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African Journal of
Business Management
Volume 5 Number 28 16 November, 2011
ISSN 1993-8233

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Prof. Wilfred Isioma Ukpere
Department of Industrial Psychology and People
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Faculty of Management,
University of Johannesburg,
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Department of Educational Management,
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University of Malaya, 50603 Kuala Lumpur,
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University of Fort Hare
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University of Craiova, Romania
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VIT Business School,
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Vellore 632014 (India)
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University of Johannesburg
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Michigan Technological University
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United Arab Emirates University
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University of Botswana
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University of Botswana
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Islamic Azad University of Babol,
Iran
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Polytechnic of Namibia,
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Taiwan Institute of Economic Research
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National Taipei University of Nursing and Health
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University of the Western Cape
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Bucharest Academy of Economic Studies, Department
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College of Economics and Management,
China Agricultural University
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district,
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Dr. Maurice Oscar Dassah
School of Management, IT and Governance
University of KwaZulu-Natal
Post Office Box X54001
Durban
4000
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Editorial Board
Dr. Peide Liu
Business Administration School,
Shandong Economic University, China
Dr. Marwan Mustafa Shammot
King Saud University, P.O.Box 28095 ,
Riyadh 11437
Kingdom of Saudi Arabia.
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University of Wollongong in Dubai,
Knowledge Village, Block 15 PoBox 20183,Dubai
UAE
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6965 Cumberland Gap Pkwy, Harrogate, TN
USA
Dr. Hooman Attar
Amirkabir University of Technology
Iran
Prof. Luis Antonio Fonseca Mendes
University of Beira Interior –
Business and Economics Department -
Estrada do Sineiro – Polo IV – 6200-209 Covilhã
Portugal
Dr. Wu, Hung-Yi
Department of Business Administration
Graduate Institute of Business Administration
National Chiayi University No.580, Xinmin Rd., Chiayi City
60054, Taiwan (R.O.C.)
Dr. Shu-Fang Luo
No.28, Da-Ye S. Road, Lin-Hai Industrial Park,
Hsiao-Kang, 812, Kaohsiung City
Taiwan
Dr. Ahmad.M.A.Ahmad Zamil
King Saud University, P.O.Box 28095 ,
Riyadh 11437
Kingdom of Saudi Arabia
Dr. Paloma Bernal Turnes
Universidad Rey Juan Carlos
Dpto. Economía de la Empresa
Pº de los Artilleros s/n
Edif. Departamental, Desp. 2101
28032 Madrid, España
Dr. Mario Javier Donate-Manzanares
Facultad de Derecho y Ciencias Sociales Ronda de Toledo,
s/n 13071 Ciudad Real
Spain
Dr. Mohamed Abd El Naby Mohamed Sallam
Faculty of Commerce -
University of Kafr El-Sheikh
Egypt
Dr. Guowei Hua
NO. 3 Shangyuancun, Haidian District, Beijing 100044,
School of Economics and Management, Beijing Jiaotong
University,
China.
Dr. Mehdi Toloo
No. 136, Forsate Shirazi st., Islamic Azad University, Central
Tehran Branch, Tehran, P. O. Box 13185.768.
Iran.
Dr. Surendar Singh
Department of Management Studies, Invertis University
Invertis village, Bareilly - Lucknow Highway,
N.H.-24, Bareilly (U.P.) 243 123
India.
Dr. Nebojsa Pavlovic
High school “Djura Jaksic”
Trska bb, 34210 Raca,
Serbia.
Dr. Colin J. Butler
University of Greenwich
Business School, University of Greenwich, Greenwich, SE10
9LS,
London, UK.
Prof. Dev Tewari
School of Economics and Finance
Westville Campus
University of Kwa-Zulu Natal (UKZN)
Durban, 4001
South Africa.
Dr. Olof Wahlberg
Mid Sweden University,
851 70 Sundsvall
Sweden

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African Journal of Business Management
International Journal of Medicine and Medical Sciences
Table of Contents: Volume 5 Number 28 16 November, 2011
ences
ARTICLES
Research Articles
Comparative perspectives on environmental accounting elements
in France and the United Kingdom 11265
Voicu Dan DRAGOMIR and Elena Roxana ANGHEL-ILCU
An assessment of Knowledge Management (KM): A consideration
of information, culture, skills and technology 11283
Alireza Anvari, Gholam Abbas Alipourian, Rohollah Moghimi,
Leila Baktash and Majid Mojahed
Trading behaviors among major investors in the United States Dollar
(USD) currency futures markets: Evidence from South Korea 11295
Dar-Hsin Chen, Hwey-Yun Yau, Chin-Lin Chuang and Po-Cheng Kuo
Employees’ perceptions regarding social health insurance: A case
of Kinshasa, Democratic Republic of Congo 11309
T. Kayiba and E. M. Rankhumise
Establishment of a temporary workforce transaction mechanism
using a real option approach 11316
Ying-Chyi Chou
Networking in the Kenyan informal sector: An attempt to manage
the market failures 11323
Ongong'a J. O. and Abeka E. O.
The three components of organizational commitment on in-role
behaviors and organizational citizenship behaviors 11335
Chun-Chen Huang and Ching-Sing You

Table of Contents: Volume 5 Number 28 16 November, 2011
Table of Contents: Volume 5 Number 33 9 November, 2011
ARTICLES
Table of Contents: Volume 5 Number 33 21 December, 2012
Table of Contents: Volume 5 Number 1 4 January, 2012
Factors affecting process orientation in Iranian Social security
organization’s hospitals 11345
Somayeh Hessam, Shaghayegh Vahdat and Shahaboddin Shamshirband
The impact of personality type on Chinese part-time MBA students’
performance 11352
Adahi Moulaye M’hamed Taher and Jin Chen
Using fuzzy cognitive map and structural equation model for market-
oriented hotel and performance 11358
Cheng-Hua Wang, Shiu-Chun Chen and Kuan-Yu Chen
Extension of determinants of capital structure: Evidence from Pakistani
non- financial firms 11375
Fawad Ahmad, Juniad-ul-Haq, Rao Umer Nasir, Mohsin Ali, Wasim Ullah
Sustainable technology management indicators: Objectives matrix
approach 11386
Maja Todorovic, Maja Levi Jaksic, and Sanja Marinkovic
The change of consumers’ behavior in crisis conditions: A psychological
approach to the empirical evidence from Romania 11399
Amalia Pandelica and Ionut Pandelica
A holistic application of process capability indices 11413
Nyamugure Philimon, Maposa Daniel, Sigauke Caston, Chiyaka Edward
and Denwick Munjeri

Table of Contents: Volume 5 Number 28 16 November, 2011
Table of Contents: Volume 5 Number 33 9 November, 2011
ARTICLES
Table of Contents: Volume 5 Number 33 21 December, 2012
Table of Contents: Volume 5 Number 1 4 January, 2012
Performance evaluation of open end and close end mutual funds in
Pakistan 11425
Bilal Nafees, Syed Muhammad Amir Shah and Safiullah Khan
Training needs assessment practices in corporate sector of Pakistan 11435
Syed Kamran Sherazi, Irfan Ahmed, Muhammad Zubair Iqbal,
Muhammad Umar and Kashif-ur-Rehman
Exploring agency problems in corporate governance from the
perspective of economic ethics of the capitalist market 11442
Hsiang-Yi Lin and Chih-Wen Huang
Do best and worst innovation performance companies differ in terms
of intellectual capital, knowledge and radicalness? 11450
Carmen Cabello-Medina, Antonio Carmona-Lavado, Ana Pérez-Luño and
Gloria Cuevas-Rodríguez
Section 404 of the Sarbanes-Oxley act and its capital market effects 11467
Maria Mirela Dobre
High-tech companies’ readiness assessment for alternative workplaces 11476
Jun Ha Kim and Yi-Kai Juan
Franchisee perceived relationship value and loyalty in a franchising
context: assessing the mediating role of franchisee satisfaction and
the moderating role of franchisee characteristics 11487
Weiping Chen

Table of Contents: Volume 5 Number 28 16 November, 2011
Table of Contents: Volume 5 Number 33 9 November, 2011
ARTICLES
Table of Contents: Volume 5 Number 33 21 December, 2012
Reasons Table to create of Contents: a new venture: Volume A determinant 5 Number of entrepreneurial 1 4 January, 2012
profiles 11497
Virginia Barba-Sanchez and Carlos Atienza-Sahuquillo
An application of supplier selection in supply chain for modeling of
intangibles: A case study of multinational Food Coffee industry 11505
Gopal Agarwal and Lokesh Vijayvargy
Strategies to improve the level of employee motivation in the fast
food outlets in Cape Town, South Africa 11521
Nnenna E. Ukandu and Wilfred I. Ukpere
Debt (a real hurdle in the economic growth of Pakistan): A time
series analy 11532
Mehboob Ahmed and Maryam Shakur
Equality investment strategy evaluation during the financial crisis:
Using TOPSIS approach 11539
Horng Jinh Chang, Chun-Ming Chien and Ching Ya Hsiao

African Journal of Business Management Vol. 5 (28), pp. 11505-11520, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.1280
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
An application of supplier selection in supply chain for
modeling of intangibles: A case study of multinational
Food Coffee industry
Gopal Agarwal 1 * and Lokesh Vijayvargy 2
1 Department of Mechanical Engineering, Malaviya National Institute of Technology (MNIT), Jaipur, Rajasthan,
302017, India.
2 Jaipuria Institute of Management, Jaipur, Rajasthan, 302033, India.
Accepted 8 August, 2011
The objective of this paper is to present a comprehensive method for the evaluation and selection of
suppliers’ offers in food industry. The supplier/vendor selection is a decision-making problem at the
strategic management level that involves a semi-structured process. The inclusion of both tangible and
intangible criteria in evaluation and selection of the best among the offers provided by various
suppliers is a complex problem. Along with this the complexity of supply chain relationships and
enterprise strategies especially within enterprise clusters aggravates decision making. Application of
supply chain concepts has been made in service industry as no such work has been attempted earlier.
Personnel qualification roster (PQR) coffee company is selected which is offering specialty coffee along
with fusion food through its more than 130 outlets. Food and drink supply chain is relatively new
domain and in India not much work has been done. Analytic hierarchy process (ANP) is solved with the
help of software “super decisions”. The case study validates the applicability of proposed models and
provides insight in to the role of intangible factors in decisions related to supply chain. The
recommendations made will not only result in stream lining of supply chain processes in PQR coffee
company but will result in substantial savings also due to reduced lead time, reduced inventory level,
better service level and effective control and coordination among the partners.
Key words: Inbound supply chain, intangibles, analytic hierarchy process (ANP), vendor selection.
INTRODUCTION
Decision making in supply chain is crucial as it involves
multi-criteria, multi- level, multi-objective and multipersonal
decisions. The emphasis on higher co-operation
and co-ordination among the partners of supply chain is
key issue, which requires strategies suitable to systemoptimal
performance where the point of focus shifts from
local to global optimal. Decisions at the interface between
supplier and manufacturer depend on trade-offs between
various factors. Some of the factors are tangible and
general in nature, while some other are situation specific
and intangible in nature. Multi-criteria decision making
*Corresponding author. E-mail: lokeshvijay79@gmail.com.
tools like AHP and ANP are gaining wide applicability and
attempt has been made to make use of them in issues
related to inbound supply chain. The priority coefficients
thus found by these techniques are used in optimization
techniques to get desired results. Application of supply
chain concepts has been made in service industry as no
such work has been attempted earlier.
The uninterrupted supply of small quantities of the raw
materials, paper and packaging material, crockery,
cleaning and clothing materials and fresh materials to
scattered outlets all across the country is biggest
challenge faced by PQR Coffee Company. There is large
number of items in inventory list, a big supplier base and
fluctuating demand with long duration of realization of
paybacks as small quantities of raw material are

11506 Afr. J. Bus. Manage.
consumed in each unit sale. The pressure on supply line
is enormous due to Just-In-Time (JIT) environment of
supply with weekly supply schedule, small storage space
at outlets and short life cycles of ingredients. The use of
3PL provider at one hand takes off the burden from the
company’s shoulder but at the same time creates problem
due to inadequate communication, lack of control
and poor coordination. The stream lining of supply can
only be achieved by aligning all suppliers in the chain
with the distributing agency and inbound logistics. Vendor
selection plays significant role in the future relations and
capability to work in supply chain environment.
The suppliers can be the biggest assets to the
organization but poor choice can make them biggest
liability also. This paper, thus deals with issues related to
supply. Firstly a new model for inventory classification is
proposed to classify items so that appropriate strategy
can be adopted. For select items vendor selection model
based on analytic hierarchy process (ANP) is proposed
to show procedure involved and steps in software “super
decisions” are shown using windows for easy understanding.
There is no vendor rating system presently in
order at PQR coffee company and thus a suitable, easy
to comprehend and yet simple in nature vendor rating
model based on decision matrix is proposed for existing
vendors. A brief theoretical orientation of each issue is
presented to show the work already done and to justify
the selection of prioritizing model. A brief implementation
plan is presented to show involvement of cross-functional
team.
LITERATURE REVIEW
Ghodsypour and Brian (1998) mentioned that supplier
selection decision-making problem involves trade-offs
among multiple criteria that involve both quantitative and
qualitative factors, which may also be conflicting. In other
words, buyer-supplier relationship based on only the
price factor has not been appropriate in supply chain
management. Consideration must also be given to other
important strategic and operational factors such as quality,
delivery, flexibility etc. Supplier selection decisions
must include strategic and operational factors as well as
tangible and intangible factors in the analysis, Sarkis et
al. (2002); Weber et al. (1991) have proposed multi
objective approach to vendor selection. Their methodology
provides a useful decision support system for a
purchasing manager faced with multiple vendors and
tradeoffs such as price, delivery reliability and product
quality. Wagner et al. (1989) have evaluated the relative
importance of quality, cost, delivery performance and
other supplier attributes. According to seventy four
articles discussing supplier selection criteria, quality was
perceived to most important followed by delivery
performance and other supplier attributes, Weber (1991).
In supply chain context other criteria like ability to work as
strategic alliance, technological compatibility, adoptability
to new management style, ability to participate in product
development etc. have become extremely important.
Mandal and Deshmukh. (1993) used interpretive structural
modeling (ISM) for vendor selection and identified
11 most important criteria. Good suppliers can help
manufactures during the development of new products
and processes, with long term quality improvements and
cost reductions and can provide enhanced delivery
performances, Goffin et al. (1997).
Cebi et al. (2003) used an integrated approach for
supplier selection in which supplier selection problem has
been structured as an integrated lexicographic goal
programming and AHP model including both quantitative
and qualitative conflicting factors. Cabi et al. (2003)
carried out vendor selection for a Turkish manufacturing
company, which has been operating for almost 40 years
in production of dry mixed food and drink products. They
proposed that in the food company, the most important
factors are quality, delivery and cost. Although, rich
literature is available for vendor selection process but use
of ANP is not tried and in this section an attempt has
been made to model intangible factors in vendor
selection relevant in the context of PQR coffee company.
Some of these criteria are interdependent and thus ANP
is fit to be used as prioritizing tool.
The decision making in vendor selection often involves
intangible factors like brand image, supplier’s reputation,
ability to innovate, adoptability to change etc. which are
non quantifiable and thus making the job of decision
maker tough as one has to rely on subjective and intuitive
thinking. Some of these factors are interdependent also
and degree of interdependency varies from context to
context leading to further complications. However, intangibles
can be quantified through relative measurement
(priorities).
These priorities along with normalized measure of
tangibles can be used in a linear programming model for
optimization of desired objective functions. ANP is used
for finding out the priority coefficients, which can be used
in limited partnership (LP) for appropriate vendor
selection.
BACKGROUND OF ANP
AHP and ANP are multicriteria decision-making tools,
which are argued to possess qualitative (decision model
development) and quantitative (decision model analysis)
components. AHP models a hierarchical decision
problem framework, which consists of multiple levels
specifying unidirectional relationships. ANP models a
network structure that relaxes the hierarchical and
unidirectional assumptions in AHP to allow interdependent
relationships in the decision making framework.
Although, the two decision tools possess the same
qualitative and quantitative procedures to structure and
analyse a decision problem, ANP needs further quantitative
steps to solve a network decision problem. For

details of the ANP method, refer to Saaty (1996). Those
who want to skip the complicated mathematical algorithm
and look for using any commercially available ANP
software, consult the website of expert choice. Inthis
study, only a brief description of the method is provided,
which is based on Cheng and Li (2001) who suggested
that ANP is composed of four qualitative (1 to 4) and five
quantitative (5 to 9) steps:
1) To state the decision problem – The topmost level is to
state the decision problem. This starts the decomposition
of further levels down the structure until final level that is
usually the scenarios or alternatives to be selected.
2) To make sure that the decision problem is to be solved
by ANP – As already stated, ANP is used to structure a
decision problem into a network form. For solving strictly
hierarchical model, AHP is sufficient.
3) To structure the unstructured decision problem – The
topmost decision problem level is abstract in nature. It
must be decomposed into a set of manageable and
measurable levels until the level of criteria for assessing
the scenarios or alternatives.
4) To determine who the raters are – Those who are
responsible for making the decision are the raters for
completing a questionnaire.
5) To design a questionnaire for eliciting data from raters
– It is suggested to use the pairwise comparison, which
can elicit more information to assign weights to the rated
elements. It is common to use the 9-point priority scale to
estimate the relative importance between paired
elements (Saaty, 1980).
6) To calculate the eigenvector of each of the developed
matrices – Each decomposed level with respect to a
higher level forms a matrix. It is necessary to calculate
the eigenvector for the elements of this matrix. For the
algorithm, refer to Saaty (1980) or Cheng and Li (2001).
7) To measure the consistency ratio (CR) of each of the
matrices to find out the inconsistency of rating – One of
the best reasons to use pairwise comparison and matrix
is to measure the CR to ascertain that raters are
consistent in rating. If the CR value cannot pass the
acceptable level, it is certain that the raters rated
arbitrarily or mistakenly.
8) To form the supermatrix by the eigenvectors of the
individual matrices (also known as submatrices) (Saaty,
1996) – The eigenvectors of each of the developed
matrices should gather together to form a supermatrix.
9) To compute the final limit matrix – In order to compute
the final limit matrix, the supermatrix, which has been
ensured of column stochastic, has to raise to high power
until weights have been converged and remain stable
(Sarkis, 2002).
METHODOLOGY
A set of criteria covering wide range of parameters is submitted in
the form of table and opinion of expert is taken to select pertinent
criteria for vendor selection in the context of PQR coffee company.
Agarwal and Vijayvargy 11507
Apart from this an unstructured opinion is also sought. Cost being
one of the most important parameters has not been included in this
analysis as the alternatives chosen are cost competitive and thus a
detailed analysis is required to select one of them on the basis of
comprehensive analysis of various other factors and ultimately the
priorities obtained with ANP can be seen in the context of cost
parameter and an appropriate decision can be taken. The priorities
obtained from the ANP can be directly used in linear programming
model as the coefficients in the objective function to get the
required distribution of the demand among the suppliers which can
satisfy a set of constraints related to lead time, plant capacity of
supplier etc. Since PQR Company is buying the hot chocolate
fudge from a single source, as the demand is not very high, there is
no need of applying the optimizing tool here.
CASE: PQR COFFEE COMPANY
PQR coffee company limited, south Asia’s largest retailer of fine
specialty coffees, was established in February, 2000 to recreate the
ambience and experience of the typical Italian neighborhood
espresso bars. PQR coffee aims to provide a comfortable place for
people to relax and unwind over a cup of coffee.
PQR coffee company was established by Turner Morrison group
as specialty coffee retailer offering fine espresso based beverages.
It places strong emphasis on the quality of coffee beans and the
process of preparing, rich aromatic coffee. This 100% Arabica
coffee is sourced from Tata coffee’s plantations in Karnataka, India.
The Tata coffee company is in strategic alliance with PQR with
34.3% stakes in the company. TCL is currently exclusive supplier of
coffee blends to PQR for its entire range of offerings. This alliance
has given TCL access to the value added market through PQR’s
expanding consumer base while PQR is benefited by access to
TCL’s technical and blend experience on specialty coffee.
Supply chain of PQR
Supply chain operations of PQR are of paramount importance as
geographical differences and distances among various retail outlets
are very high. India, with its diversity and cultural differences, is not
comparable to any other country of the world. Here every few
hundred kilometers, there is change in language, traditions, habits,
taste and behavioural patterns. PQR has its outlets in all major
cities of India like Delhi, Mumbai, Kolkata, Chennai, Bangalore,
Hyderabad, Chandigarh, Goa, Pune, Ahmedabad, Lucknow,
Kanpur, Shimla, Ludhiana, Baroda, Jaipur and Dehradun. It has its
international operations at Lanka and Dubai. Figure 1 shows
supply chain of company.
We can broadly classify operations of PQR’s supply chain into
three categories.
(1) Central supply chain
(2) Local supply chain.
(3) Foods supply chain.
Central supply chain
PQR has 4 regional offices at Delhi, Mumbai, Bangalore and
Kolkata. There is a centralized supply chain for dry items, which
have sufficient shelf life. PQR head office at Delhi manages supply
of 134 items comprising of raw materials, paper and packing items,
cleaning material, crockery/cutlery, stationery and uniform,
merchandising items etc. However, there are few vendors who have
the capability of supplying directly to the regional centers and an
understanding to affect this has been evolved; still the bulk of
material movement occurs from Delhi. The vendors for these

11508 Afr. J. Bus. Manage.
Figure 1. Supply chain of PQR coffee company.
materials are mostly located around Delhi and there are more than
100 vendors presently.
Company has 3PL arrangement for all its logistics needs
including warehousing, record keeping, consolidating, transporting
and distributing various items of centralized supply chain. For
thispurpose 1-year contract has been signed with Safepress private
limited. Safexpress is among the topmost 3PL providers in the
country with Rs. 300 crore turnovers and a fleet of 2500 dedicated
vehicles. The Safex also controls the distribution to various outlets
from warehouse. The various details of this supply chain are
discussed under sub heading of “mapping of current procedure”.
Local supply chain
Perishable items like milk, cream, ice cream, ice cubes and other
milk-based products are sourced locally and each city has its own
supplier base. In case of more than one outlet in a city, all are
sourcing from same vendors to get economies of means.
Food supply chain
To focus on core competence of providing specialty coffee, PQR
has no kitchen in its outlets. All eatables like sandwiches, tikka,
pasta, rolls, desserts and ice creams ,etc are sourced from outside.
For this purpose local suppliers are identified and contracts are
signed. For example in case of Delhi, care caterers and Taj Tacs
are supplying sandwiches, fusion meals and desserts, snacks
respectively. A cold supply chain with temperature ranging between
4 and 6 degree centigrade is maintained for daily supply of these
items, which have shelf life of 24 to 36 h. In case of few outlets,
which run all around the clock, there is second supply of food items
in the evening. The outlets pass on the daily sales data to head
office electronically using e-mail.
Vendor selection parameters
Vendor selection is multi-criteria, multi-people and multi-layer
decision-making process, which requires a great deal of analysis of
many variables most of which are intangible in nature. Few
pertinent attributes are listed in Table 1. Kindly put your preferences
for each criteria depending upon your subjective assessment by
putting ( ) in appropriate column. Based on the response of
experts on the attributes hierarchy has been formed, shown in
Figure 2 and used in ANP software “super decisions”
Vendor selection for hot chocolate fudge
This ingredient is used in large quantity with average consumption
(including all regions) of 4500 kg worth around Rs.500000 per
month. Following specifications are mentioned by the product
development department of the organization:
(1) Description: The product is dark brown I n color and is

Table 1. The parameter for selection of the vendor.
Agarwal and Vijayvargy 11509
No. Attribute Extremely important Very important Moderately important Very little important Not at all important
Cost criterion
1 Produc price
2 Cost reduction plan
Service criterion
3 Flexibility
4 Problem solving
5 Reaction to demand
Quality criterion
6 Product specification
7 Supplier’s certification
8 Durability
9 Ergonomic quality
Cycle time criterion
10 Delivery lead time
11 Development speed
Suppliers profile criterion
12 Reputation
13 Financial status
14 Market share
15 Production facility of capacity
16 Advertising
Risk Criterion
17 Low quality of delivered product
18 Production delays
19 Delivery delays
Relationship criterion
20 Compatibility with levels and Functions of buyer firm
21 Supplier customer base
22 Ability to identify needs
23 Supplier availability
smooth in texture. It has a sweet dark chocolate flavourwith
well-rounded cocoa, vanila and dairy notes.
(2) Chemical: Specifications is shown in Table 2
(3) Microbiology: it is shown in Table 3
(4) Packaging: It must be sufficient to protect the product
throughout distribution and shelf life. Packing parameters
are shown in Table 4.
(5) Labeling: Each unit and corrugated box shall be
properly labeled to indicate - Product name, Net wt., batch
no., Date of manufacture, Manufacturer’s address, and
ingredient declaration.
(6) Shelf life: 6 months from the date of manufacturing.

11510 Afr. J. Bus. Manage.
Selection of v endor
Cost Service Quality Cycle
t ime
a) Product
p rice
b) Cost
r eduction
p lan
a
b
c) Flexibility
d) Problem
s olving
e) Reaction
t o
d emand
c
d e
f) Product
specification
g)Supplier
certification
h) D urability
i) E rgonomic
q uality
j) Delivery
l ead
t ime
k) Develop -
- ment
s peed
f g j
h
Brand Co. Known Co.
i
k
Suppliers
p rofile
l) R eputation
m) F inancial
s tatus
n) M arket
s hare
o) P roduction
f acility of
c apacity
p) A dvertising
Risk Relation
- sh ip
q) Low
q uality of
d elivered
p roduct
r) P roduction
d elays
s) D elivery
d elays
l q
m n
Figure 2. Representation of ANP based evaluation model for the selection of vendor.
Table 2. Chemical specifications.
Total soluble solids 66.0 to 69.0 %
PH value 6.05 to 6.55
Viscosity (brookfield) More than 5000 cps
Table 3. Microbiological specifications.
Standard plate count Less than 5000
Yeast and mould Less than 100
(7) Storage: Ambient – well-sealed corrugated box in a cool, dry
place.
(8) Continuity guaranty: All shipments shall be uniformly high
quality, and shall have been prepared and stored under strictly
sanitary conditions, in accordance with good manufacturing prac-
o
p
r s
Economy Co.
t) Compatibility
with l evels
and f unctions
of buyer f irm
u) Supplier
s ustomer
b ase
v) Ability t o
i dentify
n eeds
w) S upplier
a vailability
tices (GMP’s) and shall conform to all provisions of the prevention
of food adulteration act, as amended.
Presently there is only one supplier catering to the need of the hot
chocolate fudge and there is one supplier earmarked as back up
t
v
u
w

Figure 3. The design of the problem.
Table 4. Packaging specifications.
Type Multilayered plastic film
Pack size 1 Kg. per unit
Box capacity 12 units per box
supplier. Recently a new company has pitched for supply of the
product and a comparison between these three suppliers is done to
find out their relative priorities based on number of factors. The
identity of the suppliers is not disclosed and they are named as
a. Vendor A- existing supplier
b. Vendor B- back up supplier
c. Vendor C- new supplier
The pairwise comparison input is obtained from the supply chain
manager (vendor development) and available information about the
supplier with the organization along with general opinion of other
supply chain staff. Software super decision is used to save the
precious time of concerned people and to show the practicability of
the approach. However, the method has been explained to
managers to get their full involvement. The following steps are
involved in modeling the problem using the software.
Step 1
Formation of network with goal, clusters and subnets: The problem
is first designed in the software by making clusters and the
corresponding nodes and connections. The vendor selection
problem is designed as hierarchical network with the goal as the
topmost cluster. This is linked to another cluster containing cost,
Agarwal and Vijayvargy 11511
service, quality and cycle time, etc criteria as its nodes. The
network is shown in Figure 3.
Step 2
The clusters and nodes under all 7 subnets are shown in Figure
4. Each subnet consists of two clusters that is attributes and
alternatives.
Step 3
Node comparisons: This involves comparison of nodes with respect
to a control criterion. One of node comparison is shown in Figure 5
Step 4
Generation of weighted and limiting supermatrix: In this step, the
generation of the weighted and the limiting supermatrices for all the
four sub networks. The unweightage supermatrix of problem is
shown in Table 7. Then clusters’ priority weights were calculated by
using expert opinion. Multiply this priority weight by the
unweightage supermatrix had the weightage matrix.the final step is
calculation of the limiting priorties of weighted super matrix which is

11512 Afr. J. Bus. Manage.
Figure 4. Various subnets with clusters.
Figure 5. Node comparisons.
shown in table 8. One of the factor’s limiting supermatrix is shown in
Figure 6
Step 5
The score obtained at the subnet level are raised to the goal level
and limit matrix is obtained for goal. Figure 7 shows weighted score
for each cluster.
Step 6
The values obtained from the synthesis are taken at the level of the
goal and overall synthesis for the model is achieved as shown in
Figure 8.
Step 7
Sensitivity analysis: In this the variations in the priority of the

Figure 6. Limiting super matrix for subnet business criteria.
Figure 7. Limit matrix at goal level.
alternatives with respect to change in the weightage of the control
criteria can be observed. One such graph is presented in Figure 9
to show the variations in the priorities of the alternative with
respect to business criteria. Table 5 shows score of vendor with
repect to each factor. It tells about ranking of vendor with respect
to various factors.
RESULTS AND DISCUSSION
Based on the priorities obtained from ANP, vendor A has
highest priority coefficient of 0.448 followed by vendor B
with 0.326 and last placed is vendor C with overall priority
of 0.226. Clearly vendor A is best choice. The vendor A is
also the present supplier and is also supplying to famous
brands like Mc-Donald etc. and the case company is
Agarwal and Vijayvargy 11513
more or less in good touch with this supplier. The results
are indicators of the personal preferences which the
analyst has as the pairwise comparison are based on his
knowledge, word of mouth information available to him
and judgments. One of reasons could be the first hand
experience of the present supplier, which the analysts
have in comparison to the here-say, and written details of
other two alternatives. One more important point is that
the vendor A is located very close to Delhi as compare to
other vendors. This has bearing on the lead-time, ease of
communication, person-to-person contact and trust. This
suggests that the case company should strengthen the
tie with vendor A and should try to forge a strategic
alliance with vendor A. It is worth while to mention here
that this supplier is supplying many other items like syrup,

11514 Afr. J. Bus. Manage.
Figure 8. Synthesis for the goal.
Figure 9. Sensititivity analysis with respect to cost.

Table 5. Score of vendor with different attribute.
Agarwal and Vijayvargy 11515
Attribute
Vendor A
Total priority Rank
B
Total priority Rank
C
Total priority Rank
Cost 0.2158 2 0.2512 1 0.1484 3
Service 0.2531 1 0.1631 2 0.0838 3
Quality 0.2535 1 0.1677 2 0.0788 3
Cycle time 0.2636 1 0.1622 2 0.0742 3
Risk 0.1564 3 0.1614 2 0.1821 1
Suppliers profile 0.2677 1 0.1604 2 0.0720 3
Relationship 0.2645 1 0.1588 2 0.0767 3
Table 6. Final results of vendor selection problem.
Serial no. Product Vendor Priorities Remarks
A 0.448
1 Hot chocolate fudge B 0.326 Select vendor A
C 0.226
2 Sugar sachet
3 Tomato ketchup
sauces and other topping to the case company. However
the priority of the back up vendor is lower than the new
vendor considered and this is attributed to better quality
and flexibility capabilities of the vendor B as compare to
vendor C. Thus it is suggested that, the vendor C be
replaced by vendor B for back up vendor. Although, at
present all the three supplier selected for analysis are
individually capable for supplying the total quantity but as
the PQR Company is planning to expand at fairly rapid
rate, which will result in rise in demand and there fore
option must be kept open to buy from more than one
supplier at a time. This will minimize the risk and provide
economy in transportation and other logistics cost due to
large geographical spread of the outlets. Similar studies
are carried out for sugar sachet, tomato ketch-up and in
all 9 vendors are evaluated (Appendix 2). The complete
results for vendor selection are given in Table 6.
Conclusion
With the advent of revolution in communication and
information technology, supply chain management has
got significant momentum and is acknowledged as vital
strategic function in most organizations. The performance
measure of supply chain is important building block for
D 0.370
E 0.226
F 0.403
G 0.247
H 0.581
I 0.170
Select vendor F
Select vendor H
decision-making. The existing models are not adequate
in taking intangible factors in to consideration. To take a
system optimal decision, trade offs between many
conflicting enablers has to be analyzed. In present study
issues related to inbound supply chain are chosen. To
identify the specific key intangibles qualitative techniques,
like SWOT, PEST, and NGT etc can be used. Vendor
selection using ANP is a unique attempt and showed
promising results. The theoretical analysis of various
issues is very convincing and motivates one to go for real
case study. Thus application of models is verified by
taking real case study of PQR coffee company, which is a
key player in food and drink outlets chain in India and
abroad. Since emphasis is on incorporating intangibles in
decision-making, that’s why service industry is chosen.
Service industries do not have an out bound supply chain
as service providers are also producers of service. Due to
perishability of service a very strong inbound supply
chain is essential and this provided lot of scope for such
a study. The use of ANP provides an unparallel framework
for vendor selection. In all 9 vendors are considered
for 3 different products and ANP is used to get priorities
based on 21 decision criteria. With the growing complexities
in business world and influence of number of
intangible factors, they can prove to be extremely essential
and are in fact inevitable. The recommendations to

11516 Afr. J. Bus. Manage.
case company are based on the details provided by the
managerial staff of the PQR coffee company and they are
highly context specific and may not be useful for other
organizations of similar nature.
REFERENCES
Cebi F, Bayraktar D (2003). An integrated approach for supplier
selection. Logist. Inform. Manage., 16(6): 395-400.
Ghodsypour SH, O’Brian C (1998). A decision support system for
supplier selection using an integrated analytic hierarchy approach
and linear programming. Int. J. Prod. Econ., 56/57: 199-212.
Goffin K, Szwejczewski M, New C (1997). Managing supplies: when few
can mean more. Int. J. Phys. Distrib. Logist. Manage., 27(7): 422-
436.
Mandal A, Deshmukh SG (1993). Vendor selection using interpretive
structural modeling (ISM). Int. J. Oper. Prod. Manage, 14(6): 52-59.
Saaty TL (2009). Applications of Analytic Network Process in
Entertainment. Iranian J. Oper. Res., 1(2), 41-55.
Saaty, TL (1996). Decision Making for Leaders. RWS Publications,
4922 Ellsworth Avenue, Pittsburgh, PA 15213.
Sarkis, J, Talluri,S, (2002). A model for strategic supplier selection. J.
Supply Chain Manage., 38(1): 18-23.
Wagner J, Ettenson R, Parrish J (1989). Vendor selection among retail
buyers: An analysis by merchandise division. J. Retailing. 65: 58-77.
Weber CA, Current JR, Benton WC, (1991). Vendor selection criteria
and methods. Eur. J. Oper. Res., 50: 2-18.

APPENDIX
Appendix 1
Table 7. Unweighted supermatrix of supplier selection by using ANP.
Criteria
Cost
Service
Quality
Cycle time
Risk
Supplier profile
Relationship
Agarwal and Vijayvargy 11517
Cost Service Quality Cycle time Risk Supplier profile Relationship
a b c d e F g h i j k l m n o p q r s t u v w
a 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
b 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c 0 0 0 0.75 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d 0 0 0.5 0 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
e 0 0 0.5 0.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
f 0 0 0 0 0 0 0.6 0.28 0.29 0 0 0 0 0 0 0 0 0 0 0 0 0 0
g 0 0 0 0 0 0.29 0 0.14 0.11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
h 0 0 0 0 0 0.33 0.3 0 0.61 0 0 0 0 0 0 0 0 0 0 0 0 0 0
i 0 0 0 0 0 0.38 0.2 0.57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
j 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
k 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
l 0 0 0 0 0 0 0 0 0 0 0 0 0.25 0.25 0 0 0 0 0 0 0 0 0
m 0 0 0 0 0 0 0 0 0 0 0 0.8 0 0.75 0 0 0 0 0 0 0 0 0
n 0 0 0 0 0 0 0 0 0 0 0 0.2 0.75 0 0 0 0 0 0 0 0 0 0
o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.14 0.14 0.15 0.07 0 0 0 0
p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.33 0 0.31 0.46 0.18 0 0 0 0
q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15 0.22 0 0.32 0.25 0 0 0 0
r 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.45 0.57 0.48 0 0.5 0 0 0 0
s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.07 0.065 0.076 0.77 0 0 0 0 0
t 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.63 0.69 0.71
u 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15 0 0.09 0.09
v 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.47 0.21 0 0.19
w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.38 0.15 0.22 0

11518 Afr. J. Bus. Manage.
Table 8. Limited Super matrix of supplier selection by using ANP.
Criteria
Cost
Service
Quality
Cycle time
Risk
Supplier profile
Relationship
Cost Service Quality Cycle time Risk Supplier profile Relationship
a b c d e F g h i j k l m n o p q r s t u v w
a 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
b 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
c 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
d 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
e 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
f 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
g 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
h 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
i 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
j 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29
k 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
l 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
m 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
n 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
o 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
p 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
q 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
r 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
s 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
t 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
u 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
v 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
w 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Appendix 2
Vendor selection for sugar sachets.
Agarwal and Vijayvargy 11519

11520 Afr. J. Bus. Manage.
Vendor selection for tomato ketchup.

African Journal of Business Management Vol. 5(28), pp. 11375-11385, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.218
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Extension of determinants of capital structure:
Evidence from Pakistani non-financial firms
Fawad Ahmad 1 *, Juniad-ul-Haq 1 , Rao Umer Nasir 1 , Mohsin Ali 2 , Wasim Ullah 3
1 Faculty of Management Sciences, International Islamic University Islamabad, Pakistan.
2 Environmental Sciences Department, International Islamic University Islamabad, Pakistan.
3 Accounting and Finance department, Faculty of Management Sciences, International Islamic University Islamabad,
Pakistan.
Accepted 20 June, 2011
This study tried to determine the influence of set of explanatory variables on the capital structure
determination for Pakistani non-financial firms by using panel data. This study also finds the
applicability of two capital structure theories (pecking order theory and trade-off theory) in Pakistani
non-financial sector. This study used five previously studied variables (profitability, size, growth,
tangibility of assets, non-debt tax shield), and added three new variables (tax, liquidity and payout),
which were not used previously in Pakistani context. This research used data from 336 non-financial
firms over the period of 5 years (2005-2009). This study used fixed effect random model regression
analysis to analyze determinants of capital structure. The results showed that industry type play
important role in determining capital structure. The results showed that out of eight variables five (size,
tangibility of assets, non-debt tax shields, liquidity and payout) are statistically significantly related to
leverage, remaining three are statistically insignificantly related with leverage. Two expected relation
are accepted while six are rejected after empirical analysis. This study identifies that industry type,
liquidity and payout ratio play important role, whereas tax does not play important role in identifying
capital structure Pakistani non-financial firms.
Key words: Determinants, capital structure, pecking order theory, trade-off theory.
INTRODUCTION
One of the important decisions that firms mangers are
concerned with is relating capital structure. The capital
structures of financial and non-financial firms are different
because of different nature of operations and financial
conditions. All non-financial firms are mainly associated
with production, therefore, there main requirement for capital
is to acquire production facilities such as buildings,
machineries, equipment and raw material.
Numbers of theories are presented relating capital
structure, starting point is considered as Irrelevance
theory, presented by Millers and Modigliani (1958).
*Corresponding author. E-mail: fawad.msfin89@iiu.edu.pk Tel:
092-332-9174212.
Abbreviations: POT, Pecking order theory; TOT, Trade-off
theory.
Millers and Modigliani (1958) argued that external
borrowing has no effect on firm’s value. Irrelevance
theory assumes certain conditions; later on researchers
found that all assumptions have significant effect on
capital structure determination.Two main theories, that is,
Pecking order theory (POT) and Trade-off theory (TOT)
play important role in determining capital structure. This
study investigated the impact of both POT and TOT in
determining capital structure of non-financial firms. POT
assumes that firms meet their capital requirement
through internal funds use first, before going for external
borrowing and equity issuance. POT also argued that the
firms do not have any target leverage ratio (Mayers and
Majluf, 1984; Mayers, 1984). Therefore firms uses
following capital preferences, that is, accumulated
earnings, short term borrowing, long term borrowing and
equity issuance in order to meet their capital
requirements (Donaldson, 1961). According to TOT, firms
assumes target debt ratio by balancing the costs and

11376 Afr. J. Bus. Manage.
benefits of equity and debt. The TOT assumes that target
debt ratio maximizes the firm value and reduces external
claims (Titman, 1984).
Over the last ten years numerous studies have been
conducted on capital structure determinants, but most
studies were based on developed countries data (Mira,
2002; Frank and Goyal, 2002; Bevan and Danbolt, 2002;
Daskalakis and Psillaki, 2005; Mazur, 2007; Elsas and
Florysiak, 2008; Serrasqueiro and Ragao, 2009).
There are also studies conducted by using developing
countries data (Kester, 1986; Allen and Mizuno, 1989;
Diranyeh, 1992; Bennett and Donnelly, 1993; Kunt and
Maksimovic, 1994; Lasfer, 1995; Panday, 2001; Ak-
Sakran, 2001; Omet and Nobanee, 2001; Al-Hayjneh,
2001; Bhaduri, 2002; Fattouh, 2003; Chen, 2004; Bauer,
2004; Bauer, 2004; Delcoure, 2007; Teker et al., 2009;
Chakraboraty, 2010).
In Pakistan limited work had been done relating determinants
of capital structure. Shah and Hijazi (2004) had
done initial work on determinants of capital structure.
Later on, previous work was extended by Shah and Khan
in 2007 by using panel data regression analysis and new
variables. Hajazi and Tariq (2006) conducted study by
using cement industry data, and Rafiq et al. (2008)
worked on Chemical industry data. Walliulah and Nishat
(2008) worked on the dynamics of capital structure.
This study added three (tax, liquidity, payout ratio) new
determinants of capital structure for Pakistani nonfinancial
firms.
This study used eight firm level determinants of capital
structure on the basis of previously used variables, less
commonly used variables and most importantly on the
basis of availability of data for maximum number of nonfinancial
firms. Other variables can also be used but the
data for most of variables is missing. Therefore only
those variables are used for which complete data is
available.
LITERATURE REVIEW
Previous studies conducted in developing countries
According to ‘Irrelevance theory’ of presented by Millers
and Modigliani (1958), studies relating capital structure
are divided into two groups, that is, capital structure
determinants and effects of capital structure on firm’s
value.
This study relates to the first group. Myers (1977)
proved a significant relation between operating risk and
leverage. Ferri and Jones (1979) used four determinants
of capital structure, that is, business risk, industry type,
operating leverage and firm size. The results proved that
firm size and operating leverage are significantly related
to leverage. The previous research of Carleton and
Silberman (1977) and Marsh (1982) showed that
independent variables including fixed assets, growth
opportunities, operating risk, firm size, and non-debt tax
shields were positively related with leverage. Variables
such as expenditures of advertisement, research and development,
insolvency, volatility of earnings, profitability,
and uniqueness of products were negatively related with
leverage.
In 1981 Aggarwal ignored industry type as variable and
used growth rate, international risk and profitability and
showed that they are not significantly related with
leverage. Aggarwal argued that country effect is an important
factor in determining capital structure. According
to Park (1998) national culture can also be used as an
independent variable.
Myres and Majluf (1984) proved that capital structure is
positively correlated with firm size, while profitability can
either be negative or positively related to leverage. De
Angelo and Masulis (1980) analyzed non-debt tax shield
as determinant and argued that non-debt tax shield like
depreciation is replicable by tax deduction of interest
payments. Kim and Sorensen (1986) proved that nondebt
tax shield is negatively associated with leverage, but
researches by Homaifar et al. (1994) and Ozkan (2001)
proved significant positive relation between non-debt tax
shields and leverage. By using dividend policy Smith and
Warner (1979) showed a significant relation among
dividend policy and capital structure.
Kester (1986) had conducted a comparison study
between U.S and Japanese firms; he showed that
profitability is significantly negatively related to leverage.
Allen and Mizuno (1989) by using book and market value
of Japanese companies found a negative association
between leverage and profitability. Rajan and Zingales
(1995) conducted research by using G7 countries data
for comparing the capital structure and its factors. They
concluded that the results of almost every sample country
were similar but some were different due to some other
factors like taxation policies and insolvency or bankruptcy.
They also observed that determinants of capital
structure for U.S (tangible assets; size, profitability and
growth) were of same importance for the rest of other
developing countries.
Kunt and Maksimovic (1994) used ten developing
countries sample and found that liquidity, assets, and
industry effects were more significantly related than firm
size, firm growth and tax effects. These results also
proved that leverage is negatively related to net fixed
assets, suggesting inefficiency in long-term debt market
working in developing countries. Booth et al. (2001) also
used ten developing countries data. They used assets
tangibility, average tax rate, size, business risk, profitability
as independent variables. The results showed that
the more profitable the firm having free internal cash flow,
the lower the debt ratio. Booth et al. (2001) argued that
the variables affecting the leverage in developed
countries have the same significant affect on the debt
ratio in developing countries. The long-term borrowings in
developing countries were lower than those of developed

countries due to the agency costs of borrowing are high
in developing economies.
Bennett and Donnelly (1993) conducted a study by
taking UK firms into consideration and showed that
capital structure decision is effected by profitability,
assets structure, non-debt tax shield and size. In addition
Homaifar et al. (1994) proved that inflation is positively
related to leverage.
In India, Bhaduri (2002) found that assets structure,
uniqueness, firm size, growth, and cash flows
significantly influence the capital structure of firms. They
proved that small size firms are mainly more dependent
on short-term borrowing whereas the large firms are
more dependent on long-term borrowing.
Deesomsak et al. (2004) proved that liquidity, firm size,
share price performance and non-debt tax shield play
influencing role in determining the leverage level in the
Australian and Eastern Asian firms. The results proved
that these four variables were positively related with
leverage. In most countries firm size and capital structure
were positively related.
Huang and Song (2005) used that data from Chinese
market and empirically proved that leverage was negatively
associated with managerial shareholdings, nondebt
shields, firm growth opportunities, profitability, where
as positively associated with tangibility of assets and firm
size. Fattouh et al. (2005) showed that profitability, firm
size, non-debt tax shields and tangibility of assets were
positively related to leverage.
Delcoure (2007) conducted study by using Central and
Eastern European countries data. In this study Delcoure
showed that non-debt tax shield, assets tangibility and
taxes were positively related to leverage, where as
negative exist between leverage and profitability. Teker et
al. (2009) analyzed the Turkish firm’s data and found that
return on assets, tangibility of assets were statistically
significantly positively associated with the firms leverage.
Shahjahanpour et al. (2010) does not used the most
commonly used variables but used variables that were
not often used, these variables are product uniqueness,
dividend policy, non-debt tax shields, liquidity, and
effective tax rate.
In recent study in India, Chakraboraty (2010) found that
profitability, size of firms and uniqueness are positively
related to leverage, where as assets tangibility and nondebt
tax shields are positively related to leverage. The
study showed contradicting results relating growth opportunity
as two models used given the opposite results. In
Pakistan Shah and Hijazi (2004) used four explanatory
variables (growth, profitability, tangibility and size). In
2007 Shah analyzed textile industry data and used three
explanatory variables (size, tangibility and profitability). In
Pakistan Hajizi and Tariq (2006), Shah and Khan (2007),
Rafiq et al. (2008), and Walliulah and Nishat (2008) used
profitability, tangibility, size, growth, volatility of earning,
and non-debt tax shields as explanatory variables in their
studies.
Ahmad et al. 11377
Firms level determinants of capital structure
This study used eight firm level determinants of capital
structure on the basis of previously used variables, less
commonly used variables and most importantly on the
basis of availability of data for maximum number of nonfinancial
firms. There are many other variables that can
be used to determine the capital structure of non-financial
firms of Pakistan such as research and development
expenses (R and D), but the problem is availability of R
and D data for all firms. Therefore in this study only those
variables are selected for which majority of firms had
data.
Profitability
There exist two opposite views relating relationship
between profitability and leverage. POT assumes that
firm first uses its accumulated earnings and then goes for
external financing. Therefore most profitable firms uses
internal financing (Myers, 1984), results in reducing the
firm leverage level. As a result POT assumes negative
relationship between leverage and profitability.
Long and Malits (1985), Baskin (1989), Michaelas et al.
(1999), Al-Sakran (2001), Doberz and Fix (2003) and
Chen (2004) have empirically proved negative relation
between leverage and profitability.
Studies conducted by using Pakistan firm’s data by
Shah and Hijazi (2004), Shah (2007), Hajazi and Tariq
(2006), Shah and Khan (2007), Rafiq et al. (2008),
Walliulah and Nishat (2008) empirically proved negative
relationship between leverage and profitability.
According to TOT, firms are expected to have stable
cash flows, and are having more debt serving capacity.
The increase in debt and stable cash flows provides more
benefits as interest payments are tax deductable, results
in reduction in cost of capital. Jenson (1986) argued that
firms with free cash flows and expected stable cash flows
should get benefit of leverage. The increase level of
leverage provides reduction in tax payments and
prevents the blockage of free cash flow, resulting in
increase in liquidity for firm. Hence TOT assumes positive
relationship between leverage and profitability.
Size
POT assumes that there is negative relation between firm
size and leverage. The larger size more information will
be disclosed by firms to the outsiders as compared to the
small sized firms. Larger firms with less asymmetry of information
may issue equity more than external financing
(Rajan and Zingales, 1995). The small firms with asymmetry
of information would not be able to raise equity
because of undervaluation of equity, and no collateral for
long term debt, as a result short term debt can be used

11378 Afr. J. Bus. Manage.
by small firms. Mazur (2007) and Chakraborty (2010)
proved empirically negative relationship between
leverage and firm size.
According to TOT larger firms are well diversified,
having stable cash flows and their chances of bankruptcy
are less as compared to small firms. Therefore larger
firms prefer leverage and are having high level of
leverage (Mayers and Majluf, 1984). Due to the large
size, high level of fixed assets, economics of scale, stable
cash flow and creditworthiness larger firms have the
bargaining power over lender and can borrow at relatively
lower rate (Marsh, 1982).
Scott (1972), Carleton and Silberman (1977), Marsh
(1982), Mayers and Majluf (1984), and Wiwattanakantang
(1999) found empirically positive relation between firm
leverage and firm size. Studies conducted by using
Pakistan firms data by Shah and Hijazi (2004), Rafiq et
al. (2008), Hajazi and Tariq (2006), Shah and Khan
(2007), and Walliulah and Nishat (2008) empirically
proved positive relation between leverage and firm size,
where as Shah (2007) found negative relation between
leverage and firm size.
Growth
POT assumes that growing firm requires high capital;
internal funds are insufficient to meet requirements, and
so firms use external borrowing. This results increase in
level of leverage.
Hence POT assumes positive relationship between
leverage and growth (Drobez and Fix, 2003). Marsh
(1982) argued that the firms having high growth
opportunity are having high debt ratio. The firms with
higher growth opportunity may invest in high risk projects,
increasing the chances of bankruptcy and lowering the
opportunity of growth to zero (Myers, 1984; Harris and
Raviv, 1990). This makes creditor reluctant to lend funds
at lower rates or for long term (Mayers, 1977). To full fill
capital needs firms can use short term debt or convertible
bonds, resulting in positive relationship between short
term debt or leverage and growth. Marsh (1982); and
Cassar and Holmes (2003) have proved empirically
positive relation between leverage and firm growth.
Studies conducted using Pakistan firms data by Shah
and Hijazi (2004), Shah (2007), Hajazi and Tariq (2006),
Shah and Khan (2007), Rafiq et al. (2008), Walliulah and
Nishat (2008) empirically proved negative relationship
between leverage and firm growth.
Tangibility of assets
Fixed assets play important role in leverage level of firms.
The firms with higher level of fixed assets have higher
tendency of external borrowing by keeping fixed assets
as collateral.
The firms having fixed assets would keep fixed assets
as collateral with the lender. On default, tangible assets
would be seized, preventing firms from bankruptcy or
from incurring agency costs. Thus, firms having fixed
assets would more actively use leverage because of
fewer chances of bankruptcy and hence positive relationship
exists between leverage and fixed assets.
Berger and Udell (1998) argued that lending by the
banks depend on the collateral provided by the firms.
Similarly, firms having fixed assets can borrow at lower
rates because of their ability to provide assets as
collateral (Jensen and Meckling, 1976). This reduces the
risk bearded by the lenders and increased firms debt
level.The firms having high level of fixed assets would
prefer to borrow by using tangible assets as collateral
because of high equity issuing costs and asymmetry of
information makes the issued equity undervalued (Scott,
1977). Feri and Jones (1979), Titman and Wessels
(1988), and Chakraborty (2010) supported empirically the
positive relation between leverage and tangible assets.
Studies conducted using Pakistan firms data by Shah
and Hijazi (2004), Shah (2007), Hajazi and Tariq (2006),
Shah and Khan (2007), Rafiq et al. (2008) and Walliulah
and Nishat (2008) empirically proved positive relation
between leverage and tangibility of assets.
Non debt tax shields
The non-debt tax shield reduces the level of earnings,
which results in the reduction of expected level of interest
tax savings and reduces the advantage of using high
debt financing. If the firm has non-debt tax shield advantage,
then they can rely on them, because of bankruptcy
costs of increasing debt or chance of losing any debt tax
advantage (De Angelo and Masulis, 1980).
TOT suggests the greater use of debt to take advantage
of the interest tax shields, hence a positive relation
is suggested between tax and debt, but here it is
assumed that if firm has non-debt tax shield then it
should be used, which makes the lower interest tax benefit
for firms having high debt. Therefore, TOT assumes a
negative relationship between leverage and non-debt tax
shields. Many researchers have suggested that
depreciation deduction and investment tax credits can be
used as non-debt tax shields and they can be used as
alternative to the interest deduction benefit of the debt
financing. Most of studies had found negative relationship
between leverage and non-debt tax shields (Huang and
Song, 2006). Shahjahanpour et al. (2010) and
Chakraborty (2010) found positive relationship between
leverage and non-debt tax shields.
Tax
Mayers and Majluf (1963) argued that firms would finance
entirely through external financing because of tax

deductions of interest payments, however this
assumption would not apply if debt is interest free.
According to TOT with the increase in effective tax rate
external borrowing of firm increases.
Therefore, a positive relationship exists between
leverage and effective tax rate.
MacKie (1990), Huang and Song (2006), and
Shahjahanpour et al. (2010) proved positive relationship
between tax and leverage.
Liquidity
There are two opposite views relating the relationship
between liquidity and leverage.
According to TOT the more liquid firm would use
external financing due to their ability of paying back
liabilities and to get benefit of tax-shields, resulting in
positive relationship between liquidity and leverage.
POT assumes that the more liquid firm would use first
its internal funds and would decrease level of external
financing, resulting in negative relation between liquidity
and leverage. Most studies have found the negative relationship
(Mazur, 2007; Shahjahanpour et al. 2010). In this
study negative relationship between liquidity and
leverage is expected.
Payout ratio
Dividend policy is mainly ignored in empirically studies in
determining the capital structure.
Beattie et al. (2004), and Frank and Goyal (2004) found
that dividend policy proved to be very important
determinant in their analysis.
The dividend payment by the firms decreases the level
Where β1 = intercept (has constant value), β2 , β3, β4, β5, β6, β7, β8,
β9 , = the slope of coefficients, µit = error term, ΐ = cross sectional
unit, t = time period, LVG = Leverage, PRO = Profitability, SZ =
Size, GRW = Growth, TNG = Tangibility of assets, NDTS = Nondebt
tax shields, TAX = Tax, LIQ = Liquidity, PYOUT = Payout ratio.
Data and sample size
Panel data was used in this study. For each non-financial firm data
was collected from State bank of Pakistan publication “Balance
Sheet Analysis of Joint Stock Companies Listed on Karachi Stock
Exchange 2004-2009”.
Out of 417 non-financial firm’s data given in 2009, 336 firms had
fulfilled the data requirement of this study.
The sample size of this study was 336 non-financial firms with 5
years of data.
Ahmad et al. 11379
of internal funds, resulting in the increase in demand for
external financing. This results in positive relationship
between leverage. POT also supports the positive
relationship, but this theory disagrees when there are
sufficient internal funds with the firm. So according to
POT, firm should first use its internal sources then should
go for borrowing. Beattie et al. (2004) argued that growth
opportunity and profitability are closely associated with
the firm dividend policy of firms. When firm pay dividend
to shareholders its internal funds decreases, resulting in
lowering of funds for investment. When firm is expecting
growth then it would adjust its dividend payout in such
way that it would have sufficient funds for investment. In
this study dummy variable is used for payout ratio. In
Pakistan firms do not pay dividend every year therefore
the number of firms paying dividend each year of time
period under consideration becomes very small, for this
reason dummy variable was used. This enables more
firms to qualify for analysis. In this study “1” was assign to
the firms paying dividend in given year and “0” was
assign to the firm’s not paying dividend. Shahjahanpour
et al. (2010) found positive relationship between payout
ratio and leverage. The Table 1 shows the measurement
and expected signs of the independent variables.
MATERIALS AND METHODS
In this study panel data regression analysis was used to determine
relationship between leverage and eight independent variables. The
panel data consist of both time series (5 years data) and cross
sectional data (non-financial firms). This study analyzed 336 nonfinancial
firms (cross sectional units) over five years, as same five
years data is collected for all firms this type of panel is known as
balanced panel.
Fixed effect approach and random approach are used in the
panel data regression analysis. This study used fixed effect model
regression analysis. The model with eight variables is given as:
EMPIRICAL ANALYSIS
Descriptive analysis
The Table 2 shows the descriptive statistics of leverage
and eight independent variables. The leverage had a
mean (median) value of 0.702196 (0.660151), indicating
that the total assets of non-financial firms are 70.2196%
are financed through leverage. Remaining less than 30%
are financed through equity. The percentage of debt is
high as compared to the equity because most of the non-
financial firms are capital intensive and require high level
of investments in fixed assets, machineries etc to start
operations.
Profitability had a mean (median) of 0.070631
(0.069629), indicates that non-financial firms earns
(1)
(1)

11380 Afr. J. Bus. Manage.
Table 1. Variables, their measures and expected signs.
Determinant Measures Expected sign
Leverage total debt/ total assets
Profitability profit before tax / total sales -
Size logarithm of total sales +
Growth percentage change in total sales -
Assets tangibility fixed assets or tangible assets / total assets +
Non-debt tax shields depreciation + amortization/ total assets -
Tax total tax / earnings before tax -
Liquidity current assets/ current liabilities -
Payout ratio Dummy variable used, 0 for no dividend payment, 1 for dividend payment +
Table 2. Descriptive statistics of dependent and independent variables.
Variable Mean Median Maximum Minimum Standard deviation
Leverage 0.702196 0.660151 9.117647 -0.128405 0.504954
Profitability 0.070631 0.069629 26.51903 -14.66667 0.87021
Size 7.27849 7.253364 13.32562 -0.510826 1.792523
Growth 4.772331 6.14169 70.7381 -0.987024 4.252983
Tangibility 0.510154 0.528738 0.992392 0 0.220479
Non-debt tax shields 0.036249 0.032974 0.546608 0 0.026289
Tax 0.156664 0.080493 51.24062 -34.66667 1.958385
Liquidity 1.501608 0.9505 95.55 -0.143 3.892494
Payout 0.379023 0 1 0 0.485288
7.0631% profit before tax on their total sales. The profit
before tax is lower because of the greater use of
leverage. The interest payments are already deducted
before tax. Size registered the mean (median) of 7.27849
(7.253364), showing that there is 727.849% of increase
in total sales of firms. This high percentage is mainly
because of the inflation and increasing prices of goods.
Growth had a mean (median) of 4.772331 (6.14169)
indicating that the sales of firms has increased by
477.2331% each year. The reason for the growth of firms
is same as the size, that is, prices of the products had
increased considerably over time.
Tangibility had a mean (median) of 0.510154
(0.528738), showing that of total assets 51.0154% are
fixed assets as non-financial firms are mainly concerned
with the production of goods, therefore they require
machinery, equipments infrastructure etc for their operations.
Therefore non-financial firms having most of their
assets as fixed assets, whereas financial firms are more
concerned with liquidity, hence they are having large
portion of assets as current assets.
Non-debt tax shield had a mean (median) of 0.036249
(0.02974), indicating that 3.2974% of total assets are
accounted to depreciation each year. This 3% value is
high because as discussed before that non-financial firms
had almost 52% investment in fixed assets. This shows
that non-financial firms can rely on depreciation as non-
debt tax shield, because it reduces the earning before
tax. Hence, less taxable income firm would be having,
resulting in low tax payments.
Tax had a mean (median) value of 0.156664
(0.080493), registering that 15.664% of earning before
tax is paid as tax by non-financial firms. This percentage
is lower as compared to other countries, because most of
the firms in Pakistan show negative earnings by implicitly
showing higher values of raw material, lowering sales etc.
Liquidity had a mean (median) value of 1.501608
(0.9505), indicating that the firms had capital to pay back
their current liabilities. After paying their liabilities they still
had 0.501608 of excess current assets. This shows that
firms meeting easily the current portion of their long term
liabilities and had excess cash for their operations.
Payout (measured through dummy variable, 1 for the
firms paying and 0 for not paying dividend in given year)
had a mean value of 0.379023, indicating that 37.9023%
of firms paid dividend to their shareholders, where as
remaining firms do not paid dividend because of negative
earnings and accumulation of earnings for new projects.
Correlation matrix
The Table 3 shows the summary of correlation co-efficient
between leverage and eight independent variables.
The sign in the table indicates the relationship between

Table 3. Correlation matrix.
Leverage Profitability Size Growth Tangibility Non-debt tax shields Tax Liquidity Payout
Leverage 1
Profitability -0.048831 1
Size -0.196232 0.083717 1
Growth -0.119977 0.052206 0.4307 1
Tangibility 0.189672 -0.066344 -0.187415 -0.206692 1
Non-debt tax shields 0.144883 -0.044437 -0.022943 -0.079963 0.350686 1
Tax -0.020018 0.003967 0.07534 0.046746 -0.029632 0.001277 1
Liquidity -0.143564 0.291325 -0.149532 0.023241 -0.271997 -0.151116 0.000626 1
Payout -0.267809 0.098308 0.398932 0.335655 -0.313433 -0.05511 0.060857 0.050505 1
the variables. Positive sign indicates the positive
relationship whereas negative sign indicate the
negative relationship. The table indicated that
leverage is negatively related with the profitability.
This shows that firms leverage level increases
with the fall in profitability. This relation is
supported by POT.
The co-efficient of correlation between leverage
and size is -0.196232 indicating the negative
relationship. This result shows that as the firm
size increases, its demand for leverage
decreases. POT supports the negative relationship
between leverage and size. The reason for
negative relation is that large size firms accumulate
earnings and uses it to finance new projects.
Due to the asymmetry of information and good
reputation large firms prefer to issue equity.
Leverage is negatively correlated with growth
indicating that the leverage requirement of firms
decreases as the total sales increases. The
coefficient of correlation between leverage and
tangibility had a value of 0.189672, showing a
positive relation. This means that as the fixed
assets increases leverage level also increases.
The main reason for this relation is that the financial
institutions prefer lending to firms having high
level of assets and can provide assets as
collateral.
The table shows a positive relation between
leverage and non-debt tax shields. This result
indicates that leverage level increases with nondebt
tax shields. This result is opposite to TOT.
The correlation co-efficient between leverage and
tax had a value of -0.020018, showing a negative
relation between leverage and tax. This result is
also contradicting with TOT and previous studies
findings.
Leverage and liquidity had a negative correlation
co-efficient. This indicates that with the
decrease in liquidity external borrowing of the firm
increases. The negative relationship supports the
POT. The negative sign means that as the firm
utilize all of its accumulated capital then it borrows
from outside to meet short term obligations and
operating capital requirement.
The table shows that leverage and payout are
negatively correlated, indicating that as firms pays
earnings as dividend, and then they full fill their
requirements from external borrowing. The relationship
of size, tangibility, non-debt tax shields,
liquidity, payout with leverage are empirically
confirmed by fixed effect model where as
Ahmad et al. 11381
remaining three gives opposite results.
Fixed effect model results
To empirically analyze the relationship between
leverage and eight independent variables, panel
data fixed effect model approach is used. Results
of fixed effect model are given Table 4. The Table
4 shows that R-squared value is 0.176603 indicating
that 17.6603% variance in dependent
variable (leverage) can be explainable through
eight independent variable used.
The result shows that the five variables are
statistically significantly related to the leverage.
The table also shows that the intercept does not
change significantly over time as the t-statistics
value for D_2006, D_2007 and D_2009 are less
than two, where as for D_2008 t-statistics is
higher than two. The table shows that the
industrial classification does matter in determining
the capital structure of firms, as the t-statistics
value of all the industrial are significantly high.
This shows that industry type is one of the important
factors in determining the capital structure of
firm. Previous study of Shah and Khan (2007)

11382 Afr. J. Bus. Manage.
Table 4. Fixed effect model results.
Variable Coefficient Standard Error t-Statistic Prob.
Profitability 0.011685 0.013757 0.849414 0.3958
Size -0.021098 0.008257 -2.554998 0.0107
Growth 0.002073 0.005613 0.369391 0.7119
Tangibility 0.279347 0.058538 4.772081 0
Non-debt tax shields 1.601033 0.474597 3.373458 0.0008
Tax 0.000542 0.00584 0.092823 0.9261
Liquidity -0.012191 0.003176 -3.838542 0.0001
Payout -0.175844 0.027776 -6.330849 0
D_2006 0.057738 0.035347 1.633458 0.1026
D_2007 0.059054 0.035704 1.654017 0.0983
D_2008 0.085717 0.035829 2.392431 0.0168
D_2009 0.058255 0.035654 1.633925 0.1025
D_TEXTILE 0.670281 0.065203 10.27998 0
D_CHEMICAL 0.556261 0.061472 9.048975 0
D_ENGINERRING 0.689003 0.062184 11.08009 0
D_SUGARNALLIED 0.869406 0.064528 13.47323 0
D_CEMENT 0.503799 0.080093 6.290178 0
D_FUELNENERGY 0.680027 0.078186 8.697562 0
D_TRANSPORTATION 0.792579 0.091627 8.650011 0
D_TOBACCO 1.824259 0.130586 13.96977 0
D_JUTE 0.587983 0.128641 4.570722 0
D_VANASPETI 0.783382 0.129698 6.040041 0
D_MIS 0.638457 0.05818 10.97385 0
R-squared 0.176603
Adjusted R-squared 0.165657
Table 5. Summary of results.
Determinant Predicted signs by theories Expected sign Observed sign
Profitability -(POT), +(TOT) - +
Size -(POT), +(TOT) + -
Growth +(POT), -(TOT) - +
Assets tangibility +(POT), +(TOT) + +
Non-debt tax shields -(TOT) - +
Tax +(TOT) - +
Liquidity -(POT), +(TOT) - -
Payout ratio +(POT) + -
had found insignificant relation between capital structure
and industrial classification. This is one of the main
finding of this study that there exist a statistically
significant relationship between capital structure and
industrial classification. In this study paper and board
industry is taken as intercept. The detail result of each
variable is given below.
Profitability
The Table 5 shows that the coefficient value between
leverage and profitability had a value of 0.011685,
indicating a positive relationship. The relationship is
statistically insignificant with t-statistics value of 0.849414
and p-value of 0.3958. This result rejects the expected
relationship between leverage and profitability for this
study. This result indicates that with the increase in
profitability of firms, leverage level raises. The positive
relationship is supported by TOT. The argument for the
relation is that with the increase in profitability, firm ability
to pay back loan increases. With profitability, goodwill
and status of the firm improves in market. Agency and
information asymmetric costs decreases and firms are
having free cash flows to meet their obligations. Similarly

an interest payment also reduces taxable income,
resulting in less tax payments. Previous studies such as
Ooi (1999) proved positive relationship between leverage
and profitability.
Size
The results show that a negative relationship between
leverage and size with the co-efficient value of -0.021098.
This relation is statistically significant with t-statistics
value of -2.554998 and p-value of 0.0107. This result
rejects the expected relation sign between leverage and
size.
The result indicates that with the increase in size of
firm, its leverage level decreases. This result is in line
with POT, which also suggests the negative relationship
between leverage and size. The large size firm has more
free cash flows, accumulated earnings and does not
require to disclose any information to the outsiders in
case of equity financing. The other main reason of
preferring equity is that with good reputation of large size
firms and free cash flows outsiders expect the increase in
value of firm. This results in overvaluation of firm equity.
Thus firms get benefit of overvalued equity by issuing
new equity. Previous studies have also proved negative
relationship between leverage and size (Mazur, 2007).
Growth
The co-efficient value between leverage and growth had
opposite result as was expected in this study, as indicated
by the value 0.002073. The relationship is statistically
insignificant with t-statistics value of 0.369391 and pvalue
of 0.7119. This result rejects the expected sign of
relationship between leverage and growth. The result
suggests that with the percentage increases in total
sales, leverage level of the firm also raises. The positive
relation between leverage and growth is also supported
by POT.
Tangibility of assets
The results show a statistically significant positive relation
between leverage and tangibility of assets with coefficient
value of 0.279347, t-statistics value of 4.772081
and p-value of 0. The result indicates that with the increase
in tangible assets leverage level of the firm raises.
The results accept the expected positive signs for this
relationship. The firms with high level of fixed assets can
keep assets as collateral while getting loans. Financial
institutions also prefer firms that can provide collateral.
As a result, the leverage level of firms rises. Previous
studies conducted in Pakistan also found positive
relationship between leverage and tangibility of assets.
Non-debt tax shields
Ahmad et al. 11383
The table shows that non-debt tax shields is positively
and statistically significantly related with the leverage
having co-efficient value of 1.601033, t-statistics value of
3.373458 and p-value of 0.0008. This result rejects the
expected relationship for this variable.
The result suggests that with the increases in non-debt
tax shields leverage also increases. The argument for
positive relation can be that most of Pakistani firms try to
reduce the tax payments.
To do so they generally implicitly record the increase in
raw materials costs, show lower sales etc. the use of both
non-debt tax shields and leverage reduces the taxable
income of the firm. First non-debt tax shields reduces the
operating income, and then interest payments are
deducted from operating income, leaving small earning
before tax.
Tax
The results indicate that tax is positively and insignificantly
related with leverage, having co-efficient value of
0.000542, t-statistics value of 0.092823 and p-value of
0.9261. The result accepts the expected positive relationship
between leverage and tax. Previous study
conducted by Shahjahanpoor et al. (2010) also found
positive relation. The positive relation is also supported
by TOT. The result indicates that with the increase in tax
rate leverage level also increases. As interest payments
are tax deductible, therefore firm uses more debt to
reduce earnings before tax. As the earning before tax
decreases, taxable income reduces resulting in reduction
of tax payments.
Liquidity
The result indicates a statistically significant negative
relationship between liquidity and leverage with co-efficient
value of -0.012191, t-statistics value of -3.838542
and p-value of 0.0001. The result accepts the expected
negative relationship between leverage and liquidity.
Previous studies conducted by Mazur, 2007 and
Shahjanhanpoor et al. (2010) also found negative
relationship. POT also supports the negative relationship.
The result suggests that more liquid firm would reduce
the level of leverage by using their own earnings and
accumulated earnings.
Payout ratio
The result suggests that payout is negatively and statistically
associated with leverage. The co-efficient value for
relation is -0.175844, having t-statistics value of -6.330849

11384 Afr. J. Bus. Manage.
and p-value of 0.
This result rejects the expected sign for relationship. In
this study dummy variable is used for payout. The result
indicates that with payout, leverage level decreases. The
reason for decrease is that Pakistani firms mainly do not
pay dividend to their shareholders because of negative
earnings and accumulation of earnings for new projects
or investments.
Firms only pay dividend when they are having excess
of earnings after meeting their internal current and future
funds demand. As the internal earnings are enough,
therefore after paying dividend, firms still maintain capital.
This is the main reason for negative relationship.
Conclusion
This study aims at adding new determinants of capital
structure literature for Pakistani non-financial firms by
using the book leverage as dependent and set of eight
variables including three variables (tax, liquidity and
payout ratio) checked for first time for Pakistani firm’s
data. This study used panel data for 336 non-financial
firms over the period of 2005-2009.
This study investigates that POT and TOT, to what
extent explains the Pakistani non-financial firm’s capital
structure determinants.
The results showed that out of eight variables five (size,
tangibility of assets, non-debt tax shields, liquidity and
payout) are statistically significantly related to leverage,
indicating that, these five variables play important role in
determining the capital structure of Pakistani nonfinancial
firms. The remaining three are statistically
insignificantly related with leverage. Two expected
relation are accepted while six are rejected after empirical
analysis. The results show that industrial type play very
important role in determining capital structure of Pakistani
non-financial firms. The results showed that negative
relation of size, liquidity and positive relation of growth
with the leverage are in consistent with the POT. Similarly
the positive relation of profitability and tax are in
consistent with the TOT.
LIMITATIONS AND FUTURE RESEARCH
This research used Pakistani non-financial firm’s data, so
the results of this study could not be generalized in any
other sector of Pakistan economy (banks, service sector
etc) or in any other country non-financial firms. Future
studies can be conducted by using the data from other
sectors of Pakistan economy or other developing country
non-financial data. Similarly, this study uses the book
leverage as dependent variable and eight explanatory
variables. Future research can include the market
leverage and other explanatory variables.
Future studies can be conducted to investigate the
influence of risk on non-financial firms.
REFERENCES
Aggarwal R (1981). International Differences In Capital Structure
Norms: An Empirical Study Of Large European Companies. J.
Manage. Int. Rev., 21: 75-88.
Allen DE, Mizuno H (1989). The Determinants of Corporate Capital
Structure: Japanese Evidence. J. Appl. Econ.. 21: 569-585.
Al-Sakran S (2001). Leverage Determinants In The Absence Of
Corporate Tax System: The Case Of Non-Financial Publicly Traded
Corporation In Saudi Arabia. J. Manage. Financ., 27(10): 58-86.
Baskin J (1989). An Empirical Investigation of the Pecking Order
Hypothesis. Financ. Manage., 18: 26-35.
Beattie V, Goodacre A, Thomson SJ (2004). Diversity And
Determinants Of Corporate Financing Decisions: Survey Evidence.
Working Paper University of Stirling.
Bennett M, Donnelly R (1993). The Determinants Of Capital Structure:
Some UK evidence. Br. Account. Rev., 25: 43-59.
Berger AN, Udell GF (1998). The economics of small business finance:
The roles of private equity and debt markets in the financial growth
cycle. J. Bank. Financ., 22: 613-74.
Bhaduri S (2002). Determinants of Corporate Borrowing: Some
Evidence from the Indian Corporate Structure. J. Econ. Financ., 26:
200-215.
Booth L, Aivazian V, Demirguc-Kunt A, Maksimovic V (2001). Capital
Structure In Developing Countries. J. Financ., 56: 87-130.
Carleton Willard T, Silberman IH (1977). Joint Determination Of Rate Of
Return And Capital Structure: An Econometric Analysis. J. Financ.
32: 811-821.
Cassar G, Holmes S (2003). Capital Structure and Financing of SMEs:
Australian evidence. J. Account. Financ., 43: 123-147.
Chakraborty I (2010). Capital Structure In An Emerging Stock Market:
The Case Of India. J. Res. Int. Bus. Financ., 24: 295-314.
Chen JJ (2004). Determinants Of Capital Structure Of Chinese-Listed
Companies. J. Bus. Res., 57: 1341–1351.
De Angelo H, Masulis R W (1980). Optimal Capital Structure Under
Corporate And Personal Taxation. J. Fin. Econ., 8: 3-29.
Deesomsak R, Paudyal K, Pescetto G. (2004). The Determinants Of
Capital Structure: Evidence From The Asia Pacific region. J. Multi.
Financ. Manage., 144(5): 387-405.
Delcoure N (2007). The Determinants Of Capital Structure In
Transitional Economies. J. Int. Rev. Eco. Financ., 16: 400-415.
Donaldson G (1961). Corporate Debt Capacity: A Study of Corporate
Debt Policy and the Determinants of Corporate Debt Capacity.
Working paper Division of Research Harvard Business School
Harvard University Cambridge MA.
Dorbetz W, Fix R (2003). What are the Determinates of the Capital
Structure? Some Evidence for Switzerland. Working paper: 4/03.
WWZ/Department of Finance.
Fattouh B, Pasquale S, Laurence H (2005). Capital Structure in South
Korea: A Quantile Regression Approach. J. Dev. Econ., 76: 231-
250.
Ferri MG, Jones W H (1979). Determinants of Financial Structures: A
New Methodological Approach. J. Financ., 34(3): 631-644.
Frank MZ, Goyal VK (2004). Capital Structure Decisions: Which Factors
Are Reliably Important? EFA 2004 Maastricht Meetings Paper No.
2464: Tuck Contemporary Corporate Finance Issues III Conference
Paper.
Harris M, Raviv A (1991). The Theory of Capital Structure. J. Financ.
46: 297–355.
Hijazi ST, Tariq YB (2006). Determinants of capital structure: A case for
Pakistani Cement Industry. Lah. J. Econ., 11(1): 63-80.
Homaifar G, Ziets J, Benkato O (1994). An Empirical Model Of Capital
Structure: Some new evidence. J. Bus. Financ. Account., 21(1): 1-
14.
Huang SG, Song F M (2005). The Determinants of Capital Structure:
Evidence from China. J. China Econ. Rev., pp. 1-23.
Jensen M (1986). Agency Costs of Free Cash Flow Corporate Finance
and Takeovers. Am. Econ. Rev., 76: 323-329.
Jensen M, Meckling W (1976). Theory of the Firm: Managerial Behavior
Agency Costs and Capital Structure. J. Financ. Econ., 3: 305-360.
Kester WC (1986). Capital And Ownership Structure: A Comparison Of
United States and Japanese Manufacturing Corporations. Financ.

Manage., pp. 5-16.
Kim WS, Sorenson EH (1986). Evidence On The Impact Of The Agency
Costs Of Debt On Corporate Debt Policy. J. Financ. Q. Anal., 21(2):
131-144.
Kunt A, Maksimovice V (1994). Capital Structure in Developing
Countries: Evidence from Ten Countries. Working Paper the World
Bank Policy Research Paper, p. 1320.
Long M, Malitz L (1985). The Investment Financing Nexus: Some
Empirical Evidence. Mid. Corp. Financ. J., 3: 53-59.
MacKie M, Jeffrey K (1990). Do Taxes Affect Corporate Financing
Decisions? J. Financ., 45(5): 1471-93.
Marsh P (1982). The Choice between Equity and Debt: An Empirical
Study. J. Fin. 37: 121-144.
Mazur K (2007). The Determinants of Capital Structure Choice:
Evidence from Polish Companies. J. Int. Atl. Econ. Res., 13: 495-514.
Michaelas N, Chittenden F, Poutziouris P (1999). Financial Policy And
Capital Structure Choice In UK SMEs: Empirical evidence from
company panel data. Small Bus. Econ., 12: 113-130.
Modigliani F, Miller MH (1958). The Cost of Capital Corporate Finance
and the Theory of Investment. J. Am. Econ., Rev. 48: 261-275.
Myers SC (1977). Determinants of corporate borrowing. J. Financ.
Econ., 5: 147-75.
Myers SC (1984). The Capital Structure Puzzle. J. Financ., 39(3): 575-
592.
Myers SC, Majluf N S (1984). Corporate Financing and Investment
Decisions When Firms have Information that Investors do not have.
J. Financ. Econ., 13(2): 187-221.
Ooi J (1999). The Determinant of Capital Structure: Evidence on Uk
Property Companies. J. Prop. Invest. Financ., 17(5): 464-480.
Ozkan A (2001). Determinants of Capital Structure and Adjustment to
Long Run Target: Evidence from UK Company Panel Data. J. Bus.
Financ. Account., 28(1/2): 175-198.
Pandey IM (2001). Capital Structure and Firm Characteristics: Evidence
from an Emerging Market. Indian Institute of Management
Ahmedabad Working Paper.
Park H (1998). The Effect of National Culture on the Capital Structure of
Firms. Int. J. Manage., 15: 204–211.
Ahmad et al. 11385
Rafiq M, Iqbal A, Atiq M (2008). The Determinants of Capital Structure
Of The Chemical Industry in Pakistan. Lah. J. Econ., 13(1): 139-158.
Rajan RG, Zingales L (1995). What Do We Know About Capital
Structure? Some Evidence from International Data. J. Financ. 50(5):
1421-1460.
Scott DE (1972). Evidence on the Importance of Financial Structure. J.
Fin. Manage. 1: 45-50.
Shah A, Hijazi T (2004). The Determinants of Capital Structure of Stock
Exchange-Listed Non-Financial Firms In Pakistan. Pak. Dev. Rev.,
43: 605-618.
Shah A, Khan S (2007). Determinates Of Capital Structure: Evidence
From Pakistani Panel Data. Int. Rev. Bus. Res. Paper, 3(4): 265-282.
Shahjanhanpour, A Ghalambor, Aflatooni A (2010). The Determinants
Of Capital Structure Choice In The Iranian Companies. Int. Res. J.
Financ. Econ., 56: 167-178.
Smith CW, Warner JB (1979). On Financial Contracting: An Analysis of
Bond Covenants. J. Financ. Econ., 7: 16-117.
Teker D, Ozlem T, Ayca T (2009). Determinants of Capital Structure for
Turkish Firms: A Panel Data Analysis. Int. Res. J. Financ. Econ., 29:
179-187
Titman S (1984). The Effect of Capital Structure on The Firm’s
Liquidation Decision. J. Financ. Econ., 13: 137-52.
Titman S, Wessels R (1988). The Determinants of Capital Structure
Choice. J. Financ., 43: 1-19.
Waliullah, M Nishat (2008). Capital Structure Choice in an Emerging
Market: Evidence from Listed Firms in Pakistan. Paper presented in
21st Australian Finance and Banking Conference.
Wiwattanakantang Y (1999). An Empirical Study on the Determinants of
Capital Structure of Thai Firm. J. Pac. Bas. Financ., 7: 371–403.

African Journal of Business Management Vol. 5(28), pp. 11532-11538, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.1629
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Debt (a real hurdle in the economic growth of Pakistan):
A time series analysis
Mehboob Ahmed 1 and Maryam Shakur 2 *
1 Department of management sciences,Bahria University, Islamabad.
2 Allama Iqbal Open University, Islamabad, Pakistan.
Accepted 13 September, 2011
This study aims to highlight the problems created by the debt (external debt) to economic growth of
Pakistan. Time series data from the FY1981 to FY 2008 is used. The analysis includes five variables.
Growth rate of GDP per capita is taken as a dependent variable while external debt to GDP ratio,
investment to GDP ratio, population growth rate and trade openness are independent variables. The
ADF- Unit Root Test is applied to check the stationarity of data. The co-integration estimation is applied,
which shows the long run relationship between external debt and growth rate of GDP per capita.
Furthermore, the Granger Causality Vector Error Correction (GCVEC) method has proved unidirectional
relationship between external debt and growth rate of GDP per capita. There is no doubt that
other macroeconomic variables also affecting the economic growth but the long term relationship of
debt and economic growth has proved that the main economic indicator behind the low economic
growth is debt.
Key words: Debt, economic growth, Pakistan.
INTRODUCTION
Debt, domestic and external, is a major tool of removing
saving-investment gap of developing countries like
Pakistan. Country needs financial capital to fill its savinginvestment
gap, that is, to boost up its investment to
attain economic growth. Unfortunately, economic facts of
the country do not show such picture due to many
reasons including lack of proper planning and it’s in time
implementation, liquidity problem, donor based strategies,
political instability, large and persistent fiscal and
current account balance of payments deficits, wasteful
government spending, undertaking of low economic
priority development projects, insufficient exchange rate
adjustment, lack of international market competitiveness,
weakening of terms of trade, higher interest rates, decline
in external resource inflow, lower export earnings and
lower domestic output. We can say that these financial
and political factors are the main reason of country’s high
indebtedness.
Pakistan is facing two debt problems in the form of huge
*Corresponding author. E-mail: maryamshakur@yahoo.com.
domestic/internal debt and very large external debt. More
than 50% of the debt is external in nature. We will
discuss external debts separately because servicing of
external debt is a huge burden on the economy. The way
the debts are increasing whether pubic or external, is a
threatening situation of a country’s economic condition.
Many empirical findings have proved that, debt burden
depresses the economic growth of the country because it
affects many of the economic variables directly or
indirectly. It also discourages the structural and fiscal
reforms taken by the government which increases the
level of economic growth of the country. This problem is
not faced only by Pakistan, but by all the low income
countries facing the same economic situation because of
heavy external debt. We have quoted many of such
findings in the literature review and methodology of this
study.
Although, debt is very useful in financing the financial
gaps especially in case of fulfillment of balance of
payment financing needs but places the future repayment
obligation on the economy. This obligation is very hard to
meet for the developing countries like, Pakistan, because
of weak macro-economic performance and many other

easons as earlier mentioned. These debt inflows create
serious problems to the economy because it is not able to
manage its debt obligations. Debt sustainability is the
major issue for Pakistan, because of weak economic
condition; the country is unable to repay its debt properly.
This unsustainable debt situation forces to reallocate the
resources of the country towards debt servicing. This
situation definitely affects negatively investments, developmental
programs and poverty reduction initiatives.
This reallocation of resources depresses the economic
growth.
Besides, all the economic and political factors
discussed and faced by Pakistan and other developing
countries, Pakistan has been facing a war situation since
9/11. The war of terrorism has affected the economic
pillar of the country because we are fighting with the
terrorists inside the country. This depresses the foreign
as well as domestic investment incentives which in turn
decreases the economic growth and affects the stability
of other macroeconomic indicators negatively. Decrease
in investment might be managed but a severe capital
flight adversely affects the economy. The major portion of
the revenue received by the government is used in
defense expenditures which leave with a very nominal
amount for the repayment of the debt, public investments
and for other social works of the government.
Objectives and organization of the study
The purpose of the study is to highlight the impact of debt
on growth of Pakistan, by finding the causality between
them.
REVIEW OF THE LITERATURE
The debt burden of Pakistan is increased from $50 to $51
billion to $55 to $56 billion after receiving $5.28 billion
from friends of Pakistan and donors’ conference held in
Tokyo on 17 th April 2009, the analysis was made by
Ahmed (2009). He further analyzed that the current
account and budget deficit came down but this was as a
result of the cut down of development projects and
withdrawal of subsidies. Mustafa (2009), focused on the
government’s review of first nine months of the FY2008
to 2009, which showed a $3.4 billion increase in external
debt and liabilities. Internal debt burden is also increased
because of the lack of availability of external financing.
Similarly, Almas (2008) highlighted that short term
borrowing is the major cause of increase in debt burden.
In addition, he analyzed that maturity of Sukuk and Euro
bonds are another burden on the economy.
The multiple co-integration procedures were made by
Hameed et al. (2008) which resulted in a negative long
run relationship between debt and economic growth. The
negative relationship between external debt and
Ahmed and Shakur 11533
economic is confirmed by Presbitero (2007) after
analyzing this relationship by using GMM methodology
on two different models; one for economic growth and the
other, for investment. The same analysis was made by
Schclarek (2004) by exploring four different variables that
is, GDP per capita growth rate, total factor productivity
growth rate, capital accumulation growth rate, and private
saving rate with data set of a panel of 59 developing and
24 industrial countries.
The improper utilization of the debt created debt crisis
side by side the donor’s agenda, corruption, capital flight
and low saving rate has affected the growth rate of the
country. Kemal (2005) was commented in his paper.
Standard growth model and public investment model was
used by Clements et al. (2005). They took data from
1970 to 1999 for 55 low income countries and analyzed
strong and direct external debt-growth relationship than
investment-growth relationship. Gill and Pinto (2005)
suggested that developing countries should aim low
public indebtedness, as sovereign debt dampens
economic growth. He examined the growth, capital flow
and crisis literature for the developing countries with
access to international market.
Pattillo et al. (2004) used the data of 61 countries over
the period of 1969 to 1998. They applied growthaccounting
framework and found that doubling the
average external debt level reduces growth of both per
capita physical capital and total factor productivity by
almost one percentage point. In other words, large debt
stocks negatively affect growth by dampening both
physical capital accumulation and total factor productivity.
Pattillo et al. (2002) tested the impact of economic growth
by using four different methodologies including OLS
estimation, instrumental variables with lagged values,
fixed effects and GMM with and without dummies and
without investment. The analysis used 3 years average
panel data of 93 developing countries and got the same
result. In addition to that, Sidique and Malik (2001)
examined the debt-growth relationship for South Asian
countries by using Fixed Effect Model and got non-linear
relationship between them.
The negative casual impact of the debt on economic
growth was estimated by Chowdhary (2001) on both 35
HIPCs and 25 non-HIPCs through sensitivity tests and
the mixed, fixed and random coefficient approach that
allowed heterogeneity in the causal relationship between
debt and growth. Increase in external debt obligations
depresses investment and hence, the economic growth
of the recipient countries. Chaudhary and Anwar (2001)
estimated the Debt Laffer Curve (DLC) for South Asian
countries by applying both Debt Laffer Curve with OLS
technique and Debt Laffer Curve with price elasticity
problem by using time series data with sample size from
1970 to 1972 to 1994 to 1995. Pakistan becomes a
stateless nation, because of increase in debt,
bureaucracy, war with India, nuclear explosion and
military take-over as explained by Zaman (2001).

11534 Afr. J. Bus. Manage.
Table 1. Growth rate of GDP (%) at constant factor cost.
Variable 1960’s 1970’s 1980’s 1990’s 2000’s
Growth rate of GDP (%) 6.8 4.8 6.5 4.6 4.8
Source: Economic Survey of Pakistan (2009 - 2010).
Table 2. Macroeconomic indicators (%).
Variable FY90 FY95 FY00 FY05 FY06 FY07 FY08
Inflation 6.0 13.0 3.6 9.3 7.9 7.8 12.0
GDP growth 4.6 5.1 3.9 9.0 5.8 6.8 5.8
Reserves 529.0 743.1 1352.3 12597.9 13122.0 15646.0 11378.2
FD/GDP 6.1 4.9 5.4 3.3 4.3 4.3 7.0
Exports 4,926.0 7,759.0 8,191.0 14,400.5 16,387.0 17,278.0 20,125.0
Trade balance -2,485.0 -2,537.0 -1,411.0 -4,352.5 -8,237.0 -9,711.0 -15,285.7
Trade balance/GDP -5.1 -3.5 -1.9 -3.9 -6.4 -6.7 -8.8
Money growth 17.5 17.2 9.4 19.3 14.9 19.3 15.3
M2/GDP 39.9 36.6 45.6 44.7 46.6 44.7
Reserve money growth 15.4 9.4 25.1 17.6 10.2 20.9 21
Source: State Bank of Pakistan. All values except exports and trade balance are in percentage while these are in billion US $.
Present debt situation
Debt burden creates a number of financial problems
which led to persistent budget and current account deficit.
Country’s both real and fiscal sectors are suffering from
internal and external debt. If we look at our economic
condition, it appears that not only the budget deficit but
deficiency in savings and its negative effect on balance of
payment are the major causes of foreign debt burden.
The absence of proper debt management is severely
effecting the monetary and fiscal operations. This
situation places an additional burden on external
accounts as the greater amount of resources is diverted
to debt servicing.
The external debt history of the country shows the
ever-increasing debt burden of the country. The growth
indicators also showed a decreasing trend (Table 1). The
stagnant situation of government revenues and exports
resulted in increasing debt burden. As seen from Figure
1, the debt burden became almost doubled from 1970 to
1978 to 1978 to 1983.
The growth situation of the country varied tremendously
during this decade. It was at its highest in FY2004 to
2005 at 9% but fell drastically in FY2008 to 2009 to 1.2%.
This situation affected all the economic indicators of the
country.
The depressed economic growth throughout the
decade did not allow the 7.5 and 9.0% growth rate in
FY2003 to 2004 and 2004 to 2005 respectively to trickle
down. A continuous fall in economic growth depressed
almost all the economic indicators as shown in Table 2.
METHODOLOGY
Data and variables
The data used is time series of the sample size of 28 observations
taken from 1980 to 1981 to 2007 to 2008. The data of growth rate
of GPD, imports, exports, investment and population is taken from
various issues of Pakistan economic survey (PES), while the data
of growth rate of GDP is taken from the Statistical Periodicals of the
State Bank of Pakistan. All the data taken is constant at the base
year 1999 to 2000 and is in logarithmic form.
The model
The model used (Siddique and Malik, 2001) which relates the
economic growth with investment, trade openness and population
can be written as follows:
G t =α0+α1EDt+ α2INV t +α3PG t +α4OPt + ε t (1)
RESULTS INTERPRETATION
Augmented Dicky-Fuller unit root test is applied to the
time series data to check the stationarity. Our results
have shown that all the variables are non-stationary and
have order of integration 1 as shown in Table 3. We
applied the Johansen’s Co-integration method to find out
the long run relationship between the variables. The
method introduced by Granger (1981) applied the
Johansen’s Cointegration method to find out the long run
relationship between the variables after applying the
ADF- Unit Root Test. We checked the order of integration

Table 3. Augmented Dicky-Fuller unit root test results.
Ahmed and Shakur 11535
Variable Order of integration Calculated 1% critical value 5% critical value
G t Level Intercept -2.241510 -3.7204 -2.9850
Intercept and trend -2.319 -4.374 -3.6027
None 0.138799 -2.6603 -1.9552
1 st difference Intercept -5.1919 -3.7343 -2.9907
EDt Level Intercept -2.4332 -3.7076 -2.9798
Intercept and trend -0.4998 -4.355 -3.994
None -4.844 -2.656 -1.9546
1 st difference Intercept -5.027232 -3.7204 -2.985
INVt Level Intercept -1.9247 -3.7076 -2.9798
Intercept and trend -3.1323 -4.355 -3.394
None 0.1147 -2.256 1.955
1 st difference Intercept -4.149 -3.72 -2.985
PG t Level Intercept 0.0946 -3.7076 -2.979
Intercept and trend -2.1574 -4.355 -3.594
None -2.0166 -2.653 -1.9546
1 st difference Intercept -4.0147 -3.7204 -2.985
OPt Level Intercept -1.205961 -3.7076 -2.9798
Intercept and trend -1.5768 -4.355 -3.594
None 0.458233 -2.6560 -1.9546
1 st difference Intercept -6.010937 -3.7204 -2.9850
Table 4. Normalized cointegrated coefficients.
G t EDt INVt PG t OPt C
1.000000 0.781755 (0.11309) -2.51082 (0.41047) 5.470588 (2.00170) 0.629635 (0.30902) -4.04729
Log likelihood 214.0255
Table 5. Correlation matrix.
Variable G t EDt INVt PG t OPt
G t 1 -0.69004 0.626016 0.419777 0.003262
EDt -0.69004 1 -0.76974 -0.86098 0.424606
INVt 0.626016 -0.76974 1 0.526181 -0.15624
PG t 0.419777 -0.86098 0.526181 1 -0.69219
OPt 0.003262 0.424606 -0.15624 -0.69219 1
of the data which is I (1) from the co-integration results
shown in Table 4. As such, we can write the equation as:
G t = -0.781755EDt+ 2.510822INVt -5.470588PG t -
0.629635OPt (2)
Equation 2 indicates that growth rate of GDP per capita
has negative relationship with external debt, population
growth and trade openness while positive with investment
(Hameed et al. (2008). Equation 2 has proved that
increase in external debt depresses economic and
similarly the population growth. The trade openness is
effecting negatively because of the larger portion of
imports comparative to the exports. The investment is
showing a strong and positive relationship with growth
rate of GDP per capita.
In Table 5, results of correlation matrix has shown
strong but negative correlation of growth rate of GDP per

11536 Afr. J. Bus. Manage.
Table 6. Granger Causality tests: Pairwise Granger causality tests.
Null hypothesis Obs F-Statistic Probability
EDt does not Granger cause G t
G t does not Granger cause EDt
24
6.68447
0.50310
0.00350
0.68522
INVt does not Granger cause G t
1.10280 0.37521
24
G t does not Granger cause INVt 3.89071 0.02761
LPGR does not Granger cause G t
1.57548 0.23203
24
G t does not Granger cause LPGR 1.05713 0.39314
OPt does not Granger cause G t
0.43459 0.73103
24
G t does not Granger cause OPt 0.66783 0.58331
INVt does not Granger cause EDt
0.13941 0.93511
25
EDt does not Granger cause INVt 3.70177 0.03100
PG t does not Granger cause EDt
4.92439 0.01138
25
EDt does not Granger cause PG t 1.78358 0.18633
LTOP does not Granger cause EDt
3.16992 0.04955
25
EDt does not Granger cause OPt 3.94576 0.02517
PG t does not Granger cause INVt
1.78496 0.18607
25
INVt does not Granger cause PG t 1.25805 0.31845
OPt does not Granger cause INVt
0.21746 0.88299
25
INVt does not Granger cause OPt 0.39620 0.75731
OPt does not Granger cause PG t
1.61645 0.22063
25
PG t does not Granger cause OPt 0.92434 0.44909
Sample: 1981 2008; Lags: 3.
capita with external debt to GDP ratio as the value of
correlation coefficient is 0.7 approximately and is
negative, investment to GDP ratio and population growth
rate has moderate and positive correlation. The values of
their correlation coefficients are 0.626 and 0.419
respectively. In spite of cointegration test, the correlation
among the trade openness and growth rate of GDP per
capita has shown positive but weak correlation. The
causality test of the said data has shown that external
debt is the only factor that is affecting the GDP growth
rate in the long run. This is showing the impact of debt
overhang to the GDP. The remaining variables show
short run relationship between them. From Table 6, by
taking 3 years lag value, the long run relationship is
observed between the external debt to GDP ratio and
growth rate of GDP per capita as its F-stat is 6.68447
which is significant at 5% level of significance. The null
hypothesis which states that external debt to GDP ratio
does not Granger cause is rejected. As such, there is no
causality among all the other variables.
The VECM results in Table 7 shows negative and long
run relationship between debt and growth rate of GDP
per capita. It is the case with population growth rate, but
investment and trade openness are showing shot run
relationship with growth rate of GDP per capita.
Conclusion
The main focus of the study is to highlight the impact of
external debts on the economic growth of the country.
The magnitude of the debt burden is increasing day by
day which is increasing the dependency of Pakistan on
external resources. The economic situation is getting
worse with the passage of time; therefore Pakistan will
not be able to stand on its feet, if same situation of external
borrowing will prevail. This situation has been very
clearly highlighted and the unit root test results confirmed
that all the variables are integrated of the same order.
The cointegration has shown the long run relationship

Million dollars
35000
30000
25000
20000
15000
10000
5000
0
External Debt (million $)
1965-70 1970-78 1978-83 1983-88 1988-93 1993-98
Figure 1. External debt in million dollars.
Table 7. Vector error correction estimates (VECM).
Years
Ahmed and Shakur 11537
Error correction D(G t) D(EDt) D(INVt) D(PG t) D(OPt)
-1.134577 -0.030798 0.203612 -0.077553 -0.032792
CointEq1
(0.50962) (0.11687) (0.09377) (0.02600) (0.08343)
(-2.22633) (-0.26352) (2.17141) (-2.98255) (-0.39302)
D(G t (-1))
D(G t (-2))
D(EDt (-1))
D(EDt (-2))
D(INVt (-1))
D(INVt (-2))
D(PG t (-1))
-0.082858 0.028698 -0.132755 0.065485 0.000396
(0.40122) (0.09201) (0.07382) (0.02047) (0.06569)
(-0.20651) (0.31190) (-1.79826) (3.19885) (0.00602)
0.005807 0.052106 -0.093552 0.024636 0.027463
(0.26970) (0.06185) (0.04962) (0.01376) (0.04416)
(0.02153) (0.84246) (-1.88517) (1.79030) (0.62196)
-2.463829 0.407044 -0.035481 0.089757 -0.485873
(1.20197) (0.27564) (0.22116) (0.06133) (0.19679)
(-2.04983) (1.47671) (-0.16043) (1.46355) (-2.46904)
-3.181019 0.238919 -0.193351 0.026694 -0.078511
(1.39596) (0.32013) (0.25685) (0.07123) (0.22855)
(-2.27873) (0.74632) (-0.75276) (0.37478) (-0.34352)
-0.683163 -0.099492 0.325175 -0.171440 0.138019
(1.66979) (0.38293) (0.30724) (0.08520) (0.27338)
(-0.40913) (-0.25982) (1.05837) (-2.01226) (0.50486)
-3.820465 -0.324311 -0.002430 0.129275 -0.231316
(1.46836) (0.33673) (0.27018) (0.07492) (0.24040)
(-2.60185) (-0.96311) (-0.00899) (1.72550) (-0.96221)
3.015626 -0.083025 -0.478817 -0.370289 0.708842
(4.38520) (1.00564) (0.80687) (0.22375) (0.71795)
(0.68768) (-0.08256) (-0.59342) (-1.65496) (0.98732)

11538 Afr. J. Bus. Manage.
Table 7. Contd.
D(PG t (-2))
D(OPt (-1))
D(OPt (-2))
C
R 2
-0.356436 -1.074190 0.364986 0.043120 -0.241976
(4.56890) (1.04777) (0.84067) (0.23312) (0.74802)
(-0.07801) (-1.02522) (0.43416) (0.18497) (-0.32349)
0.116537 0.185625 -0.307245 -0.166967 0.014619
(1.80709) (0.41442) (0.33250) (0.09220) (0.29586)
(0.06449) (0.44792) (-0.92404) (-1.81086) (0.04941)
-1.628275 -0.073192 0.014068 0.005792 -0.164618
(1.74649) (0.40052) (0.32135) (0.08911) (0.28594)
(-0.93231) (-0.18274) (0.04378) (0.06500) (-0.57571)
0.420073 0.013548 0.009122 -0.017165 0.057593
(0.18406) (0.04221) (0.03387) (0.00939) (0.03013)
(2.28226) (0.32096) (0.26934) (-1.82777) (1.91121)
0.790036 0.289633 0.445264 0.721816 0.552042
Adj. R 2 0.597569 -0.361536 -0.063245 0.466813 0.141414
Sum sq. resids 1.506131 0.079209 0.050991 0.003921 0.040371
S.E. equation 0.354275 0.081245 0.065186 0.018076 0.058002
F-statistic 4.104788 0.444789 0.875627 2.830623 1.344384
Log likelihood -0.832408 34.51016 39.79540 70.57912 42.59790
Akaike AIC 1.069367 -1.875847 -2.316284 -4.881593 -2.549825
Schwarz SC 1.658394 -1.286820 -1.727257 -4.292566 -1.960798
Mean dependent -0.023968 0.072970 -0.004494 -0.009541 0.011331
S.D. dependent 0.558464 0.069628 0.063218 0.024755 0.062597
Determinant residual covariance
1.24E-14
Log likelihood 214.0255
Akaike information criteria -12.41879
Schwarz criteria -9.228226
Sample (adjusted): 1985 to 2008; Included observations: 24 after adjusting endpoints; Standard errors and t-statistics in
parentheses.
between debt and economic growth that is proved further
through Granger causality and Vector Error Correction
Estimates that there is a unilateral relationship between
debt and economic growth.
REFERENCES
Government of Pakistan (2002). A Debt Reduction and Management
Strategy. Report of Debt Reduction and Management Committee.
Islamabad. Ministry of Finance.
Kemal AR (2005). Macroeconomic Management: Breaking out of the
Debt Trap. Lahore J. Econ., (Special Edition)
Pakistan Economic Survey (2006-07). Govt. of Pakistan, Islamabad:
Ministry of Finance.
Siddique R, Malik A (2001). Debt and Economic Growth in South Asia.
Pakistan Dev. Rev., 40(4): 677-688.
Siddique R, Siddique R (2001). Determinants of Debt Rescheduling in
Pakistan. Pakistan Dev. Rev., 40 (4): 689-704.
Schclarek A (2004). Debt and Economic Growth in Developing and
Industrial Countries.
Zaman A (2001). The Economics of Stateless Nation: Sovereign Debt
and Popular Well-being in Pakistan. Pakistan Dev. Rev., 40(4): 1121-
1134.
Pattillo C, Poirson H, Ricci L (2002). External Debt and Growth. Financ.
Dev., 39(2).
Mustafa K (2009). External Debt hit $49.7bn, Domestic Debt
Rs483.2bn. The News May 5 th .
Ahmad A (2009). Mounting External Debt Amid Slowing GDP Growth.
Bus. Financ. Rev., The News April 27 th .
Hamid A, Ashraf H, Chaudhary MA (2008). External Debt and its Impact
on Economic and Business Growth in Pakistan. Int. Res. J. Financ.
Econ., 20
Clements B, Bhattacharya R, Nguyen TQ (2005). Can Debt Relief Boost
Growth in Poor Countries? IMF Economic Issues No.34.

African Journal of Business Management Vol. 5 (28), pp. 11450-11466, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.600
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Do best and worst innovation performance companies
differ in terms of intellectual capital, knowledge and
radicalness?
Carmen Cabello-Medina*, Antonio Carmona-Lavado, Ana Pérez-Luño and
Gloria Cuevas-Rodríguez
Pablo de Olavide University, Sevilla,Spain.
Accepted 5 October, 2011
This paper has differentiated “best” from “worst” innovative companies, taking into account three
separate bodies of literature, that is, the intellectual capital, knowledge-based view and innovation
literatures. Based on a sample of 181 firms that belong to the manufacturing and services industries,
our findings showed that the best innovation performance companies (considering both financial and
nonfinancial dimensions of innovation success), which also presented higher firm performance, had
systematically higher scores for all dimensions of intellectual capital (human, organizational and social
capital), for knowledge exchange and combination than the worst innovation performance companies.
However, with regard to knowledge types, our results were not as conclusive. There were no
differences between best and worst innovation performance companies in terms of their systemic
knowledge. Codified and simple knowledge were related to best innovation performance companies
only in their financial dimension. Finally, regarding radicalness, firms with more innovation success
were those which provided new products or services that incorporated new technology and new
customer benefits (uniqueness), while firms with less innovation success launched new products or
services that were unfamiliar or difficult to understand by customers. These findings contribute to the
innovation literature providing a pretty full picture of best and worst innovative companies.
Key words: Intellectual capital, human capital, social capital, organizational capital, knowledge, radicalness,
innovation performance.
INTRODUCTION
Looking for the features that explain the differences
between the most and the least successful innovative
companies has been a challenging concern for both
academics and practitioners. As Slowinski and Sagal
(2010) suggest, firms are complex entities and a practice
that works well in one firm may not produce the same
results in another. However, establishing a set of “best
practices” is important as they can work well in a wide
variety of firms and can be adapted to a wide range of
environments. Besides, these practices can allow us not
*Corresponding author. E-mail: mcabmed@upo.es. Tel:
+034954349359. Fax: +034954348353.
only to understand the characteristics of successful
organizations but develop benchmarks for “excellent
organizations” (Cocks, 2009). The patterns or configurations
of successful innovative firms could act as reference
models that simplify and accelerate process innovation
(Barros, 2007).
Best practices, which have been defined as tactics or
methods that have been shown through real-life implementation
to be successful (Dooley et al., 2002), have
been studied from several approaches. One of the most
extensive studies in this field has been the Product Development
and Management Association (PDMA) survey
(Barczak et al., 2009; Griffin, 1997), which tries to determine
which practices are more commonly associated with
firms that are more successful in new product

development (NPD). Dooley et al. (2002) analyze a large
and heterogeneous number of potential best practices for
each stage of the NPD process, from the customer
requirements stage to the product improvement and
disposal stage. Cooper et al. (2004a, 2004b) also discuss
the results of the American Productivity and Quality
Center study on performance and best practices in new
product development. They highlight a set of best
practices organized into three categories: The culture and
climate within the business in support of product
innovation, the role of senior management and the nature
of project teams and how they are organized.
Nevertheless, these previous researches lack a global
reference to the mechanisms that successful companies
use to manage knowledge in their innovation activities.
Edvinsson and Sullivan (1996) discuss the role that
knowledge plays in the process of innovation, and develop
a model for managing the firm’s intellectual capital.
Ross and Ross (1997) and Bontis et al. (1999) review the
different tools available to measure the intangible
resources, those considered key to develop innovation.
Although other researchers such as Coombs and Hull
(1998) and Hidalgo and Albors (2008) examine certain
knowledge management practices for innovation, they
propose them as a set of techniques and tools more than
as tactics (Dooley et al., 2002) useful for success.
Then, beyond the search for a set of the specific best
practices for innovation success, our approach tries to
improve our understanding of the differential features that
characterize the most successful companies in terms of
innovation activities, focusing on several aspects related
to the way in which these companies are managing their
knowledge and the type of innovation developed. One
interesting approach to deal with this issue is the
intellectual capital perspective. Stewart (1997) defines
intellectual capital as a set of knowledge, information,
intellectual property and experience that can be put to
use to create wealth in the organization. Nahapiet and
Ghoshal (1998) and Subramaniam and Youndt (2005)
conceptualize it as the sum of all knowledge that firms
utilize for competitive advantage. While the terms used to
label the various intellectual capital components may
differ, they consist of three basic components (Nahapiet
and Ghoshal, 1998; Reed et al., 2006; Subramaniam and
Youndt, 2005): Human capital, organizational capital and
social capital. Human capital, at an individual level, refers
to the knowledge and capabilities of the employees who
work for the firm. The second aspect, organizational
capital, corresponds to those components of explicit
knowledge that may be documented and recorded. Third,
social capital can be defined as knowledge resources
generated by interpersonal networks, which are, therefore,
embedded and available within those networks of
relationships (Nahapiet and Ghoshal, 1998). Regarding
the last aspect of intellectual capital, social capital, it must
be considered both in its internal and external
Carmen et al. 11451
perspective in order to embrace the knowledge that is
being shared between the individuals pertaining to the
company and individuals pertaining to the companies’
partners for innovation activities.
This approach (intellectual capital) would not be
complete if we did not consider the dimensions and
processes of knowledge that are embedded in the
innovation activities (Bontis, 1998), given that the ability
to create new knowledge enables firms to both innovate
and to outperform their rivals (Grant, 1996; Kogut and
Zander, 1992). We analyze the dimensions of knowledge
(systemic versus autonomous; tacit vs. explicit; complex
vs. simple) and the knowledge exchange and combinations
that lead to more successful innovations.
Finally, innovativeness or radicalness of innovation,
which can be conceptualized as the degree of new knowledge
embedded in the innovation (Dewar and Dutton,
1986), is the characteristic of new products or services
that we study as another determinant of innovation
success (Szymanzki et al., 2007).
Based on the previous statements, the main objective
of this research is to identify the features that differentiate
the most successful innovative companies from the least
successful, in relation to the three relevant topics in the
field of innovation research, which are intellectual capital,
knowledge and radicalness. Therefore, our research
question is: “Do best and worst innovation performance
companies differ in terms of intellectual capital,
knowledge and radicalness?”
Our criterion to identify the best and the worst innovation
performance companies is innovation success (or
new products or innovation performance), which has
been usually considered as commercial success, in terms
of sales, profitability or market share from new products
(Kock, 2007; Montoya-Weiss and Calantone, 1994;
Szymanski et al., 2007). All of these issues are indicators
of the financial dimension of innovation performance.
Nevertheless, few studies capture the nonfinancial
performance of innovation, which also represents positive
consequences to the firm: company image, retention of
existing customers, attraction of new customers, profitability
of other company products, competitive advantage,
etc. (Avlonitis et al., 2001; Salavou and Avlonitis, 2008).
We will study both financial and nonfinancial innovation
success.
This paper makes several contributions to research.
First, we bring together three separate bodies of
literature—the intellectual capital, knowledge-based view
and innovation literatures- providing a more accurate
description of the best and worst innovative companies.
Second, we focus on the R and D and innovation departments.
Most of the aspects of intellectual capital and
knowledge have been studied previously by referring to
the whole organization (Subramaniam and Youndt,
2005). Our approach provides a richer understanding of
the determinants of innovation success, with direct

11452 Afr. J. Bus. Manage.
impli- cations for R and D managers. Third, our research
contributes to the innovation literature examining both the
financial and nonfinancial performance dimensions of
innovation success (Avlonitis et al., 2001).
Conceptual framework: Separating best from worst
innovation performers
Three topics (intellectual capital, knowledge and
radicalness) have been chosen to explain the features of
the most successful companies with regard to their
innovation activities.
Intellectual capital
Edvinsson and Malone (1997) posit that intellectual
capital is a two-level construct: Human capital (the knowledge
created by, and stored in a firm’s employees) and
structural capital (the embodiment, empowerment, and
supportive infrastructure of human capital). They then
divide structural capital into organizational capital (knowledge,
created by, and stored in a firm’s information
technology systems and processes that speeds the flow
of knowledge through the organization) and customer
capital (the relationships that a firm has with its customers).
Bontis (1998) also discusses customer capital as
one aspect of what he calls ‘relational capital’, or the
capital that encompasses all external relationships. As
Reed et al. (2006) suggest this relational capital view is
similar, to that referred to as external social capital by
sociologists (Burt, 1992; Coleman, 1988) and management
theorists (Nahapiet and Ghoshal, 1998; Stewart,
1997; Youndt et al., 2004).
As it is clear from above, the names of the various intellectual
capital components differ; however, three basic
components of intellectual capital can be considered:
Human capital, organizational capital and social capital
(Subramaniam and Yound, 2005), which represent
different approaches adopted by organizations for
accumulating and utilizing the knowledge. In the
paragraphs below we propose how and why these issues
are present in the most successful innovative companies.
Human capital at an individual level refers to the
knowledge and capabilities of the employees who work
for the firm. The second aspect, organizational capital,
corresponds to those components of explicit knowledge
that may be documented and recorded. The proper
management of organizational capital may lead to the
preservation of the knowledge generated within the firm
through codification and documentation in some way that
can be accessed and used readily by any company member,
which has been called codification strategy (Hansen
et al., 1999). Third, social capital can be defined as knowledge
resources generated by interpersonal networks,
which are, therefore, embedded and available within
those networks of relationships (Nahapiet and Ghoshal,
1998). Social capital affects information and it influences
and promotes solidarity among these actors (Adler and
Kwon, 2002). Regarding this last aspect of intellectual
capital, social capital, it must be considered both in its
internal and external perspective in order to embrace the
knowledge that is being shared between the individuals
pertaining to the company and individuals pertaining to
the companies’ partners for innovation activities.
Human capital: How should be the individual
knowledge involved in innovation activities?
The existing literature on innovation has emphasized the
role of individual knowledge as one of the primary
resources for innovation, and it is clear that a firm’s ability
to produce new products and other organizational capabilities
is inextricably linked to its human capital (Laursen,
2002; Lopez-Cabrales et al., 2006). Then, we expect that
the most innovative companies will have the highest
levels of human capital.
Our reasoning must consider the two sides of this
individual knowledge. First, its value, that is, “its potential
to improve the efficiency and effectiveness of the firm,
exploit market opportunities, and/or neutralize potential
threats” (Lepak and Snell, 2002); second, its uniqueness,
that is, the degree to which an employee is irreplaceable
and idiosyncratic, and his or her rare and firm-specific
knowledge, skills, and abilities (Barney, 1991) are difficult
to transfer to other positions and for other firms to
duplicate (Lengnick-Hall and Lengnick-Hall, 2003; Lepak
and Snell, 1999).
In companies with a high level of value in its human
capital, employees represent the greatest collection and
diversity of skills, they are flexible in acquiring new skills,
are willing to experiment and apply new procedures and
can contribute to identifying new market opportunities
(Costa and McCrae, 1992; Subramanian and Youndt,
2005; Taggar, 2002). Thus, companies with this kind of
employee are more likely to enhance their innovative
performance.
Furthermore, in companies with a high level of knowledge
uniqueness, employees are irreplaceable and idiosyncratic.
They can generate competitive differentiation
because their specialized knowledge, which contributes
to the development of new ideas and products, may be
difficult for other firms to duplicate (James, 2002; Lepak
and Snell, 1999; Lengnick-Hall and Lengnick-Hall, 2003).
Thus, we expect that the value and uniqueness of human
capital will be much higher in the most successful
innovative companies than in the least successful ones.
Organizational capital: Why preserving knowledge
through documentation is important for innovation
activities?
Organizational capital represents the memory of the

organization and it has been defined as archival information
about the firm’s history that could be considered
in current decision making processes (Walsh and
Ungson, 1991). This memory of the organization is
expressed through organization processes, databases,
documents, patents and manuals that organizations use
to store and retain knowledge (Wright et al., 2001;
Youndt et al., 2004). The question is why organizations
should be interested in preserving all this knowledge.
Valuable knowledge, once captured and codified, can be
systematically transmitted and disseminated, and other
individuals can use it in new contexts (Sorensen and
Lundh-Snis, 2001). In this way, proper and active
consultation of up-to-date reliable and accessible internal
knowledge could have a positive influence on innovation
success, as has been demonstrated by Leenders and
Voermans (2007). Thus, organizations where
organizational capital is appropriately managed have
institutionalized knowledge and codified experience
stored in databases, routines, etc., all of which are
available for its members, who can put them into practice
for new products. We expect that the level of
organizational capital will be much higher in the most
successful innovative companies than in the least
successful ones.
Internal social capital: What should be the
relationships between individuals involved in
innovation activities?
Two main dimensions of social capital are noteworthy:
The structural dimension and the relational dimension
(Granovetter, 1992; Nahapiet and Ghoshal, 1998). Our
research is focused on the relational dimension, as it can
better explain innovation performance (Moran, 2005).
The central argument is that innovation mostly depends
on the quality of relationships established between the
people involved (relational dimension), rather than on the
density, connectivity and hierarchy of such relationships
(structural dimension). We expect that the quality of
relationships between people involved in innovation
activities will be much better in the most successful
innovative companies than in the least successful ones.
The importance of the relational dimension of social
capital for innovation is based on its effect on the three
conditions for knowledge exchange and combination,
which are required by successful innovations. These
conditions are access to parties for exchange and
combination of knowledge, anticipation of value through
exchange and combination, and the motivation of parties
to engage in knowledge creation through data exchange
and combination (Nahapiet and Ghoshal, 1998).
Beyond the arguments of Nahapiet and Ghoshal, the
importance of the relational dimension of social capital for
innovation can be mainly argued in terms of relational
closeness and trust. The reasoning that supports this
Carmen et al. 11453
argument has to do with the idea of innovation as the
result of the cooperation and interpersonal relations
established between the people involved. When two
parties trust each other, they are more willing to share
their resources, which in turn will improve innovation
performance (Tsai and Ghoshal, 1998). Furthermore,
Moran (2005) suggests that although an actor may have
access to several people who are potentially critical
sources of information for innovation, it is the quality of
past interactions that will influence whom he or she is
likely to approach and engage. Then, if there is a close
relationship, people will be more willing to support and
encourage innovative ideas, as the individuals involved
are able to give the confidence needed to turn ideas into
successful projects.
Summarizing, where trust and friendship levels are high
(high level of social capital), people are more willing to
engage in social exchange and cooperative interactions,
such as relying on others, asking for help, and having
spontaneous conversations and unplanned meetings, as
well as sharing information, knowledge and resources
(Lee et al., 2005). Then, one could expect that most
successful innovative companies have a high level of
social capital than the least successful ones.
External social capital: What should be the
relationships with the partners for innovation
activities?
Besides internal relationships, firms establish in the
course of their business activities a variety of interfirm
ties (buyer–supplier relationships, strategic alliances, and
joint ventures, among others) that enable them to
exchange a variety of information and knowledge, and
overcome the inherent risks associated with the innovation
process (Gopalakrishnan et al., 2008; Sivadas and
Dwyer, 2000). As with internal social capital, the partners’
intention and willingness to cooperate and exchange
knowledge depends on trust (Fukuyama, 1995; Kale et
al., 2000; Ring and Van de Ven, 1994) or on the level of
social capital embedded in the relationships (Yli-Renko et
al., 2001). The literature review by De Man and Duysters
(2005) suggests that intensive types of alliances have a
positive impact on innovation because these close and
trustworthy collaborations between organizations can
promote a more efficient transfer of complementary
knowledge.
This idea that strong interfirm linkages, often characterized
by long-lasting, repeated and socially dense
relationships, favor innovation success is not new in the
managerial arena. This relational embeddedness
enhances information utilization and enables firms to
proceed more efficiently by reducing concerns about the
loss of proprietary skills and knowledge and diminishing
the likelihood of conflict regarding goals and implementation.
For Inkpen and Tsang (2005), an atmosphere of

11454 Afr. J. Bus. Manage.
trust will contribute to the free exchange of knowledge
between partners, because decision makers should not
feel that they have to protect themselves from others’
opportunistic behavior. This provides a normative
environment that guarantees the actual execution of
knowledge recombinant processes (Padula, 2008). Trust
is needed for collaboration in innovation activities
because the drafting of complete, detailed contracts can
make the creation of knowledge and innovation difficult or
even impossible (Blomqvist et al., 2005). Besides, highly
interconnected (cohesive) network structures promote
more intense interactions between partner firms’
personnel, allowing knowledge to be more meaningfully
understood and more effectively exchanged, combined
and utilized (Coleman, 1988). Wu et al. (2007) and Zollo
et al. (2002) state that organizations can usually acquire
external knowledge and partner-specific experience that
are complementary so as to increase their innovation
performance. Besides the advantages related to effective
complementary knowledge transfer, other direct effects of
external social capital on (financial and nonfinancial)
innovation success should be highlighted: enhancing the
speed of new product development (De Man and
Duysters, 2005; Rindfleisch and Moorman, 2001), and
building an advantage in quickly establishing a new
technology, thereby augmenting the penetration and
establishment of new standards (Schilling, 1998). We
expect that the most successful innovative companies will
be involved in better interorganizational relationships than
the least innovative performers. Thus, summarizing the
features of intellectual capital, the most successful
innovative firms differ from the least innovative firms in
that they have valuable and unique human capital, a
large quantity of stored firm information (organizational
capital) and strong internal and external relationships
based on mutual feelings of attachment and trust (internal
and external social capital).
Knowledge-based view
Research involving organizational knowledge has
emphasized the importance of different dimensions of
knowledge. However, there has been little consistency in
classifying knowledge. One of the first studies that widely
analyzed knowledge dimensions was that of Winter
(1987), who states that knowledge is compounded in the
following four dimensions. The first one refers to the tacit
character or possibility that knowledge can be
communicated in a symbolic way from its possessor to
another person, in a way in which the recipient finally
knows as much as the originator of knowledge. The
second dimension is knowledge observability. This
dimension covers the possibility of observing knowledge
in its use. That is, knowledge observability is the extent to
which the necessary underlying knowledge is revealed by
its use. The third dimension is knowledge complexity, or
the quantity of information necessary to characterize a
particular item of knowledge. Lastly, Winter (1987)
establishes the systemic dimension as knowledge
dependence on a system, or the necessity of combining
knowledge with other elements of knowledge to make it
useful. Each one of these dimensions is represented in a
continuum, in such a way that the knowledge located
near the left end of each dimension presents bigger problems
for its transfer and imitation than that knowledge
located near the right end. This paper considers that all of
these dimensions have some effect on innovation
success. However, based on more recent studies such
as Gopalakrishnan et al. (1999) and Subramaniam and
Venkatraman (2001), we have included the observability
dimension along with the tacitness dimension. Therefore,
we are going to deeply analyze how tacit/explicit,
complex/simple and systemic/autonomous knowledge is
present in best and/or worse innovative companies.
Tacit versus explicit knowledge: Why explicit
knowledge can be the seed of innovation success?
Knowledge tacitness is the most common knowledge
dimension (Gopalakrishnan et al., 1999; Grant, 1996;
Nonaka, 1994; Polanyi, 1966). Polanyi (1966) classifies
human knowledge in two categories. On the one hand,
Polanyi (1966) distinguishes explicit or codified knowledge,
which is the knowledge that can be transferred
through a formal language. That is, it is the knowledge
that can be transmitted without the loss of its integrity if
the transmitter and receiver share the syntactic rules
necessary for its decipherment (Kogut and Zander,
1992). On the other hand, he defines tacit knowledge as
having a personal quality that makes its formalization and
communication difficult (Nonaka, 1994). Explicit knowledge
is expressed verbally or in writing, while tacit
knowledge is not verbalized or may even be nonverbalizable,
intuitive and not articulable (Hedlund, 1994).
Explicit knowledge is easy to process, while tacit
knowledge is difficult to articulate and to transmit in a
systematic and logical form (Gopalakrishnan et al., 1999).
To disseminate tacit knowledge among the members of
an organization, it is necessary to transform it into words
or numbers that all will understand. It is in fact during the
conversion from tacit to explicit knowledge that new
knowledge is created (Nonaka and Takeuchi, 1995).
Therefore, we agree with some theoretical studies that
consider that tacit knowledge is the seed of innovation
(Nonaka, 1994). However, when the innovation is fully
developed, such tacit knowledge would have already
been converted into explicit knowledge (Pérez-Luño,
2009). There are three reasons for such a statement.
Firstly, tacit knowledge is personal and cannot be communicated.
Secondly, product or service innovations, by
definition, need to be observable and based on codified
knowledge. Thirdly, innovations require firms to assimilate

customers’ needs into the design and production of the
product or service, and the changes must be clearly observable
to the customers (Gopalakrishnan et al., 1999).
Therefore, in order to obtain successful innovations,
organizations need to have been able to convert the
personal tacit knowledge into explicit knowledge and/or
organizational capital.
Complex versus simple knowledge: How can
complex knowledge create successful innovations?
Pringle (1951) defines knowledge complexity as the
number of parameters needed to define a system. This
way, the quantity of information required to transfer a
piece of complex knowledge is very high. The more complex
the knowledge is, the higher the number of abilities,
routines, technologies and interdependent resources
related with this knowledge (Zander and Kogut, 1995).
Therefore, the complexity increases the quantity of
information necessary for an effective transfer
(Gopalakrishnan et al., 1999; Kogut and Zander, 1992;
Subramaniam and Venkatraman, 2001; Zander and
Kogut, 1995). Simple knowledge, on the other side, may
be easily obtained from an outside source and, typically,
the cost of developing such knowledge is unjustifiable if it
is available elsewhere (Gopalakrishnan and Bierly, 2001).
Complex knowledge is required for most production
processes and there is a positive relation between knowledge
complexity and innovation success. There are a
couple of reasons for such statements. First,
Gopalakrishnan and Damanpour (1994) define the
complexity of an innovation using three characteristics:
Its difficulty, its intellectual sophistication, and its originality.
Pelz (1985) also associates knowledge complexity
with originality. Therefore, to develop successful innovations
(something novel), companies need to use some
degree of original-complex (or less simple) knowledge.
Second, the internal development of innovations based
on complex knowledge familiarizes the organization’s
personnel with the difficult and original elements of the
innovation (Kogut and Zander, 1992), and consequently
reduces imitation risk and allows firm to appropriate the
innovation rents. Then, we expect that the most successful
innovative companies will have more complex
knowledge than the least innovative ones.
Systemic versus autonomous knowledge: Why
successful innovations are based on systemic
knowledge?
It has been mentioned that the dependent (systemic) or
independent (autonomous) character of knowledge refers
to the necessity or not of combining knowledge with other
elements so that it is of use (Winter, 1987). This way,
independent or autonomous knowledge can be used
Carmen et al. 11455
without the necessity of being combined with previous
knowledge, while the dependent or systemic knowledge
requires this combination to be useful (Gopalakrishnan et
al., 1999; Winter, 1987). In this sense, an innovation
could be viewed as autonomous if it can be developed
and implemented as a black box and plugged into related
components or processes (Gopalakrishnan et al., 1999).
However, we consider that to develop successful
innovations, the organizational knowledge used in the
innovation process should be dependent or systemic.
That is, the innovation process requires the organization
to combine its existing knowledge with new knowledge.
Thus, we expect that the knowledge will be more
systemic in the most successful innovative companies
than in the least successful ones.
Thus, summarizing knowledge features, the most
successful innovative firms differ from the least innovative
firms in that they have knowledge that is more explicit,
more complex and more systemic.
Why is knowledge exchange and combination
important for innovation?
The ability to create new knowledge enables firms to both
innovate and to outperform their rivals, that is, it is related
to innovation success (Grant, 1996; Kogut and Zander,
1992). Collins and Smith (2006) state that such ability
results from the collective ability of employees to
exchange and combine knowledge (Nahapiet and
Ghoshal, 1998). That is, the knowledge possessed by
individuals must be transferred to the group level and the
organization as a whole so that it can be applied, giving
rise to innovations (Nonaka and Takeuchi, 1995).
The relevance of knowledge exchange and
combination for innovation has been theoretically argued
in several studies. Cohen and Levinthal (1990) consider
that the interaction between individuals who possess
different knowledge improves the organization’s ability to
innovate. Thus, Seidler-de Alwis and Hartmann (2008)
find that organizations that promote knowledge sharing
processes are more successful in innovation. Collins and
Smith (2006) found that, knowledge sharing was a great
indicator of firm performance (understanding firm performance
as the revenue from new products and services).
Therefore, we expect that the most successful innovative
companies will achieve higher degrees of knowledge
exchange and combination than the least successful
performers.
Radicalness of Innovation
The concept of radicalness has been defined broadly as
the magnitude of change or degree of novelty of the
innovation (Gatignon et al., 2002; Tidd et al., 1999).
Radicalness will be analyzed here at a macro level, that
is, with regard to the world, the market or the industry in

11456 Afr. J. Bus. Manage.
which the company operates (Garcia and Calantone,
2002; Johannessen et al., 2001). At a macro level, a distinction
has usually been made between two dimensions:
Technology and market (Gatignon et al., 2002; Gatignon
and Xuereb, 1997; OECD/Eurostat, 1997; De Brentani,
2001; Chandy and Tellis, 1998, 2000).
The technological dimension of radicalness determines
the extent to which the technology involved in a new
product is different from prior technologies (Chandy and
Tellis, 1998) or whether a new product incorporates a
substantially different core technology (Chandy and
Tellis, 2000).
The market dimension has two different meanings in
the literature. One, it is defined as uniqueness/new
customer benefits, that is, in terms of the extent to which
the new product fulfils key customer needs better than
existing products (Chandy and Tellis, 1998) or provides
substantially higher customer benefits (Chandy and
Tellis, 2000); but on the other hand, it is defined as
newness to customers in the sense of customer
unfamiliarity (Kock, 2007) or as “the degree to which the
new product/service varies from current customer
consumption requirements and experiences, and thus the
degree of learning and adoption effort required by
customers” (Atuahene-Gima, 1996a). It also refers to the
time or difficulty in understanding the new product/service
concept or its advantages (Avlonitis and Salavou, 2007).
The three different meanings of radicalness are likely to
relate differently to the best and worst innovative performers.
Uniqueness or new customer benefits, associated
with relative advantage and differentiation, are expected
to be higher for the most successful innovative
companies (Atuahene-Gima, 1996a, 1996b; Calantone et
al., 2006; Kock, 2007; Song et al., 2010; Kim and
Atuahene-Gima, 2010). Newness to customers, linked to
customer change, uncertainty and risk, is probably higher
for the least successful innovative companies (Atuahene-
Gima, 1996b; Calantone et al., 2006; Kock, 2007).
Some authors argue that radical innovations (Chandy
and Tellis, 1998; Sorescu, Chandy, and Prabhu, 2003) or
technology-based breakthrough innovations (Zhou et al.,
2005) involve both a new technology and greater
customer benefits. Gatignon et al. (2002) also posit that
radical innovation advances the price/performance
frontier by much more that the existing rate of progress.
Thus, technological radicalness and new customer benefits
seem to go together. Indeed, a positive relationship
between [technological] radicalness and commercial or
innovation success has been found in previous research
(Gatignon and Xuereb, 1997; Gatignon et al., 2002).
Therefore, the technological dimension of radicalness
should have a higher level for best than for worst
innovative performers.
Summarizing, we have argued that the most successful
innovative companies have several common features
related to the sides of intellectual capital, the types and
process of knowledge and the degree and dimensions of
innovation radicalness, which differentiate them from the
least successful ones. Table 1 provides a summary of the
previous statements.
In addition to the main variables analyzed in this study
related to innovation success, intellectual capital,
knowledge and radicalness, we also included firm
performance to confirm that innovation success leads to
positive consequences or results for the company as
shown by previous research (Paladino, 2007). Other firm
variables such as size, age and industry will also be
examined.
MATERIALS AND METHODS
Sample and data collection
The companies that have been chosen for the study belong to
manufacturing industries: Mechanical machinery and equipment,
and service industries: Software or computer programming
services, and research and development services. These industries
have a relatively high percentage of innovative companies (that is,
companies developing a successful product or process innovation
in the 2003–2005 period), according to the latest available
Technological Innovation in Companies Survey from Spain (INE,
2007). Companies have to carry out new product development or
improvements of existing products, and have at least 50 employees
in the cases of both the manufacturing industry and software or
computer programming services, and at least 20 in the case of
research and development services (because of its relatively small
size). The study population is composed of 537 companies (extracted
from the SABI, a database that contains financial information of
Spanish and Portuguese companies), which met all these
requirements.
Data collection was conducted via survey. RandD managers
responded to questions about intellectual capital and knowledge
and both RandD managers and marketing managers were invited
to respond to questions about innovation success, radicalness and
firm performance. In this way, we reduced the potential commonmethod
variance bias. In some firms, we only received responses
from one or two managers. This could be explained in part because
some companies were relatively small and only one person
occupied these positions. Previous research has also utilized from
three to one respondent by firm (Ramani and Kumar, 2008). Data
were collected during 2008.
One hundred and eighty-one responses were received, which
provided a response rate of 33.7%. Table 2 gives information about
the companies included in the sample. A chi-squared test applied to
a contingency table with both the companies included and not
included in the sample and the industry categories (manufacturing
and services) is not statistically significant (� 2 (1) = 1.744, p � 0.05).
The t-test of equality of means for independent samples shows that
the difference in the mean score is not statistically significant
between both groups of companies regarding the number of employees
(t (535) = .392, p � 0.05) and age (t (535) = –.462, p � 0.05).
Therefore, it seems that there is not a problem of nonresponse bias
in our data because of industry, company size and age.
Measures
Most of the measures have been adapted from measurement
scales used and validated by previous research. We asked about

Table 1. Characteristics of best innovative companies.
1. Valuable and unique human capital
2. High level of organizational capital
3. High level of internal and external social capital
4. Explicit, complex and systemic knowledge
5. High level of knowledge exchange and combination
6. High technological radical innovations, high new customer benefits and low newness to customers
Table 2. Sample of companies.
Variable Number of companies Proportion (%)
Industry
Manufacturing 97 53.6
Services 84 46.4
Number of employees (size)
Up to 49 20 11.0
50–99 77 42.5
100–249 56 30.9
250–499 20 11.0
500 or more 8 4.4
Age (years)
10 or younger 42 23.2
11–20 50 27.6
21–30 42 23.2
Over 30 47 26.0
Total 181 100.0
new or significantly improved products/services introduced by the
company in the previous five years to measure innovation success
and radicalness. Intellectual capital and knowledge are measured
within the R and D department. Table 3 shows the measures used
in our study.
Within-firm agreements between managers were assessed by
the interrater agreement measure, rwg, developed by James et al.
(1984, 1993). This indicator ranges from 0 (complete disagreement)
to 1 (complete agreement). In general, the median rwg values
obtained suggest an acceptable degree of agreement or consistency
between the respondents (Chen et al., 2008). Therefore, we
averaged the scale items from multiple respondents to form single
ratings for each construct and company.
Given that the measurement scales used were based upon an
exhaustive review of the relevant literature concerning the
constructs under study, we can initially affirm its content validity. An
exploratory factor analysis was performed separately for each
dimension or construct, and those factors with eigenvalues greater
than 1 were selected. All the items in each dimension or construct
loaded in one factor (unidimensionality). However, in the technological
dimension of radicalness, the item R-T1 had a low and
negative factor loading, in newness to customers the item R-NC4
also had a low factor loading, and in systemic knowledge the item
SYS1 loaded in another factor. All the three items were deleted.
With regard to reliability, Cronbach’s alpha exceeded the minimum
Carmen et al. 11457
value of 0.7 recommended by Nunnally and Bernstein (1995) for all
the measures (Table 3). Thus, these measures seem to be reliable
and valid. Number of employees (firm size), age and industry are
objective data obtained from the SABI database.
Analysis
Best practices studies methodology comparing best and worst
performers has the advantage of providing an overall view of how
companies differ in terms of a large number of variables (Cooper et
al., 2004).
Two groups of firms were created based on their scores of
innovation success, for both financial and non-financial performance
dimensions. The least successful innovative companies are
below the 35th percentile and the most successful are above the
65 th percentile in both dimensions of innovation success separately.
For the financial performance dimension of innovation success,
there are 63 firms in the best group and 71 firms in the worst group.
For the nonfinancial dimension, there are 72 best and 83 worst
innovative companies. One-factor ANOVA will test for the existence
of statistically significant differences in the scores of the variables
analyzed between best and worst performers.
The number of companies in each group can vary depending on
the analyzed variable because of missing data.

11458 Afr. J. Bus. Manage.
Table 3. Measurement scales.
Innovation success (at the firm level)
(adapted from Avlonitis et al. (2001) new service performance scale)
Regarding the new or significantly improved products/services introduced by the company in the previous five years:
Financial performance dimension (� = .922)
IS-FP1: They were profitable.
IS-FP2: Their total sales were high.
IS-FP3: They had a large market share.
IS-FP4: They exceeded their profit objectives.
IS-FP5: They exceeded their sales objectives.
IS-FP6: They exceeded their market share objectives.
Nonfinancial performance dimension (� = .846)
IS-NFP1: They had a positive impact on the company’s perceived image.
IS-NFP2: They improved the loyalty of the company’s existing customers.
IS-NFP3: Their introduction enhanced the profitability of other company products.
IS-NFP4: They attracted a significant number of new customers to the company.
IS-NFP5: They gave an important competitive advantage to the company.
Intellectual capital (within the R and D department)
Human Capital (� = .901)
(adapted from Subramaniam and Youndt’s (2005) scale)
Regarding the employees of the R and D department:
HC1: They are highly skilled.
HC2: They are widely considered the best in our industry.
HC3: They are creative and bright.
HC4: They are experts in their particular jobs and functions.
HC5: They develop new ideas and knowledge.
Organizational capital (� = .759)
(adapted from Subramaniam and Youndt’s (2005) scale)
Regarding the R and D department:
OC1: It uses patents and licenses as a way to store knowledge.
OC2: Much of its knowledge is contained in manuals, databases, etc.
OC3: Culture (stories, rituals) contains valuable ideas, ways of doing business, etc.
OC4: It embeds much of its knowledge and information in structures, systems, and processes.
Internal social capital (� � .941)
(adapted from Merlo et al.’s (2006) scale)
ISC1: Overall, the intentions of those in my department are good.
ISC2: Members of my department are always honest and trustworthy.
ISC3: Members of my department exhibit a great deal of integrity.
ISC4: I fully trust members of my department.
External social capital (� = .865)
(based on article by Maurer and Ebers (2006))
ESC1: Overall, a climate of cooperation and trust exists in our agreements with other companies for the development of new
products and the improvement of existing products.
ESC2: Companies with which we collaborate exhibit a high degree of commitment to our projects.
Knowledge (within the R and D department)
Regarding the knowledge that incorporates new or significantly improved products/services introduced by the company in the
previous five years:

Table 3 Contd.
Carmen et al. 11459
Tacit Knowledge (� = .805)
(based on articles by Hansen et al. (1999), Subramaniam and Venkatraman (2001) and Norman (2002))
TAC1: It is easy to comprehensively document in manuals and report (reversed).
TAC2: It can be precisely communicated through written documents (reversed).
TAC3: It is easy to comprehensively understand from written documents (reversed).
TAC4: It is obvious to all competitors (reversed).
TAC5: It is easy to identify without personal experience in the area (reversed).
Complexity (� = .866)
(based on articles by Winter (1987), Subramaniam and Venkatraman (2001) and Gopalakrishnan, Bierly and Kessler (1999))
COM1: It requires prior learning in other technologies and related knowledge.
COM2: It requires a large quantity of information.
COM3: It is technologically sophisticated and difficult to implement.
COM4: It is complex (vs. simple).
Systemic knowledge (� � . 789)
(adapted from Gopalakrishnan, Bierly and Kessler’s (1999) systemic versus autonomous scale)
SYS1: It is independent of other products and services offered by the organization (reversed). *
SYS2: Its users need to be in contact with other departments within the organization.
SYS3: Its implementation requires knowledge about other systems within the organization.
Knowledge exchange and combination (� = .945)
(adapted from Collins and Smith’s (2006) scale)
Regarding the employees of the R and D department:
KEC1: They see benefits from exchanging and combining ideas with one another.
KEC2: They believe that by exchanging and combining ideas they can move new projects or initiatives forward more quickly than by
working alone.
KEC3: At the end of each day, they feel that they have learned from each other by exchanging and combining ideas.
KEC4: They are proficient at combining and exchanging ideas to solve problems or create opportunities.
KEC5: They are capable of sharing their expertise to bring new projects or initiatives to fruition.
KEC6: They are willing to exchange and combine ideas with their co-workers.
Radicalness (at a macro level)
Technological dimension (� = .896)
(adapted from Gatignon et al.’s (2002) radicalness scale)
Regarding the new or significantly improved products/services introduced by the company in the previous five years:
R-T1: They represented a minor improvement over the previous technology (reversed). *
R-T2: They were based on a revolutionary change in technology.
R-T3: They were a breakthrough innovation.
R-T4: They led to products that were difficult to replace with substitutes using older technology.
R-T5: They represented a major technological advance in the subsystems.
Uniqueness/New customer benefits (� = .856)
(adapted from Avlonitis and Salavou’s (2007) new product uniqueness scale)
R-U/NCB1: They offer more possibilities to customers.
R-U/NCB2: They offer unique, innovative features to customers.
R-U/NCB3: They cover more customer needs.
R-U/NCB4: They have more uses.
R-U/NCB5: They are of higher quality.
R-U/NCB6: They are superior in technology.

11460 Afr. J. Bus. Manage.
Table 3 Contd.
Newness to customers (� = .845)
(adapted from Avlonitis and Salavou’s (2007) product newness to customer scale)
R-NC1: They required a major learning effort by customers.
R-NC2: It took a long time before customers could understand its full advantages.
R-NC3: The product/service concept was difficult for customers to understand.
R-NC4: They were not known and tried in the market. *
Firm performance (� = .930)
(adapted from Zahra’s (1996) firm performance index: Satisfaction with the company’s achievement of six goals, weighted by its
perceived importance)
FP1: Return on investment.
FP2: Return on equity.
FP3: Sales growth.
FP4: Net profit margin.
FP5: Market share.
FP6: Return on assets.
Other variables
Firm Size (number of employees)
Age (2008 - company foundation date)
Industry (manufacturing versus services)
All the measures use a seven-point scale, with the exception of firm performance, which ranges from 1 - 49 and control variables. *Eliminated in an
exploratory factor analysis.
RESULTS
Means, standard deviations, and one-factor ANOVA for
the groups of best and worst innovative companies
according to levels of innovation success, intellectual
capital, knowledge, radicalness and firm performance,
are shown in Table 4 for the financial dimension of
innovation success and in Table 5 for its nonfinancial
dimension.
First, we checked that there are statistically significant
differences between the best and the worst performers in
terms of innovation success. One-factor ANOVA showed
for both the financial (Table 4) and the non-financial
(Table 5) dimensions that the best groups had a higher
mean score than the worst group of innovative firms
(P

Table 4. One-factor ANOVA on the financial performance dimension of innovation success.
Variable Group n Mean SD F-statistic
Financial performance dimension
Worst
Best
63
71
3.8113
5.4840
0.56864
0.45226
358.896***
Human capital
Organizational capital
Internal social capital
External social capital
Tacit knowledge
Complexity
Systemic knowledge
Knowledge exchange and combination
Technological dimension
Uniqueness/New customer benefits
Newness to customers
Firm performance
Firm size
Age
regard to age for the financial dimension, and with regard
to industry (financial dimension: � 2
(1) = 1.017, P�0.05);
Worst 54 5.2704 0.89688
Best 58 5.5603 0.74503
Worst 54 4.7199 1.11141
Best 58 5.1379 1.07240
Worst 54 5.7870 0.99457
Best 58 6.0905 0.76225
Worst 52 4.8245 1.14491
Best 57 5.3070 1.00531
Worst 56 3.5089 0.95371
Best 60 3.0583 0.69874
Worst 56 4.4576 1.05509
Best 60 3.8097 1.23359
Worst 56 4.2500 1.30993
Best 60 4.0167 1.25623
Worst 54 5.3256 0.92913
Best 57 5.6740 .79950
Worst 63 4.5159 1.18572
Best 71 4.9354 0.93930
Worst 63 5.1750 0.87085
Best 71 5.4117 0.75274
Worst 63 3.7831 0.99590
Best 71 3.2473 1.16575
Worst 62 22.6871 8.42694
Best 68 29.5923 7.00463
Worst 63 164.98 256.710
Best 71 273.34 851.239
Worst 63 21.87 14.983
Best 71 20.11 14.020
Carmen et al. 11461
3.483†
4.103*
3.312†
5.487*
8.505**
9.178**
0.959
4.498*
5.208*
2.848†
8.077**
25.979***
0.944
0.493
nonfinancial dimension: � 2
(1) = 2.682, P�0.05). However,
for the nonfinancial dimension of innovation success,

11462 Afr. J. Bus. Manage.
Table 5. One-factor ANOVA on the nonfinancial performance dimension of innovation success.
Variable Group n Mean SD F-statistic
Non-Financial performance dimension
Worst
Best
72
83
4.4949
5.8438
0.52890
0.38123
337.728***
Human capital
Organizational capital
Internal social capital
External social capital
Tacit knowledge
Complexity
Systemic knowledge
Knowledge exchange and combination
Technological dimension
Uniqueness/New customer benefits
Newness to customers
Firm performance
Firm size
Age
younger firms were better performers than older firms.
DISCUSSION
This paper discussed the different features that
Worst 61 5.2377 0.86990
Best 69 5.6783 0.69173
Worst 61 4.7848 0.99507
Best 69 5.2518 0.96765
Worst 61 5.6578 0.88533
Best 69 6.2156 0.64272
Worst 57 4.8158 1.26775
Best 65 5.3308 0.89689
Worst 63 3.3556 0.99592
Best 73 3.2311 0.86694
Worst 63 3.8922 1.23226
Best 73 4.1712 1.17565
Worst 63 4.0357 1.46866
Best 72 4.1736 1.16177
Worst 61 5.3975 0.88603
Best 68 5.8076 0.77778
Worst 72 4.3061 1.04187
Best 83 5.1150 0.84483
Worst 72 4.9487 0.80460
Best 83 5.6259 0.62023
Worst 72 3.4151 1.09113
Best 83 3.5475 1.04445
Worst 70 23.2786 7.90556
Best 81 28.3829 6.54433
Worst 72 169.49 259.616
Best 83 217.45 666.277
Worst 72 26.47 15.999
Best 83 19.75 14.481
10.320**
7.342**
17.165***
6.831*
.608
1.822
.370
7.836**
28.460***
34.904***
.594
18.837***
.329
7.543**
characterize the best and worst innovative companies,
focusing on several aspects related to the way in which
these companies manage their knowledge and the radicalness
of the innovations they develop. Three separate
bodies of literature (the intellectual capital, knowledgebased
view and innovation literatures) have been

ought together in our research as a promising
approach, which suggests convincing theoretical
explanations for our proposals. Our research provided
interesting findings regarding the topics discussed:
intellectual capital (human, organizational and social
capital), knowledge-based view and innovation
radicalness.
Regarding the elements of intellectual capital, successful
innovative companies have shown high levels of
human capital. That is, their employees are highly skilled,
creative, and willing to develop new ideas and knowledge
(value of human capital). Furthermore, these individuals,
experts in their particular jobs and functions, are
irreplaceable and idiosyncratic, and their specialized
knowledge allows the company to generate a competitive
differentiation (uniqueness of human capital). Although
we have focused our intellectual capital analysis on R
and D and innovation departments of innovative
companies, and one could suppose that human capital is
a strategic resource for all of them, there are clear
differences in the degrees of capabilities, creativity,
experience, development of new ideas and knowledge
and so on between the best and worst innovative
companies.
Regarding organizational capital, institutionalized
knowledge stored in the form of standard operating
procedures, routines and scripts, is present in the most
successful innovative companies to a greater extent than
in less innovative ones. Thus, the best performers
successfully manage the process through which valuable
knowledge is institutionalized and the memory of the
organization is built. This process of codification and
storing of knowledge allows these companies to systematically
transmit and disseminate it in ways that people
involved in innovation activities can use it in other new
projects.
Internal social capital, that is, a high level of quality of
the relationships between individuals involved in innovative
activities, also seems to be a feature of the best
performers. Although organizational capital was revealed
to be a valuable means of knowledge sharing, relationships
between individuals add irreplaceable benefits for
knowledge transmission. Sveiby (1997) stated that,
“many managers tend to believe that a word written is a
word understood. They forget that the receiver of information,
not the giver, gives it meaning.” In this sense, this
work provides empirical evidence that high-quality
relationships between individuals within a firm contribute
to its ability to create value in the form of successful
innovations. The proximity, familiarity, trust and respect
inherent in these relationships make people more willing
to engage in knowledge exchange and cooperative
interactions in their innovation activities. Similarly, the
strength of the interorganizational relationships (external
social capital) is also more present in the most successful
innovative companies. As we had argued, when
Carmen et al. 11463
companies are involved in interorganizational agreements
characterized by a high level of trust, the transfer
of knowledge is improved, knowledge is more meaningfully
understood and more effectively exchanged,
combined and utilized, and firms are more willing to
spend more time and financial resources on innovative
activity, all of which may positively influence the success
of innovation agreements.
Regarding knowledge, as expected, we have found that
knowledge exchange and combination is a characteristic
of the most successful innovators (using both the
financial and nonfinancial indicators). This is important
because previous literature already states that the ability
to create new knowledge enables firms to both innovate
and to outperform their rivals (Grant, 1996; Kogut and
Zander, 1992). Our study went further by explaining that
the collective ability of employees to exchange and
combine knowledge is really the base of innovation
success.
On the side of knowledge dimensions, we have obtained
interesting findings. Consistent with our previous
statements, we found higher levels of explicit knowledge
(versus tacit) in successful innovators, but only in the
financial dimension of innovation success. If fact, one
could argue that items for explicit knowledge are closed
to organizational capital (as codifiability is used as
equivalent to explicit knowledge). Therefore, it makes
sense to obtain similar results when analyzing less tacit
knowledge and higher organizational capital. That is, our
results for the financial dimension of successful
innovators are consistent along two different but related
bodies of research: the knowledge based view and the
intellectual capital. However, other findings regarding the
noncomplex knowledge within financial successful innovation
firms, and no differences at all in any dimension in
the type of knowledge (tacit-explicit, complex-simple,
systemic-autonomous) within nonfinancial successful
innovation firms, require further discussion. That is, we
have found that best and worse innovative companies
differ in both intellectual capital (taking into account all of
the studied dimensions) and knowledge exchange and
combination. However, the type of knowledge is not really
useful to discriminate between both types of firms. In this
sense, arguments as those proposed by Cohen and
Levinthal (1990) and Kim and Atuahene-Gima (2010),
where the key for obtaining competitive advantages is the
process of absorptive capacity and/or learning (and not
the type of knowledge absorpted or learned), could
explain our findings.
Regarding innovation radicalness, we observed that
firms with more innovation success provide uniqueness
or new customer benefits, while firms with less innovation
success are those which launch new products or services
that are unfamiliar or difficult to understand by customers.
This is consistent with the framework of Rogers (1995), in
which the relative advantage of an innovation is positively

11464 Afr. J. Bus. Manage.
related to its rate of adoption, and its complexity is
negatively related. It is also congruent with the
Technology Acceptance Model (TAM) by Davis (1989),
where perceived usefulness and perceived ease-of-use
are determinants of intention to use a new technology.
Companies should develop products or services with
clear advantages in comparison with competitors and
reduce the learning effort required by customers. The
technological radicalness is greater for the best than for
the worst performers, showing that investing in R and D
to develop new technologies translates into superior
innovation success.
Finally, we found that companies with greater innovation
success have also greater firm performance. This
finding demonstrates that the more innovative the
company, the more profitable it is.
Our research suggests interesting practical implications.
It seems that managers should pay attention to all
the dimensions of a firm’s intellectual capital. Having a
human capital with high levels of capabilities, creativity
and experience appears critical in becoming an outperforming
innovative firm. Similarly, R and D managers
should design systems by which knowledge is codified,
documented and stored in such a way that people have
easy access to it. Furthermore, improving the quality of
relationships between people should be a concern for
managers. Encouraging techniques specifically designed
to promote trustworthy collaborations not only within the
firm but with other firms/institutions seems to foster
innovation success. In general, managers should keep in
mind that knowledge sharing and combination are
characteristics of the most successful innovators, who
usually develop more radical innovations in the sense
that they provide uniqueness or new customer benefits.
This research has some limitations. First, the sample of
companies was small and belongs to only three Spanish
industries. Therefore, there is no guarantee that the
results obtained can be generalized to other sectors.
Second, regarding intellectual capital, we focus on the
relational side of social capital, and cognitive and
structural sides are not analyzed, which needs to be
addressed in future research. Third, the use of crosssectional
data showed us the differences between best
and worst innovative companies at a moment in time.
Longitudinal studies would be necessary to clarify
whether our results change over time. Finally, although it
was beyond the scope of this study, future research
should analyze how firms can take advantage of their
knowledge to transform their innovations into intellectual
assets. This issue has been demonstrated to be critical in
guaranteeing the rights of ownership, and ultimately the
firms’ benefits appropriation.
Summarizing, as conclusions of this research, we
highlight the relevance of the three sides of intellectual
capital (human, social and organizational) for innovation
success. Also, the processes of knowledge exchange
and combination are more determinants for innovation
success than the type of knowledge. Companies which
develop radical innovations also achieve a higher level of
success. Finally, firm performance is associated with
innovation success.
ACKNOWLEDGEMENT
This research has been supported by the Spanish
Ministry of Innovation and Science (Research Project
ECO2010-21859).
REFERENCES
Adler PS, Kwon S-W (2002). Social Capital: Prospects for a new
concept. Acad. Manage. Rev., 27(1): 17–40.
Atuahene-Gima K (1996a). Differential Potency of Factors Affecting
Innovation Performance in Manufacturing and Services Firms in
Australia. J. Prod. Innov. Manage., 13(1): 35–52.
Atuahene-Gima K (1996b). Market Orientation and Innovation. J. Bus.
Res., 35(2): 93–103.
Avlonitis GJ, Salavou HE (2007). Entrepreneurial Orientation of SMEs,
Product Innovativeness, and Performance. J. Bus. Res., 60(5): 566–
575.
Avlonitis GJ, Papastathopoulou PG, Gounaris SP (2001). An
Empirically-Based Typology of Product Innovativeness for New
Financial Services: Success and Failure Scenarios. J. Prod. Innov.
Manage., 18(5): 324–342.
Barney JB (1991). Firm Resources and Sustained Competitive
Advantage. J. Manage., 17(1): 99–120.
Barczak G, Griffin A, Kahn KB (2009). Perspective: Trends and Drivers
of Success in NPD Practices: Results of the 2003 PDMA Best
Practices Study. J. Prod. Innov. Manage., 26(1): 3–23.
Barros O (2007). Business process patterns and frameworks: Reusing
knowledge in process innovation. Bus. Proc. Manage. J., 13(1), 47–
69.
Blomqvist K, Hurmelinnab P, Seppänena R (2005). Playing the
Collaboration Game Right—Balancing Trust and Contracting.
Technovation, 25(5): 497–504.
Bontis N (1998). Intellectual Capital: An Exploratory Study that
Develops Measures and Models. Manage. Decis., 36(2): 63–76.
Bontis N, Dragonetti NC, Jacobsen K, Ross G (1999). The Knowledge
Toolbox: A Review of the Tools Available to Measure and Manage
Intangible Resources. Eur. Manage. J., 17(4): 391–402.
Burt RS (1992). Structural Holes: The Social Structure of Competition.
Cambridge, MA: Harvard University Press.
Calantone RJ, Chan K, Cui AS (2006). Decomposing Product
Innovativeness and its Effects on New Product Success. J. Prod.
Innov. Manage., 23(5): 408–421.
Chandy R, Tellis G (1998). Organizing for Radical Product Innovation:
The Overlooked Role of Willingness to Cannibalize. J. Mark. Res.,
35(4): 474–487.
Chandy R, Tellis G (2000). The Incumbent’s Curse? Incumbency, Size,
and Radical Product Innovation. J. Mark., 64(3): 1–17.
Chen M-H, Chang Y-C, Hung S-C (2008). Social Capital and Creativity
in R&D Project Teams. R&D Manage., 38(1): 21–34.
Cocks G (2009). High performers down under: Lessons from Australia’s
winning companies. J. Bus. Strateg., 30(4): 17–22.
Cohen WM, Levinthal DA (1990). Absorptive Capacity: A New
Perspective on Learning and Innovation. Admin. Sci. Q., 35(1): 128–
152.
Coleman JS (1988). Social Capital in the Creation of Human Capital.
Americ. J. Sociol., 94: 95–120.
Collins CJ, Smith KG (2006). Knowledge Exchange and Combination:
The Role of Human Resource Practices in the Performance of High-

Technology Firms. Acad. Manage. J., 49(3): 544–560.
Coombs R, Hull H (1998). Knowledge Management Practices and Path-
Dependency in Innovation. Res. Policy 27(3): 237–253.
Cooper RG, Edgett SJ, Kleinschmidt EJ (2004a). Benchmarking Best
NPD Practices—I. Res. Technol. Manage., 47(1): 31–43.
Cooper RG, Edgett SJ, Kleinschmidt EJ (2004b). Benchmarking Best
NPD Practices—II. Res. Technol. Manage., 47(3): 50–59.
Costa P, McCrae R (1992). Neo PI-R Professional Manual, Odessa, FL:
Psychological Assessment Resources.
Davis FD (1989). Perceived Usefulness, Perceived Ease of Use, and
User Acceptance of Information Technology. MIS Q., 13(3): 319–340.
De Brentani U (2001). Innovative versus Incremental New Business
Services: Different Keys for Achieving Success. J. Prod. Innov.
Manage., 18(3): 169–187.
De Man A, Duysters G (2005). Collaboration and Innovation: A Review
of the Effects of Mergers, Acquisitions and Alliances on Innovation.
Technovation, 25(12): 1377–1387.
Dewar RD, Dutton JE (1986). The Adoption of Radical and Incremental
Innovations: An Empirical Analysis. Manage. Sci., 32(1): 85–103.
Dooley K, Subra A, Anderson J (2002). Adoption Rates and Patterns of
Best Practices in New Product Development. Int. J. Innov. Manage.,
6(1): 85–103.
Edvinsson L, Sullivan P (1996). Developing a Model for Managing
Intellectual Capital. Eur. Manage. J., 14(4): 356–364.
Edvinsson L, Malone MS (1997). Intellectual Capital: Realizing your
Company’s True Value by Finding its Hidden Brain Power. New York:
Harper Business.
Fukuyama F (1995). Social Capital and the Global Economy. Foreign
Aff., 74(5): 89-103.
Garcia R, Calantone R (2002). A Critical Look at Technological
Innovation Typology and Innovativeness Terminology: A Literature
Review. J. Prod. Innov. Manage., 19(2):110–132.
Gatignon H, Xuereb JM (1997). Strategic Orientation of the Firm and
New Product Performance. J. Mark. Res., 34(1): 77–90.
Gatignon H, Tushman M, Smith W, Anderson P (2002). A Structural
Approach to Assessing Innovation: Construct Development of
Innovation Locus, Type, and Characteristics. Manage. Sci., 48(9):
1103–1122.
Gopalakrishnan S, Bierly P (2001). Analyzing Innovation Adoption
Using a Knowledge-Based Approach. J. Eng. Technol. Manage.,
18(2): 107–130.
Gopalakrishnan S, Bierly P, Kessler EH (1999). A Reexamination of
Product and Process Innovations Using a Knowledge-Based View. J
High Technol. Manage. Res., 10(1): 147–166.
Gopalakrishnan S, Damanpour F (1994). Patterns of Generation and
Adoption of Innovation in Organizations: Contingency Models of
Innovation Attributes. J. Eng. Technol. Manage., 11(2): 95–116.
Gopalakrishnan S, Scillitoe JL, Santoro MD (2008). Tapping Deep
Pockets: The Role of Resources and Social Capital on Financial
Capital Acquisition by Technology Firms in Biotech-Pharma
Alliances. J. Manage. Stud., 45(8): 1354–1376.
Granovetter MS (1992). Problems of Explanations in Economic
Sociology. In: Networks and Organizations: Structure, Form, and
Action, eds. N. Nohria and R. Eccles. Boston: Harvard Business
School Press, pp. 25–26.
Grant RM (1996). Toward a Knowledge-Based Theory of the Firm.
Strateg. Manage. J., 17: 109–122.
Griffin A (1997). PDMA Research on New Product Development
Practices: Updating Trends and Benchmarking Best Practices. J.
Prod. Innov. Manage., 14(6): 429–458.
Hansen MT, Nohria N, Tierney T (1999). What’s Your Strategy for
Managing Knowledge? Harvard Bus. Rev. 77(2): 106–116.
Hedlund G (1994). A Model of Knowledge Management and the N-Form
Corporation. Strateg. Manage. J., 15: 73–90.
Hidalgo A, Albors J (2008). Innovation Management Techniques and
Tools: A Review from Theory and Practice. R&D Manage., 38(2):
113–127.
INE (2007). Encuesta sobre Innovación Tecnológica en las Empresas
2005. Madrid: Instituto Nacional de Estadística.
Inkpen A, Tsang EW (2005). Social Capital, Networks and Knowledge
Transfer. Acad. Manage. Rev., 30(1): 146–165.
Carmen et al. 11465
James LR, Demaree RG, Wolf G (1984). Estimating Within-Group
Interrater Reliability with and without Response Bias. J. Appl.
Psychol., 69(1): 85–98.
James LR, Demaree RG, Wolf, G (1993). An Assessment of Within-
Group Interrater Agreement. J. Appl. Psychol., 78(2): 306–309.
James W (2002). Best HR Practices for Today’s Innovation
Management. Res. Tech. Manage., 45(1): 57–60.
Johannessen JA, Olsen B, Lumpkin GT (2001). Innovation as Newness:
What is New, How New, and New to Whom? Eur. J. Innov. Manage.,
4(1): 20–31.
Kale P, Singh H, Perlmutter H (2000). Learning and Protection of
Proprietary Assets in Strategic Alliances: Building Relational Capital.
Strateg. Manage. J., 21(3): 217–237.
Kim N, Atuahene-Gima K (2010). Using Exploratory and Exploitative
Market Learning for New Product Development. J. Prod. Innov.
Manage., 27(4): 519–536.
Kock A (2007). Innovativeness and Innovation Success—A Meta-
Analysis. Zeitschrift für Betriebswirtschaft, 2: 69–89.
Kogut B, Zander U (1992). Knowledge of the Firm, Combinative
Capabilities, and the Replication of Technology. Organ. Sci., 3(3):
383–397.
Laursen K (2002). The Importance of Sectoral Differences in the
Application of Complementary HRM Practices for Innovation
Performance. Int. J. Ec. Bus., 9(1): 139–156.
Lee S, Wong P, Chong C. (2005). Human and Social Capital
Explanations for R&D Outcomes. IEEE Trans. Eng. Manage., 52(1):
59–68.
Leenders MA, Voermans CA (2007). Beating the Odds in the Innovation
Arena: The Role of Market and Technology Signals Classification
andNoise. Ind. Mark. Manage., 36(4): 420–429.
Lengnick-Hall M, Lengnick-Hall C (2003). HR’s Role in Building
Relationship Networks. Acad. Manage. Exec., 17(4): 53–63.
Lepak DP, Snell SA (1999). The Human Resource Architecture: Toward
a Theory of Human Capital Allocation and Development. Acad.
Manage. Rev., 24(1): 31–48.
Lepak DP, Snell SA (2002). Examining the Human Resource
Architecture: The Relationships among Human Capital, Employment,
and Human Resource Configurations. J. Manage., 28(4): 517–543.
Lopez-Cabrales A, Valle R, Herrero I (2006). The Contribution of Core
Employees to Organizational Capabilities and Efficiency. Hum.
Resour. Manage., 45(1): 81–109.
Maurer I, Ebers M (2006). Dynamics of Social Capital and their
Performance Implications: Lessons from Biotechnology Start-Ups.
Adm. Sci. Q., 51(2): 262–278.
Merlo O, Bell SJ, Mengüc B, Whitwell G (2006). Social Capital,
Customer Service Orientation and Creativity in Retail Stores. J. Bus.
Res., 59(12): 1214–1227.
Montoya-Weiss MM, Calantone R (1994). Determinant of New Product
Performance: A Review and Meta-Analysis. J. Prod. Innov. Manage.,
11(5): 397–417.
Moran P (2005). Structural vs. Relational Embeddedness: Social
Capital and Managerial Performance. Strateg. Manage. J., 26(12):
1129–1151.
Nahapiet J, Ghoshal S (1998). Social Capital, Intellectual Capital, and
the Organizational Advantage. Acad. Manage. Rev., 23(2): 242–266.
Nonaka I (1994). A Dynamic Theory of Organizational Knowledge
Creation. Organ. Sci., 5(1): 14–37.
Nonaka I, Takeuchi H (1995). The Knowledge Creating Company.
Oxford: Oxford University Press.
Norman PM (2002). Protecting Knowledge in Strategic Alliances.
Resources and Relational Characteristics. J. High Technol. Manage.
Res., 13(1): 177–202.
Nunnally J, Bernstein I (1995). Teoría Psicométrica. McGraw-Hill:
México.
OECD/Eurostat (1997). Oslo Manual. Proposed Guidelines for
Collecting and Interpreting Technological Innovation Data. Head of
Publications Service. Paris: OECD.
Padula G (2008). Enhancing the Innovation Performance of Firms by
Balancing Cohesiveness and Bridging Ties. Long Range Plan., 41(4):
395–419.
Paladino A (2007). Investigating The Drivers of Innovation and New

11466 Afr. J. Bus. Manage.
Product Success: A Comparison of Strategic Orientations. J. Prod.
Innov. Manage., 24(6): 534–553.
Pelz DC (1985). Innovation Complexity and the Sequence of Innovating
Stages. Knowledge: Creation, Diffusion, Utilization 6(3): 261–291.
Pérez-Luño A (2009). Does knowledge mediate the relationship
between the environment and the decision to innovate or imitate?.
Int. J. Intel. Prop. Manage., 3(1): 6–22.
Polanyi M (1966). The Tacit Dimension. New York: Anchor Day.
Pringle JWS (1951). On the Parallel between Learning and Evolution.
Behavior, 3(3): 175–215.
Ramani G, Kumar V (2008). Interaction Orientation and Firm
Performance. J. Mark., 72(1): 27–45.
Reed KK, Lubatkin M, Srinivasan N (2006). Proposing and Testing an
Intellectual Capital-Based View of the Firm. J. Manage. Stud., 43(4):
867–893.
Rindfleisch A, Moorman C (2001). The Acquisition and Utilization of
Information in New Product Alliances: A Strength-of-Ties Perspective.
J. Mark., 65(2): 1–18.
Ring PS, Van de Ven AH (1994). Developmental Processes of
Cooperative Interorganizational Relationships. Acad. Manage. Rev.,
19(1): 90–118.
Rogers E (1995). Diffusion of Innovations. New York: The Free Press.
Ross G, Ross J (1997). Measuring your Company’s Intellectual
Performance. Long Range Plan., 30(3): 413–426.
Salavou H, Avlonitis G (2008). Product Innovativeness and
Performance: A Focus on SMEs. Manage. Decis., 46(7): 969–985.
Schilling M (1998). Technological Lockout: An Integrative Model of the
Economic and Strategic Factors Driving Technology Success and
Failure. Acad. Manage. Rev., 23(2): 267–284.
Seidler-de Alwis R, Hartmann E (2008). The use of tacit knowledge
within innovative companies: knowledge management in innovative
enterprises. J. Know. Manage., 12(1): 133–147.
Sivadas E, Dwyer FR (2000). An Examination of Organizational Factors
Influencing New Product Success in Internal and Alliance-Based
Processes. J. Mark., 64(1): 31–49.
Slowinski G., Sagal MW (2010). Good practices in open innovation.
Res. Technol. Manage., 53(5): 38-45.
Song LZ, Di Benedetto CA, Song M (2010). Competitive advantage in
the first product of new ventures. IEEE Trans. Eng. Manage., 57(1):
88–102.
Sorensen C, Lundh-Snis U (2001). Innovation through Knowledge
Codification. J. Inform. Technol., 16(2): 83–97.
Sorescu AB, Chandy RK, Prabhu, JC (2003). Sources and Financial
Consequences of Radical Innovation: Insights from Pharmaceuticals.
J. Mark., 67(4): 82–102.
Stewart TA (1997). Intellectual Capital: The New Wealth of
Organizations. Doubleday/Currency, New York, USA.
Subramaniam M, Venkatraman N (2001). Determinants of
Transnational New Product Development Capability: Testing the
Influence of Transferring and Deploying Tacit Overseas Knowledge.
Strateg. Manage. J., 22(4): 359–378.
Subramaniam M, Youndt MA (2005). The Influence of Intellectual
Capital on the Types of Innovative Capabilities. Acad. Manage. J.,
48(3): 450–463.
Sveiby KE (1997). The New Organizational Wealth: Managing and
Measuring Knowledge-Based Assets. Berrett-Koehler, New York,
USA.
Szymanski DM, Kroff MW, Troy LC (2007). Innovativeness and New
Product Success: Insights from the Cumulative Evidence. J. Acad.
Mark. Sci., 35(1): 35–52.
Taggar S (2002). Individual Creativity and Group Ability to Utilize
Individual Creative Resources: A Multilevel Model. Acad. Manage. J.,
45(2): 315-330.
Tidd J, Bessant J, Pavitt K (1999). Managing Innovation. Integrating
Technological, Market and Organizational Change. John Wiley &
Sons, Chichester, UK
Tsai W, Ghoshal S (1998). Social Capital and Value Creation: The Role
of Intrafirm Networks. Acad. Manage. J., 41(4): 464–476.
Walsh JP, Ungson GR (1991). Organizational Memory. Acad. Manage.
Rev., 16(1): 57–91.
Winter SG. (1987). Knowledge and Competence as Strategic Assets.
In: The Competitive Challenge: Strategies for Industrial Innovation
and Renewal, ed. D. J. Teece. Cambridge, M.A: Ballinger, 159–184.
Wright PM, Dunford BB, Snell SA (2001). Human Resources and the
Resource Based View of the Firm. J. Manage., 27(6): 701–721.
Wu SH, Lin LW, Hsu MY (2007). Intellectual Capital, Dynamic
Capabilities and Innovative Performance of Organizations. Int. J.
Technol. Manage., 39(3/4): 279–296.
Yli-Renko H, Autio E, Sapienza, HJ (2001). Social Capital, Knowledge
Acquisitions, and Knowledge Exploitation in Young Technology-
Based Firms. Strateg. Manage. J., 22(6/7): 587–613.
Youndt MA, Subramaniam M, Snell, SA (2004). Intellectual Capital
Profiles: An Examination of Investments and Returns. J. Manage.
Stud., 41(2): 335–361.
Zahra SA (1996). Technology Strategy and New Venture Performance:
A Study of Corporate-Sponsored and Independent Biotechnology
Ventures. J. Bus. Vent., 1(4): 289–321.
Zander U, Kogut B (1995). Knowledge and the Speed of the Transfer
and Imitation of Organizational Capabilities: An Empirical Test. Org.
Sci., 6(1): 76–92.
Zollo M, Reuer JJ, Singh H (2002). Interorganizational Routines and
Performance in Strategic Alliances. Organ. Sci., 13(6): 701–713.
Zhou KZ, Yim CK, Tse DK (2005). The Effects of Strategic Orientations
on Technology- and Market-Based Breakthrough Innovations. J.
Mark., 69(2): 42–60.

African Journal of Business Management Vol.5 (28), pp. 11283-11294, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1069
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
An assessment of Knowledge Management (KM): A
consideration of information, culture, skills
and technology
Alireza Anvari*, Gholam Abbas Alipourian, Rohollah Moghimi, Leila Baktash and
Majid Mojahed
1 Department of Industrial Management, Gachsaran Branch, I.A.U, Gachsaran, Iran.
Accepted 10 February, 2011
The impact of knowledge management on organizational performance has been a popular research
topic in recent years. However, it seems to be a lack of empirical studies that measure knowledge
orientation in educational environments, even though knowledge orientation and universities are
considered complementary organizational approaches. This paper explores the level of knowledge
management based on four pillars: information, culture, skills and technology. The methodology
involved both qualitative and quantitative research to assess and evaluate knowledge management
based on literature in the university. Data from 124 participants were analyzed. The instrument used
was a standardized research questionnaire on knowledge management, the internal correlation of
which was calculated through Cronbach’s alpha of 91%, and then analyzed through SPSS. In general,
the findings indicated that there were some tokens of knowledge management that were ranked above
the average level (culture, skills and technology); but that information was lower than average. The
concrete tokens for knowledge management were developing at an increasing pace. Moreover, the
results showed that there was a significant difference in the perception and experience of knowledge
management in the university between two groups (lecturers and staff). Furthermore, there was some
evidence, to imply a trend of knowledge management development. A ranking of existing and desired
conditions was presented. The paper provides empirical data on knowledge orientation in a university
environment, and contributes to the research on the impact of knowledge management on
organizational performance.
Key words: Knowledge management, culture, skills, information, technology, university.
INTRODUCTION
The competitive conditions in organizations today have
become more widespread and varied than they were in
the past. This context is changing so rapidly that for the
majority of organizations the pace is far too rapid for them
to be able to respond to and keep up with. In other words,
as soon as there is a change in competitive conditions to
which the organization attempts to react and adapt, the
next change takes place and the organization needs to
*Corresponding author. E-mail: ar_anvar@yahoo.com. Tel:
0060172090167.
adjust itself to the new conditions in order to maintain its
position and survive.
Hence, an organization is more successful if its
employees work together in a safe space and culture. An
organization that is unable to continuously develop,
share, mobilize, cultivate, put into practice, review and
spread knowledge will be unable to compete effectively
(Rampersad, 2002). This is the reason why the ability of
an organization to improve existing skills requires new
technology, information and experience in organizational
culture.
Universities are no exception. Since they are centers
for the production and distribution of knowledge, they

11284 Afr. J. Bus. Manage.
Figure 1. KM based on four pillars.
Information
need to have sufficient potential for both greater
dynamism and stability. Later, establishment of
innovation and consequently creating new knowledge
have been regarded as important achievements for
academic institutions, and such institutions have mainly
focused their attempts on promoting knowledge and
enriching intellectual capital through the implementation
of their existing resources. These resources include not
only information but also all the intellectual capital as well
as culture, skills, technology and human resources, all of
which need to be identified and used systematically
through proper management methods. The emergence of
knowledge management (KM) from the management of
information is indicative of attempts in the field to bring
together internal intellectual capital and the resources
produced internally with external resources, and to
spread the institutions’ activities beyond issues of
education and research, to encompass official
procedures of executive processes. In order to achieve
this goal, Islamic Azad University of Firouzabad, as a
centre for the creation and distribution of knowledge, like
any other organization, requires KM to be implemented to
handle the potentialities and commitments of skilled
employees by identifying methods for creating,
recognizing, implementing and distributing organizational
knowledge. Thus, at the organizational level, KM
emphasizes the creation, utilization and development of
an organization’s collective intelligence (Loh et al., 2003).
Therefore, in order for knowledge to be enhanced
effectively, interaction is necessary between
technologies, techniques and people to allow an
organization to manage its knowledge effectively (Bhatt,
2001). Hence, the relationship between culture,
information, technology and skills must be considered.
In order to measure this ability, this article introduces a
knowledge management based on culture, skills,
information and technology, and in this research, an
attempt is made to identify the level of KM based on four
pillars (culture, skills, information and technology). The
research consisted of both qualitative and quantitative
methods.
Moreover, the following questions are addressed in this
research in relation to the literature:
1. What is the contribution of the literature to the field of
KM assessment in organizations?
2. Is there any evidence for KM within universities?
3. What are the employees’ perspectives toward KM?
4. What is the relative importance of the four pillars
(culture, skills, information and technology) in the
“existing condition” and “desired condition”?
KNOWLEDGE MANAGEMENT AND THE FOUR
PILLARS (CULTURE, SKILLS, INFORMATION,
TECHNOLOGY)
Knowledge is a function of culture, skills, information
(Rampersad, 2002) and technology. Culture is the totality
of standards, values, views, principles and attitudes of
individuals within a particular context that underscore
their behaviour and functioning. Skills refer to people’s
capabilities, abilities and personal experiences; it relates
to what people can do, what they know and what they
understand. Information comprises the definition given to
data or information obtained according to certain
conventions. Technology is a tool, instrument and
resource for the creation, utilization and transformation of

knowledge (Rampersad, 2002). (Figure 1).
Culture is the totality of standards, principles, values,
views, perspectives and attitudes of individuals that
determine their reactions and performance. The
conversion processes is triggered spirally, and is therefore
called the “knowledge spiral”; knowledge creation
and sharing become part of the culture of an organization
(Yang and Wu, 2008). The political and cultural
environments are known from the analysis of knowledge
culture because effective knowledge management cannot
take place without extensive behavioral, cultural and
organizational change (Davenport and Prusak, 2000).
Imani and Mackenzie (2004) argue that the relationship
between culture and knowledge management is much
more complex; their findings suggest that culture not only
creates in advance a context for knowledge sharing, but
from a conceptual standpoint extends to encompass
social tacit knowledge, and that knowledge sharing and
tacit knowledge are inextricably linked.
Skills refer to individuals’ aptitudes, talents, capacities,
capabilities, abilities and personal experience; it relates to
how people can make a connection between
understanding, knowing and doing. The results of a study
show that the firms in the sample recognize the need for
multi-skilled personnel to exploit the advantages
stemming from the adoption of information and
communication technology (ICT) (Spanos et al., 2002).
The ability of a university to improve existing skills and
acquire new ones is its strongest competitive advantage,
and as a result technology is enabling individual “expert”
knowledge to be transformed into knowledge that is
widely attainable.
Information is considered as an organized set of data,
and knowledge is perceived as meaningful information
(Bhatt, 2001). Information includes the meaning given to
data or information acquired. Many organizations are
developing information systems designed particularly to
facilitate the sharing and integration of knowledge (Alvani
and Leinder, 2001). The activities involved in the process
are analyzed to provide supplementary information and to
enable the identification of the types of knowledge
needed for proper implementation (Castellanos et al.,
2004).
Instead of focusing on transaction costs in the markets
for knowledge, organizations need to develop internal
capabilities to enable them to leverage technological
knowledge externally (Lichtenthaler, 2008). The process
of creating, acquiring and utilizing knowledge is held to
improve organizational performance (Laudon and
Laudon, 2005). In order to achieve the desired outcome,
organizations must not only build appropriate ICT
infrastructures but must also integrate human resources,
computer systems, network technologies and other
corresponding organizational arrangements to effectively
obtain, store and utilize knowledge (Jafari et al., 2009).
Tseng (2008) divides knowledge networks into five subtechnological
fields: electrical engineering, audiovisual
Anvari et al. 11285
technology, telecommunications, computer technology
and the semiconductor field. Document and KM
technologies merely repackage that information for easier
use (Schultze and Boland, 2000).
A common element in many KM research frameworks
is organizational culture. For the most part it is assumed
that technology plays a key role in the processes involved
in KM. A broader view examines KM requirements from
three perspectives: (a) information-based; (b) technologybased;
and (c) culture-based (Alavi and Leidner, 2001;
Karlsen and Gottschalk, 2004), skills and information.
The last of these viewpoints highlights the importance
of organizational culture in the KM process.
Complications at the organizational level have been
described, including culture, power (Hall and Goody,
2007) and technology (Fernandez and Sabherwal, 2004).
Not all KM processes require high levels of investment in
technology. More importantly, the successful use of the
technology is often dependent on the incorporation of KM
behaviour into the organizational culture. Employing more
technology has the potential to further free individuals
from commodity work by increasing their efficiency and
therefore allowing them to engage in value-adding
activities. However, such an implementation of
technology would require a different kind of analysis of
system requirements, one that examined not only what
the analysts do, but also what they should be doing. In
order to reduce the technology knowledge gap, deliberate
measures will be required to build scientific and
technological capacities of universities.
A REVIEW OF KNOWLEDGE MANAGEMENT
In recent years, various researchers have discussed
different aspects of KM, which can be classified in
several categories. So, distribution, application areas and
the resources is presented in Table 1. In addition, on the
basis of a review of more than 100 papers of KM
performance evaluation, the classifications shown in
Table 2 can also be used.
Assessing knowledge management in universities
With regard to knowledge management in universities,
Sarkar Arani (2005) has focused on the challenges in
Japan and the prerequisites for internationalization of
knowledge management in universities, as well as
universities’ duties to produce knowledge and KM. On the
basis of his conclusion he stated that in a world in which
the strong boundaries of the past are collapsed by
information technology and communication, and the
stability of past findings is reduced, educational institutions,
including universities and their curriculums, should
be reconstructed so that mutual understanding is possible
through an expansion of international cooperation.
This dynamic recognition and cooperation helps us to

11286 Afr. J. Bus. Manage.
Table 1. General classification of knowledge management.
Perspective
category
Organizational
knowledge
Theoretical and
fundamental concepts
of knowledge
management
Categories of
knowledge
management
Relationship between
knowledge
management and IT
Resources
Argyris, 1990; Sommerville and Dalziel, 1998; Goffee and Jones, 2001; Rampersad, 2001; Hall and
Andriani, 2002; Hwang, 2003; Albers and Brewer, 2003; Kakabadse et al., 2003; Bose, 2004; Vlok,
2004; Goh, 2004; Abdullah et al., 2005; Wu and Wang, 2006; Montequín et al., 2006; Fernandez et al.,
2006; Gillingham and Roberts, 2006; Golban and Kianzade, 2006; Shen et al., 2007; Gumus, 2007;
Tseng, 2008; Kayakutlu and Buyukozkan, 2008; Chang and Wang, 2009; Chen and Lin, 2009; Wen,
2009.
Barney, 1991, 1996; Von Krogh and Roos, 1995; Wernerfelt, 1984, 1995; Teece, 1998, 2000;
Wheelwright and Clark, 1992; Argyris, 1990; Sveiby, 2001; Schultze and Leidner, 2002; Hall and
Andriani, 2002; Rodrigues and Martis, 2004; Wong and Aspinwall, 2004, 2006; Papoutsakis and Vallès,
2006; Park, 2006; Gillingham and Roberts, 2006; Lin, 2007; Thitithananon and Klaewthanong, 2007;
Alrawi, 2007; Tseng, 2008; Wu et al., 2008; McFarlane, 2008; Chen and Lin, 2009.
Drucker, 1990, 1991; Grant, 2000; Nonaka and Takeuchi, 1995; Sveiby, 1992, 1997; Von Krogh et al.,
1998, 2000a, 2000b; Applegate et al., 1999; Bontis, 1999; Alavi and Leidner, 2001; Kakabadse et al.,
2003; McNurlin and Sprauge, 2004; Jafari et al., 2005; Khadivar et al., 2005; Lee et al., 2005;
Gillingham and Roberts, 2006; Montequín et al., 2006; Papoutsakis and Vallès, 2006.
Quinn, 1992; Drucker, 1993; Nonaka and Takeuchi, 1995; Prusak, 1997; Davenport and Prusak, 2000;
Bhatt, 2001; Schultze and Leidner, 2002; Hijazi and Kelly, 2003; Wu and Wang, 2006; Papoutsakis and
Vallès, 2006; Rodrigues et al., 2006; Alhawary and Alnajjar, 2008.
develop a better and more realistic picture of our
history; to reach a higher level of self-awareness and
self-consciousness; and to prepare sufficiently for
international understanding and cooperation, especially in
national, regional and global issues. It also enables more
higher education institutes to participate in the field of the
production of science (Sarkar Arani, 2005). In the
assessment of KM in universities within this framework,
there is a question cited:
How well does the model of KM systems in universities
reflect the success indexes and theoretical foundations of
the general model of KM systems?
Basically, this approach can be employed for
implementing and developing KM systems, with a
recommendation that theories should be investigated
through identifying the manner in which the observed
data are reflected. Jamshidi and Nemati (2008) examined
knowledge sharing and experience of social capital
development within IT units in a university. They showed
that there was a significant difference between the
knowledge-sharing process and the social capital
experience.
Specific research questions
The research questions of the study were as follows:
1. At what level is KM at Firoozabad Islamic Azad
University?
Total
2. What is the priority of the four pillars (culture, skills,
information and technology) in the “existing condition”
and “desired condition”?
3. How can KM be put into practice in universities, and
how can strategies be provided for enhancing the
effectiveness of KM at Firoozabad Islamic Azad
University?
METHODOLOGY OF THE STUDY
The present study involved a survey of all faculty members and
staff at Firoozabad Islamic Azad University. The population of the
study was selected through stratified sampling. The data obtained
from the 124 participants in the sample (more than 40% of the
population) were analyzed. Descriptive statistical methods such as
percentage, mean and standard deviation were used. Depending
on the type of variable, the t-test and correlation coefficients were
applied in order to investigate the correlation.
Participants
Questionnaires were sent to participants (staff and lecturer) with
significant responsibility in order to measure the level of KM: 140
faculty members and university staff were selected through
stratified random sampling and investigated through standardized
instruments for the management of knowledge designed by the
researchers. The collected data was analyzed using SPSS.
Of the 140 questionnaires distributed, 131 were completed by
employees, resulting in 124 usable responses (58 staff and 66
lecturers). There were 76 male and 48 female respondents. Their
ages were as follows:
23% were younger than 30 years; 51% were aged 30 to 40 years;
24
25
20
12

Table 2. KM categories based on specific aspect.
Anvari et al. 11287
Perspective Classifications/categories Author(s)
Methodbased
Marketing cost methods, return on assets, direct intellectual capital, score card Bontis, 1999
Major-focusbased
Benchmarking focus, performance measurement focus, Skandia Business Navigator, value
focus
Liebowitz et al., 2000
Knowledge
steps
Knowledge creation, knowledge validation, knowledge presentation, knowledge distribution,
and knowledge application activities, knowledge capitalization, knowledge balancing
Bhatt, 2001
Indicatorbased
General management, leadership style, strategic vision, internal process, human resources Rampersad, 2002
Methodbased
The balanced score card, economic value-added, Skandia Business Navigator Bose, 2004
Area-based Background/structural factors, knowledge production, knowledge integration Vlok, 2004
Area-based
Knowledge measurement in products and processes, measurement of knowledge value in
internal organization, measurement of organizational conditions based on KM processes
Khadivar et al., 2005
Methodbased
Direct intellectual capital, score card, marketing cost methods, return on assets Jafari et al., 2005
Indicatorbased
Context indicator, input indicator, process indicator, output indicator Natakuatoog, 2005
Knowledge
applied
Knowledge creation, knowledge accumulation, knowledge sharing, knowledge utilization,
knowledge internalization
Leea et al., 2005
KM – aspects Psychological, culture, process, functionality, architecture Abdullah et al., 2005
KM – aspects Technology, process, people
Montequín et al.,
2006
Model-based
Cognitive model, network model, community model, quantum model, philosophy-based
model, general intellectual capital (IC) measurement model
Kakabadse et al.,
2003; & Montequín et
al., 2006
Indicatorbased
Knowledge or information quality, perceived knowledge management system (KMS)
benefits, user satisfaction, and system use were used as dependent variables in evaluating
KMS success
KM process (knowledge acquisition, knowledge conversion, knowledge application and
Wu and Wang, 2006
Indicatorbased
knowledge protection), KM effectiveness (individual-level and organizational-level KM
effectiveness) and socio-technical support (organizational support and information
technology diffusion) based on the previous literature
Lin, 2007
KM – aspects People, structures and processes McFarlane, 2008
Analysisbased
Qualitative analysis, quantitative analysis, non-financial indicator analysis, financial indicator
analysis, internal performance analysis, external performance analysis, project-orientated
analysis, organization-orientated analysis
Chen and Lin, 2009
Different Employee traits, strategy factors, superintendent traits, audit and assessment, Chang and Wang,
aspects organizational culture, operating procedures, information technology
2009
and 26% were over 40 years. Meanwhile, 78% were married and
22% were single.
In terms of education levels, 11 people had less than a
Bachelor’s degree; 47 held a Bachelor’s degree; 30 had a Master’s
degree; and 36 had a PhD (doctorate).
With regard to work experience, 34 respondents had 1 to 5 years;
43 had 6 to 10 years; and 47 had more than 10 years (Table 3).
Thus, the following groups contained the largest number of
respondents:
1. Position: lecturer.
2. Gender: male.
3. Marital status: married.
4. Age group: 30 to 40 years.
5. Education level: Bachelor and PhD.
6. Experience: more than 10 years.
Sampling design
Four sets of measures were adopted and used to measure each of
the four constructs, namely, culture, skills, information and
technology. These measures were subjected to a formal pre-test by
a number of managers and experts. Some minor modifications were
carried out to clarify the meanings of some items. A variety of KM
approaches and systems must be employed in organizations in
order to deal effectively with the diversity of knowledge types and
attributes (Alavi and Leidner, 2001). According to Alrawi (2007)
there are many aspects of KM, and the way in which it is applied in
organizations depends on the structure of the organization.

11288 Afr. J. Bus. Manage.
Table 3. Details of respondents’ demographic.
Demographic
Characteristics
Job groups
Gender
marital
Age
Education level
Years of
experiences
However, the structure, processes and procedures of KM have not
yet been defined as a tangible standard, and it is difficult to find
comprehensive and explicit reference criteria (Wen, 2009).
An internal consistency analysis was carried out separately for
each variable in the theorized model by calculating Cronbach’s
alpha (the reliability coefficient). The results in Table 3 show that
the Cronbach coefficient for all the variables in the model were
above the critical value of 0.7 (Nunnally, 1978). Hence, the authors
concluded that all the items had been appropriately assigned to
each variable. The instrument developed also had content validity,
since the selection of measurement items was based on an
exhaustive review of the literature and a detailed evaluation by
academics and practitioners. Content validity depends on how well
the researchers have created the measurement items to cover the
content domain of the variable being measured (Nunnally, 1978).
The study used a five-point rating scale, that is, from 1 (strongly
disagree) to 5 (strongly agree). The reliability alpha (α) of different
variables and sample items for each variable are discussed.
FINDINGS / RESULTS OF THE STUDY
The correlation and validity of the instrument’s
statements were calculated using the Cronbach method.
The correlation for all the subscales of KM were high
and significant at 0.01; the correlation for the skills
indicator was ranked highest (r = 0.919), culture was
ranked second (r = 0.876), technology was ranked third (r
= 0.860) and information was ranked the lowest (r =
0.840) (Table 4).
The Cronbach’s alpha value for culture was 0.82, and
Category
Specification
Frequency Percent Cumulative
Staff 58 46.8 46.8
Lecturers 66 53.2
100.0
male 76 61.3 51.3
female 48 38.7
100.0
married 97 78.2 78.2
single 27 21.8
100.0
40 33 26.6
100.0
Under bachelor 11 8.1 8.1
Bachelor degree 47 37.9 46
master 30 24.2 70.2
PhD 37
29.8
100.0
1-5 34 27.4 27.4
6-10 43 34.7 61.1
>10 47 37.9 100.0
for skills 0.84. Among the indicators, information had the
lowest value at 0.78 and technology was 0.86.
Fortunately, the reliability coefficient for KM as a whole
was very strongly (Table 4), an alpha value of 0.91
indicating that the research instrument has high validity.
Moreover, the minimum alpha value for subscales was
0.78, which is a rather high value.
Description of data
Normal distribution
Table 5 shows the mean, standard deviation, skewness
and kurtosis for four indicators (culture, skills, information
and technology) and the total for KM. The normality of the
distribution of variables was assessed based on kurtosis
and skewness, with the resulting exploratory analysis
showing a strong degree of normality for the KM scale.
Means of different variables
The mean values of the different variables are presented
in Table 5. These are mean values on a five-point scale
(1 = strongly disagree; to 5 = strongly agree) of the four
indicators within KM. The average scores of the
indicators were moderate, with the exception of

Table 4. Output of statistical information.
Indicator No Cronbach's alpha Mean Correlations Sig.
Culture 8 0.82 24.8 0.876 ** 0.000
Skills 12 0.84 37.9 0.919 ** 0.000
Information 9 0.78 25.9 0.840 ** 0.000
Technology 10 0.86 31.46 0.860 ** 0.000
KM – Total 39 0.91 120
Table 5. Output of descriptive statistics.
Anvari et al. 11289
Indicators Total No No Mean SD Skewness Kurtosis
Culture 124 8 24.8 0.40804 -0.213 -0.299
Skills 124 12 37.9 0.6753 -0.766 -0.067
Information 124 9 25.9 0.43446 -0.286 -0.597
Technology 124 10 31.46 0.50998 -0.588 -0.206
KM – Total 124 39 120 1.77 0.796 0.149
information, which was lower than average, indicating
that the respondents believed that the level of KM in
relation to these criteria was less than average. In fact,
the employees did not express positive opinions on the
following:
1. Integrating management information systems.
2. Knowledge networks.
3. Knowledge transaction.
4. Knowledge documented.
5. Up-to-date knowledge.
6. Knowledge transformation.
It appears that information is more challengeable and
tangible than the other indicators, so is more apparent to
employees. Moreover, the indicators for leadership and
process skills, technology learning and utilization
commitment of others scored lower than average, while
discussing openly, problem solving, purchasing modern
technology, technical skills and culture-making
commitment had maximum scores.
Indicators of KM: Data analysis
The main objective of this research was to identify and
investigate the pattern for establishing KM in a university.
In the other words, this research sought to answer the
questions of whether there are any signs or evidence in a
university for knowledge-based management, and of how
this new and efficient pattern can be implemented or
strengthened in a university. The minor objectives of the
study included studying the demographic features such
as gender, age, educational attainment level and the
types of respondents in the study (faculty members and
staff); studying the parameters of knowledge-based
management, such as the general style of management,
leadership style, strategic vision and internal
management processes within a university; and
investigating the status of human resources in a
university. According to Table 6, the mean values for the
two groups (staff and lecturers) vary. This indicates that
there is a significant difference between the approach of
staff and lecturers in relation to culture, skills, information
and technology. In addition, the ranges of standard
deviations in the measures differ between the two
groups. It appears that the lecturers were more focused
in their approach. Thus, the lecturers’ assessments were
more positive because they have more information and a
deeper and wider vision.
It is obvious that the statements of measures needed to
encompass a wider vision of KM, since following the
promotion of facilities by the university to achieve KM,
lecturers appeared to be more satisfied with the situation.
The results of participants’ rankings of the parameters
are shown in Table 7.
Also, the results of an essay-type question on the desired
condition of KM based on four pillars (culture, skills,
information and technology) are showed in Tables 8 and
9.
Also as shown a comparative scoring in Table.10;
priority of existing and desired conditions respect of staff
(approximately) is vice versa. It means staff expectances
and requests’ is contrary with there is existed. But
respects of lecturer priority of existing and desired
conditions (approximately) are the same. Meanwhile,
totally (respect of all employees) is moderate.
DISCUSSION ON THE FINDINGS
According to Wong and Aspinwall (2004, 2006), KM is an

11290 Afr. J. Bus. Manage.
Table 6. Output of group statistics.
Items Position N Mean SD
Culture
Staff
Lecturer
58
66
23.9138
25.5909
5.84683
2.78445
Skills
Information
Technology
KM – Total
Staff 58 35.241 9.5409
Lecturer 66 40.277 3.7018
Staff 58 23.1552 5.14970
Lecturer 66 28.3182 2.89381
Staff 58 29.1897 6.59467
Lecturer 66 33.4545 3.77515
Staff 58 111.5000 24.73243
Lecturer 66 127.6212 8.95762
Table 7. Rankings of the existing condition of KM.
Index staff lecturers all
Culture 1 3 3
Skills 2 1 1
Information 4 4 4
Technology 3 2 2
Table 8. Output of scores allocated to desired condition of KM.
Indicator
Staff
score %
Lecturer
score % score
Total
%
Culture 105 0.18 104 0.16 209 0.17
Skills 139 0.24 230 0.35 369 0.30
Information 167 0.29 160 0.24 342 0.27
Technology 169 0.29 166 0.25 320 0.26
Total 580 1 660 1 1240 1
important strategy for improving organizational
competitiveness and performance; how knowledge-based
organizations can be evaluated has become one of the
most important issues in KM (Wu and Wang, 2008).
When managers have greater recognition of KM, they
have better understanding of the issues and realize its
importance. Meanwhile, Chen and Lin (2009) have
described the benefits that the KM project can bring to
companies, and expressed the urgency of taking the
initiative. According to Kidwell et al. (2001), KM should
not be seen as an extreme change: the concern should
be to focus on a thorough implementation of KM.
According to the results described above, one of main
problems for the university is the lack of or weak
procedures and suitable organizational structures. The
results of another study (Wen, 2009) named “procedures,
people, supporting organizational structure and
information technology” as the four key ingredients of
success for KM.
Furthermore, in rankings reported by Wen (2009), the
priorities of a number of criteria were identified:
information, staff, wisdom, knowledge and data.
Meanwhile, in the current research the lowest score was
given to information (data collection, sharing and transfer
is lower than the mean). Moreover, research by Alhawary
and Alnajjar (2008) showed that the information systems
technology had a significant impact on knowledge
creation and conversion.

Table 9. Rankings of the desired condition of KM.
Index Staff Lecturers All
Culture 4 4 4
Skills 3 1 1
Information 2 3 2
Technology 1 2 3
Table 10. A comparative ranking of the existing and desired condition.
Anvari et al. 11291
Index
Existing
Staff
Desired Existing
Lecturers
Desired
All employees
Existing Desired
Culture 1 4 3 4 3 4
Skills 2 3 1 1 1 1
Information 4 2 4 3 4 2
Technology 3 1 2 2 2 3
The results of Alhawary and Alnajjar (2008) indicated
that there were no significant differences in the
perceptions of academic staff at Jordanian universities in
terms of the use of information systems technology for
the purpose of knowledge creation and conversion.
However, the results of the current research showed
there was a significant difference in the perception of the
two groups (staff and lecturers). Research by Jamshidi
and Nemati (2008) showed that there was a significant
difference between the knowledge-sharing process and
social capital experience. It can be said that there was a
significant difference between groups’ approaches to
knowledge sharing and the social capital concept
(Jamshidi and Nemati, 2008). The problem appears to be
related to the age of Firouzabad University (22 years),
since there is a correlation between the history of the
institution and its ability to respond to the challenges of
the 21st century knowledge economy (Cranfield and
Taylor, 2008).
In summary, there is a permanent process of change in
which culture and KM both develop and influence/affect
each other, whether or not the culture in question is
appropriate. In both cases culture changes whether it is
by formulate or involuntary. Therefore, the issue is
whether the change is radical or incremental, even if it
one were to overlook subcultures and neglect the
debates on the impossibility of shaping culture in very
compound social groupings such as international
universities. In addition, reducing the technology and
knowledge gap will require deliberate measures to build
the scientific and technological capacities of universities.
Also, the analysis of the interviews showed that cultural
differences have an impact on the meanings of KM
practices, but in different ways and at different levels
(Kivrak, 2009).
As result, attempts should be made to implement virtual
education/e-learning, e-books, virtual libraries, e-classes,
e-colleges and dual-mode e-learning, and to create
research centers. Universities have three core functions:
1. Learning and teaching (increase of human
capital/investment, entertainment services/consumption).
2. Scientific research (knowledge production,
“theoretical/empirical”) and information storage.
3. Provision of services to third parties.
Hence, some of the objectives of universities are:
knowledge creation, knowledge and transfer issuance to
service based on social requirements, development,
culture-making, and the creation of equal opportunities. It
appears that there is a need to apply various types of
skill: educational skills, technical skills, social skills, legal
skills and appraisal skills.
In the end, according to the literature review, the results
of the questionnaires and interviews with managers and
some experts and lecturers in the university, the highest
scores for implementation of a dynamic network system
were:
Executive information system; management information
system; workgroup support system; transaction
processing system; inter-organizational system;
customer-integrated system; decision support system.
Conclusion
According to the findings of this study, there are
observable concrete indexes, signs and evidences of KM
in the fields of research, official, civil, scientific,

11292 Afr. J. Bus. Manage.
educational and digital facilities at the university, and they
are increasing, though not very rapidly. Furthermore,
from the point of view of the lecturers and staff of the
university being studied, there have been advances in the
parameters of KM (culture, skills and technology) in the
university at average and above average level. The index
of information has not been very successful in the
research environment, and has been evaluated as weak.
This calls for the principals of the university and other
similar universities to take action. No significant
relationship was found between KM and some variables
such as age, gender and education. However, the study
found that there was a significant relationship between
KM and the groups (lecturer/staff) and years of
experience. Overall, there are some strategies that could
increase the effectiveness of KM in the university.
A comparative ranking of the existing condition (Table
10) was ([technology=skills] > culture > information),
while the ranking of desired condition was (skill >
information > technology > culture). As a result,
considering the combination of the qualitative and
quantitative research in this study, the overall score for
KM as a whole was above average, and the development
trend of KM is appropriate for the age of the university
(22years). However, it is proposed that organizational
knowledge should be improved or reorganized, especially
in terms of the process of KM, knowledge creation,
sharing, utilization and transformation, in order to bring
the university up to date, and that this should be
considered as a future project.
The limitations of this study were research problems in
the research environment, the difficulties of generalizing
the findings obtained to other similar environments, and
the weakness of the research and experimental effects
relating to KM. In order to rapidly establish the
management of knowledge in the research environment,
with reference to the findings, some theoretical
suggestions were provided for the university principals
and researchers, as well as some practical strategies for
the managers of organizations and executive managers.
REFERENCES
Abdullah R, Selamat MH, Sahibudin S, Alias, RA (2005). “A Framework
For Knowledge Management System Implementation In Collaborative
Environment For Higher Learning Institution”, J. Knowledge Manage.
Practice, 6, available at http://www.tlainc.com/articl83.htm (accessed
1 December 2009).
Alavi M, Leidner DE (2001). “Review: Knowledge Management and
Knowledge Management Systems: Conceptual Foundations and
Research Issues”, MIS Quarterly. 25(1): 107-36.
Albers GA, Brewer S (2003). “Knowledge Management and the
Innovation Process: The Eco-Innovation Model”, Journal of
Knowledge Management Practice, Vol. 4, available at
http://www.tlainc.com/jkmpv4.htm (accessed 1 December 2009).
Alhawary FA, Alnajjar FJ (2008). “Impact Assessment of I/S
Technology Utilization On Knowledge Creation And Conversion: An
Empirical Study In Jordanian Universities”, Journal of Knowledge
Management Practice, Vol. 9, No. 1, available at
http://www.tlainc.com/articl151.htm (accessed 1 December, 2009).
Alrawi K (2007). “Knowledge Management and the Organization's
Perception: A Review”, Journal of Knowledge Management Practice,
Vol. 8, No. 1, available at http://www.tlainc.com/articl131.htm
(accessed 1 December 2009).
Anderson JC, Gerbing DW (1998). “An updated paradigm for scale
development incorporating unidimensionality and its assessment”, J.
Market. Res., 25 (2): 186–92.
Applegate LM, McFarlan FW, McKenney JL (1999). Corporate
Information Systems Management: Text and Cases, 5th edition,
Irwin/McGraw-Hill, USA.
Argyris C (1990). Overcoming Organizational Defenses: Facilitating
Organizational Learning, Prentice-Hall, Englewood Cliffs, NJ.
Armistead C (1999). “Knowledge Management and Process
Performance”, J. Knowledge Manage., 3(2): 143-57.
Barney JB (1991). “Firm Resources and Sustained Competitive
Advantage”, J. Manage., 17(1): 99-120.
Barney JB (1996). “The Resource-based Theory of the Firm”,
Organizational Sci., 7(5): 66-77.
Bell De Tienne K, Dyer G, Hoopes C, Harris S (2004). “Toward a model
of effective knowledge management systems: Conceptual
foundations and research issues”, J. Leadership Organ. Stud., 10(4)
26-43.
Bhatt GD (2001). “Knowledge management in organizations: examining
the interaction between technologies, techniques, and people”, J.
Knowledge Manage., 5(1): 68–75.
Bontis N (1999). “Managing organizational knowledge by diagnosing
intellectual capital: framing and advancing the state of the field”,
International Journal of Technology Management. 18, Nos. 5/6/7/8.
Bontis N (2001). “Assessing knowledge assets: a review of the models
used to measure intellectual capital”, Int. J Manage. Rev., 3 (1):41-
60.
Bose R (2004). “Knowledge management metrics”, Indust. Manage.
Data Sys., 104 (6): 457–68.
Castellanos AR, Rodríguez JL, Ranguelov SY (2004). “University
R&D&T capital: What types of knowledge drive it?”, J. Intellectual
Capital, 5(3): 478–99.
Chang TH,Wang TH (2009). “Using the fuzzy multi-criteria decision
making approach for measuring the possibility of successful
knowledge management”, Infor. Sci., 179: 355–70.
Chen H-J, Lin T-C (2009). “Exploring source of the variety in
organizational innovation adoption issues - An empirical study of
managers’ label on knowledge management project issues in
Taiwan”, Expert Systems with Applications, 36: 1380-90.
Choi B, Lee H (2003). “An empirical investigation of KM styles and their
effect on corporate performance”, Infor. Manage., 40: 403–17.
Choy SC, Suk CY (2005). “Critical Factors in the Successful
Implementation of Knowledge Management”, Journal of Knowledge
Management Practice, Vol. 6, No.1, available at
http://www.tlainc.com/articl90.htm (accessed 1 December 2009).
Cranfield DJ, Taylor J (2008). “Knowledge Management and Higher
Education: a UK Case Study”, Electronic J. Knowledge Manage.,
6(2): 85–100.
Davenport TH, Prusak L (2000). Working Knowledge: How
Organizations Manage What They Know, Harvard Business School
Press, USA (paperback edition).
Davenport TH, Short JE (1990). “The New Industrial Engineering:
Information Technology and Business Process Redesign”, Sloan
Manage. Rev., 31:11-27.
Drucker PF (1990). “The Emerging Theory of Manufacturing”, Harv.
Bus. Rev., 68(3): 94-102.
Drucker PF (1991). “The New Productivity Challenge”, Harv. Bus. Rev.,
69 (6): 69-79.
Drucker PF (1993). Post-Capitalist Society, Butterworth-Heinemann,
Oxford, UK.
Fernandez IB, Sabherwal AGR (2004). Knowledge Management -
Challenges, Solution, and Technologies, Pearson Prentice Hall,
London.
Fernandez JT, Segura SL, Salmeron JL, Moreno JR (2006).
“Operational Knowledge Management System Design in Total Quality
Management: Small and Medium Size Companies”, J. Knowledge
Manage. Practice, 7: 1, available at
http://www.tlainc.com/articl105.htm (accessed 1 December, 2009)

Gheshmi A (2004). “Knowledge-based Organization's Business
Excellence Architecture Model”, Paper presented at the Fourth
International Conference of Industrial Engineering, 12-17 June,
Tarbiat Modarres University, Tehran.
Gilbert M, Lebold M, Probst G (2002). “Five Styles of Customer
Knowledge Management, and How Smart Companies Use Them To
Create Value”, Eur. Manage. J., 20 (5): 459-69.
Gillingham H, Roberts B (2006). “Implementing Knowledge
Management: A Practical Approach”, Journal of Knowledge
Management Practice, Vol. 7, No. 1, available at
http://www.tlainc.com/articl107.htm (accessed 1 December 2009).
Goffee R, Jones G (2001). “Followership - It’s Personal Too”, Harv. Bus.
Rev., 79 (11): 63-70.
Goh ALS (2005). “Harnessing knowledge for innovation: an integrated
management framework”, J. Knowledge Manage., 9(4): 6-18.
Golban I, Kianzade M (2005). “Knowledge Management”, J. Manage.
Deve., 116 (1): 20-25.
Gold AH, Malhotra A, Segars AH (2001). “Knowledge management: an
organizational capabilities perspective”, J. Manage. Information Sys.,
18(1):185-214.
Grant RM (1996a). “Towards a Knowledge-based Theory of the Firm”,
Strategic Management Journal, Special issue: Knowledge Firm. 17:
1069-86.
Grant, R.M. (1996b), “Prospering in Dynamically-competitive
Environments: Organizational Capability as Knowledge Integration”,
Organ. Sci., 7(4): 375–87.
Grant RM (2000). “Shifts in the World Economy: The Drivers of
Knowledge Management”, in Despres, C. and Chauvel, D. (Eds),
Knowledge Horizons: The Present and the Promise of Knowledge
Management, Butterworth-Heinemann, Boston, MA.
Gumus H (2007). “The Effect of Communication on Knowledge Sharing
In Organizations”, Journal of Knowledge Management Practice, Vol.
8, No. 2, available at http://www.tlainc.com/articl133.htm (accessed 1
December 2009).
Hall H, Goody M (2007). “KM, culture and compromise: interventions to
promote knowledge sharing supported by technology in corporate
environments”, Journal of Information Science. 33(2): 181–8.
Hall R, Andriani P (2002). “Managing Knowledge for Innovation”, Long
Range Planning, 35: 29-48.
Hazeri A, Sarafzade M (2007). “Knowledge Management in Universities
and Role of Libraries on it”, J. Information Demonstrator and Bulletin
of Iran Scientific Documents, Vol. 4 and 5.
Henderson JC, Venkatraman N (1993). “Strategic Alignment:
Leveraging Information Technology for Transforming Organizations”,
IBM Systems J., 32(1): 4-16.
Hijazi S, Kelly L (2003). “Knowledge Creation in Higher Education
Institutions: A Conceptual Model”, Proceedings of the 2003 ASCUE
Conference, June 8 - 12, Myrtle Beach, South Carolina.
Hwang AS (2003). “Training strategies in the management of
knowledge”, J. Knowledge Manage., 7(3):156–66.
Imani Y, Mackenzie K (2004). “Knowledge Management and Culture: A
Misunderstood Paradox”, International Journal of Knowledge, Culture
and Change Management, Vol. 4, print article: ISSN 1447-9524,
MC04-0109-2004; pdf: ISSN 1447-9575, MC04-0109-2004; available
at. http://ijm.cgpublisher.com/products_index
Jafari M, Ebnerasoul SA, Rezaee J, Akhavan P (2005). “An exploratory
study on knowledge assets measurement”, Paper presented at the
Fifth International Conference of Industrial Engineering, 3- 6 May,
Science and Industry University, Tehran.
Jamshidi L, Nemati MA (2008). “An investigation of knowledge share
and experience on social capital development within IT units of
Shahid Beheshti University”, Paper presented at the Knowledge
management conference, February 20-22, Razi Conference Center,
Tehran.
Kakabadse NK, Kakabadse A, Kouzmin A (2003). “Reviewing the
knowledge management literature: towards a taxonomy”, J.
Knowledge Manage., 7(4): 75-91.
Karlsen JT, Gottschalk P (2004). “A study of factors affecting knowledge
transfer in IT projects”, Eng. Manage. J., 16(1): 3-10.
Kayakutlu G, Buyukozkan G (2008). “Assessing knowledge-based
resources in a utility company: Identify and prioritise the balancing
factors”, Energy, 33: 1027-37.
Anvari et al. 11293
Khadivar A, Elahi H, Nezafati N (2005). “Study, classification and
comparison of Knowledge Measurement methods”, Paper presented
at the Fifth International Conference of Industrial Engineering, 3-6
May, Science and Industry University, Tehran.
Kidwell JJ, Vander Linde KM, Johnson SL (2000). “Applying Corporate
Knowledge Management Practices in Higher Education”, Educause
Quarterly. 4: 28–33.
Kivrak S, Ross A, Arslan G (2009). “Impacts of Cultural Differences on
Knowledge Management Practices in Construction”, Paper presented
at the Fifth International Conference on Construction in the 21st
Century (CITC-V), Collaboration and Integration in Engineering,
Management and Technology, 20-22 May, Istanbul, Turkey.
Kowch EG (2005). “The Knowledge Network: A Fundamentally New
(Relational) Approach To Knowledge Management and The Study Of
Complex Co-Dependent Organizations”, Journal of Knowledge
Management Practice, Vol. 6, 1available at
http://www.tlainc.com/articl87.htm (accessed 1 December 2009).
Lakshman, C. (2007), “Organizational knowledge leadership: A
grounded theory approach”, Leadership Organ. Deve. J., 28(1): 51-
75.
Laudon KC, Laudon JP (2005). Management Information Systems:
Managing the Digital Firm, 9th edition, Prentice Hall, India.
Lee KC, Lee, S, Kang IW (2005). “KMPI: measuring knowledge
management performance”, Inform. Manage., 42:469-82.
Liebowitz J, Rubenstein-Montano B, McCaw D, Buchwalter J, Browning
C, Newman B, Rebeck K, and the Knowledge Management
Methodology Team (2000), “Knowledge Audit”, Knowledge Process
Manage., 7(1): 3–10.
Lin H-F (2007). “A stage model of knowledge management: an
empirical investigation of process and effectiveness”, J. Inform. Sci.,
33: 643.
Lichtenthaler U (2008). “Leveraging technology assets in the presence
of markets for knowledge”, Eur. Manage. J., 26:122-34.
Loh B, Tang A-C, Menkhoff T, Chay YW, Evers HD (2003). “Applying
knowledge management in university research”, Singapore
Management University, available at http://home.tonline.
de/home/hdevers/Papers/Loh-Tang-Menkhoff-Chay-Evers2003new.pdf
(accessed 1 September 2009).
McFarlane DA (2008).“Toward A Knowledge Management Body of
Knowledge (KMBOK): A Philosophical Discourse in KM Concepts
and Ideas”, J. Knowledge Manage. Practice, 9: 4, available at
http://www.tlainc.com/articl167.htm (accessed 1 December 2009).
McNurlin BC, Sprague RH, Jr (2004). Information Systems
Management in Practice, 6th edition, Pearson Educational Inc., New
Jersey.
Moffett S, McAdam R, Parkinson S (2003). “Technology and people
factors in knowledge management: an empirical analysis”, Total
Quality Manage., 14(2): 215-24.
Montequín VR, Fernández FO, Cabal VA, Gutierrez NR (2006). “An
integrated framework for intellectual capital measurement and
knowledge management implementation in small and medium-sized
enterprises”, J. Inform. Sci., 32: 525.
Nonaka I, Takeuchi H (1995). The Knowledge-Creating Company,
Oxford University Press, Boston, MA.
Nunnally JC (1978). Psychometric Theory, McGraw-Hill, New York, NY.
Papoutsakis H, Vallès RS (2006). “Linking Knowledge Management and
Information Technology to Business Performance: A Literature
Review and a Proposed Model”, J. Knowledge Manage. Practice, 7:
1, available at http://www.tlainc.com/articl106.htm (accessed 1
December 2009).
Park JH (2006). “The Role Of Trust On Knowledge Creation In A Virtual
Organization: A Social Capital Perspective”, J. Knowledge Manage.
Practice, 7: 4, available at http://www.tlainc.com/articl125.htm
(accessed 1 December 2009).
Prusak L (1997). Knowledge in Organizations, Butterworth-Heinemann,
USA.
Quinn JB (1992). Intelligent Enterprise: A Knowledge and Service
Based Paradigm for Industry, The Free Press, New York, NY.
Rampersad HK (2001). Total Quality Management; an executive guide
to continuous improvement, Springer- Verlag, New York.
Rampersad H (2002). “Increasing Organizational Learning Ability Based
On A Knowledge Management Quick Scan”, Journal of Knowledge

11294 Afr. J. Bus. Manage.
Management Practice, Vol. 3, available at
http://www.tlainc.com/jkmpv3.htm (accessed August, 2006).
Rodrigues LLR, Gayathri RS, Rao S (2006). “Empirical Study Based
Evaluation of KM Models in the IT Sectors: Implications for Quality
Outcomes”, J. Knowledge Manage. Practice, 7: 3, available at
http://www.tlainc.com/articl116.htm (accessed 1 December, 2009).
Rodrigues LLR, Martis MS (2004). “System Of Human Resource And
Knowledge Management in Engineering Education”, J. Knowledge
Manage. Practice, 5: 1, available at http://www.tlainc.com/articl77.htm
(accessed 1 December, 2009).
Sarkar Arani MR (2005), Knowledge Management, research or
knowledge creation, Ghoo Press, Tehran. Schultze U, Boland Jr, RJ
(2000), “Knowledge management technology and the reproduction of
knowledge work practices”, J. Strategic Inform. Sys., 9(2-3): 193-212.
Schultze U, Leidner DE (2002). “Studying knowledge management in
information system research: Discourses and theoretical
assumptions”, MIS Quarterly, 26(3): 213–242.
Shen PD, Lee T-H, Tsai CW, Chen YF (2007). “Case Studies on the
Implementation of Knowledge Management in the Distribution
Industry: Worldpeace and Alltek”, Electronic Commerce Stud., 5(3):
263-286.
Sommerville J, Dalziel S (1998). “Project Teambuilding – the
applicability of Belbin’s team-role self-perception inventory”, Int. J.
Project Manage., 16(3):165-171.
Spanos YE, Prastocos GP, Poulymenakou A (2002). “The relationship
between information and communication technologies adoption and
management”, Inform. Manage., 39: 659–675.
Sveiby KE (1992). “Strategy formulation in Knowledge-intensive
industries”, in Hussey, D.E. (Eds), Int. Rev. Strat. Manage., 3: 167-
186.
Sveiby KE (1997), The New Organizational Wealth – Managing and
Measuring Knowledge-Based Assets, Berrett-Koehler, San
Francisco, CA.
Sveiby KE (2001).“A Knowledge-based Theory of the Firm To Guide
Strategy Formulation”, J. Intellect. Capital, 2(4): 344-358.
Teece DJ (1998). “Capturing Value from Assets: The New Economy,
Market, and Intangible Assets”, California Manage. Rev., 40(3): 57-
79.
Teece DJ (2000). “Strategies for Managing Knowledge Assets: The
Role of Firm Structure and Industrial Context”, Long Range Planning,
33(1): 35-54.
Thitithananon P, Klaewthanong T (2007). “Knowledge Management is a
Perfect Education Development Tool: Is Thailand’s Higher Education
Really Ready to Embrace it?” J. Knowledge Manage. Practice, 8: 2,
available at http://www.tlainc.com/articl135.htm (accessed 1
December, 2009).
Tseng S M (2008). “Knowledge management system performance
measure index”, Expert Systems with Applications. 34: 734-745.
Venkatraman N (1994). “IT-Enabled Business Transformation: From
Automation to Business Scope Redefinition”, Sloan Manage. Rev.,
35(2): 73-87.
Vlok D (2004). “An Assessment of the Knowledge Processing
Environment in an Organisation – A Case Study”, Master’s thesis,
Rhodes Investec Business School, Rhodes University.
Von Krogh G, Roos J (1995),“A Perspective on Knowledge,
Competence and Strategy”, Personnel Rev., 24(3): 56-76.
Von Krogh G, Ichigo K, Nonaka I (1998). “Knowledge Enablers”, in Von
Krogh, G., Roos, J. and Kleine, D. (Eds.), Knowing in Firms:
Understanding, Managing and Measuring Knowledge, Sage, London,
pp. 25–35.
Von Krogh G, Ichigo K, Nonaka I (2000a). Enabling Knowledge
Creation. How to Unlock the Mystery of Tacit Knowledge and
Release the Power of Innovation, Oxford University Press, New York,
NY.
Von Krogh G, Nonaka I, Nishiguchi T (Eds.) (2000b). Knowledge
Creation: A Source of Value, McMillan, New York, NY.
Wangenheim CG, Lichtnow D, Wangenheim A, Comunello E (2001).
“Supporting Knowledge Management in University Software R&D
Groups”, Springer Verlag, Berlin/Heidelberg, pp. 52-66.
Wen Y-F (2009). “An effectiveness measurement model for knowledge
management”, Knowledge-Based Systems, Knowledge-Based
Systems archive. 22: 5.
Wernerfelt B. (1984), “A resource-based view of the firm”, Strat.
Manage. J., 5(2): 272-280.
Wernerfelt B (1995). “The resource-based view of the firm: ten years
after”, Strat. Manage. J., 16(3): 1986–1998.
Wheelwright SC, Clark KB (1992). Revolutionizing Product
Development: Quantum Leaps in Speed, Efficiency, and Quality, The
Free Press, A Division of Macmillan, Inc., New York, NY.
Willcoxson L (2004). Using the Learning Organization Concept for
Quality Development within a University, University of Southern
Queensland, Toowoomba, Australia.
Wong KY, Aspinwall E (2004). “Characterizing knowledge management
in the small business environment”, J. Knowledge Manage., 8(3): 44-
61.
Wong KY, Aspinwall E (2006). “Development of a knowledge
management initiative and system: a case study”, Expert Systems
with Applications, 30 (4): 633-641.
Wu J-H, Wang Y-M (2006). “Measuring KMS success: A respecification
of the DeLone and McLean’s model”, Inform. Manage., 43:728–39.
Wu L-C, Ong C-S, Hsu Y-W (2008). “Knowledge-Based Organization
Evaluation”, Decision Support Systems. 45: 541-549.
Yang HI, Wu TC-T (2008). “Knowledge sharing in an organization”,
Technological Forecasting & Social Chang., 75: 1128-1156.

African Journal of Business Management Vol. 5(28), pp. 11539-11545, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.2219
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Equality investment strategy evaluation during the
financial crisis: Using TOPSIS approach
Horng Jinh Chang 1 , Chun-Ming Chien 1 * and Ching Ya Hsiao 2
Graduate Institute of Management Sciences, Tamkang University, No.151, Yingzhuan Rd., Danshui Dist., New Taipei
City 25137, Taiwan, R.O.C.
Accepted 26 September, 2011
During the financial crisis time, fear has taken over the mood of investors. There are so many
uncertainties during a crisis. This time, the financial crisis came with a credit crunch. At such time, the
contrarian investors were looking for better investment objectives which may increase profits in the
future by mean reversion effect. But how can they be confident that the chosen investment objectives
can survive the credit crunch or the crisis. The study addressed a case of a Taiwanese bank selecting
an appropriate combination of equalities by Markowitz’s Mean-Variance Method in portfolio
management to deal with market risk in the crisis. The Technique for order preference by similarity to
ideal solution method (TOPSIS) was also used to deal with credit risk during the credit crunch in this
financial crisis. In setting the criteria weight setting and selecting the best strategy, the opinions of
experts were fully considered - from bank counselors in academia and the members of the bank asset
and liability management committee who are management from the Loan, Business, and Risk
management departments, who are all experienced in their professional fields. This study proved that
financial institutions could use TOPSIS in-group decision in the investment sector.
Keyword: Financial crisis, Markowitz, Mean-variance, TOPSIS.
INTRODUCTION
A local US sub-prime problem caused one of the largest
investment banks, Lehman Brothers, to fall into
bankruptcy on September 15 th , 2008. The effect was
enormous and it became a global financial crisis due to
contagion among markets.
Barber and Odean (2008) find that both extremely
positive and extremely negative returns lead to significant
*Corresponding author. E-mail:
george87917802@yahoo.com.tw. Tel: +886-2-26215656. Fax:
+886-2-8631-3214.
Abbreviations: TOPSIS, Technique for order preference by
similarity to ideal solution method; BIS, Bureau of Industry and
Security; CFOs, Chief Financial Officers; MCDM, method of
multiple criteria decision making; MV, mean-variance criterion;
MCDM, multiple criteria decision making; TSE, Taiwan Stock
Exchange; ALCO, Asset and Liability Committee; ROE, return
on equity; EMH, efficient market hypothesis.
buying behavior of investors. In Taiwan during the financial
crisis period, the buying behavior of both individuals
and institutional investors were less emotional (Yu and
Hsieh, 2010). Most investors are reluctant to purchase
cheaper equities due to future uncertainty. For momentum
investors, purchasing assets at a lower price is
against their principal of “following the trend”.
But for the contrarian investors, it is good timing to
invest during a time of crisis, when most investors overreact
to the market. They can purchase the assets at a
price lower. The contrarian investors’ trading strategy
focused on, “lean against the wind”, but this financial
crisis came with a credit crunch, which brought credit
uncertainty for the chosen investment objectives.
The returns on the stock markets are generally
regarded as the leading indicator of the future economic
situation. If the investors purchased assets when the
crisis was at a confirmed end, then the investors would
lose the chance of buying at a cheaper price.
Unlike previous crises, the 2008 crisis occurred in the

11540 Afr. J. Bus. Manage.
midst of a credit crisis of historic breadth and depth.
Many firms were under financial constraint by the banks
due to much bank capital being deducted by their toxic
assets, which became liquidity insufficient during the
current crisis. The banks are not able to loan to firms like
they used to, and are not able to maintain their capital
ratio under Bureau of Industry and Security (BIS) regulation.
Murillo, John and Campbell (2010) surveyed 1050
Chief Financial Officers (CFOs) in the US, Europe and
Asia.
They found evidence indicating that the constrained
firms planned deeper cuts in capital, employment and
technology; furthermore, the firms have to bypass attractive
projects or sell more assets to fund the operation.
Sudheer and Amiyatosh (2011) provide evidence that the
adverse capital shocks to banks negatively affects
borrower performance. Firms that relied mostly on banks
for capital experienced a greater decline in capital expenditure
and profitability than did other firms. In this case,
during the financial crisis, the traditional factors such as
the mean and variance of historical returns data did not
quite meet the requirements for setting up a good investment
strategy. The purpose of this study is aimed at
commercial banks which addressed a strategy to invest
in the stock market during the financial crisis using both
the mean and standard deviation for the market risk, and
the technology for order preference by similarity to ideal
solution (TOPSIS) method for credit risk.
The formation of trading strategy by a Taiwanese
commercial bank
There generally exist four parts to the formation of financial
assets allocation strategy: appropriate timing, the
allocations of funds, trading strategy, and portfolio
selection.
The failure of Lehman Brothers in 2008 has made the
global stock markets drop a lot, and caused a lot of chain
reactions in other asset markets globally. Many governments
have joined together to offer some policy
incentives or loosen monetary policy, trying to restore the
2 2
p E WaR a WbR b WaE Ra WbE Rb
peoples’ confidence and prevent further decline. At the
time of the crisis, all the positive and negative news
flowed into the markets. There is no knowing if the
current policy incentives were useful, or if the current
economic situation is declining further. Neither is it known
whether the investment objectives can survive or boom
after the financial crisis. There are too many uncertainties
during a crisis period.
Trying to minimize the effect of uncertainties, the investors
had to adopt a totally different approach; combining
not only market risk concerns, but also concerns of credit
risk concerns for the trading objectives in the crisis
period.
This study aimed to introduce TOPSIS, a method of
multiple criteria decision making (MCDM), and weights
each expert’s opinion equally.
METHODS AND DATA COLLECTION
The Mean-Variance criterion (MV)
Markowitz (1952) proposed The Mean-Variance criterion (MV) to
set up the investment portfolio selection to reduce the systematic
risk.
Yang (2004) A portfolio refers to a collection of more than one
category of securities or assets. Portfolio theory discusses how
investors should form portfolios through establishing investment
strategies to maximize rates of return under a fixed-risk situation, or
to minimize risks under a fixed-return situation.
The expected return on portfolios is:
E R W E R
n
p i i
i 1
E Wa Ra E Ra Wb Rb E Rb
i
Where Wi is the weight of target security i in the i 1
portfolio. E(Ri) is the expected return of target security i in the
portfolio. That is, the expected return on portfolios is the weighted
mean of expected return of securities.
2
p
The risks of portfolios are:
E WaRa WaE Ra WbRb WbE Rb
2 2
2 2
a a a 2 a b a a b b a b b
E W R E R W W R E R R E R W R E R
2 2
2 2
a a a 2 a b a a b b b b b
W E R E R W W E R E R R E R W E R E R
2 2 2 2
Wa a 2WaW
b ab W b b
(2)
2
(1)
n
2
W
1

Chang et al. 11541
If a portfolio consists of N securities, the number of variances will be squared-N, which includes N variances and N(N-1) covariances.
W W ... W 2WW 2 WW ... 2W
W
2 2 2 2 2 2 2
p 1 1 2 2 n n 1 2 12 1 3 13 n n 1 n( n 1)
1
W , V
2
n
2
n n
i
i 1
i i j
i 1 j 1
ij
i j
W WW
If a portfolio includes N securities, and
i
N
2
i
,
ij
represents the average covariance, the equation is presented as
follows:
2
2 1 n 1 1
p V N N 1 ij
i 1
N N N
2 2
1 N N
NV 2
N N
2
1 1
V 1 ij
N N (4)
N
1
2
, 0, 0
ij
p
If N V 1 0 ij ij
.
in other
words, if a portfolio is comprised of an extremely large number of
securities, the variance effect of individual security almost vanishes;
the majority of the variances in the portfolio are covariances. Therefore,
investors can diversify the potential risks of securities through
holding different categories of securities; however, the common
risks among securities cannot be diversified.
TOPSIS method
TOPSIS method is presented in Chen and Hwang (1992), with
reference to Hwang and Yoon (1981). The basic principle is that the
chosen alternative should have the shortest distance from the ideal
solution and the farthest distance from the negative-ideal solution.
This approach has been successfully applied in many fields. (Tzeng
et al., 2002, 2005; Mahmoodzadeh et al., 2007).
The decision maker wants to solve a multiple criteria decision
making (MCDM) problem. A MCDM problem can be concisely
expressed in matrix format as;
1 C 2 C … n C
A 1 f 11 f 12 … f 1n
A 2 f 21 f 22 ... f 2n
A m f m1
fm 2 … f mn
W w1, w2,..., w n
where
A1, A2,..., A j
are possible alternatives among which
C1, C2,..., C j
are criteria with
decision makers have to choose,
(3)
f ij
which alternative performance are measured, is the rating of
alternative i A C j wj with respect to criterion , is the weight of
C j
criterion .
The TOPSIS procedure consists of the following six steps:
(1) Calculate the normalized decision matrix. The normalized value
rij is calculated as;
J
ij ij
2
ij
j 1
r f f
J 1,..., J ;
i 1,..., n .
rij refer the normalized values of each performance measure,
refers the I alternative at j criteria.
(2) Calculate the weighted normalized decision matrix. The
weighted normalized value Vij is calculated as;
v wr J 1,..., J; i 1,..., n .
ij i ij
(3) Determine the ideal (A * ) and negative-ideal (A - ) solution.
* * *
1 ,..., A v vn
j v i I j v i I
' ''
max ij , min ij ,
* *
1 ,..., A v vn
j v i I j v i I
' ''
min ij , max ij ,
Where is associated with benefit criteria and is associated with
risk criteria.
(4) Calculate the separation measures using the n-dimensional
Euclidean distance. The separation of each alternative from the
Dj ideal solution is given as;
n
*
*
Dj v , j 1,..., J.
ij vi
i
1
Similarly, the separation from the negative-ideal solution
given as;
2
f ij
D j
is

11542 Afr. J. Bus. Manage.
n
Dj , j 1,..., J.
ij i
i 1
v v
Table 1. shows the contents of each portfolio, means, and standard deviations.
S/No. stock stock Mean (Returns) Standard deviation (Risk)
1 S1101 S1216 -0.046 2.4721
2 S1101 S1301 -0.21195 2.2145
3 S1101 S2002 -0.1978 2.2422
4 S1101 S2330 -0.593 2.2538
5 S1216 S1301 0.02305 2.0823
6 S1216 S2002 0.0372 2.1074
7 S1216 S2330 -0.358 2.1297
8 S1301 S2002 -0.12875 1.7979
9 S1301 S2330 -0.52395 1.8301
10 S2002 S2330 -0.5098 1.8735
2
C
(5) Calculate the relative closeness to the ideal solution
j
. The
relative closeness of the alternative aj with respect to A * is defined
as;
C D D D j J
*
j j / j j , 1,..., .
(6) Finally, rank the preference order.
Data collection
For this study, we gather the daily close stock price from
Reuters from July 2 nd , 2007, to December 31 st , 2008.
Totally 377 observations were used and using 100*log
(PSTt /PSTt-1) as the returns of the stocks. The data of
average cash dividend rate, the return on equity (ROE),
the current ratio, and the debt to net worth ratio of the last
three years (2004, 2005, and 2006) were gathered from
Taiwan Stock Exchange (TSE).
A practical case
The mid capital commercial bank A is a financial institution
in Taiwan. At the beginning of 2009, the president of
bank A was under budgetary pressure from the Board.
During the financial crisis period, he believed that the
relationship with shareholders at good firms is more
beneficial to bank A than solely as a loan provider. From
his past experience, he thought that the bank could hold
a few good stocks for a period of time during the current
financial crisis. The returns will be much higher than
those of fixed income securities. He tried to form an asset
allocation plan to fulfill his 2009 budget, not only with
fixed income securities but with investment positions in
stocks. He asked the Treasury Department to form a
stock selection plan and made a proposal to the Asset
and Liability Committee (ALCO) to discuss and ask an
academic counselor to find a method for choosing the
best portfolio from the proposal, with other criteria for
concern like credit constrained brought by the current
financial crisis.
The traders of the Treasury Department used to buy
and sell stocks over the short term (usually holding their
trading positions for only several weeks). To avoid
systematic risk, they decided to choose five stocks in the
stock index (TW50) from five major industries which are
cement, food manufacturing, chemical manufacturing,
steel and electronic industry. Each stock is that of the
leading firm in their respective industry. The stock quotes
are: S1101, S1216, S1301, S2002 and S2330. They form
the portfolio with two stocks of equal weight. To reduce
uncertainty, they formed the portfolio based on two
factors: the expected returns (mean of the portfolio
returns) and the risk (the standard deviation of the
portfolio). They gathered the stock price from Reuters
and calculated the returns and volatility of the portfolio
from July 2, 2007, to December 31, 2008. In total, 10
portfolios were formed by this method. illustrated at Table
1.
The academic counselor suggested using the TOPSIS
approach as an evaluation of equality investment
strategy. In establishing the criteria, six principles toward
the target firms were established through numerous discussions
among the ALCO members: revenue, standard
deviation of the equity return, cash dividend rate, ROE,
current ratio, and the debt to net worth ratio. They regard
revenue as cash generating power of the firm; standard
deviation of the equity return as the market risk; cash
dividend rate standards for the yields of the investment
objectives which is in comparison with the bank’s funding
cost, ROE as the profitability of the firm, current ratio as
the firm’s ability to repay its short term loan which stands
for the chance of the firm to pass the credit crunch and
debt to net worth ratio as the measurement of the firm’s
leverage.
Table 2 illustrates the returns, the standard deviation of
each portfolio from July 2, 2007 to December 31, 2008,

Table 2. The combination yield, standard deviation, average cash dividend, ROE, current ratio and debt to net
worth ratio of each portfolio.
No. com yield com SD
cash dividend
rate
ROE current ratio
debt to net
worth ratio
1 -0.05 2.47 1.38 10.97 169.59 56.47
2 -0.21 2.21 3.37 16.09 241.08 46.58
3 -0.20 2.24 2.51 17.19 204.46 37.49
4 -0.59 2.25 2.26 17.28 350.44 30.05
5 0.02 2.08 3.08 16.11 197.52 56.30
6 0.04 2.11 2.21 17.20 160.90 47.20
7 -0.36 2.13 1.96 17.29 306.88 39.77
8 -0.13 1.80 4.21 22.32 232.39 37.32
9 -0.52 1.83 3.96 22.41 378.37 29.88
10 -0.51 1.87 3.09 23.51 341.75 20.79
Chang et al. 11543
Table 3. The criteria weight differs by department. It reflects the weight given by each department or position from their professional
fields.
Criteria
Senior
management
Business
department
Academic
counselor
Risk
management
department
Loan
department
Average
com yield 0.35 0.29 0.15 0.11 0.21 0.222
com SD 0.12 0.08 0.3 0.33 0.09 0.184
cash dividend rate 0.25 0.22 0.1 0.08 0.17 0.164
ROE 0.06 0.15 0.05 0.08 0.11 0.09
current ratio 0.11 0.15 0.2 0.15 0.19 0.16
Debt to net worth ratio 0.11 0.11 0.2 0.25 0.23 0.18
the average cash dividend rate, the ROE, the current
ratio, and the debt to net worth ratio of the last three
years (2004, 2005, and 2006). The data collected from
TSE. The members of ALCO could assign the relative
importance (weight) of each criterion. The members are
the heads of the Business, Loan, and Risk Management
Departments; an academic counselor; three vice
presidents; and the president. The average values of
weights are illustrated differs by department. It reflects
the weight given by each department or position from
their professional fields. Table 3 shows the weight
normalized decision matrix used by Equation (1).
In order to determine the ideal and non-ideal solutions,
the next step is to utilize equations (2) and (3); the results
are A+ = (0.052, 0.022, 0.025, 0.012, 0.023, and 0.025);
and A- = (-0.003, 0.016, 0.008, 0.005, 0.010, and 0.009).
Subsequently, we employed Equations (4) and (5) to
calculate the separation of each alternative solution from
the ideal solution. We then obtained TOPSIS ranking lists
through Equation (6), and the results are presented in
Table 4.
The results of Table 5 indicate that alternative 9 may be
considered the best for maximizing the expected benefits
concerning the credit crunch and the risk for bank A.
DISCUSSION
The Efficient Market Hypothesis (EMH) assumes all the
investors are rational and the rational would give the
security a fair value with the integrated market news.
Since 1980, there are strands of literature, which indicate
lots of market situation that cannot be explained by EMH.
Lee et al. (1999) found the Asymmetric Information exist
among the big individual investors, small individual
investors and institutional investors. The Asymmetric
Information makes different investors doing different
trading pattern at the same news going public. At the
crisis time, it is hardly hearing any good news. If the
investors making the trading strategy by the current
released news or forecast by the economists, a feeling of
fear and pessimist would exist at the market. The investors
were not able to make sound judgment even the
equalities were under their value.
At past crisis, the institutional investors made the
equality trading strategy based on the type of crisis they
faced. For example, at the period of Asian Crisis 1997,
most institutional investors focused on the export
industries because of the vast devaluation of the local
currency against USD.

11544 Afr. J. Bus. Manage.
Table 4. Shows the weight normalized decision matrix used by Equation (1).
No. com yield com SD cash dividend rate ROE current ratio debt to net worth ratio
1 0.0041 0.0217 0.0081 0.0055 0.0105 0.0253
2 0.0188 0.0194 0.0197 0.0080 0.0149 0.0209
3 0.0175 0.0196 0.0147 0.0086 0.0127 0.0168
4 0.0525 0.0197 0.0132 0.0086 0.0217 0.0135
5 -0.0020 0.0182 0.0180 0.0080 0.0122 0.0252
6 -0.0033 0.0185 0.0130 0.0086 0.0100 0.0211
7 0.0317 0.0187 0.0115 0.0086 0.0190 0.0178
8 0.0114 0.0158 0.0246 0.0111 0.0144 0.0167
9 0.0464 0.0160 0.0232 0.0112 0.0234 0.0134
10 0.0451 0.0164 0.0181 0.0117 0.0212 0.0093
Table 5. Separation measure and ranking of each alternative.
The financial crisis started since the collapse of
Lehman Brothers on 2008. With the contagion effect, the
banking sector is quite affected by the collapse of
Lehman Brothers. It brought a greater amount of
systemic risk and affected not only the banking sector but
also the non-banking sectors. Bordo and Haubrich (2009)
conclude with the severe financial events are associated
with severe recessions. The financial crisis this time
associated with credit crunch and no one knows what
kind of industries would be survived this crisis. In such
case, an investment strategy this time dealt not only with
equalities price movement but also with the credit issue
of the equalities.
The Treasury Department of the financial sector at
Taiwan dealt with lot of financial issue like the interest
rate or the foreign exchange issue. With credit issue, their
judgment was always based on the credit rating by the
credit agencies. But with the lots sudden downgraded
CDO at 2007, the credit rating became not that confident
by the Treasury Department. An investment strategy with
sound credit concern should be emphases this time,
which makes it totally different from the previous trading
strategy. Yu and Hsieh (2010) confirm the buying
behavior is mitigated by the financial crisis of 2007.
The Treasury Department of Bank A makes an
S/No. c+ c- Ranking
1 0.738556 0.261444 9
2 0.50475 0.49525 5
3 0.608729 0.391271 7
4 0.263225 0.736775 2
5 0.697897 0.302103 8
6 0.802079 0.197921 10
7 0.455749 0.544251 4
8 0.572257 0.427743 6
9 0.225162 0.774838 1
10 0.327939 0.672061 3
investment strategy based on Mean-Variance approach.
Before the crisis, the best option is judged with only two
factors: mean is the expected yield and variance stands
for the risk. This kind of strategy was popular among the
financial institutions before the 2007 financial crisis. The
credit concern of the investment securities hasbecome
important during the crisis.
We use the TOPSIS Method as equality investment
strategy evaluation during the financial crisis. Through
numerous discussions, the ALCO members established
six principles toward the target firms: revenue, standard
deviation, and cash dividend rate, ROE, current ratio, and
the debt to net worth ratio. After establishing the criteria,
the members of ALCO then assign the relative importance
(weight) of each criterion. With the criteria setting to
aid for the better investment strategy, we have chosen
the most suitable investment strategy for this crisis from
the Treasury Department’s proposals.
This study aimed to introduce TOPSIS, a method of
MCDM, and weighs each expert’s opinion equally.
Conclusion
Before the 2008 financial crisis, the Treasury Department

of the financial institution set up the investment strategy
with two main factors: yield and risk. The previous studies
used standard deviation of the portfolio as risk and mean
return of the portfolio as yield. To seek for higher returns
and lower risk, it evolves two types of investors: momentum
investors and contrarian investors. The momentum
investors would liquidate all the long positions and sell
the short position as the pulling back of the Stock
Exchange Index at the financial crisis. The contrarian
investors would buy the securities at their lows but not
sure if the investment objectives can survive at the credit
crunch time. According to Yang et al. (2006), they find the
institutional investors at Taiwan would be momentum
trading at purchasing and become contrarian at selling
securities. At the time of the financial crisis, the buying
behavior of both institutions and individual investors
became less emotional than used to be (Yu and Hsieh,
2010). In such the financial intuitional investors would
take negative attitude toward buying stocks at crisis
period. This kind of attitude would not be helpful at
choosing the investment objectives, which are lower than
their true value.
At the beginning of 2009 - which was already three
months after the collapse of Lehman Brothers - the
president of Bank A thought that it might be a good time
to invest in the stock market, since there was less market
risk; but, considering the credit issue at credit crunch
time, it was even more important to set up good criteria to
choose good credit quality objectives. Based on the
advice of the bank’s counselor, Bank A used the TOPSIS
method to conduct an Equality Investment Strategy
Evaluation with some credit criteria.
With professional advice of experts and from senior
management, the related departments, and academics,
the portfolio returns, standard deviations, and debt to net
worth ratios have been of greatly concern to the
members of the ALCO committee.
Considered with portfolio returns, cash dividend rates,
and the current ratio, alternative 9 is relatively strong
among its alternatives.
At the financial crisis period, the Treasury Department
faced a lot of uncertainties and the trading pattern has
become less motional at the crisis time (Yu and Hsieh,
2010). To minimize the effect of uncertainties, the investors
had to adopt a totally different approach; combining
not only market risk concerns, but also concerns of credit
risk concerns for the trading objectives in the crisis
period. In such case, a different investment strategy
approach at the financial crisis period is necessary for the
investors. There are a lot of studies using TOPSIS as a
solution for group decision making. Most of previous
Chang et al. 11545
literatures, the experts choose the investment strategy
based on past quantitative data. Hsu and Tsou (2009)
use TOPSIS at selecting the best investment objectives
at the portfolio.
However, there are many quantitative and qualitative
factors affecting the success of the investment strategy
during the financial crisis. Limited studies had used
TOPSIS at choosing investment strategy during the
financial crisis. This study empirically proved that the
TOPSIS method can be applied to the financial field with
MCDM issues at financial crisis period. When decision
makers meet with uncertainty, few suitable portfolios
based on the MCDM can be formed, and they can
choose their own best portfolio based on their criteria.
REFERENCES
Barber BM, Odean T (2008). All that glitters: the effect of attention and
news on the buying behavior of individual and institutional investors.
Rev. Financ. Stud., 21: 785-818.
Chen SJ, Hwang CL (1992). Fuzzy Multiple Attribute Decision Making:
Methods and Applications. Springer-Verlag, Berlin.
Hsu CH, Tsou CS (2009). Multi-criteria Portfolio Optimization Analysis
by Combining K-MOPSO with TOPSIS. Soochow J. Econ. Bus., 65:
109-138.
Hwang CL, Yoon K (1981), Multiple Attribute Decision
Making-Methods and Applications. Springer. New York.
Lee YT, Li JC, Liu YJ (1999). Trading Patterns of Big versus Small
Players in An Emerging Market: An Empirical Analysis. J. Baking
Financ., 23(5): 701-725.
Mahmoodzadeh S, Shahrabi J, Pariazar M, Zaeri, MS (2007). Project
Selection by Using Fuzzy AHP and TOPSIS Technique. World Acad.
Sci. Eng. Technol., 30: 333-338.
Markowitz H (1952). “Protfolio Selection”. J. Financ., 7: 77-91.
Murillo C, Graham RJ, Campbell RH (2010). The real effects of financial
constraint: evidence from a financial crisis. J. Finan. Econ., 97(3):
470-487.
Tzeng GH, Tsaur SH, Laiw YD, Opricovic S (2002). Multicriteria
Analysis of Environmental Quality in Taipei: Public Preferences and
Improvement Strategies. J. Environ. Manage., 65: 109-120.
Sudheer C, Amiyatosh P (2011). The effect of banking crisis on bankdependent
borrowers. J. Finan. Econ., 99(1): 116-135.
Tzeng GH, Lin CW, Opricovic S (2005). Multicriteria Analysis of
Alternative-fuel Buses for Public Transportation. Energy Policy, 33:
1373-1383.
Yang GL (2004). “Financial Economics”. Wu-Nan Book Inc..
Yang SL, Chen HY, Yu CH (2006). Trading Institutional Ownership and
Institutional Investors’ Trading Behavior. J. Finan. Stud., 14(1): 41-72.
Yu HYi, Hsieh SF (2010). The effect of attention on buying behavior
during a financial crisis: Evidence from the Taiwan stock exchange.
Int. Rev. Finan. An. doi: 10.1016/j.irfa.2010.08.002.

African Journal of Business Management Vol.5 (28), pp. 11295-11308, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1076
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Trading behaviors among major investors in the United
States Dollar (USD) currency futures markets: Evidence
from South Korea
Dar-Hsin Chen 1 , Hwey-Yun Yau 2 *, Chin-Lin Chuang 3 and Po-Cheng Kuo 4
1 Department of Business Administration, National Taipei University, Taiwan.
2 Department of Accounting and Information, National Taipei College of Business, Taiwan.
3 Department of Finance, Southern Taiwan University, Taiwan.
4 Department of Business Administration, National Taipei University, Taiwan.
Accepted 15 June, 2011
The purpose of this paper is to investigate the behaviors of major traders (investment companies,
banks, and foreigners) and return volatility of Won/USD futures in the South Korea currency market.
The results indicate that there exists a significantly positive relationship between currency market
volatility and unexpected trading volume in terms of both banks and foreigners, and unexpected open
interest is also associated positively with market volatility for all three major traders. Regarding the
asymmetric effect, only the banks’ trading volume and foreigners’ net positions are asymmetric on
volatility direction. In spot markets, it is found that there exists uni-directional causal relationships in
terms of investment trust companies and foreigners. On the other hand, for futures markets we also
discover one-way causal relationships in both banks and foreigners. Based on the dispersion of beliefs
models and noise trading theories, investment trust companies appear to be uninformed in Won/US
futures markets.
Key words: Market volatility, trader behaviors, currency futures, net positions.
INTRODUCTION
The purpose of this study is to uncover the linkage
between volatility range of daily exchange rates and
trading volumes among United States Dollar (USD)
futures in South Korea. After the inception of derivatives
trading in early 1970s, research interests focusing on the
effects of financial derivatives on their underlying assets
have grown dramatically. Trading of derivatives attracts
not only hedgers but also speculators whose trading
*Corresponding author. E-mail: yauhy2006@gmail.com,
yauhy@webmail.ntcb.edu.tw. Tel: +886-2-23226365.
Abbreviations: USD, United States Dollar; BP, British pound;
DM, Deutsche mark; JY, Japanese yen; CD, Canadian dollar;
ITC, investment trust companies; KOSCOM, Korea Securities
Computing Corporation; F, Foreigners; B, Banks; OI, Open
Interest; ADF, Augmented Dickey-Fuller; AIC, Akaike
information criterion; VAR, vector autoregression.
behaviors might destabilize spot prices and trading
volumes. Kaldor (1939) indicates that futures could
provide an opportunity of speculation and destabilize the
cash markets more. Consequently, an increase in trading
futures contracts might cause an increase in spot
volatility.
After futures were introduced and traded on major
stock exchanges, the economic literature intensified the
debate on the impact of derivatives trading on spot price
volatility. According to Bessembinder and Seguin (1993),
an increase in volatility lowers the demand for currency,
commodity, and interest rate futures markets. Chatrath et
al. (1996) further indicate that futures activity has a
positive impact on the conditional volatility changes in
exchange rate, and has a weaker feedback from
exchange rate volatility to futures activity. Bhargava and
Malhotra (2007) then find mixed relationships between
futures trading and volatility in spot rates of BP, DM, JY
and CD against USD over the period of 1982 through

11296 Afr. J. Bus. Manage.
March 2000.
The trading of derivatives has grown tremendously,
especially in South Korea’s financial markets. Futures
contracts are traded in the Korea Exchange (KRX),
accounting for 18% of all contracts traded around the
world. The Bank for International Settlements (BIS)
triennial survey of foreign exchange and derivatives
market activity also shows that the turnover in traditional
foreign exchange markets increased to $3.2 trillion in
April 2007. Hence, the trading of exchange derivatives
plays a decisive role.
During the peak of the financial crisis in December
1997, South Korea’s exchange rate system shifted to a
totally free floating mechanism from a fluctuating one,
while implementations of foreign exchange transactions
replaced ex-management and accelerated the
liberalization of assets. Due to a lack of proper risk
management, the government realized the importance of
derivative tool and has issued a wide range of derivative
products. In order to avoid facing volatility in exchange
rate and foreign exchange risk, South Korea launched
currency futures trading in 1999 and the trading volumes
has grown dramatically. This study then intends to
examine the connection between daily volatility of
exchange rate and major traders’ trading behaviors. To
further address this connection, this paper not only uses
trading volume and open interest, but also uses net
positions, which could distinguish hedgers from
speculators or day traders. Finally, the impact of foreign
exchange rate on the trading behaviors of market major
participants and if traders’ trading destabilizes the
markets would be discussed as well.
Our study introduces a test of the dispersion of beliefs
models and noise trading theories in futures markets,
assuming that there exists a relationship between trading
activity and volatility that depends on the information that
traders possess.
Literature review
The mixture of distribution hypothesis (MDH) was
addressed by Clark (1973), under the assumption that
daily price change is supposed as being a random
variance and also a sum of intra-day price changes. Clark
(1973) supports that there is a positive relationship
between the absolute value of price change and trading
volume, while price volatility also is impacted by trading
volume directly. Epps and Epps (1976) bring up the
framework named the two-parameter portfolio selection
model, which considers the market to be composed of
short and long positions. Following the assumption of
MDH, Tauchen and Pitts (1983) address the general
model, which describes that under the assumption of the
fixed traders, the square of price change is the positive
function of the covariance of trading volume. There exists
a positive correlation between price volatility as well as
trading volume. As with the suggestion of Clark (1973),
there is large volatility when the largest transactions
happen. Luc et al. (2005) shed new light on the mixture of
the distribution hypothesis by means of the weekly
exchange rate volatility of the currency Norwegian Krone
(NOK). The novelty of their study is that the impact of
changes in the number of information events is positive
and statistically significant. Recent studies about the
impact of information intensity on exchange rate volatility
mostly support MDH. Copeland (1976) brings another
essential model - the sequential information arrival model
that explores price volatility and trading volume - under
the major assumption that market traders receive the
latest information randomly and continuously; or in other
words, every single trader does not receive a new
message simultaneously. On the contrary, the new
message is only sent to one trader, and therefore the
final price equilibrium results after all traders attain the
same information. Karpoff (1987) criticizes that those
traders who do not attain information might not acquire
any information from the market price or the trading
behavior of those traders who do attain information.
The conclusion - when market traders are all optimistic
or pessimistic, the trading volume is the largest - is
inconsistent with the empirical findings. MCarthy and
Najand (1993) test near-month contracts from the
Chicago Mercantile Exchange (CME) of daily currency
futures during the period from January 1979 to May 1990.
Their results indicate a positive relationship between the
absolute value of price change and trading volume other
than the Japanese Yen. Volumes and lagged absolute
returns are also found to be related. Fleming (1997)
analyzes the volume-volatility relation of U.S. treasury
securities, and finds out that there is a positive impact on
the stock of foreign markets. Daigler and Wiley (1999)
indicate that the general public and traders who are
uninformed drive the positive relation between volume
and volatility. Trades by floor traders are often associated
with decreased volatility, indicating the contemporaneous
relation between volatility and net position by categorized
traders - speculators and hedgers in conjunction with
small traders.
Previous studies, though, contribute little about the
effects of trading in futures on the underlying spot market,
with most of them focusing on the linkage between
exchange rate volatility and stock price and return. Some
studies provide empirical results that futures trading could
destabilize the spot market. Bae et al. (2004) investigate
that introducing Korea Composite Stock Price Index
(KOSPI) futures trading has resulted in both larger spot
price volatility and greater market efficiency (allowing for
quicker adjustment of market prices to information)
overall. Their study suggests that there exists volatility
spillover to stocks against futures. Chen and Shen (2004)
also find a common volatility factor that drives the
dynamics of stock return and exchange rate.
There are still many veins of studies regarding the
causal relationship between futures trading and currency

market volatility. Darrat et al. (2002) suggest that index
futures trading might not be blamed for the observed
volatility in the spot market. However a stronger and
more consistent support alternative posture that volatility
in the futures market is an outgrowth of a turbulent cash
market. Bessembinder and Seguin (1993) over argue
whether more prosperous futures trading activity is
associated with greater equity volatility and suggest
equity volatility is positively related to spot trading activity
and to contemporaneous futures trading shocks. Adrangi
and Chatrath (1998) examine the relationship between
exchange rate variability and futures trading activity in the
context of disaggregated open interest. The techniques
employed allow for more specific inferences regarding
which groups of traders contribute to exchange volatility.
Their results suggest that while ‘typical’ levels of futures
commitments are not destabilizing, surges in the level of
the commitments of large speculators and small traders
do cause exchange rate volatility. The actual release of
the commitment-of-traders data, however, has no impact
on spot prices.
Yang et al. (2005) examines the lead-lag relationship
between futures trading activity (volume and open
interest) and cash price volatility for major agricultural
commodities. The results of Granger causality tests show
that an unexpected increase in futures trading volume
leads an increase in cash price volatility for most
commodities. Likewise, there is a weak feedback causal
relationship between open interest and cash price
volatility. These findings are consistent with the
destabilizing effect of futures trading on agricultural
commodity markets. Bhargava and Malhotra (2006) find
that speculators and day traders destabilize the market
for futures, though whether hedgers stabilize or
destabilize the market is not inconclusive. The results
suggest that speculators’ demand for futures goes down
in response to increased volatility. They also indicate that
open interest activity either stabilizes or destabilizes
markets for speculators.
Agnieszka and Samuel (2007) investigate Euro
currency futures on the U. S. dollar, British pound,
Japanese yen, Swiss franc, Swedish krona, and
Canadian dollar. Their most important finding is that
speculative trading in futures has a day-to-day
destabilizing effect on the volatility of both the spot and
futures exchange rates for all currencies. On the other
hand, there is evidence that some lagged activities of
hedgers can stabilize the volatility of spot and futures
exchange rates. Cai et al. (2008) use a new highfrequency
data set to investigate informational linkages in
the euro–dollar and dollar–yen exchange rates across
five trading regions. Information is proxied by exchange
rate return, direction of return, volatility, trading activity,
and order flow. They find that informational linkages are
statistically significant at both own-region and inter-region
levels, but own-region spillovers dominate in economic
significance, especially for volatility and trading activity.
DATA AND METHODOLOGY
Data
Chen et al. 11297
Most previous studies have investigated the effects of futures
trading on spot market volatility, index price, or commodity price,
but only some research focuses on currency futures. In this paper
we especially focus on relationships between volatility of Won/USD
exchange rate and USD futures in South Korea’s financial markets.
We categorize South Korea’s capital markets into three major
traders - investment trust companies (ITC), banks, and foreigners -
who frequently have high trading activities in the spot and futures
foreign exchange markets. Therefore, this paper further investigates
dynamic relationships between these three major traders and return
volatility among the spot, USD futures and exchange rate markets.
The daily data used in this study covers the period from years of
2004 to 2008 which obtained from the Korea Securities Computing
Corporation (KOSCOM).
Methodology
We assume that traders use their expectations of the volatility range
of the futures return to adjust their spot or USD futures positions in
advance. We explore if the fluctuation of price change affects spot
and USD futures trading behaviors among the three major traders
and relationship between spot and USD futures volume. In addition,
we also consider the variation of net positions, which can be
defined as long contracts less the short contracts.
The proxy for the level of trading activity is trading volume, which
is standardized by open interest, according to Chatrath et al.
(1996). Daily futures volume largely impacts on speculation, since a
hedger’s transaction is composed of minor proportions of daily
futures volume. Open interest generally represents longer-thanintraday
positions that mostly capture hedge activity, while futures
volume relatively to open interest reflects speculation. Therefore,
open interest may provide more information on trading activity than
volume alone. Bessembinder and Seguin (1993) indicate the
relation between volatility, volume and open interest among
agricultural, financial and metal futures in eight countries. In this
study we not only take trading volume of each trader, but also adopt
open interest, which as a result it represents the market momentum
and provides certain information to market traders. Moreover, we
incorporate with net position, which is defined as long contracts less
short contracts, to measure the position each trader holds.
Where represents the daily clothing price or rates of the series i
at day t, that i equal to USD futures prices and Won/USD rates as
spot price. represents the daily clothing price at day t-1, we
measure the variance of return in the data series t by using .
On the other hand, we define the volatility of both the trading
volume and open interest as a logarithm of the ratio of the daily
trading volume is as following:
(1)
, (2)

11298 Afr. J. Bus. Manage.
where represents the trading volume of the series i at day t,
that i equal to investment companies (In), Foreigners (F), Banks
(B), as well as Open Interest (OI), respectively. represents
the trading volume of series i at day t-1, and is the rate of the
change of volume in the data series t.
Estimation of conditional means and volatilities
We follow the procedure as that of Bessembinder and Seguin
(1993) and Schwert (1990), which is referred to as the Schwert
volatility estimator. This procedure allows for an unbiased
estimation of conditional daily standard deviations on observable
variables. In order to measure market depth, we consider open
interest and net position. Bessembinder and Seguin (1993)
conclude that expected open interest may mitigate volatility.
Typically, unexpected open interest not only helps explain volatility,
but also volume, and no matter how the changes fluctuate, large
changes in unexpected open interest increase volatility.
We test the impact of volume and net positions on the volatility of
USD returns in each trader’s series, following Bessembinder and
Seguin (1993), in order to take the phenomenon of volatility
clustering into consideration. We add the lagged volatility variance
into our formula to capture the unsymmetrical effects on return
change, and we also adopt unexpected return. Conditional means
and volatilities are estimated as:
represents the daily returns of USD futures, is the four dummy
variables for the day of the week, and is the unexpected return or
residuals. denotes estimated unexpected returns, which are used
to estimate daily standard deviations, using the transformation:
(3)
(4)
We also have representing the expected volume
of each trader on day t, where represents the unexpected
volume of the three major traders, and k represents the three major
traders: investment trust companies, banks, as well as foreigners.
Here, and are equal to expected open interest and
unexpected open interest in USD futures markets. Both and
represent expected net positions and unexpected net
positions from the three major traders on day t. Net positions are
defined as the long open interests less the short open interests in
our study.
To partition each trader’s activity into expected and unexpected
components, we first test whether the series of each trader’s
volume and open interest are stationary or not, whereby all tests for
stationarity are conducted with the Augmented Dickey-Fuller (ADF)
test for a unit root. As recommended by Bessembinder and Seguin
(1993) and Valeria and Yiuman (2008), Equations 3 and 4 are
estimated sequentially. The lagged return is included in Equation 3
to allow for short-term shifts in expected returns. The inclusion of a
lagged unexpected return captures possible asymmetry in the
relation between return and volatility. Lagged volatilities are
included in Equation 4 to account for the effect of volatility’s
persistence. It is well known that volatility is positively related to an
unexpected shock and negatively associated with an unexpected
shock in the spot markets, as Wang (2002) supports the same
aspect.
Asymmetric model
To examine the relation between the net positions of the three
major traders and the volatility of USD futures, we take a similar
procedure as in Bessembinder and Seguin (1993) and regress the
volatility estimator on lagged volatilities, and expected and
unexpected trading activities, including trading volume and open
interest, as well as expected and unexpected net positions by each
trader. In other words, we add the dummy variables of both volume
and net positions into the estimation Equation 5 to test whether the
impact of unexpected change on volume and traders’ positions is
asymmetric or not. The empirical model is of the following form:
(5)
Here, and represent the dummy variables of volume
and net position, respectively. When the unexpected activity is
greater than one, the dummy variable is equal to one, meaning that
there exists a positive impact. Instead, as the unexpected activity is
greater than one, the dummy variable is equal to zero, which
means there exists a negative impact. The coefficients of the
activity series imply that the marginal impact effect is negative ( ,
), while on the other hand, the positive marginal impact can be
estimated from the sum of the coefficients of unexpected activity
and the product of unexpected activity in conjunction with activity
dummy variables ( , + ).
GARCH estimation
In order to realize the causality relationship between each trader’s
trading activity and market dynamics in both the spot and futures
markets, we employ an estimated GARCH series to measure both
Won/USD rate volatility and USD futures return volatility. Previous
studies suggest that GARCH (1,1) should be the default when
working with financial data in general and the variance of the spot
and futures rates in particular. The parameterization for Conditional
Variance is shown in Equation 6 as follows:
where . In general speaking, the GARCH
variance equation can be written as:
where is the variance estimated by the GARCH model in time t.
The coefficients measure the impact of the sectoral volatility
shocks, whereas the coefficients accounts for the identical shock
(6)
(7)

Table 1. Summary statistics for returns and overall trading activities.
Variables Returns Won/USD
Open
interest
Total vol.
Chen et al. 11299
Volumes by type of trader Net positions by type of trader
Banks ITC Foreigners Banks ITC Foreigners
Mean 0.02 -0.09 0.006 0.049 -0.04 -0.03 0.04 -1.47 -44.49 -42.40
Std. Error 0.01 56.35 0.04 20.39 42.67 36.66 31.44 368.89 2035.47 4041.95
Skewness -0.52 -0.18 3.52 0.10 -0.04 -0.44 -0.43 -0.41 0.10 -0.02
Kurtosis 8.34 4.35 38.32 3.87 3.64 4.82 4.93 5.98 4.73 3.79
J-B 1531.12 102.02 66918.37 41.71 21.69 212.54 231.83 495.58 157.17 32.79
ADF Test -36.175*** -13.675*** -11.6516*** -13.89*** -12.744*** -12.795*** -14.37*** -20.07*** -10.20*** -23.06***
This table presents the descriptive statistics for the logarithm values of USD futures trading volume on investment companies , banks, and foreigners. Net
positions mean the long volume less the short volume. Return and Won/USD rate both measure the change rate of the daily closing pric e on USD
futures and the volatility of the Won exchange rate. All measures are in logarithm process from January 2, 2004 to December 30, 2008. *** represents
significance at the 0.01 level, ** represents significance at the 0.05 level, and * represents significance at the 0.10 level . All data are obtained from
January 2, 2004 to December 30, 2008.
to volatility from the previous day.
VAR models
Here we conclude whether trading activities of each trader in the
USD futures markets influence the volatility of the Won/USD rate
and futures return performing the method of the vector
autoregression (VAR) approach, which is commonly used for
forecasting the interrelated series and for measuring the impact of
random disturbances on the system of variables. Moreover, VAR
treats every endogenous variable as a function of the lagged values
of all endogenous variables in a system. Hence, we use VAR to
determine the interaction in the market among the activities of the
three traders (investment companies, banks, as well as foreigners)
and the change in the Won/USD rate. The application of VAR
models in this study can be written as follows:
(8)
(9)
(10)
(11)
where , , and are coefficients of constant, lagged regressor
and lagged independent variables respectively; j is the number of
lags; Vol equals volatility of the Won/USD rate and USD futures; Vi
and NPi represent logarithm of trading volume and net positions for
banks (i=B), investment trust companies (i=In), as well as foreigners
(i=F). As described by Agnieszka and Samuel (2008) and Bhargava
and Malhotra (2006), the appropriate number of lags for VAR
models is determined by performing each VAR model with one to
four lags, and also relying on the lowest reported value for Akaike
information criterion (AIC) and Schwarz information criterion (SIC).
After running the VAR models above, we use Granger casualty test
to figure out if traders’ activities, including trading volume and their
net positions, significantly affect the volatility of the Won/USD rate
and futures return, or on the contrary, whether the change in the
Won/USD rate and futures return have a significant impact on the
trading activities of each trader.
EMPIRICAL RESULTS
Descriptive statistics
Table 1 reports the summary statistics for the returns, the
Won/USD rate, and trading activities of each trader.
Table 1 represents the mean daily return, the Won/USD
rate, and the logarithm of the ratio of overall trading
volume and open interest in USD futures. Futures return
is also the logarithm of the daily closing prices of the
contract closest to expiration, except within the delivery
month.
When the change in the second nearest contract is
used, the results are obtained with the means, standard
error, skewness, and kurtosis in conjunction with the
Jarque-Bera normality test, which shows how the
distribution is not like a normally distributed series.
According to the results as follows, we conclude that the
mean daily return, overall open interest, and trading
volume are all positive, but the Won/USD rate is negative,
which means the trend of the Won exchange rate was
undergoing depreciation over the period from 2004 to
2008.
Table 1 also reports summary statistics for the trading
volume and net positions, which are long contracts less
short contracts for each trader. It appears that the
logarithm of the volume of banks and investment
companies is negative, whereas instead the logarithm of
the volume of foreigners is positive. In terms of net
positions, banks, ITC, and foreigners are all negative,
implying hedgers always take net long positions, while
speculators take net short positions. We may conclude
that the three major traders in South Korea’s currency
markets mainly undertake speculator positions. For each
of the series, the results of the ADF tests show that these
data reject the null hypothesis of having a unit root and
being stationary. The existence of a unit root has
implications for decomposing a variable into expected
and unexpected components.

11300 Afr. J. Bus. Manage.
Table 2. Time series models of daily return.
Variables ITC Banks Foreigners
Intercept 0.0067, (4.04) *** 0.0063, (3.63) *** 0.0060, (3.75) ***
Day of the week dummy
Monday -0.0024, (-1.38) -0.0020, (-1.26) -0.0015, (-0.89)
Tuesday -0.0011, (-0.63) -0.0015, (-0.93) -0.0007, (-0.41)
Wednesday -0.0018, (-1.03) -0.0018, (-1.14) -0.0015, (-0.91)
Thursday -0.0013, (-0.76) -0.0006, (-0.39) -0.0012, (-0.70)
Sum of 10 Lagged Volatilities -0.3447, (2.24) ** -0.3224, (3.13) *** -0.3151, (3.01) ***
Sum of 10 Lagged Unex. Returns -0.3018, (0.05) -0.1768, (0.09) -0.2836, (0.09)
Durbin-Watson 2.0122 2.0096 2.0090
Adjusted R 2 0.0130 0.0086 0.0012
Net position is defined as long volume less short volume, and volume and net positions are decomposed into expected and unexpected
components based on the AR (p) model, while volatility is estimated by using the Schwert volatility estimator obtained from Equation (3).
Test statistics for individual coefficients are t statistics for the null hypothesis that the coefficient is zero. Test statistics for the sum of lagged
volatilities and sum of lagged unexpected returns are F statistics for the null hypothesis that the sum of the coefficients of lagged volatilities
and lagged unexpected returns is zero, respectively (Table 1).
Table 3. Overall trading activity and volatility.
Variables ITC Banks Foreigners
Intercept 0.004, (3.54) *** 0.006, (5.25) *** 0.0043, (3.96) ***
Expected volume -1.14E-05, (0.52) -3.93E-05, (-2.33) ** -3.11E-05, (-0.85)
Unexpected volume 7.37E-06, (0.61) 4.98E-05, (4.96) *** 1.28E-04, (9.39) ***
Expected open interest 3.83E-04, (2.66) *** 3.1E-05, (2.36) ** 4.95E_04, (3.36) ***
Unexpected open interest 1.85E-04, (3.77) *** 2E-05, (4.59) *** 3.42E-04, (6.78) ***
Expected net position -5.80E-08, (-0.05) 3.86E-06, (0.85) 9.10E-07, (1.56)
Unexpected net position -3.83E-07,m (-1.96) ** 9.68E-07, (0.96) -1.70E-07*, (-1.69)
Day of the week dummy
Monday -0.0013, (-1.05) -0.0006, (-0.48) -0.0018, (-1.51)
Tuesday -0.0032, (-2.49) ** -0.0031**, (-2.70) ** -0.0037, (-2.98) ***
Wednesday 0.0005, (0.40) -0.0002, (-0.16) -0.0009, (-0.72)
Thursday -0.0006, (-0.47) -0.0012, (-1.02) -0.0007, (-0.54)
Sum of 10 Lagged Volatilities 0.8032, (61.47) *** 0.6311, (71.22) *** 0.8164, (56.55) ***
Sum of 10 Lagged Unex. Returns 0.0380, (5.72) *** -0.2723, (7.11) *** -0.2868, (7.28) ***
Durbin-Watson 1.9969 2.0156 2.0316
Adjusted R 2 0.2738 0.1546 0.3262
Net position is defined as long volume less short volume, and volume and net positions are decomposed into expected and unexp ected
components based on the AR model, while volatility is estimated by using the Schwert volatility estimator obtained from Equation (3). Test
statistics for individual coefficients are t statistics for the null hypothesis that the coefficient is zero. Test statistics for the sum of lagged
volatilities and sum of lagged unexpected returns are F statistics for the null hypothesis that the sum of the coefficients of lagged volatilities and
lagged unexpected returns is zero, respectively (See Table 1).
Estimation of conditional means and volatilities
The estimations of the conditional means of daily returns
are shown in Table 2. According to this table, the
independent variables have somewhat explanatory power
for realized returns, with the largest adjusted R 2 (1%).
Most of dummy variables are insignificant, however, both
lagged unexpected returns and lagged volatilities are
negative, but only the lagged volatilities are significant at
level of 0.05. Bessembinder and Seguin (1993) indicate
that volatility is positively related to trading volumes and
find the impact of unexpected volume on volatility is
greater than that of expected one. The net positions of
speculators and small traders, on average, are positively
associated with volatility, while there is a negative impact
between an unexpected change in the net positions of
hedgers and volatility.
Table 3 presents the results of regressing daily volatility

Table 4. Relationship between daily return volatility and trader positions
Chen et al. 11301
Variables ITC Banks Foreigners
Intercept 0.0042, (3.50) 0.0075, (5.74) *** 0.0035, (2.73) ***
Expected volume 1.10E-05, (0.49) -4.03E-05, (-2.40) ** -3.29E-05,m (-0.89)
Unexpected volume 7.80E-06, (0.64) 4.08E-05, (4.79) *** 1.27E-04, (9.29) ***
Expected open interest 0.0004, (2.59) *** 2.79E-04, (2.13) ** 4.85E-04, (3.27) ***
Unexpected open interest 0.0002, (3.73) *** 1.93E-04, (4.30) *** 3.34E-04, (6.60) ***
Expected positions -6.85E-08, (-0.05) 4.05E-06, (0.90) 9.06E-07, (1.55)
Unexpected positions -3.78E-07, (-1.94) * 7.43E-07, (0.74) -1.70E-07*, (-1.69)
D× unexpected volume 0.0006, (0.61) -0.0019, (-2.74) *** -7.80E-04, (-1.03)
D× unexpected positions -0.0007, (-0.91) -0.0003, (-0.49) 0.0019**, (2.31)
Day of the week dummies
Monday -0.0013, (-1.04) -0.0004, (-0.37) -0.0017, (-1.42)
Tuesday -0.0032, (-2.50) ** -0.0029, (-2.50) ** -0.0036, (-2.91) ***
Wednesday 0.0004, (0.35) -0.0003, (-0.24) -0.0009, (-0.74)
Thursday -0.0006, (-0.50) -0.0011, (-0.99) -0.0008, (-0.67)
Sum of 10 lagged volatilities 0.8032, (61.47) *** 0.6311, (71.22) *** 0.8164, (56.55) ***
Sum of 10 lagged unex. returns 0.0380, (5.72) *** -0.2723, (7.11) *** -0.2868, (7.28) ***
Durbin-Watson 1.9958 2.0205 2.0168
Adjusted R 2 0.2733 0.1588 0.3289
Allowing for the asymmetric activity.
All trading activities, including trading volume and net positions, are decomposed into expected and unexpected components following the
method of AR. Volatility is transferred by the Schwert estimator. Term D is a dummy variable that is equal to one for a positive shock and zero for
a negative shock otherwise. Test statistics for each coefficient are t statistics for the hypothesis that the coefficient is zero. Test statistics for the
sum of lagged volatilities and sum of lagged unexpected returns are F statistics for the null hypothesis that the sum of the coefficients of lagged
volatilities and lagged unexpected returns is zero, respectively (See Table 1).
estimates on expected as well as unexpected overall
trading activity variables. According to the results, we find
a Tuesday effect, which is associated with volatility
negatively in South Korea’s currency markets. Our study
further finds that the explanatory of both the sum of
lagged volatilities as well as the sum of lagged returns for
volatility is significant among three traders. The
coefficients on expected volume are negative for all and
there is an insignificant effect, with only the bank series
displaying significance at 0.05 levels. For unexpected
volume, they show the same signs, with a positive and
significant relationship to volatility, other than the series of
investment companies. Consistent with the results of
Bessembinder and Seguin (1993), the coefficient on
unexpected and expected trading volume is positive and
significant, along with the coefficient estimate on
expected and unexpected open interest. These results
show that all of coefficients are significant positively at
0.05 levels. There is evidence that change in open
interest facilitates market depth, and the MDH is
supportive in South Korea’s currency market.
The estimated coefficients on expected net positions
are all positive, but all t-statistics are insignificant. In
contrast, we find that the negative impacts in the net
positions of each trader are associated with an increase
in volatilities, while the coefficients of unexpected net
positions are significant, except for banks. The
coefficients of unexpected net positions on investment
companies and foreigners are both negative, which
means these two traders perform hedging strategies for
the most of time. However, the coefficients on banks are
negative, indicating that they usually have speculator
positions. It is noted that the adjusted R 2 values on these
three type investors are from 0.15 to 0.32, and the value
for foreigners is the highest among the three traders.
Asymmetric model
Admati and Pfleiderer (1988) point out that market depth
may depend on whether changes in volume to be
expected or unexpected. They contend that markets
should be deeper when trading volume is expected to be
higher. Both positive and negative shocks may have
different impacts on price volatilities. Table 4 explores
whether positive and negative shocks of unexpected

11302 Afr. J. Bus. Manage.
trading volume and net positions have varied impacts on
volatility. The coefficients of activity series show the
marginal impact effect is negative ( , ), on the other
hand, the positive marginal impact can be estimated from
the sum of the coefficients of unexpected activity and
product of unexpected activity in conjunction with activity
dummy variables ( , + ). If the absolute value of
the interaction variable is greater than that for the
unexpected variable, then the effect of a positive shock
on volatility is greater than that of a negative effect. Our
results from Table 3 indicate there is no significant effect
on the interaction variable of unexpected volume ( )
except for the series of banks.
We also find the interaction variable of unexpected net
positions to have significant explanatory power for the
series of foreigners. The coefficient of unexpected
volume is positive except for banks, and the coefficient
for the unexpected volume is negative other than
investment trust companies. Sum of the coefficients for
unexpected volume and for the interaction variable, which
represents the positive effect on volatility, is also negative
except for investment companies.
This reflects that the effect of a positive shock on
volatility is larger than a negative shock, but only
significant in the series of banks. We briefly suggest that
both positive and negative shocks in the volumes of the
investment companies are associated with an increase in
volatility.
In terms of the banks, both positive and negative
shocks are related to a decrease in volatility. Finally, a
positive shock upgrades the volatility, but a negative
shock lowers the volatility. However, for the foreigners,
the effect of a negative shock among the three traders is
smaller than that of a positive shock.
We also observe the interaction variable of unexpected
net positions ( ), since the coefficient estimates on
unexpected net positions are all negative except the
banks, and the coefficient estimates for the interaction
variable are also negative except the foreigners. Hence,
the sum of the coefficient estimates for unexpected net
positions and that for the interaction variable are negative
other than for the foreigners, but their absolute values are
larger than the absolute value of the coefficient for
unexpected net positions. Therefore, we conclude that
the effect of a positive shock on volatility is greater than
that of a negative shock.
This suggests that both a positive shock and a negative
shock are associated with a decrease in volatility for the
investment companies. In terms of the banks, a positive
shock is related to a decrease in volatility and a negative
shock is related to an increase in volatility. In the end, a
positive shock upgrades the volatility and a negative
shock lowers the volatility for the foreigners. As with the
unexpected volume, the effect of a negative shock
among three traders is smaller than that of a positive
shock for the net positions of each trader, respectively.
Vector autoregression (VAR) models
Trading volume in futures and the spot won/ United
States Dollar (USD) rate volatility
Here we examines the relationship between the spot
Won/USD rate volatility, which is a proxy by GARCH
model, and the USD futures trading volume, which is
standardized by natural logarithm. This study defines
trading volume as the proper measure capturing the
speculative activities in the USD futures market. In
addition, we also investigate the influence that past
fluctuations of the Won/USD rate have on present
volatility. The VAR results are in Tables 5 to 8. The most
interesting finding that can be drawn is that when trading
volume is used as the independent series and the
volatility of the Won/USD rate is the dependent series,
only the coefficients for all lag terms on the series of
investment companies are positive and significant at 0.01
levels. It means that only the speculation of investment
companies has a day-to-day destabilizing effect on the
Won/USD rate market. On the contrary, under the inverse
relationship for when the volatility of the Won/USD rate is
used as the independent series and trading volume is the
dependent series, we indicate there is one lag term with
at least explanatory power that is significant for the
variable of each trader’s trading volume. The fourth lag’s
coefficient is positive and significant at 0.05 levels for the
series of banks, and the second lag’s coefficient is
negative and significant simultaneously at 0.1 levels for
the series of investment companies. Moreover, the first
lag’s coefficient is negative and significant, but the
second lag’s coefficient is positive and significant at least
at the 0.1 levels instead.
Net positions in futures and the spot won/ United
States Dollar (USD) rate volatility
The net positions we use as measures capture the hedge
activities on USD futures in South Korea’s currency
markets. In order to further indicate the nature of the
relationship between the trading activities of hedging and
the volatility of the Won/USD rate, we employ the VAR
models. The most interesting findings are obtained when
the net positions are used as the independent series in
the VAR models, and the volatilities of the Won/USD rate,
proxied by GARCH (1,1), are used as the dependent
series in the VAR models. Through such a model we
examine the impact of the lagged values of net positions
on the volatility of the Won/USD, showing the stabilizing
and destabilizing effects that traders have on the
exchange rate volatility. Among the three traders’ series,
only significant lags are obtained in the banks’ series
which are found to destabilize the Won/USD rate volatility
at three lagged times, indicating that an increased
number of net positions on the last three trading days
with larger volatility.

Table 5. VAR results with volume and estimator of WON/USD rate by Garch.
Chen et al. 11303
Independent variables Lag
Dependent variables
Won/USD rate garch t-value
Panel A: banks
Dependent variables
Volume t-value
Won/USD Rate -1 0.077 2.70 *** -3155.893 -1.09
-2 0.044 1.56 ** 3358.98 1.17
-3 -0.194 -6.87 *** -3336.618 -1.16
-4 -0.015 -0.54 5281.646 1.81 **
Volume −1 -6.99E-09 -0.03 -0.579 -20.50 ***
−2 -2.11E-07 -0.68 -0.415 -13.13 ***
-3 -2.60E-07 -0.84 -0.303 -9.59 ***
-4 -1.21E-07 -0.43 -0.143 -5.09 ***
Panel B: Investment trust companies
Won/USD Rate −1 0.076 2.71 *** -1792.910 -0.70
−2 0.045 1.62 * -3919.197 -1.52 *
-3 -0.188 -6.70 *** 3211.495 1.24
Volume −1 7.39E-07 2.41 *** -0.459 -16.45 ***
−2 9.41E-07 2.92 *** -0.299 -10.10 ***
-3 8.29E-07 2.73 *** -0.212 -7.60 ***
Panel C: foreigners
Won/USD Rate −1 0.080 2.87 *** -3176.045 -1.36 *
−2 0.044 1.58 * 4990.350 2.13 ***
−3 -0.196 -6.97 *** -1086.863 -0.46
Volume −1 2.57E-07 0.76 -0.269 -9.60 ***
−2 -1.01E-07 -0.29 -0.147 -5.13 ***
−3 -4.51E-08 -0.13 -0.189 -6.77 ***
See Table 1.
When we take volatility as an independent series and the
trading activities of net positions as a dependent series,
then according to the results from Table 6, only the
second to fourth lags are jointly significant, while the
second and the third lags are positive to the net positions
of investment companies, but the fourth lag is negatively
associated with it. The findings show that in the period of
the last three trading days the volatility of the Won/USD
rate increases, and the net positions of investment
companies indeed diminish. However, the net positions of
investment companies are positively to the volatility of the
Won/USD rate on the last four days. For the remaining
series, no evidence is found that Won/USD volatility
significantly affects the net positions of USD futures.
The last important question, as far as the Won/USE rate
is concerned and which could be obtained from VAR
models, is whether the present Won/USD rate depends
on the volatility in the days before. For the three traders’
series, there exists a strong and positive relationship
between the last two trading days’ volatility and the
volatility at the present time (t=0). This means that
volatility increases two days before or the previous day
which are followed by an intensified volatility today.
Moreover, for both the series of banks and foreigners, the
level of the volatilities at lag three days negatively and
significantly influences the present time, while the level of
volatility at lag three and four days is also associated
negatively and significantly with the present time for the
series of investment companies.
Trading volume in futures and the United States
Dollar (USD) futures return volatility
Table 7 represents the results from the VAR models
testing the relationship between the trading volume in the
three traders’ series and volatility of the USD futures
returns. When trading volume of USD futures is used as
the independent series and volatiles of futures return are
the dependent series, only the coefficients for lagged
volume are individually significant for all lags for the

11304 Afr. J. Bus. Manage.
Table 6. VAR results with net positions and estimator of WON/USD rate by garch.
Independent variables Lag
Dependent variables
USD futures return garch t-value
Panel A: banks
Dependent variables
Volume t-value
Won/USD Rate -1 0.082 2.93 *** 2512.093 0.09
-2 0.043 1.52 * -2254.647 -0.08
-3 -0.194 -6.90 *** -14274.22 -0.50
Net Positions −1 1.50E-08 0.38 -0.115 -4.06 ***
−2 -1.21E-08 -0.43 -0.103 -3.63 ***
-3 6.06E-08 2.17 ** -0.114 -4.01 ***
Panel B: investment trust companies
Won/USD Rate −1 0.077 2.70 *** 136791.5 0.85
−2 0.046 1.63 * -221745.3 -1.40 *
-3 -0.192 -6.80 *** -204558.0 -1.29 *
-4 -0.0161 -0.56 564037.6 3.50 ***
Net Positions −1 5.10E-09 1.00 -0.103 -3.63 ***
−2 -1.48E-09 -0.29 -0.036 -1.25
-3 -6.20E-09 -1.22 -0.033 -1.16
-4 3.40E-09 0.67 -0.006 -0.22
Panel C: foreigners
Won/USD Rate −1 0.078 2.86 *** -219578.0 -0.71
−2 0.043 1.53 * -678758.9 -2.18 ***
−3 -0.196 -6.97 *** -689430.8 -2.21 ***
Net Positions −1 -1.38E-09 -0.54 -0.162 -5.69 ***
−2 -2.09E-09 -0.81 -0.054 -1.87 **
−3 -1.92E-09 -0.75 -0.062 -2.17 ***
See Table 1.
series of investment companies, except the first lag. For
the series of banks and foreigners, we indicate there is
no significant lag impact of trading volume on the volatility
of USD futures returns. However, as far as the series of
investment companies are concerned, all lags are
positive, implying that this positive relationship between
volume and the volatility exists for consecutive days (t=-2,
-3, -4). Therefore, it can be concluded that the three
traders in USD futures markets have no immediate (day
to day) destabilizing effect on the variability of USD
futures returns.
In order to investigate the inverse relationship, trading
volume of USD futures is used as the dependent series
while the GARCH estimation of USD futures returns are
the independent series. The result shows that some lags
of volatility can be said to affect trading volume. For the
series of banks, all lags are positive significantly except
the fourth lag, while the second lag has a significant
impact on the trading volume of investment companies.
In the end, only the coefficient of the first lag is positively
significant for the series of foreigners. In brief conclusion,
we find all significant lags of the individual effect of the
increase in volatility do stimulate trading volume. It is
definitely worth noticing, that the impact of the first lag
volatility (yesterday) is much higher than the impact of the
lags of higher order (days before yesterday) for the series
of banks.
To examine whether today’s volatility of the USD
futures markets depends on the volatility in the past or
not, lagged values of USD futures returns volatility are
treated as the independent series, while today’s volatility
is the dependent series. We indicate that only the

Table 7. VAR Results with volumes and estimator of USD futures return by garch.
Independent
variables
Lag
Chen et al. 11305
Dependent variables Dependent variables
USD futures return garch t-value Volume t-value
Panel A: banks
USD futures return -1 -0.026 -0.91 41578.35 8.49 ***
-2 -0.010 -0.34 10040.25 2.00 **
-3 0.025 0.85 7443.879 1.48 *
-4 -0.074 -2.54 *** -1550.37 -0.31
Volumes −1 4.66E-08 0.28 -0.592 -20.95 ***
−2 7.10E-08 0.39 -0.426 -13.47 ***
-3 2.95E-07 1.60 -0.309 -9.77 ***
-4 1.62E-07 1.01 -0.152 -5.50 ***
Panel B: investment trust companies
USD futures return −1 -0.0269 -0.94 3073.711 0.69
−2 -0.0083 -0.29 8879.265 1.98 **
-3 0.0257 0.90 1160.663 0.26
-4 -0.0649 -2.27 *** -583.041 -0.13
Volumes −1 1.98E-07 1.10 -0.491 -17.33 ***
−2 3.12E-07 1.61 * -0.343 -11.23 ***
-3 3.75E-07 1.94 ** -0.284 -9.32 ***
-4 2.93E-07 1.63 * -0.150 -5.30 ***
Panel C: foreigners
USD futures return −1 -0.0263 -0.92 26978.15 6.67 ***
−2 -0.0125 -0.43 4273.283 1.04
−3 0.0319 1.10 -1026.039 -0.25
Volumes −1 1.42E-07 0.71 -0.276 -9.83 ***
−2 -1.08E-07 -0.53 -0.150 -5.21 ***
−3 2.48E-07 1.27 -0.185 -6.73 ***
See Table 1.
coefficients of the fourth lags for both series of banks and
investment companies are found to individually,
significantly affect the volatility at present time (t=0), and
hence for the series of banks and investment companies.
We interpret this by saying that their volatilities in USD
futures markets from four days ago have a negative
effect on the volatiles today.
Net positions in futures and the USD futures return
volatility
When the net positions are treated as the independent
series in the VAR models and the futures volatility is the
dependent series, the results show that only the series of
investment companies have a destabilizing effect on the
volatility for the first lag and second lag. This implies that
an increase in the number of net positions in the near
past (yesterday and two days ago) causes increased
volatility in USD futures markets at the present time (t=0).
When the net positions are used as the dependent
series, which are explained by lagged futures volatility,
for the series of banks we indicate that the coefficients of
first lag and second lag are both negative and related
significantly to the net positions, suggesting that
increased volatility in the USD futures markets yesterday
and two days ago results in decreased futures contracts
today. Therefore, it can be concluded that banks’ net
positions have a stabilizing effect on the variability of
futures volatility. On the other hand, we also investigate
that the coefficient of the first lag is positively significant,
but the second lag is negatively significant for the series
of foreigners, implying that an increase in volatility
yesterday causes lower net positions today in USD
futures markets. If the volatility in USD futures markets
increases two days ago, then it can be expected that the

11306 Afr. J. Bus. Manage.
Table 8. VAR Results with net positions and estimator of USD futures return by garch.
Independent variables Lag
Dependent Variables
USD futures return garch t-value
Dependent variables
Net positions t-value
Panel A: Banks
USD futures return -1 -0.0283 -0.99 -363731.1 -7.40 ***
-2 -0.0047 -0.16 -137767.5 -2.74 ***
Net positions −1 1.01E-08 0.62 -0.115 -4.07 ***
−2 -2.19E-09 -0.14 -0.085 -3.08 ***
Panel B: Investment Trust Companies
USD futures return −1 -0.0296 -1.04 338428.2 1.22
−2 -0.0101 -0.36 123980.1 0.45
Net Positions −1 3.85E-09 1.32 * -0.102 -3.59 ***
−2 3.95E-09 1.35 * -0.036 -1.28
Panel C: Foreigners
USD futures return −1 -0.0277 -0.96 -6192649.0 -12.02 ***
−2 -0.0149 -0.49 2600352.2 4.77 ***
−3 0.0393 1.29 * -4421.895 -0.01
Net positions −1 -1.22E-09 -0.77 -0.113 -3.96 ***
−2 1.13E-09 0.72 -0.047 -1.67 **
−3 4.76E-10 0.32 -0.052 -1.96 **
See Table 1.
net positions of foreigners increase today.
Only the series of foreigners show that the present
futures volatility depends on its past volatility. An
individually significant and positive coefficient is found for
the third lag in the series of foreigners. This result shows
that increased volatility in USD futures markets three
days ago has a positive impact on volatility in the futures
markets at the present time (t=0).
Granger causality
In order to determine the lead-lag (causal) relationship,
we employ the method of Granger Causality, which is a
proper technique to more rigorously examine if there is
any causation between variables.
Trading activities and the spot Won/USD rate
volatility
Table 9 displays the results from the Granger Causality
test for the Won/USD rate volatility, which is proxied by
GARCH. We also focus on the different measures of the
trading activities, including trading volume and the net
positions. The results of the Granger Causality test reveal
that three sets of significant uni-directional (one-way)
causality exist: volume Granger causes Won/USD rate
GARCH and Won/USD rate GARCH, Granger causes net
positions for investment trust companies; and Won/USD
rate GARCH Granger causes net positions for foreigners.
The results prove for the series of investment
companies that the trading volume for investment
companies has a significant impact on Won/USD rate
volatility, but there is no evidence that the changes in the
Won/USD rate influence the level of the trading volume
for any series. Therefore, it can be concluded that, there
does not exist a bi-directional causal relationship
between trading activities in trading volume and the
Won/USD rate in USD spot currency markets. As far as
the net positions of each trader are concerned, it is found
that the fluctuation of the Won/USD rate actually
influences the level of the net positions for both
investment companies and foreigners. However, there is
no evidence to explain the opposite relation between
Won/USD rate volatility and the net positions of each
trader, such that changes in net positions cause the level
of volatility in USD spot currency markets.
Trading activities and the USD futures return
volatility
A similar test is employed to examine the relationship

Table 9. Results of granger causality test for Won/USD rate market.
Null hypothesis F-statistics
Panel A: Banks
Volume does not granger cause Won/USD rate garch 0.2349
Won/USD rate garch does not granger cause volume 1.9247
Net positions do not granger cause WON/USD rate garch 1.7365
Won/USD rate garch does not granger cause net positions 0.0896
Panel B: investment trust companies
Volume does not granger cause Won/USD rate garch 4.3622 ***
Won/USD rate garch does not granger cause volume 1.4007
Net positions do not granger cause WON/USD rate garch 0.8179
Won/USD rate garch does not granger cause net positions 3.7658 ***
Panel C: foreigners
Volume does not granger cause Won/USD rate garch 0.2783
Won/USD rate garch does not granger cause volume 2.0418
Net positions do not granger cause Won/USD rate garch 0.4067
Won/USD rate garch does not granger cause net positions 3.7444 ***
See Table 1.
Table 10. Results of granger causality test for Won/USD futures market.
Null hypothesis F-statistics
Panel A: Banks
Volume does not Granger Cause USD futures return GARCH 0.7343
USD futures return GARCH does not Granger Cause volume 19.3453 ***
Net positions do not Granger Cause USD futures return GARCH 0.2093
USD futures return GARCH does not Granger Cause net positions 30.7028 ***
Panel B: Investment Trust Companies
Volume does not Granger Cause USD futures return GARCH 1.2822
USD futures return GARCH does not Granger Cause volume 1.0942
Net positions do not Granger Cause USD futures return GARCH 1.6300
USD futures return GARCH does not Granger Cause net positions 0.8228
Panel C: Foreigners
Volume does not Granger Cause USD futures return GARCH 1.0137
USD futures return GARCH does not Granger Cause volume 15.1191 ***
Net positions do not Granger Cause USD futures return GARCH 0.4339
USD futures return GARCH does not Granger Cause net positions 56.9740 ***
See Table 1.
between trading activities and the USD futures return
volatility in terms of the three traders. The results of the
Granger Causality test are shown in Table 10 that unidirectional
causality between variables exist: USD futures
return GARCH Granger causes volume and net positions
for both banks and foreigners. However, there is no
significant lead-lag (causal) relationship found in reverse
order. As far as the activities of trading volume and net
positions are concerned, the main findings suggest that
USD futures play a significant role to impact the trading
activities for both banks and foreigners.
Chen et al. 11307
To sum up, the results from Granger Causality tests
prove that there does not exist bi-directional causal
relationship between trading activities and volatilities for
both the Won/USD rate as well as USD futures return in
South Korea’s futures markets.
SUMMARY AND CONCLUSIONS
The purpose of this paper is to investigate the effect of
trading activities by type of major traders on return

11308 Afr. J. Bus. Manage.
volatility in South Korea’s USD futures markets, over the
period of January 2, 2004 through December 30, 2008.
Consistent with the mixture of distribution hypothesis, the
principal finding in this paper is that an unexpected
change (in either direction) in trading volume of both
banks and foreigners is, on average, positively
associated with volatility. In addition, unexpected open
interest is also associated positively with market volatility
for all three major traders. Regarding the asymmetric
effect, only the banks’ trading volume and foreigners’ net
positions are asymmetric on volatility direction. In spot
markets, it is found that there exists a causal relationship
in terms of investment trust companies and foreigners. It
is also found that all lags of series of the investment trust
companies have a significant and positive impact on the
volatility of the Won/USD rate, which is consistent with
the result of Granger Causality. Therefore, we conclude
that the trading volume of investment trust companies
has a day-to-day destabilizing effect on the volatility of
the Won/USD rate. Moreover, regarding the relationship
between net positions and Won/USD rate volatility, there
exist a strong relationship between the net positions of
both investment trust companies and foreigners and
Won/USD rate volatility. It seems the fluctuation of the
Won/USD rate may influence the net positions of
investment trust companies and foreigners hold.
On the other hand, for futures markets we discover
there are causal relationships in both banks and
foreigners. According to the dispersion of beliefs models
and noise trading theories, investment trust companies
appear to be uninformed in USD futures markets. In
contrast, banks and foreigners likely possess certain
private information. It is not surprising that both banks
and foreigners hold such private information, because
they also have substantial cash transactions and
potentially benefit from economies of scale in information
gathering than investment trust companies. For the
relationship between trading activities and USD futures
return, the result shows there is no bi-directional causal
relationship in the three traders’ series. Briefly speaking,
any trader in currency markets has no absolute impact on
the change of USD futures return, which means the
return, is mainly decided by the market mechanism.
REFERENCES
Admati A, Pfleiderer P (1988). “A theory intraday patterns: Volumes and
price volatility.” Rev. Financ. Stud., 1: 1-40.
Agnieszka K, Samuel A (2008). “The relationship between currency
futures trading activity and exchange rate volatility,” Unpublished
Ph.D. Dissertation, Department of Business, Economics and Law,
GÖTEBORG University.
Bessembinder P, Seguin PJ (1993). “Price volatility, trading volume, and
market depth: Evidence from futures markets.” J. Financ. Quant.
Anal., 28: 21-39.
Bhargava V, DK Malhotra (2007). “The relationship between futures
trading activity and exchange rate volatility, revisited.” J. Multinational
Financ. Manage., 17: 95-111.
Cai F, Howorka E, Wongswan J (2008). “Informational linkages across
trading regions: Evidence from foreign exchange markets.” J. Int.
Money Financ., 27(8): 1215-1243.
Chatrath A, Ramchander S, Song F (1996). “The role of futures trading
activity in exchange rate volatility.” J. Futures Mark., 20: 105-125.
Clark PK (1973). “A subordinated stochastic process models with finite
variance for speculative prices.” Econometrica, 41: 135-155.
Daigler RT, Wiley MK (1999). “The impact of trader type on the future
volume-volatility relation.” J. Financ., 54: 2297-2316.
Enders W (1995). “Applied Econometric Time Series,” John Wiley and
Sons, Inc. 2nd Edition, 480 pp.
Epps TW, Epps ML (1976). “The stochastic dependence of security
price changes and transaction volumes: Implications for the mixtureof-distribution
hypothesis.” Econometrica, 44: 305-321.
Fleming M (1997). “The round-the clock market for U.S. treasury
securities,” Federal Reserve Bank of New York. Econ. Pol. Rev., pp.
9-32.
Kaldor N (1939). “Speculation and economic stability.” Rev. Econ. Stud.,
7: 1-27.
Luc B, Pagfinn R, Genaro S (2005). “Exchange rate volatility and the
mixture of distribution hypothesis.” Empir. Econ., 30: 889-911.
Tauchen GE, Pitts M (1983). “The price variability–volume relationship
on speculative markets.” Econometrica, 51: 485-505.
Valeria M, Yiuman T (2008). “Intraday volatility in the bond, foreign
exchange, and stock index futures markets.” J. Futures Mark, 28:
313-334.
Wang C (2002). “The effect of net positions by type of trader on volatility
in foreign currency futures markets.” J. Futures Mark., 22: 427-450.
Yang J, Balyeat RB, Leatham DJ (2005), “Futures Trading Activity and
Commodity Cash Price Volatility.” J. Bus. Financ. Account., 32: 297-
323.

African Journal of Business Management Vol. 5(28), pp.11487-11496,16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.1114
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Franchisee perceived relationship value and loyalty in a
franchising context: assessing the mediating role of
franchisee satisfaction and the moderating role of
franchisee characteristics
Weiping Chen
School of Agricultural Economics and Rural Development, Renmin University of China,
Beijing, 100872, P. R. China. E-mail: chenweipingruc@ruc.edu.cn. Tel: 86-10-82509719.
Accepted 30 September, 2011
This article examined franchisee satisfaction as mediator and franchisee characteristics as moderators
of the relationship between franchisee perceived relationship value and loyalty. Using the data from 218
franchisees in 5 Chinese convenience store franchise companies, the findings revealed a partially
mediating role for franchisee satisfaction in the relationship between perceived relationship value and
loyalty. Furthermore, results showed that the relationship was stronger for franchisees with older and
higher education, but it was weaker for those franchisees with shorter relationship length.
Key words: Relationship value, loyalty, mediator, moderators, franchising, China.
INTRODUCTION
Customer loyalty, a buyer’s overall attachment or deep
commitment to a product, service, brand or organization
(Oliver and Rust, 1997; Oliver, 1999) is an important
strategic objective of managers around the world (Cooil
et al. 2007). Franchising as a key strategy in the growth
of global business activity depends on relationships
between franchisor and franchisee (Kidwell et al., 2007).
Although, in theory franchisees sign formal contracts to
acquire desirable behavior from their franchise partners
(Bracker and Pearson, 1986), formal contractual agreements
and good intentions do not guarantee satisfying
and efficacious relations (Gassenheimer et al., 1996). For
the franchisor, it is critical to establish franchisees’ loyalty
in the system (Chiou et al., 2004).
Recently, researchers have suggested that an important
determinant of loyalty is relationship value (Ulaga and
Abbreviations: CFA, confirmatory factor analysis; GFI,
goodness-of-fit index; AVE, average variance extracted; AGFI,
average goodness-of-fit index; CFI, comparative fit index; IFI,
incremental fit index; RMSEA, root mean square error of
approximation
Eggert, 2006). Relationship value has usually defined as
the trade-off between the benefits (“what you get”) and
the sacrifices (“what you give”) in a market exchange
(Zeithaml, 1988). Harmon and Griffiths (2008)
hypothesized that the franchisee perceived relationship
value is positively related to franchisee loyalty. To date,
however, no research has examined empirically the
relationship between relationship value and loyalty in the
franchising context. This study was thus designed to
determine how franchisee perceived relationship value
relates to franchisee loyalty.
The vast majority of models developed to explain the
link between relationship value and loyalty are grounded
in Fishbein and Ajzen’s (1975) reasoned action theory.
Fishbein and Ajzen’s basic proposition is that cognitive
beliefs (such as relationship value) combine to influence
affective responses (such as satisfaction), which in turn
influence behavioral intent or behavior (such as loyalty).
Many empirical studies have provided support for
cognitive-affective-behavior links in business-toconsumer
(B2C) contexts (Omar et al., 2007; Yang and
Peterson, 2004) and in B2B buyer-seller relationship
contexts (Irene et al., 2009; Callarisa et al., 2009; Barry

11488 Afr. J. Bus. Manage.
Figure 1. The model and hypotheses.
and Terry, 2008; Ulaga and Eggert, 2006). In keeping with
this previous research, the first object of this study is to
evaluate the mediation of the franchisee satisfaction
variable in explaining how the franchisee perceived
relationship value and loyalty are related.
In addition, prior research has found that individual
characteristics (for example age, education) moderate
the relationships between cognitive, affective and
behavior (Evanschitzky and Wunderlich, 2006). In terms
of relationship value and loyalty link, however, there is a
paucity of research on the issue of moderator variables
(Yang and Peterson, 2004; Chen and Tsai, 2008). In
particular, no research exists that examines the effect of
individual characteristics on the link between relationship
value and loyalty. To fill this gap, the second object of this
study is the examination of the extent to which franchisee
characteristics moderate how the franchisee perceived
relationship value relate to franchisee loyalty.
THEORY AND HYPOTHESES
Figure 1 shows the model of the relationships expected
between franchisee perceived relationship value and
franchisee loyalty. According to the model, which is based
on the reasoned action theory and information processing
theory (Moskovitch, 1982), franchisee satisfaction
mediates the relationship between franchisee perceived
relationship value and franchisee loyalty. Three
franchisee characteristics (age, education and length of
relationship) also moderate this relationship. In the
following sections, the study first discusses the link
between franchisee perceived relationship value,
satisfaction and loyalty. The study then offer predictions
about how the franchisee characteristics moderate the
relationship between franchisee perceived relationship
value and franchisee loyalty.
Mediating influence of franchisee satisfaction
Satisfaction in this context is defined as a cumulative,
global evaluation stemming from an aggregation of
transaction experiences (Parasuraman et al., 1994). Prior
research suggests that satisfaction is an affective
variable (Oliver, 1999). As the study explained briefly at
this article’s outset, affective variables can mediate the
relationship between cognitive and behavior variables.
Franchisee perceived relationship value reflects
franchisee’s rational trade-off between the costs and
benefits to be derived over the lifetime of the franchisorfranchisee
relationship and is regarded as a cognition
variable (Harmon and Griffiths, 2008), whereas
franchisee loyalty concerns behavior or a disposition to
behave positively toward a franchisor. Thus, the study
predicts that franchisee satisfaction mediates the effect of
franchisee perceived relationship value on franchisee
loyalty. However, Vakratsas and Ambler (1999)
suggested that cognition about a product may affect
purchase behavior directly for some product categories
such as paper towels and life insurance.
Gross (1997) also argued that in business markets,
purchasing managers’ decision making is mainly guided
by cognitive factors. Prior empirical research have reported
that relationship value has a positive direct effect on
loyalty (Eggert and Ulaga, 2002; Sirdeshmukh et al.,
2002; Barry and Terry, 2008). Therefore, for all the above
reasons, the study expects the mediation to be partial.
H1: Franchisee satisfaction partially mediates the
relationship between franchisee perceived relationship
value and franchisee loyalty.
Moderating influence of franchisee characteristics
As mentioned above, the moderating role of individual

characteristics on relationship value and loyalty link have
been ignored in the existing literature. This article fills this
gap by analyzing the moderating effects of three
franchisee characteristics, including age, education and
length of relationship. Specifically, the study proposed
that the association among the franchisee characteristics,
franchisee perceived relationship value and franchisee
loyalty can be explored by viewing the franchisee as an
information processor and loyalty as the output of an
information-processing system. According to the information
processing theory, franchisees have limited ability
to process information and therefore use heuristics or
schema-based forms of processing to make their
decisions. For example, franchisees may reduce their
search efforts by narrowing the choice set or relying on
some important key information cues, such as their past
behavior (Walsh et al., 2008). An information-processing
perspective on franchisee loyalty suggests that the loyalty
decision is made on different bases according to differences
in franchisee characteristics (Walsh et al., 2008;
Cooil et al., 2007; Levin et al., 2006; Evanschitzky and
Wunderlich, 2006; Homburg and Giering, 2001). Thus,
the study expected that the extent to which franchisee
perceived relationship value affects franchisee loyalty
may vary depending upon franchisee characteristics. A
brief discussion of each of the three potential moderators
follows.
Age
Prior research has found that, information-processing
capabilities decline with age (Gilly and Zeithaml, 1985).
Given the restricted information-processing capabilities of
older franchisees, the study expected these franchisees
to be more likely to rely on fewer decision criteria, such
as perceived relationship value, when they develop
loyalty to their franchisor whereas younger franchisees
seek alternative information that might also influence their
loyalty. Thus, the loyalty of older franchisees is likely to
be more positively by perceived relationship value than
that of younger franchisees. Accordingly, the study
hypothesized:
H2: Franchisee’s age moderates the relationship between
franchisee satisfaction and franchisee loyalty in such a
way that the relationship is stronger for the older than for
the younger.
Education
Individuals with higher levels of education engage more
in information processing prior to decision making,
whereas lower levels of educated individuals rely more
on fewer information cues (Capon and Burke, 1980).
Thus, because lower educated franchisees lack other
variance-explaining information cues, whereas higher
Chen 11489
educated franchisers search for additional information
cues, apart from their current perceived relationship
value, variations in perceived relationship value of lower
educated franchisees likely results in a greater change
loyalty than it would for higher educated franchisers.
Accordingly, the study hypothesized:
H3: Franchisee’s education moderates the relationship
between franchisee satisfaction and franchisee loyalty in
such a way that the relationship is stronger for those with
lower, rather than higher education.
Length of relationship
Length of relationship is defined as the number of years
that a franchisee has worked with his/her current
franchisor. Prior research suggests that customers with
positive experiences over time are less likely to defect
and are more forgiving (Anderson and Sullivan, 1993). In
particular, researchers have observed that customers’
judgments of recent exchange outcomes are influenced
by the cumulative effect of long-term experiences with the
supplier (Kalwani and Narayandas, 1995). Thus, in the
case of franchise relationships, because long-term
franchisees tend to use the cumulative experience to
evaluate the relationship exchange outcomes, they will
be more immune to variations in their current perceived
relationship value. Accordingly, the study expected that,
the loyalty of long-term franchisees is likely to be less
positively affected by their perceived relationship value
than that of newer franchisees.
H4: Length of relationship moderates the relationship
between franchisee satisfaction and franchisee loyalty in
such a way that the relationship is stronger for those with
shorter, rather than longer relationship.
MATERIALS AND METHODS
Sample and procedures
Convenience store franchise systems in China rural areas were
selected as the object for this study. In the past ten years, China
has the most franchises in the world (Wang et al., 2008). In
particular, franchising has also been a prevalent mode of entry into
the Chinese rural retailing market. Small independent stores are
being rapidly displaced by chains of convenience stores in the
countryside. According to statistics released by China’s Ministry of
Commerce, over 300,000 chain convenience stores in China rural
areas were opened as of the end of 2009. Furthermore,
convenience store franchising also typifies business format
franchising and was chosen as the target of prior academic
franchise studies (Chiou et al., 2004). Thus, the selected population
provided a rich and suitable context for this study.
Franchisees were drawn from 5 convenience store franchise
companies from Hubei, Shandong and Guangxi, three provinces of
China. Because mail and telephone surveys were likely to have a
poor response rate, the study conducted personal interviews to
gather the data for this study. Interviewees were restricted to the

11490 Afr. J. Bus. Manage.
single-unit franchise owners. The study minimized the chance of
interviewer bias by using a structured and standardized interview
process and Likert-type scales for responses whenever possible.
Prior to the survey, a focus group interview was conducted to pretest
and improve the questionnaire design. A gift worth about 1 US
dollar was provided to each respondent before the survey started.
Franchisees were given the option of having the survey read to
them and filled out by the trained student interviewers or completing
it themselves. The survey team interviewed a total sample of 256
franchisees during the period May to August, 2009. Questionnaires
with incomplete information were removed, resulting in 218 usable
questionnaires.
Measures
Dependent variable
Franchisee loyalty was measured using four items, three originally
developed by Chiou et al. (2004) and one developed by
Bordonaba-Juste and Polo- Redondo (2008) to measure
franchisees’ intention to remain in the franchise system.
Respondents indicated, on a seven-point Likert scale (1 = “strongly
disagree,” 7 = “strongly agree”), whether (1) “It is my pleasure to
introduce this franchise system to others,” (2) “I am willing to
collaborate with this franchisor in the future,” (3) “I believe that over
the long run the relationship with the franchisor will be profitable,”
and (4) “Although, I can look for other franchise systems, I still
consider the current franchise system as my first priority.” The
coefficient alpha for this measure was 0.82.
Independent variable
The franchisee perceived relationship value was measured with
four items adapted from Ulaga and Eggert (2006). The four items
were (1) “Compared to alternative franchise systems, the franchisor
adds more value to the relationships as a whole,” (2) “Compared to
alternative franchise systems, I gain more in our relationship with
the franchisor,” (3) “Compared to alternative franchise systems, the
relationship with the franchisor is more valuable,” and (4)
“Compared to alternative franchise systems, the franchisor creates
more value for us when I compare all the costs and benefits of the
relationship.” The coefficient alpha for this measure was 0.79.
Mediator
The study measured franchisee satisfaction using a scale
consisting of three items obtained from a study by Chiou et al.
(2004). In our study, respondents indicated on seven-point Likert
scale whether (1) “I am happy about my decision to choose this
franchise system,” (2) “I believe that I did the right thing when I
chose this franchise system,” and (3) “Overall, I am satisfied with
this franchise relationship.” The coefficient alpha for this measure
was 0.82.
Moderators
Age, education and length of relationship were self-reported by
respondents. Age had eight categories: 1 = “20 or under”, 2 = “21-
25”, 3 = “26-30”, 4 = “31-35”, 5 = “36-40”, 6 = “41-45”, 7 =“46-50”
and 8 = “over 50.” Education had six categories: 1 = “primary
school and below,” 2 = “middle school,” 3 = “high school,” 4 =
“vocational school”, 5 = “college” and 6 = “university and above”.
Consistent with Dant and Nasr (1998), franchisees were asked to
indicate the year in which they joined the network as a franchisee,
from which the study measured the length of relationship.
Control variables
To eliminate potential confounds, the study included two store-level
variables as controls in testing the hypotheses. The study controlled
for store size because size may affect franchisees’ attitude toward
business, which has been linked to franchisee’s loyalty
(Jambulingam and Nevin, 1999). Size was measured as the
operating area of each convenience store in square meters. The
study controlled for store region because the region is regarded as
one of the franchisee selection criteria (Olm et al., 1988) and thus
might relate to franchise relationship. Franchisees provided selfreport
data on store region: “Village” was coded 1, and “township”
was coded 0.
RESULTS
Confirmatory factor analysis
To assess the validity of the measures, the study
conducted a confirmatory factor analysis (CFA).
Parameter estimation based on the maximum likelihood
method was made with the Amos 6.0 computer package.
The adequacy of the CFA models was evaluated using
the criteria of overall fit with the data, convergent validity
and discriminant validity. The CFA model and the
standardized loadings along with t-value are presented in
Table 1.
The results indicate reasonable overall fits between the
model and the observed data. The overall fit of
measurement model was x 2
(41) = 68.26 P=0.005. The
goodness-of-fit index (GFI) was 0.95, the adjusted
goodness-of-fit index (AGFI) was 0.92, the comparative fit
index (CFI) was 0.97, the incremental fit index (IFI) was
0.97, and the root mean square error of approximation
(RMSEA) was 0.06.
Convergent validity is observed when the path
coefficients from latent constructs to their corresponding
manifest indicators are statistically significant (that is,
t>2.0; Anderson and Gerbing, 1988). As shown in the
Table 1, all items loaded significantly on their corresponding
latent construct, with the lowest t-value being
7.28, thereby providing evidence of convergent validity.
To further assess the discriminant validity of our
measures, the study followed the procedures outline by
Fornell and Larcker (1981), which requires that the
average variance extracted (AVE) for each construct
should be higher than the squared correlation between
that construct and any other construct. In the present
study, in no case was there a squared correlation
between two constructs higher than either of the
construct’s AVEs (Table 2).
Descriptive analyses
Table 2 present descriptive statistics and correlations for

Table 1. Results of confirmatory factor analysis for the measures of variables studied a
.
Chen 11491
Construct
Franchisee loyalty
Item-construct loading
Standardized t-value
Composite
reliability
Average variance
extracted
1. It is my pleasure to introduce this franchise system to others 0.74 Fixed
2. I am willing to collaborate with this franchisor in the future 0.79 11.08
3. I believe that over the long run the relationship with the franchisor will be profitable 0.84 12.00 0.84 0.57
4. Although I can look for other franchise systems, I still consider the current franchise
system as my first priority
0.58 8.09
Franchisee satisfaction
1. I am happy about my decision to choose this franchise system 0.84 Fixed
2. I believe that I did the right thing when I chose this franchise system 0.82 12.42
3. Overall, I am satisfied with this franchise relationship 0.71 10.82
Franchisee perceived value
1. Compared to alternative franchise systems, the franchisor adds more value to the
relationships as a whole
2. Compared to alternative franchise systems, I gain more in our relationship with the
franchisor
3. Compared to alternative franchise systems, the relationship with the franchisor is
more valuable
4. Compared to alternative franchise systems, the franchisor creates more value for us
when I compare all the costs and benefits of the relationship
0.64 Fixed
0.80 8.86
0.78 8.78
0.60 7.28
a n=218. , GFI =0.95, AGFI =0.92, CFI =0.97, IFI =0.97 RMSEA confidence interval (0.03, 0.08).
Table 2. Means, standard deviations and correlations among variables a .
0.83 0.63
0.80 0.50
Variable Mean SD 1 2 3 4 5 6 7
Franchisee loyalty 5.80 0.77 (0.75)
Franchisee satisfaction 5.96 0.76 0.57** (0.79)
Franchisee perceived relationship value 6.11 0.68 0.45** 0.45 (0.71)
Age 4.19 1.62 -0.09 -0.13 -0.16*
Education 2.88 1.00 -0.07 -0.11 -0.08 0.29**
Length of relationship 3.52 0.83 0.12 0.09 0.04 -0.09 0.14**
Store size 96.93 137.27 -0.01 -0.12 -0.10 0.4 0.18** 0.52**
Store region 0.73 0.44 0.04 0.11 0.16* -0.12 -0.35** -0.26** -0.39**
a n=218. For multiple-item constructs, figures on the diagonal in parentheses represent the square root of the average variance extracted. SD,
standard deviation; *P0.05 two tailed tests.
Test of hypothesis 1: Analyses of franchisee
satisfaction as a mediator
Hypothesis 1 predicts that franchisee satisfaction will
partially mediate the relationship between franchisee
perceived relationship value and franchisee loyalty.
Hypothesized mediation effect was assessed in
accordance with standards outlined by Baron and Kenny
(1986). Baron and Kenny specified three conditions that
must be satisfied in order to infer mediation: (1) The
independent variable (here, franchisee perceived
relationship value) must be significantly related to the
dependent variable (franchisee loyalty) in the absence of
the mediator (franchisee satisfaction), (2) the independent
variable must be significantly related to the mediator,
and (3) when both the independent variable and the
mediator are considered simultaneously, the direct
relationship between the independent variable and the
dependent variable should either decrease significantly
(for partial mediation) or become non-significant (for full

11492 Afr. J. Bus. Manage.
Table 3. Results of regression analysis for mediation by franchisee satisfaction a .
Variable
Controls
Model 1
Dependent variable, Loyalty
Model 2
Dependent variable, Satisfaction
Model 3
Dependent variable, Loyalty
Store size 0.03 -0.07 0.06
Store region -0.0.2 0.01 -0.03
Independent
Perceived
Relationship Value
0.46*** 0.44*** 0.25***
Mediator
Satisfaction 0.47***
0.20 0.20 0.38
R 2
Adjusted R 2
0.19 0.19 0.37
F 18.18*** 18.20*** 32.23***
an = 218, standardized regression coefficients are displayed in the table.***P˂0.001.
mediation). Table 3 summarizes the results.
As shown in Table 3, consistent with Baron and
Kenny’s first requirement for mediation, franchisee
perceived relationship value was significantly associated
with franchisee loyalty in the absence of mediator
variable (β=0.46, P

Table 4. Results of hierarchical regression analysis for moderation by franchisee characteristics a .
Chen 11493
Variable
Control
Moderator: Age
Step 1 Step 2 Step 3
Moderator: Education
Step 2 Step 3
Moderator: Relationship length
Step 2 Step 3
Store size 0.01 0.03 0.03 0.03 0.04 -0.02 -0.5
Store region 0.05 -0.02 -0.03 -0.04 -0.04 -0.01 -0.03
Main effect
PRC b
0.45*** 0.40*** 0.45*** 0.38*** 0.44*** 0.41***
Age -0.02 -0.03
Education -0.05 -0.02
Relationship length 0.11 0.15*
Interaction
PRC age 0.12 +
PRC education 0.20**
PRC relationship length -0.13*
R 2 0.00 0.20 0.21 0.21 0.24 0.21 0.23
Adjusted R 2 0.00 0.19 0.20 0.19 0.22 0.20 0.21
F 0.20 13.60*** 11.56*** 13.74*** 13.30*** 14.28*** 12.35***
ΔR 2 0.20*** 0.01 + 0.20*** 0.03*** 0.20*** 0.01 +
an = 218, standardized regression coefficients are displayed in the table; b PRC represents perceived relationship value variable.
***P

11494 Afr. J. Bus. Manage.
Figure 3. Interaction of perceived relationship value and education on loyalty.
Figure 4. Interaction of perceived relationship value and length of relationship on loyalty.
on mediators of relationship value effects and supports
the efficacy of the reasoned action theory in the
franchising context.
Secondly, prior findings on the direct effect of relationship
value on loyalty have been inconsistent. For instance,
Barry and Terry (2008) reported that relationship value
has a positive, direct effect on loyalty. However, Lam et
al. (2004) found that relationship value has no significant
direct effect on customer loyalty (customer patronage).
The result of this study suggests that, in a franchising
context, perceived relationship value is related directly to
loyalty. Thus, franchisee perceived relationship value not
only have an indirect effect on franchisee loyalty through
franchisee satisfaction, but it also have direct effect on
franchisee loyalty. This study demonstrated that the
franchisee perceived relationship value is a key driver of
franchisees’ loyalty toward the franchise system.
Thirdly, this study extends the relationship value–loyalty
link literature, which has generally ignored the issue of
moderator effects. The findings confirm the moderating
influence of franchisee characteristics (age, education
and length of relationship) on the relationship between
franchisee perceived relationship value and franchisee loyalty.
In particular, consistent with information processing

processing theory (for example Moskovitch, 1982), the
effect of franchisee perceived relationship value was
positively moderated by franchisee’s age and was
negatively moderated by franchisee’s length of
relationship.
Finally, the information processing theory was less
effective for explaining the moderating role of franchisee’s
education. More specifically, the strength of relationship
between franchisee perceived relationship value and
loyalty appeared to become stronger for franchisees with
higher levels of education than those with lower levels of
education.
MANAGERIAL IMPLICATIONS
The results of this study have two managerial implications.
First, as franchisee loyalty becomes an increasing
important concern for franchisors, understanding the
factors that influence franchisee loyalty is increasingly
important. The findings clearly suggest that franchisee
loyalty can be generated through improving the
relationship value perceived by franchisee and enhancing
franchisee satisfaction.
Secondly, the findings suggest that, given finite resources
for the franchisor, taking franchisee characteristics
such as age, education and length of relationship into
account is important in designing franchisee loyalty programs.
The resources should appropriately be allocated
to those franchisees that exhibit a strong relationship
value - loyalty link. Further, the results suggest that the
franchisor might find it beneficial to focus on the
relationship value of those franchisees with older, higher
education and shorter relationship length.
LIMITATIONS AND FUTURE RESEARCH
The current study has some limitations that should be
addressed in future research. First, obtaining data from a
single convenience store system limits the generalizability
of the results to other franchise systems. To enhance
external validity, future research should obtain data from
different categories of franchise systems.
Secondly, another limitation is the use of single-item
measures of relationship value. However, relationship
value has often been treated as a multidimensional
construct (Ulaga and Eggert, 2003; Ulaga and Eggert,
2006; Baxter, 2009). Ulaga and Eggert (2006) noted that,
“researchers may choose between a multidimensional
scale with multiple items and its unidimensional counterpart
with only few items, depending on their research
objectives. Relying on the multidimensional scale is key
for research investigating the value-creating dimensions
of a business relationship.” Future research should
include multi-item measures of this constructs and
investigate the effect of specific dimensions of
relationship value on loyalty.
Chen 11495
Thirdly, an interesting question that arises from the
findings of this study is whether other variables mediate
the relationship between franchisee perceived relationship
value and franchisee loyalty. For example, Ulaga
and Eggert (2006) and Irene et al. (2009) suggested the
mediating effect of trust and commitment on the
association of customer perceived value and loyalty in
the B2B buyer-seller relationship contexts. It would be
informative to investigate how variables such as trust and
commitment mediate franchisee perceived relationship
value–loyalty link.
Finally, a related question is to whether other variables
moderate the relationship between franchisee perceived
relationship value and franchisee loyalty. This study
focuses on selected characteristics of the franchisee as
moderating factors. A host of other franchisee
characteristics (for example, gender, financial capability,
experience) may have a moderating influence on
franchisee perceived relationship value – loyalty link.
Further, situational characteristics (for example, switching
cost, dependence) may also affect this relationship. Yang
and Peterson (2004) reported that the moderating effect
of switching cost on the association of customer
perceived value and loyalty in the setting of electronic
commerce. Thus, future research should consider the
influence of other possible moderators of
franchiseeperceived relationship value–loyalty link.
ACKNOWLEDGEMENTS
I am very grateful to this journal’s anonymous reviewers
for their helpful comments. The research was funded by
the Chinese National Natural Science Foundation grant
(No. 70972132), and the Fundamental Research Funds
for the Central Universities, and the Research Funds of
Renmin University of China (No. 11XNJ022).
REFERENCES
Aiken LS, West SG (1991). Multiple regression: testing and interpreting
interactions. Newbury Park, CA: Sage.
Anderson JC, Gerbing DW (1988). Structural equation modeling in
practice: a review and recommended two step approach. Psychol.
Bull., 103 (3): 411–423.
Anderson EW, Sullivan MW (1993). The antecedents and
consequences of customer satisfaction for firms. Mark. Sci., 12 (2):
125–143.
Baron RM, Kenny DA (1986). The moderator mediator variable
distinction in social psychological research: conceptual, strategic, and
statistical considerations. J. Pers. Soc. Psychol., 51 (6): 1173–1182.
Barry J, Terry TS (2008). Empirical study of relationship value in
industrial services. J. Bus. Ind. Mark. 23 (4): 228–241.
Baxter R (2009). Reflective and formative metrics of relationship value:
a commentary essay. J. Bus. Res., 62 (12): 1370–1377.
Bordonaba – Juste V, Polo – Redondo Y (2008). The effect of
relationship marketing strategy on franchise channels: evidence from
Spanish franchisees. J. Mark. Channels, 15 (1): 71-91.
Bracker JS, Pearson JN (1986). Planning and financial performance of
small, mature firms. Strategic Manage. J., 7 (6): 503-522.
Callarisa F, Luis J, Enrique BA, Miguel AMT, Javier SG (2009).

11496 Afr. J. Bus. Manage.
Customer loyalty in clusters: perceived value and satisfaction as
antecedents. J. Bus. Bus. Mark., 16 (3): 276–316.
Capon N, Burke M (1980). Individual, product class, and task related
factors in consumer information processing, J. Consum. Res., 12 (7):
314 –326.
Chen CF, Tsai MH (2008). Perceived value, satisfaction, and loyalty of
TV travel product shopping: involvement as a moderator. Tour.
Manage., 29 (6): 1166 –1171.
Chiou JS, Hsieh CH, Yang CH (2004). The effect of franchisors’
communication, service assistance, and competitive advantage on
franchisees’ intentions to remain in the franchise system. J. Small
Bus. Manage., 42(1): 19–36.
Cooil B, Keiningham TL, Aksoy L, Hsu M (2007). A longitudinal analysis
of customer satisfaction and share of wallet: investigating the
moderating effect of customer characteristics. J. Mark., 71 (1): 67-83.
Dant RP, Nasr NI (1998). Control techniques and upward flow of
information in franchising in distant markets: conceptualization and
preliminary evidence. J. Bus. Venturing, 13 (1): 3-29.
Eggert A, Ulaga W (1992). Customer perceived value: a substitute for
satisfaction in business markets? J. Bus. Ind. Mark., 17 (2/3): 107-
118.
Evanschitzky H, Wunderlich M (2006). An examination of moderator
effects in the four stage loyalty model. J. Bus. Res., 8 (4): 330-345.
Fishbein M. Ajzen I (1975). Belief, attitude, intention and behavior: an
introduction to theory and research. California: Addison – Wesley.
Fornell C, Larcker DF (1981). Structural equation models with unobservable
variables and measurement error. J. Mark. Res., 18 (1): 39–50.
Gassenheimer JB, Baucus DB, Baucus MS (1996). Cooperative
arrangements among entrepreneurs: an analysis of opportunism and
communication in franchise structures. J. Bus. Res., 36 (1): 67-79.
Gilly MC, Zeithaml VA (1985). The elderly consumer and adoption of
technologies. J. Consum. Res., 12 (3): 353-357.
Gross I (1997). Evolution in customer value: the gross perspective, in
Donath B (Ed.), Customer Value: Moving Forward – Back to Basics,
ISBM Report No. 13.
Harmon T, Griffiths, M (2008). Franchise relationship value: implications
for behavioral and performance outcomes. J. Bus. Ind. Mark., 23 (4):
256-263.
Homburg C, Giering A (2001). Personal characteristics as moderators of
the relationship between customer satisfaction and loyalty. Psychol.
Mark., 18 (1): 43-66.
Irene GS, Marta FD, Amparo CT (2009). The value of B2B relationship.
Ind. Manage. Data Syst. 109 (5): 593–609.
Jambulingam T, Nevin JR (1999). Influence of franchisee selection
criteria on outcomes desired by the franchisor. J. Bus. Venturing, 14
(4): 363-396.
Kalwani MU, Narayandas N (1995). Long term manufacturer supplier
relationship: do they pay off for supplier firms. J. Mark. 59 (1): 1–16.
Kidwell RE, Nygaard A, Silkoset R (2007). Antecedents and effects of
free riding in the franchisor-franchisee relationship. J. Bus. Venturing,
22 (4): 522 -544.
Lam SY, Shankar V, Err6399 amilli MK, Murthy B (2004). Customer
value, satisfaction, loyalty, and switching costs: an illustration from a
business to business service context. J. Acad. Mark. Sci., 32 (3): 293-
311.
Levin DZ, Whitener EM, Cross R (2006). Perceived trustworthiness of
knowledge sources: the moderating impact of relationship length. J.
Appl. Psychol., 91 (5): 1163–1171.
Moscovitch M (1982). A neuropsychological approach to perception and
memory in normal and pathological aging, in Craik FIM and Trehub S
(Eds.), Memory and Cognitive Processes in Aging, New York: Plenum
Press pp. 55- 78.
Neter J, Wasserman W, Kutner MH (1990). Applied linear statistical
models: regression, analysis of variance, and experimental designs.
3 rd ed. Homewood, IL: Irwin.
Oliver RL, Rust R (1997). Customer delight: foundations, findings, and
managerial insight. J. Retail., 73 (3): 311-337.
Oliver RL (1999). Whence consumer loyalty? J. Mark. 63 (SI): 33-44.
Olm KW, Eddy GG, Adaniya AR (1988). Selecting franchisee prospects.
Society of Franchising Proceedings II. San Francisco: Society of
Franchising.
Omar NA, Musa R, Nazri MA (2007). Program perceived value and
program satisfaction influences on store loyalty: insight from retail
loyalty program. Gadjah Mada Int. J. Bus., 9 (3): 355–378.
Parasuraman A, Zeithaml VA, Berry LL (1994). Reassessment of
expectations as a comparison standard in measuring service quality:
implications for further research. J. Mark., 58 (1): 111–124.
Sirdeshmukh D, Singh J, Sabol B (2002). Consumer trust, value and
loyalty in relational exchanges. J. Mark., 66 (1): 15-37.
Ulaga W, Eggert A (2005). Relationship value in business markets: the
construct and its dimensions. J. Bus. Bus. Mark., 12 (1): 73–99.
Ulaga W, Eggert A (2006). Value-based differentiation in business
relationships: gaining and sustaining key supplier status. J. Mark., 70
(1): 119-136.
Vakratsas D, Ambler T (1999). How advertising works: what do we really
know? J. Mark., 63 (1): 26-43.
Walsh G, Evanschitzky H, Wunderlich M. (2008). Identification and
analysis of moderator variables: investigating the customer
satisfaction – loyalty link. Eur. J. Mark., 42 (9/10): 977–1004.
Wang ZJ, Zhu M, Terry A (2008). The development of franchising in
China. J. Mark. Channels, 15 (2/3): 167–184.
Yang ZL, Peterson RT (2004). Customer perceived value, satisfaction,
and loyalty: the role of switching costs. Psychol. Mark., 21 (10): 799-
822.
Zeithaml VA (1988). Consumer perceptions of price, quality, and value:
a means-end model and synthesis of evidence. J. Mark., 52 (3): 2–
22.

African Journal of Business Management Vol. 5(28), pp. 11316-11322, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1285
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Establishment of a temporary workforce transaction
mechanism using a real option approach
Ying-Chyi Chou
Department of Business Administration, Tunghai University, Taiwan. E-mail: ycchou@thu.edu.tw.
Tel: +886-4-23590121 ext. 35100 or 35122. Fax: +886-4-23594107.
Accepted 21 December, 2010
To reduce labor costs and enhance profitability, many modern businesses have begun to employ
temporary labor. Temporary labor not only helps businesses by providing necessary manpower during
the busy season, it can also help businesses reduce labor costs during declining economic conditions
by outsourcing labor. To address the need for flexibility in labor supply and extend the time needed to
make labor decisions, this study presents the innovative concept of real options on temporary
workers. The purpose of such options is to hedge the demand-supply uncertainty in future labor and
wages. This study not only introduces the concept and method of real options on temporary workers
but also provides real-life empirical samples to verify the reasonableness, applicability and
practicability for issuing real options on temporary workers.
Key words: Temporary workforce, Stackelberg model, real option.
TEMPORARY WORKERS AND TEMPORARY EMPLOYMENT INDUSTRY
Although business outsourcing of product manufacturing,
parts or sales are well established practices, human
resource outsourcing is still a developing concept in
Taiwan and currently focuses only on unskilled temporary
labor. Minimizing labor costs to improve business
competitiveness by achieving a lean, efficient and highly
flexible human resource structure is without a doubt the
key element to business success. In the present business
environment of falling profit margins, increased labor
costs, rapidly changing economic conditions, unstable
investment environments and aggressive competition,
businesses around the globe are attempting to lower
costs and increase profits by gradually replacing permanent
employees with temporary workers hired from
outside sources. Using temporary labor enables organizations
to increase competitiveness by adjusting their
human resource structures. Lenz (1996) classified the
main advantages of using temporary workers as enabling
flexibility and rapid response to a changing business
environment without sustaining the costs of recruitment,
welfare or retirement. Wessel (2001) suggested that the
main advantage of temporary workers is when an investment
project is expanding but the attractiveness is gone,
the business is able to employ temporary workers to
lower costs, which allows delay of policy decisions
regarding permanent workers. Dixit and Pindyck (1994)
also agreed that when engaging in labor-related investment
decisions, businesses can utilize temporary workers
to increase the flexibility of their human resource policies.
Thus, flexibility is one of the main advantages of employing
temporary workers. Hence, the temporary worker
employment rate has tended to increase in recent years.
Currently, the trend towards employing contract workers
is expanding from non-technical to technical and skilled
labor (Brenčič, 2009; MacPhail and Bowles, 2008).
THE PROFILE OF REAL OPTIONS ON TEMPORARY
WORKERS
Past studies on transaction mechanisms using the
concept of options all employ real options as a means of
evaluating investment policy. For example, Campbell
(2002) uses options pricing theory to determine the
optimal timing of information systems investments and to
explore the effect of different investment review cycles,
while Kim et al. (2002) and Pinches (1998) include real
options theory to assess investment policies for IT
companies and Fauffman and Li (2005) analyzes the
investment timing strategy for a firm that is deciding

about whether to adopt one or the other of two incompatible
and competing technologies. Bhattacharya and
Wright (2005) developed an options model for managing
different types of uncertainties. Trigeorgis (1993) leans
toward the flexible interactive relationship between real
options and financial options. Foote and Folta (2002) and
Pinker and Larson (2003) used “real option” analyses to
determine the value of flexibility gained by using a
temporary employee workforce. They found that
businesses can expand investment or reduce risk by
using temporary workers when facing demand and
supply uncertainties in the labor market. Bellalah (2002)
applies real options to assess lease contracts while
Insley (2002) uses real options to assess investments in
the forestry industry. In a practical model research,
Nembhard et al. (2003) applies real options to product
outsourcing. However, real options are rarely applied to
problems involving manpower outsourcing. In fact, supply
and demand for high tech technical manpower are uncertain
and market price fluctuation is significant (Bhatnagar,
et al., 2007; Stratman, et al., 2004). It is advisable to use
a transaction style of options to conclude business
contracts. Therefore, applying options can resolve
problems of uncertain human resource demand in a
rapidly changing business environment (Jacobs, 2007).
DESCRIPTION OF THE MODEL
The underlying asset for real options on temporary workers is the
option on temporary workers. That is, after buyers (user enterprise)
of real options on temporary workers pay the premium to the sellers
(temporary workers agency), they are entitled to lay claim to the
seller to contract for another option on temporary workers at the
appointed exercise price (the premium of options on temporary
workers) at the expiration date. The exercise price for options on
temporary workers is the outsourcing expense that the buyer
agrees to pay the seller for a unit of labor provided, and it is
determined as soon as the real option is issued. The price can
usually be based on the human resource market price during the
time of issue; therefore, real options on temporary workers can
simultaneously hedge the risk of both uncertain human resource
demand and uncertain wages. Also, the expiration date of options
on temporary workers can be set to approximate the date when the
workers would be needed.
Notations
C: The per-unit premium provided to the seller in the outsourcing
C
Q
e
rt
( 1
F(
K))
K
Q
m
Chou 11317
Q
K
0
S
M
f ( S
M
)
dS
M
option contract.
K : The per-unit exercise price provided by the seller in the
outsourcing option contract.
Q
: The total manpower quantity determined in the outsourcing
option contract.
S : Technical manpower outsourcing market price per unit upon
contract expiration.
t : The contract period between the options contract signing and
exercise dates.
r : The risk-free short-term interest rate during the contract period.
m : The probability of the seller successfully deploying manpower,
0 < m < 1.
Z : Unit cost for surplus manpower.
T : Unit training cost for manpower.
HC : Fixed processing costs borne by the seller when deploying
manpower, whether successful or not.
D : Fixed demand volume of the buyer on the contract expiration
date.
S M : Unit market price of manpower at the beginning of the
contract.
f
( SM
)
: Probability density function for the unit market price of
manpower, with mean value μ and standard deviation σ.
F
( SM
)
: Cumulative probability function for the unit market price
of manpower.
E(PS): Expected producer surplus function of the seller.
E(Cost): Expected per unit outsourcing cost of the buyer.
Formulation
The model used here is based on the Stackelberg game model. It
assumes that the buyer is the leader and the seller is the follower,
with the follower reacting to the actions of the leader. To solve the
model, the reaction function of the seller, K=f(Q), is found by
maximizing the producer surplus of the seller, which then is plugged
into the buyer’s model. Finally, by solving for the profit maximization
of the buyer, the optimal exercise price and premium of the seller
are found, along with the optimal outsourcing quantity of the buyer.
Expected producer surplus for seller
The expected producer surplus function for seller is given as
follows:
E(PS)=
e
rt
Q
K
F(
K)
F(
K)
( K
( 1
HC
S
M
)
m)
( 1
f ( S
Q
M
)
Z
F(
K))
dS
Q
M
T
(1)

11318 Afr. J. Bus. Manage.
The first three terms are: a) the initial premium received for the
outsourcing option contract, b) the returns receivable by the seller
when the contract expires during an economic upturn, in which
case, the seller anticipates that the buyer will fully exercise the
option, c) when the economy is performing poorly, causing the
market price for manpower to fall below the exercise price, in which
case, the seller anticipates that the buyer will not exercise the
option and will instead dispatch manpower to other companies.
The last three terms are: a) the lost profit opportunity of the seller
when the contract expires in an economic upturn, leading to the
market price for manpower to exceed the exercise price, b) surplus
costs and c) expected processing costs incurred when the contract
E(Cost)=
C
Q
e
rt
( 1
K
S
M
F(
K))
f ( S
M
)
K
dS
The costs are described as follows: the premium costs that need to
be paid by the buyer when the buyer signs the option outsourcing
contract; the expected exercise costs that need to be paid for
outsourcing manpower when the contract expires when the
economy is good; the expected hiring costs that need to be paid
when the contract expires, due to an increase in demand for
manpower on contract expiry; when the buyer experiences a
manpower shortage; the need to hire more manpower from other
temporary help companies to make up for shortfalls; and the
manpower training costs that are paid by the seller to the buyer
after the completion of the contract transaction.
Setting the premium model for option outsourcing
According to the option pricing model proposed by Black and
Scholes, the formula for evaluating call options is as follows:
C
d
rt
SN(
d1)
Ke N(
d2
)
ln( S / K)
( r
t
,
/ 2)
t
2
d d t
1 , 2 1
(3)
E(PS)=
( SN(
d )
1
e
Q
rt
rt
e K
F(
K)
KN(
d
( K
HC
S
2
M
))
( 1
)
Q
f ( S
e
M
Q
M
rt
)
F(
K))
( D
( 1
D
dS
Q
M
expires in an economy downturn, in which case, the buyer will not
exercise the option, and the seller does not dispatch manpower to
other companies. Due to the professional nature of semiconductor
equipment manufacturers, temporary help companies can not
provide professional training programs for the engineers, and still
must defray the buyer's employee training costs after the
completion of the contract transaction.
Expected outsourcing costs for buyer
The expected outsourcing cost function for buyer is given as
follows:
Q)
K
0
S
M
f ( S
M
)
dS
M
( 1
F(
K))
In the situation of technical manpower outsourcing, S = outsourcing
manpower market price per unit at contract expiration, σ 2 = the
variances of price, t = the contract period between the option
contract signing date and the exercise date, r = the risk-free shortterm
interest rate during the contract period, and K = exercise price.
Additionally, N(d1) is a hedging rate, that is, the magnitude of
fluctuation of the spot market price S will affect the magnitude of
fluctuation of the call option price C. Also, N(d2) denotes the
probability that the spot market price is greater than the exercise
price when the contract expires.
THE SOLUTION PROCEDURE
The solution procedure for the Stackelberg game model involves
three main steps.
Step 1: Find the optimal reaction function K = f(Q) for the exercise
price K.
Plug Equation (3) into Equation (1) to obtain Equation (4). The
seller, to maximize the outsourcing producer surplus function, has
the reaction function K = f(Q) based on the optimal outsourcing
quantity, as follows:
F(
K))
T
K
F(
K)
Q
( 1
m
m)
Q
K
0
Q
S
M
Z
f ( S
M
Q
)
(4)
T
dS
M
(2)

Taking the derivative of E(PS) with respect to K, we find:
E ( PS )
K
e
rt
e rt
e rt
let
we get
=
Q
Q
Q
e
rt
e
rt
Q(
1
F(
K))
f ( K)
K
Q
m
Q K
f ( K)
Q limF(
S
f ( K)
HC
Q
K
f ( K)
f ( K)
( K
( 1
HC
f ( K)
S M ) f ( S
K
m)
Q
Q
f ( K)
Q(
1 F(
K))
f ( K)
K Q m Q K f ( K)
f ( K)
( 1 m)
Q Z =
Q
x
M
)
x
K
T
M
Z
Chou 11319
)
dS
Q(
1 F(
K)
f ( K)
( 1 m)
Q Z
( 1 F(
K))
f ( K)
K Q m Q K f ( K)
=
f ( K)
HC f ( K)
Q T
Q(
1 2F(
K)
f ( K)
Q ( 1 m)
( K Z)
HC Q T (5)
E(
PS )
K
0
Q ( 1 2F(
K))
f ( K)
Q ( 1 m)
( K Z)
HC
From Equation (6), since 0 < m < 1, and f(K), K, Q, Z, HC and T are
all positive, the value of the right-hand side of the equation must be
greater than zero; therefore the left-hand-side of the equation must
satisfy F(K)

11320 Afr. J. Bus. Manage.
Table 1 . The relationship between total manpower quantity, exercise price and cost.
Q
K
To solve this model, the model parameters are first set,
5 10 15 20 25 30 35 40 45
55019 56864 57607 58009 58261 58433 58558 58654 58729
E(cost) 6197439 6192227 6186321 6180183 6173942 6167645 6161316 6154965 6148600
Q
K
as follows:
50 55 60 65 70 75 80 90 100
58789 58839 58881 58917 58947 58974 58997 59036 59067
E(cost) 6142224 6135841 6129452 6123059 6116662 6110263 6103861 6091052 6078237
1) Survey analysis revealed that the monthly salaries of
employee are normally distributed: M S
~Normal (u=NT$
63,000, =NT$1,000).
2) S indicates the current market salaries of employee. In
calculating the salary and benefits, various necessary
qualifications must be considered, including educational
level, experience, required training courses and
necessary technical skills. Current market price for
manpower is S =NT$ 65000.
3) r is the risk-free nominal interest rate, which is about
1.6%.
4) t is period until the contract expires, which is set to 1
year.
5) m is the probability that, due to a poor market, the
buyer does not exercise the contract, and the seller can
deploy its manpower to other companies. Therefore the
likely probability is estimated from the current economic
conditions, the past transactions and experience of the
seller, and the manpower quality of the seller, m=0.9.
6) HC is the probable cost borne by the seller when
recruiting and deploying manpower, which can be set to
HC=NT$ 50,000.
7) Z, the unit manpower price, was identified through
surveys, and was estimated to be NT$ 30,000.
8) T is the per-unit training cost, which is estimated as NT
$3,000.
9) D is the manpower demand. Based on past buyer
demand for manpower, the manpower demand D is
estimated to be 100.
Now that the parameter values have been set, numerical
analysis is performed to solve the reaction function K =
f(Q). Table 1 summarizes the results of the numerical
analysis. In this Figure 1, we see that K and Q exhibit a
strong nonlinear relationship. Therefore, the regression
b
Q
c
Q
function K a
2 can be used. With an R-
Square value of 89.5%, the optimum regression function
for K = f(Q) is;
K
59342.
6547
28215.
2958
Q
33050.
2715
2
Q
After finding the reaction function K = f(Q) via simulation,
this is substituted into the expected outsourcing cost
function of the buyer Equation (2), yielding Equation (10).
E(Cost)=
e
S
rt
e
( 1
rt
( a
F(
a
( D Q)
b c
a
Q 2
Q
S
M
b
Q
b
Q
c
) 2
Q
c
)) ( a 2
Q
f ( S ) dS
M
Q
M
b
Q
D
c
) Q 2
Q
b c
a
Q 2
Q
0
S
M
f ( S ) dS
M
M
( 1
F(
a
b
Q
(9)
c
)) Q 2
Q
(10)
To illustrate Equation (10), the unit outsourcing cost of the
buyer may gradually decrease as the outsourcing
quantity Q increases. The relationship between K, Q and
expected outsourcing costs E (Cost) is shown in Table 1
and Figure 1. Given that the buyer must satisfy demand
quantity D = 75, that is, the total outsourcing volume is
75, which minimizes the cost of the buyer.
Conclusions
The analytical results of this study clearly indicate that
real options on temporary workers can indeed enable
labor-intensive enterprises to negotiate more effective
and helpful contracts with temporary worker agencies to
enhance hedging ability and flexibility. Real options can
thus help companies respond to changing economic
T

cycles, uncertain labor demand and supply and associa- ted risks. Whereas options have traditionally been used
Chou 11321
K
60000
59000
58000
57000
56000
55000
6220000
6200000
120
6180000
100
6160000
80
6140000
6120000
6100000
40
6080000
COST
Figure 1. The relationship between K , Q and E (Cost).
exclusively to hedge against uncertainty and volatility in
financial or real assets, this study applies options to
intellectual assets such as human resource.
ACKNOWLEDGEMENTS
The authors would like to thank the National Science
Council of the Republic of China, Taiwan for financially
supporting this research under Contract No. NSC NSC
93-2213-E-029-007 and 95-2221-E-029-001.
REFERENCES
Bellalah M (2002). Valuing lease contracts under incomplete
information:A real-options approach. Eng. Econ., 47(2): 194-212.
Bhatnagar R, Saddikutti V. Rajgopalan A (2007). Contingent manpower
planning in a high clock speed industry. Int. J. Prod. Res., 45(9):
2051-2072.
20
60
Q
Bhattacharya M, Wright PM (2005). Managing human assets in an
uncertain world: applying real options theory to HRM. Int. J. Hum.
Res. Manage. 16(6): 929-948.
Brenčič V (2009). Employers' hiring practices, employment protection,
and costly search: A vacancy-level analysis. Labour Econ., 16: 461–
479.
Campbell JA (2002). Real options Analysis of the timing of investment
decisions. Inform. Manage., 39: 337-344.
Dixit AK, Pindyck RS (1995). The options approach to capital
investment. Harvard Bus. Rev., 73(4): 105-115.
Fauffman RJ, Li X (2005). Technology competition and optimal
investment timing: a real options perspective. IEEE Trans. Eng.
Manage., 52(1): 15-29.
Foote DA, Folta TB (2002). Temporary workers as real options. Human
Res. Manage. Rev., 12: 579-597.
Insley M (2002). A real options approach to the valuation of a forestry
investment. J. Environ. Econ. Manage., 44: 471-492.
Jacobs B (2007). Real options and human capital investment. Labour
Econ., 14: 913-925.
Kim YJ, Sanders GL (2002). Strategic actions in information technology
investment based on real option theory. Decision Support System.
33: 1-11.
Lenz EA (1996). Flexible employment: positive work strategies for the

21 st century. J. Labor Res., 17: 555-566.
MacPhail F, Bowles P (2008). Temporary work and neoliberal
11322 Afr. J. Bus. Manage.
government policy: evidence from British Columbia, Canada. Int.
Rev. Appl. Econ., 22(5): 545–563.
Nembhard HB, Shi L, Aktan M (2003). A real options design for product
outsourcing. Eng. Econ., 48(3): 199-217.
Pinches GE (1998). Real options: developments and applications. Q.
Rev. Econ. Financ., 38: 533-535.
Pinker E, Larson RC (2003). Optimizing the use of contingent labor
when demand is uncertain. Eur. J. Oper. Res., 144: 39-55.
Stratman JK, Roth AV, Gilland WG (2004). The development of
temporary production workers in assembly operations: a case study
of the hidden costs of learning and forgetting. J. Oper. Manage.,
21(6): 689-707.
Trigeorgis L (1993). Real options and interactions with financial
flexibility. Financ. Manage., 22: 202-218.
Wessel D (2001). Capital: Temp workers have lasting effect. Wall Street
J. A1.www.kingjob.com/cont3-b.asp. Kingstone Humane Resources
Consulting.

African Journal of Business Management Vol. 5(28), pp. 11467-11475, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.694
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Section 404 of the Sarbanes-Oxley act and its capital
market effects
Maria Mirela Dobre
International Accounting and Financial Reporting Department, Bucharest Academy of Economic Studies, Romania or
Calea Grivitei 184 bl O ap 27, Bucharest, Romania. E-mail: mirela_dobre@yahoo.com Tel: 0040 723561788.
Accepted 14 June 2011
Starting with November 15, 2004, Section 404 of the Sarbanes-Oxley Act of 2002 (SOX 404) requires all
accelerated firms (with at least $75 million in public equity) to report on the effectiveness of their
internal controls over financial reporting. There has been some controversy regarding the burden that it
casts on companies and whether the benefits outweigh the costs of compliance. Reporting under SOX
is meant to improve investor confidence concerning the stock of a specific company by adding
credibility to its financial statements. An increase in the quality of financial information should
determine a narrowing of the bid-ask spread. I identify the cost components of the market makers bidask
spread for a sample of stocks surrounding the implementation of SOX 404. The expectation is that
market makers react to the implementation of Section 404 as if information asymmetry has diminished.
The study uses the model developed by Bollen et al. (2004) to separate the cost components of the bidask
spread for a sample of compliant firms in the period surrounding the implementation of SOX 404.
Key words: Bid-ask spread, informed trading, information asymmetry, internal controls, adverse selection cost.
INTRODUCTION
This paper investigates the market effects of Section 404
of the Sarbanes-Oxley act of 2002 (SOX404) by looking
at the changes that the passage has brought in trader’s
information asymmetry, proxied by market makers’ bidask
spreads. Before the enactment of Sarbanes-Oxley,
firms were only required to publicly disclose internal
control deficiencies if there was a change in auditor. The
study argues that if compliance with SOX 404 increases
internal control over financial reporting (ICFR), investor
confidence in annual reports will also increase. Superior
disclosures available to all traders lead to a reduction of
Abbreviations: CRSP, Center for research in security prices;
ICFR, internal control over financial reporting; ICW, internal
control over financial reporting weakness; IHP, inventory
holding premium; MHI, Modified Herfindahl Index; MW, material
weakness; Reg FD, regulation fair disclosure; SOX, Sarbanes-
Oxley act of 2002; SOX 404, Section 404 of the Sarbanes-
Oxley act; MW, material weaknesses; IT, information
technology; ND, number of dealers.
information asymmetry. An increase in the quality of
financial information should determine a narrowing of
market maker’s bid-ask spreads because the adverse
selection cost is lower. My expectation is that market
makers react to the implementation of Section 404 as if
information asymmetry has diminished, considering that
the chances of trading against better informed traders are
lower.
Information asymmetry is a situation in which one party
in a transaction has more or superior information
compared to another. This often happens in transactions
where the seller knows more than the buyer (although,
the reverse can happen as well) and can lead to adverse
selection - immoral behavior that takes advantage of
asymmetric information before a transaction.
The Bid price is the current highest price at which
someone in the market is willing to buy a stock. The Ask
price is the current lowest price that someone is willing to
sell a stock. The difference in these two amounts is called
the Bid-Ask spread. These prices are constantly
changing during each trading session as shares change

11468 Afr. J. Bus. Manage.
hands. The Bid-Ask spread is determined mainly by
liquidity. If a stock is highly liquid, meaning there is a
large volume of shares being bought and sold, the Bid-
Ask spread will be much lower. A low Bid-Ask spread is
important to traders because the extra cost that they pay
in the spread will eat away at the profits of their trades
(Kosmider, 2006).
Section 302 of the act, requires that chief executive
officers and chief financial officers evaluate quarterly the
design and effectiveness of internal controls, and report
an overall conclusion about their effectiveness. Section
404(a) of SOX outlines management’s responsibility and
requires that the annual report include an internal control
report by management which contains an assessment of
the effectiveness of internal control over financial
reporting as of the end of the most recent fiscal year.
Section 404(b) requires the auditor to make a separate
independent assessment of the company’s internal
controls over financial reporting.
Implementing stronger internal controls over financial
reporting (ICFR) is considered an important step towards
higher quality disclosures, although there has been some
criticism concerning the high costs of compliance with
Section 404. Healy and Palepu (2001) argued that the
demand for financial reporting and disclosure arises from
information asymmetry and agency conflicts between
managers and outside directors. The credibility of
management disclosures is enhanced by regulators,
standard setters, auditors (mandatory provisions for
auditor assessment of ICFR effectiveness) and other
capital market intermediaries. The passage of the
Sarbanes-Oxley Act by U.S. was meant to provide this
precise enhancement of credibility, after the market had
previously witnessed significant financial failures and
frauds. The financial reporting system is generally
regarded as a means by which shareholders can monitor
managers and, furthermore, effective ICFR is considered
a tool for mitigating the agency problem (Goh, 2009;
Hoitash et al., 2009, among others). Because strong
ICFR restrict management’s discretion over earnings
measurement, disclosures made under sections 302 and
404 provide additional measures beyond financial reports
that can reveal the extent to which corporate governance
has succeeded in reducing agency costs.
While complying with SOX 404 is considered by far
more expensive than SOX 302, a good research question
is whether all the supplementary requirements are really
necessary and meet their intended purposes, or
complying with SOX 302 does a similar job in the eyes of
investors? This issue is of great importance, as the
extension of Section 404 auditor testing to smaller U.S.
public companies remains controversial (Hoitash et al.,
2009) and has been postponed several times in the
recent years. The answer could be useful to regulators in
other countries who seek evidence on whether less
stringent internal control regimes are sufficient for high-
quality financial reporting.
Instead of looking for a general disclosure quality
measure, the study investigates the effects of a specific
type of disclosure in the market – material weaknesses
(MW) disclosures under Section 404 a) and b). Reporting
these weaknesses reflects a firm’s ability to identify
internal control risks and could be a good indicator of
future remediation of such weaknesses.
Since there are few measures for information
asymmetry between informed and uninformed traders,
previous research mainly uses the relative bid-ask spread
to proxy for it. The market bid-ask spread is the amount
by which the ask price exceeds the bid for a share. It is a
function of order-processing costs, inventory holding
costs, market maker competition and adverse selection
costs. The first three are not affected by SOX 404 so any
variation of the spread must be driven by a change in
adverse selection costs. Increased disclosure quality
driven by compliance with SOX 404 should determine a
reduction in information asymmetry between informed
and uninformed traders and therefore, a reduction of
adverse selection costs included in the bid-ask spreads.
Following Sidhu et al. (2008), the study separates the
cost components of the bid-ask spread for a sample of
compliant firms in the period surrounding the implementation
of SOX 404. Their model is based on the one
developed by Bollen et al. (2004) and investigates the
market effects of a law imposed by the SEC – Regulation
Fair Disclosure. Other authors (Brown and Hillegeist,
2007) have used the PIN (probability of informed trading)
proxy for information asymmetry, but it is not entirely
reliable (Ertimur, 2007). However, my study is related to
that of Brown and Hillegeist in that it also aims to show
that disclosure quality reduces information asymmetry.
The Sarbanes Oxley Act of 2002 and its market
effects - Prior research
This paper contributes to the literature on internal control
by further investigating the market effects of regulation
concerning internal control weaknesses disclosures.
Three types of internal control weaknesses can be
disclosed under Sections 302 and 404. Listed in increasing
order of severity, these are control deficiencies,
significant deficiencies, and MW. The primary differences
between a control deficiency and a significant deficiency
are in the probability and magnitude of the financial
statement misstatements, which may result due to the
existence of the weaknesses. A material weakness is “a
deficiency, or a combination of deficiencies, in internal
control over financial reporting, such that there is a
reasonable possibility that a material misstatement of the
company's annual or interim financial statements will not
be prevented or detected on a timely basis. Although the
initial impact in stock price of such disclosures is negative

(Litvak, 2007), other research shows that internal control
risk matters to investors and that firms reporting effective
internal controls or firms remediating previously disclosed
internal control deficiencies benefit through lower cost of
equity and higher accruals quality (Ashbaugh-Skaife et
al., 2009). Doyle et al. (2007a) show that firms which
disclose MW tend to be smaller, younger, financially
weaker, more complex, growing rapidly, or undergoing
restructuring. Also, firms with information technology (IT)
related weak components report more MW and
misstatements than firms without IT related weak
components, providing evidence on the pervasive
negative impact of weak IT controls, especially in control
environment, risk assessment, and monitoring (Klamm
and Watson, 2009).
There is also a line of research addressing the issue
whether the provisions of section 302 are sufficient for
informed investment decisions, or more restrictive,
detailed regulation of such disclosures is truly necessary,
taking into account both costs and benefits. Some critics
of SOX maintain that the costs of regulation exceed its
benefits for many corporations (Carney, 2006).
Additionally, it has been suggested that internal controls,
no matter how adequate, could not have done much to
prevent the accounting scandals that took place. The
requirements to set up and assess the efficiency of these
controls were already in place sometime before. Internal
controls are generally designed to prevent small frauds,
but the large frauds are perpetrated by those with the
authority to circumvent any policy (Sinnett, 2004). Litvak’s
research (2007) tests investor’s beliefs about costs and
benefits of SOX. Results show that stock prices have
declined for foreign firms subject to SOX, compared to
cross-listed firms not subject to SOX. Engel et al. (2007)
argue that going-private is an attractive response to SOX
for some firms. Zhang (2007) hypothesizes and finds
evidence that if the governance provisions of SOX
imposed net costs on firms, firms with corporate
governance structure weaker than optimum would incur
more costs and experience more negative cumulative
abnormal returns around the SOX rulemaking events.
Bhamornsiri et al. (2009) focus on the impact of SOX 404
requirements for cross-listed non- US companies and the
impact on external audit fees for filers during the first 2
years it was effective. Findings indicate that audit fees
increased by an average of 65% for the initial group of
filers in the first year SOX 404 was effective and by 9% in
the second year. This increase was associated with a 5%
decrease in earnings for these companies.
The study also adds to existing literature on general
effects of the Sarbanes-Oxley act. Hansen et al. (2009)
investigate the listings and delistings on US stock
exchange after SOX. Results show that the passing of
SOX was not associated with an increase in delisting
likelihood for any size quintiles. However, the implementation
of SOX 404 was significantly positively associated
Dobre 11469
with the probability of delisting for larger firms, especially
if they were performing poorly. Carter et al. (2009) find
support for the joint hypothesis that the implementation of
SOX led to a decrease in earnings management because
the reporting environment became less flexible. A more
recent paper by Ashbaug-Skaife et al. (2009) investigate
how changes in internal control quality affect firm risk and
cost of equity and finds that firms with internal control
deficiencies have significantly higher idiosyncratic risk,
systematic risk, and cost of equity. Accounting information
system quality includes not only the disclosures
the firm makes to outsiders, but also the internal control
systems that a firm has in place. The quality of
accounting information and the systems that produce that
information influence a firm’s cost of capital in two ways:
(1) direct effects—where higher quality accounting
information does not affect firm cash flows, per se, but
does affect market participants’ assessments of the
variance of a firm’s cash flows and the covariance of the
firm’s cash flows with aggregate market cash flows—and
(2) indirect effects—where higher quality information and
better internal controls affect real decisions within the
firm, including the quality of operating decisions as well
as the amount of firm resources that managers
appropriate for themselves. Chhaochharia and Grinstein
(2007) study the effects of SOX act of 2002 on firm’s
returns, taking into consideration their size and level of
compliance. Evidence shows that firms that are less
compliant have greater abnormal returns than those that
are more compliant. Also, large, less compliant firms
show positive abnormal returns while smaller, less compliant
firms show negative abnormal returns, meaning
that some provisions are detrimental to small firms.
Ogneva et al. (2008) find that, on average, internal
control over financial reporting weaknesses (ICWs) are
not directly associated with higher cost of equity, for firms
that filed first-time Section 404 reports with the SEC.
Although they find that ICW firms have higher implied
cost of equity than firms without such weaknesses, there
is no significant association between ICW and cost of
equity after controlling for analyst forecast bias and
primitive firm characteristics associated with ICWs.
Brown and Hillegeist (2007) examine the precise
mechanisms through which disclosure quality affects
information asymmetry among equity investors over a
year. Information asymmetry occurs when one or more
investors possess private information about the firm’s
value while other uninformed investors only have access
to public information. The presence of information asymmetry
creates an adverse selection problem in the market
when privately informed investors trade on the basis of
their private information. Their findings provide some
empirical support for regulators’ beliefs that high quality
disclosures make the capital markets more attractive to
‘‘ordinary’’ uninformed investors. Results indicate that
disclosure quality primarily affects information asymmetry

11470 Afr. J. Bus. Manage.
by reducing the likelihood that investors discover and
trade on private information.
Sidhu et al. (2008) examine the cost of adverse
selection before and after regulation fair disclosure (Reg
FD) became effective in 2000. The evolution is observed
through the cost components of market maker bid-ask
spreads. The market bid-ask spread is the amount by
which the ask price exceeds the bid for a share. This is
essentially the difference in price between the highest
price that a buyer is willing to pay for an asset and the
lowest price for which a seller is willing to sell it. It is a
function of order-processing costs, inventory holding
costs, market maker competition and adverse selection
costs. The first three are not affected by SOX Section
404 so any variation of the spread must be driven by a
change in adverse selection costs. Their conclusion is
that Reg FD led to an increase in adverse selection cost
(risk premium which covers losses caused by trading
against better informed traders). They use the model
developed by Bollen et al. (2004), which is also the one
that this research is based on. Sidhu et al. (2008)
conclude that Reg FD has led to an increase in the
expected cost of information asymmetry, contrary to its
objectives.
Proxy and hypotheses development
Since there are few measures for information asymmetry
between informed and uninformed traders, previous
research mainly uses the relative bid-ask spread to proxy
for it. However, most models assume that the only timeseries
variation in spread is driven by information asymmetry.
Movements in order-processing costs, inventory
holding costs and competition are considered constant. In
some cases, the adverse selection cost component of the
spread is not explicitly isolated, in which case, results
could be driven by the other components as well.
The most common proxies for information asymmetry
are briefly described by Leuz and Verrechia (2000). The
bid-ask spread is commonly thought to measure
information asymmetry explicitly. The reason for this is
that the bid-ask spread addresses the adverse selection
problem that arises from transacting in firm shares in the
presence of asymmetrically informed investors. Less
information asymmetry implies less adverse selection,
which, in turn, implies a narrower bid-ask spread. An
alternative, and perhaps less explicit, proxy for adverse
selection is trading volume in firm shares. Trading volume
is a measure of liquidity in that it captures the willingness
of some investors who hold firm shares to sell and the
willingness of others to buy. This willingness to transact
in firm shares should be inversely related to the existence
of information asymmetries. Trading volume, however,
can be influenced by a host of other factors unrelated to
information. Finally, share price volatility has been used
by prior studies as a proxy for information asymmetry. To
the extent that smooth transitions in share prices suggest
the absence of information asymmetries between the firm
and shareholders, or among investors, low levels of
volatility suggest fewer information asymmetries. Higher
disclosure should lead to a lower bid-ask spread,
increased trading volume and less share price volatility.
The study hypothesizes that if compliance with SOX
404 increases internal control over financial reporting,
investor confidence in annual reports will also increase. A
confidence increase means lower compensation
premiums incorporated in the bid-ask spread. The higher
financial information quality is, the lower the adverse
selection cost should be, assuming that the chances of
trading against better informed traders are lower. The
expectation is that market makers react to the implementation
of Section 404 as if information asymmetry has
diminished, so the adverse selection cost component of
the bid-ask spread of market makers should narrow after
the implementation of SOX 404.
H1: The adverse selection cost is a significant component
of the bid-ask spread of market makers
H2: The bid-ask spread should narrow after the
implementation of SOX 404.
THE BOLLEN-SMITH-WHALEY MODEL
Following Sidhu et al. (2008), the study separates the cost
components of the bid-ask spread for a sample of compliant firms in
the period surrounding the imple-mentation of SOX 404. The study
differs significantly; not only in time span but also in that it attempts
to simplify the Bollen et al. (2004) model of estimating the spread
components. The following are specifications of the original model:
Quoted spread = ask price – bid price (at the time of each
transaction t).
Herfindahl index
This incorporates the number of dealers (ND) making a market in a
particular stock, as well as their respective trading volumes Vi. Rate
of return volatility is σ. The returns are obtained from the Center for
Research in Security Prices daily return file, and the daily return
standard deviation is annualized using the factor √252.
A preliminary regression is used including the following variables:
inverse of trading volume modified Herfindahl Index, inventory
holding premium. This regression shows that competition among
market players also plays an important role in determining the
absolute level of the bid-ask spread.
SPRDi � �0 ��1InvTVi
��
2MHIi
��
3IHPi
� �i

Where SPRDi is the bid-ask spread of stock i, InvTVi is the inverse
of trading volume, MHIi is the modified Herfindahl Index, and IHPi is
the inventory holding premium. In this model, the specific
components of the bid-ask spreads are: α0, the minimum tick size;
α1InvTVi, order-processing costs; α2MHIi, competition; and α3IHPi,
the sum of the inventory holding and informational asymmetry
components of the spread.
The first term on the right-hand side of the equation, α0, is the
exchange mandated minimum tick size. It serves as the lower
bound for the bid-ask spread. The second term models the effects
of order-processing costs (for example, the exchange seat, floor
space rent, computer costs, informational service costs, labor costs,
and the opportunity cost of the market maker’s time). Because
these costs are largely fixed, at least in the short run, their
contribution to the size of the bid-ask spread should fall with trading
volume—the higher the trading volume, the lower the bid-ask
spread. The third term captures the effects of competition among
market makers, measured by a modified Herfindahl Index (MHIi).
The fourth term on the right-hand side of the equation is the
market maker’s “inventory-holding premium.” This premium is
demanded by the market maker to cover the expected cost of
accommodating a customer order and then having the stock price
move against him, independent of whether the trade is initiated by
an informed or an uninformed customer. IHPi is estimated as a
single at-the-money option, with no distinction drawn between
informed and uninformed traders. Assuming that the market maker
sets his inventory-holding premium (IHP) component of the bid-ask
spread such that he minimizes the risk of losing money should the
market move against him, his demanded compensation is:
IHP�
�E(
�S
�S
� 0)
Pr( �S
� 0)
According to this equation, the minimum IHP equals the expected
loss on the trade conditional on an adverse stock price movement
times the probability of an adverse stock price movement. A market
maker demands different inventory-holding premium for trades with
informed and uninformed traders. From the market maker’s
perspective, the required inventory-holding premium, IHP, equals
the sum of the expected inventory holding cost and expected
adverse selection cost components of the spread, that is,
IHP� ( 1�
p ) IHP � p IHP
I
U
I
Where pI (1 − pI) is the probability of an informed (uninformed)
trade.
The coefficient α1 is expected to be positive because it
represents the market maker’s total order-processing costs. The
coefficient α2 should be positive. The fewer the number of dealers
and the less evenly distributed the trading volume across dealers,
the higher the modified Herfindahl Index and the higher the spread.
The coefficient α3 should also be positive. The higher the expected
inventory-holding premium, the greater the bid-ask spread. This
would prove H1 true.
Sample and method
Using compliance week as a tool and search engine for company
10 K and 10 Q filings, I retrieve the names, ticker symbols and
disclosure excerpts of the companies that disclosed material weaknesses.
Next step was searching the center for research in security
prices (CRSP) daily stock file for daily trading data for the 117 companies
that disclosed material weaknesses in the month of March
2005. The reason for looking into March disclosures is that this was
I
Dobre 11471
the first large output of annual reports after the implementation of
Section 404. To see whether there has been significant change in
returns, prices or bid/ask spreads surrounding the disclosures, the
study also includes the previous and following months.
The study retrieves price and intra-day transaction information for
a three month period from February 2005 to April 2005 for each of
these companies and computes the cost components of the bid/ask
spread (Inverse of Trading Volume, the Modified Herfindahl Index
and the Inventory Holding Premium), which has been discussed in
the previous chapter. After eliminating missing tickers, zero trading
volumes and unavailable market maker count information, the
search returns 1047 complete daily observations for 57 companies.
A simplified method of computation is used as compared to the
original Bollen-Smith-Whaley model cost components. A dated
panel is built, with 57 cross-sections, observed along 62 working
days in the months of February, March and April 2005. Table 1
includes some statistics for the following series retrieved through
database search:
The Ask and Bid columns represent the closing ask and bid of a
certain stock on a particular day.
Ask or High Price is the highest trading price during the day, or
the closing ask price on days when the closing price is not
available. Bid or Low Price is the lowest trading price during the day
or the closing bid price on days when the closing price is not
available.
Price or bid/ask average is the closing price or the negative bid/
ask average for a trading day. If the closing price is not available on
any given trading day, the number in the price field has a negative
sign to indicate that it is a bid/ask average and not an actual closing
price. Negative signs were eliminated where the bid/ask average is
shown, for computation reasons and also because the negative
sign is only a symbol - the value of the bid/ask average is not
negative.
Holding Period Return: A return is the change in the total value
of an investment in a common stock over some period of time per
dollar of initial investment. Return is the return for a sale on day i. It
is based on a purchase on the most recent time previous to day i
when the security had a valid price.
Trading volume is the total number of shares of a stock sold on
day i. It is expressed in units of one share, for daily data, and on
hundred shares for monthly data. The data source for NYSE/AMEX
reports the number rounded to the nearest hundred. For example,
12,345 shares traded will be reported on the NASDAQ Stock
Exchange as 12,345 and on the NYSE or AMEX exchanges as
12,300.
Market Maker Count is the number of registered market makers
for the issue.
Number of Trades contains the number of trades made on the
NASDAQ Stock Market each date for a security. Trades on all
exchanges are connected to NASDAQ’s composite pricing network
and all paper trades are included in the count.
The study computes the Bid/Ask spread for each stock i as:
SPREAD i = Ask or High Price i - Bid or Low Price i (1)
The Inverse of Trading Volume is:
InvTV i = 1 / Trading Volume i (2)
The Herfindahl index is:
ND 2
�Vi
�
HI � �� �
(3)
i�1
V
�
�

11472 Afr. J. Bus. Manage.
Table 1. Descriptive statistics.
Statistic Ask Bid
Ask or high
price
Bid or low
price
Price or bid/ask
average
Returns
Trading
volume
Market
maker count
Number of
trades
Mean 14.51581 14.48892 14.79640 14.26376 14.50458 -0.002541 898677.5 37.52377 2255.074
Median 12.35000 12.33000 12.69000 12.17000 12.35000 -0.001372 219330.0 33.00000 804.0000
Maximum 44.59000 44.48000 45.43000 44.43000 44.43000 0.993062 1.28E+08 89.00000 228699.0
Minimum 0.640000 0.630000 0.680000 0.610000 0.630000 -0.264469 2716.000 14.00000 20.00000
Std. Dev. 9.504561 9.495058 9.633784 9.395030 9.501572 0.046177 4403696. 15.99917 8292.905
Skewness 0.731981 0.733396 0.722224 0.743770 0.731821 8.480812 23.24814 0.994929 20.08574
Kurtosis 2.908778 2.910745 2.879117 2.936008 2.906689 207.1080 652.8263 3.543677 524.8706
Jarque-Bera 96.19017 96.54525 93.93407 99.11248 96.16550 1875414. 18975829 190.2393 12248417
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Observations 1073 1073 1073 1073 1073 1073 1073 1073 1073
Values are expressed in U.S. dollars, except trading volume, market maker count and number of trades.
Where ND is the number of dealers (Market Maker Count),
V is the Trading Volume for a particular stock; Vi is the
trading volume for the respective dealer.
The Modified Herfindahl Index is:
MHIi
HIi�1
NMi
1�1
NMi
� (4)
where HIi is the Herfindahl Index and NMi is the number of
market makers.
The expected inventory-holding premium is an at-themoney
option whose value may be written
IHP� S[
2N(.
5�
E(
t))
�1]
(5)
where S is the true stock price at the time at which the
market maker opens his position, σ is the standard
deviation of security return, E (√t) is the expected value of
the square root of the time between offsetting trades, and
N(·) is the cumulative unit normal density function.
The estimated regression will be:
SPRDi � �0 ��1InvTVi
��
2MHIi
��
3IHPi
� �i
(6)
In this model, the specific components of the bid-ask
spread are: α0, the minimum tick size; α1InvTVi, orderprocessing
costs; α2MHIi, competition; and α3IHPi, the
sum of the inventory holding and informational asymmetry
components of the spread.
STATISTICS AND REGRESSION RESULTS
Figure 1 shows the evolution of stock prices,
including one standard deviation line, before and
after material weaknesses disclosures were made
in March. The mean of prices was $14.5 and there
seems to have been a decrease around the10th
of March. The companies with a higher stock price
than the mean have experienced a more dramatic
Figure 1. Mean of Price across 3 months daily observations.
downward spike, compared to companies that are
priced below the mean, as the graph shows. But
these are just average values and a more accurate

0.06
0.04
0.02
0.00
-0.02
-0.04
-0.06
Figure 2. Mean of holding period returns across observation period.
Figure 3. Histogram and statistics of the bid/ask spread.
Figure 4. Mean of the bid/ask spread across 3 months daily
observations.
analysis would have to include firm-specific
characteristics, as stock prices vary significantly from one
firm to another.
Dobre 11473
The 57 stocks analyzed across three months have
generated negative average holding period return, as
shown in Table 1. They were generally small, only adding
up to a few cents. There were some negative spikes in
February and the disclosing month of March was
dominated by poor, negative returns. The evolution
across the observation period is shown in Figure 2.
It is interesting to notice that there was a significant
positive spike around the 25th of March, right after almost
all material weaknesses were made public and the
market had time to absorb the bad news. April’s returns
varied so much from positive to negative that is difficult to
draw a conclusion as to any possible impact of the
annual reports.
Table 1 includes a summary of the main statistics
computed for each data series. The trading volume daily
average for a company was quite high, around 900.000
units traded by an average of 38 market makers. The
study has only included companies which had number of
trades, market maker count and trading volume larger
than one. The highest price for a share was 44$ while the
lowest was 63 cents. Intriguingly, the maximum return
was only 99 cents.
The bid/ask spread had a mean of 53 cents, around
370 out of the total 1073 observations were set around
that amount, as shown Figure 3.
The spread seemed to have increased significantly
around the 20th of February after a major narrowing a
few days earlier. Mid-march and mid-April also showed
reductions in the bid/ask spread, most probably around
disclosure dates (Figure 4). Reductions of the bid/ask
spread are associated, according to prior literature, with
reduced information asymmetry. However the graph on
this sample does not show a significant reduction as
hypothesized earlier (H2)
The next step was running a regression with the bid/ask
spread as a dependent variable, using the least squares
method with fixed effects. A key assumption in most
applications of least squares regression is that there are
not any omitted variables which are correlated with the
included explanatory variables. (Omitted variables cause
least squares estimates to be biased.) When the
unobserved variable varies across one dimension of the
panel but not across the other, there is a feature called
fixed effects to make up for the omitted variable.
My data panel does not include observations for each
company for each of the 62 days considered, limitation
deriving from availability of data and restrictions
explained in the sample section. It is an unbalanced data
panel; therefore a substitution of missing observations by
a constant is required. The presence of multiple observations
for each company makes estimation of the fixed
effect possible. It is a cross-section fixed effect where
there is a missing day for a certain company. The same
happens for a variable that was constant over time while
varying across companies. This would lead to a varying

11474 Afr. J. Bus. Manage.
Table 2. Regression estimates with fixed cross-section effects and fixed cross-section and period effects.
Variable Fixed cross-section effect Fixed cross-section and period effect Cross-section weight
INVTV 1904.008 (0.058) 1951.676 (0.060) 2135.953 (0.003)
MHI -1.787(0.000) -1.804 (0.000) -1.682 (0.000)
IHP 0.260 (0.000) 0.348(0.000) 0.241(0.000)
C -0.083 (0.478) -0.308(0.022) -0.042 (0.652)
R-squared 0.495 0.533 0.674
F 16.833 9.068 35.615
Prob. (F statistic) 0.00 0.000 0.000
Durbin- Watson 1.448 1.475 1.638
t-stat Prob. are in parentheses.
fixed effect.
Table 2 shows the regression results, first column
including cross-section fixed effects, second including
both cross-section and period fixed effects, third column
with cross-section weights. Overall, the third regression
seemed to be more accurate. The model explained only
half of the variation of the SPREAD (49% and 53%,
respectively), which means that it was also influenced by
other factors not included in this linear regression. R 2 was
improved (67%) when cross-section weights were
applied. However, these results are not discouraging, as
the fisher statistic showed that the model is relevant.
The modified Herfindahl index coefficient was negative
in all three regressions, therefore narrowing the bid/ask
spread, which is contrary to the model’s expectations,
and intriguing at the same time. The fewer the number of
dealers and the less evenly distributed the trading volume
across dealers, the higher the Modified Herfindahl Index
and the higher the spread should be.
These results mean that a variation of the MHI of one
unit inversely affected the SPREAD by 1.7 cents 1.8
cents and 1.6 cents, respectively. This inconsistency
might have been caused by the small number of observations
for such a volatile variable, or by the simplified
method of computing the MHI. The inverse of trading
volume had the highest coefficient and it was positive, as
expected, although, the t-statistic and respective
probability indicate that it is somewhat weakly significant
(prob. was slightly higher than the acceptable 0.05 for a
strongly significant coefficient). The Inventory Holding
Premium coefficient estimate was positive, as expected,
and significant, but only 0.26, 0.34 and 0.24, respectively.
It showed that a variation of one cent in the inventory
holding premium determined a variation of 0.34 cents in
the bid/ask spread.
Conclusions
The answer to the SOX 404 controversy could be useful
to regulators in other countries who seek evidence on
whether less stringent internal control regimes are
sufficient for high-quality financial reporting. Economic
theory suggests that a commitment by a firm to increased
levels of disclosure should lower the information asymmetry
component of the market makers’ bid/ask spread.
The regression results obtained through a simplified
version of the Bollen, Smith and Whaley model are not
entirely consistent with expectations. Estimation showed
that The inventory holding premium (which includes the
adverse selection cost component of the bid/ask spread)
does not have a large impact on the spread itself,
although it has a positive influence, proving H1 true.
The evolution of the spread has not seen a significant
downward spike after the month of disclosures
considered for this study. There may be two explanations
for this result. First, the fact that these companies are
disclosing issues related to financial reporting might send
a negative signal towards investors and inspire distrust in
the annual reports. The SEC’s objective of reducing the
information asymmetry has not been met immediately,
due to „bad news” effects. This is intuitive, but in the long
run the effect of such disclosures might be opposite.
Companies have discovered ICFR weaknesses and
might even have taken action to remediate them.
Second, this study is based on a reduced sample of
newly compliant firms. A larger sample of companies with
observations of the spread across years should show the
expected reduction.
Also noticeable were the low returns that the stocks
generated. This is consistent with previous research on
cumulative abnormal returns and overall evolution of
trading for compliant firms (Zhang et al 2007; Litvak,
2007, among others). Such poor performance is most
probably caused by the financial difficulties these
companies meet, MW disclosing companies being prone
to such problems as shown by Doyle et al. (2007a) and
Klamm and Watson (2009).
The study concludes that compliance with the Section
404 of the Sarbanes Oxley Act has not led to a reduction
of information asymmetry among traders, and of the bid/
ask spread, infirming H2. However, this study is based

ased on a short 3 month window, prior and after
implementation, and regulation effects are usually
noticeable in long the run, so it is possible a future study
with a longer time-span would show the expected spread
narrowing.
ACKNOWLEDGEMENTS
This article is a result of the project POSDRU/6/1.5/S/11
„Doctoral Program and PhD Students in the education
research and innovation triangle”. This project is co
funded by European Social Fund through The Sectorial
Operational Programme for Human Resources
Development 2007-2013, coordinated by The Bucharest
Academy of Economic Studies.
REFERENCES
Ashbaugh-Skaife H, Collins D, Kinney W, LaFond R (2009). The Effect
of SOX Internal Control Deficiencies on Firm Risk and Cost of Equity.
J. Account. Res., 47: 1-43.
Bhamornsiri S, Guinn R, Schroeder R (2009). International Implications
of the Cost of Compliance with the External Audit Requirements of
Section 404 of Sarbanes–Oxley. Int. Adv. Econ. Res., (15): 17–29.
Bollen N, Smith T, Whaley R (2004). Modeling the Bid/Ask Spread:
Measuring the Inventory-Holding Premium. J. Finance Econ., (72):
97–141.
Brown S, Hillegeist S (2007). How disclosure quality affects the level of
information asymmetry. Rev. Account. Stud. (12): 443–477.
Chhaochharia V, Grinstein Y (2007). Corporate Governance and Firm
Value: The Impact of the 2002 Governance Rules. J. Fin., (4): 1789-
1825.
Doyle J, Ge W, McVay S (2007a). Accruals Quality and Internal Control
over Financial Reporting. Account. Rev., 82(5): 1141–1170.
Doyle J, Ge W, McVay S (2007b). Determinants of weaknesses in
internal control over financial reporting. J. Account. Econ., 44: 193–
223.
Dobre 11475
Engel E, Hayes R, Wang X (2007). The Sarbanes–Oxley Act and firms’
going-private decisions. J. Account. Econ., (44): 116–145.
Ertimur Y (2007). Discussion of “How disclosure quality affects the level
of information asymmetry” Rev. Account. Stud., (12): 479 – 485.
Hansen B, Pownall G, Wang X (2009). The robustness of the Sarbanes
Oxley effect on the U.S. capital market. Rev. Account. Stud., 14:
401–439.
Healy PM, Palepu KG (2001). Information asymmetry, corporate
disclosure, and the capital markets: A review of the empirical
disclosure literature. J. Account. Econ., (31): 405–440.
Hoitash U, Hoitash R, Bedard JC (2009). Corporate Governance and
Internal Control over Financial reporting: A Comparison of Regulatory
Regimes. Account. Rev., 84(3): 839-867.
Klamm B, Weidenmier Watson M (2009). SOX 404 Reported Internal
Control Weaknesses: A Test of COSO Framework Components and
Information Technology. J. Inform. Syst., 23 (2): 1–23.
Kosmider D (2006). The Bid/ask Spread and How it Effects Trading.
Accessed online at http://ezinearticles.com/?The- Bid/ask-Spread-
and- How- it- Effects- Trading&id=139730.
Leuz C, Verrecchia R (2000). The economic consequences of
increased disclosure. J. Account. Res., (38): 91–124.
Litvak K (2007). The effect of the Sarbanes Oxley act on non-US
companies cross-listed in the US. J. Corp. Fin. (13): 195–228.
Ogneva M, Subramanyam KR, Raghunandan K (2007). Internal Control
Weakness and Cost of Equity: Evidence from SOX Section 404
Disclosures. Account. Rev., 82(5): 1255-1297.
Sidhu B, Smith T, Whaley RE, Willis RH (2008). Regulation Fair
Disclosure and the Cost of Adverse Selection. J. Account. Res.,
46(3): 697-727.
Sinnett WM (2004). Detecting fraud: will the new rules help? Finance
Exec., 20: 63–65.
Zhang I (2007). Economic consequences of the Sarbanes–Oxley Act of
2002. J. Account. Econ., (44): 74–115.
Zhang Y, Zhou J, Zhou N (2007). Audit committee quality, auditor
independence and internal control weaknesses. J. Account. Pub.
Pol., 26: 300–327.

African Journal of Business Management Vol. 5(28), pp. 11265-11282, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.694
ISSN 1993-8233©2011 Academic Journals
Full Length Research Paper
Comparative perspectives on environmental
accounting elements in France and the United Kingdom
Voicu Dan DRAGOMIR* and Elena Roxana ANGHEL-ILCU
Department of International Accounting and Financial Reporting Faculty of Accounting, The Bucharest Academy of
Economic Studies, Romania.
Accepted 16 November, 2010
The aim of this paper is to provide a comparative perspective on the output of environmental
accounting systems. For the purpose of the analysis, the methodological focus was set on monetary
elements included in the annual financial and sustainability reports. Following the tradition of
accounting literature, two accounting cultures were selected for their paradigmatic opposition: the
British and the French financial reporting systems. Using a sample of 100 companies, half extracted
from each country, the environmental elements expressed in monetary terms were subsequently
content-analyzed, following a relevant regulatory benchmark: the UK generally accepted accounting
principles (GAAP), the French chart of accounts, the European recommendations on account
preparation and corporate reporting, and the International financial reporting standards (IFRS). We also
provide empirical evidence for a phenomenon called “reporting inertia”, which refers to a certain
approach to corporate environmental reporting, where companies are using prefabricated phrases and
paragraphs to report almost the same monetary elements year after year, for long periods of time.
Finally, we discuss the mixed results in the distribution of accounting elements between the two
accounting cultures. This investigation is novel in that there is no previous study offering a very
detailed classification and analysis of several environmental accounting elements, in a European
context.
Key words: Environmental accounting, European companies, international financial reporting standards
(IFRS), comparative studies.
INTRODUCTION
“Green accounting” describes the effort of researchers,
accounting standard setters, professional organizations,
and governmental agencies to get corporations to
participate proactively in cleaning and sustaining the
environment and, moreover, to describe fully and forthrightly
their environmental activities in either their annual
reports or in stand-alone environmental disclosures. In
this respect, Elkington (1998) coined the phrase “triple
bottom line” (TBL) to suggest that financial reporting
should expand beyond traditional bottom-line income as
a measure of success which should also include information
about social and environmental performance.
Since the late 1980s, in a number of countries worldwide,
companies have been required to provide some
*Corresponding author. E-mail: voicudragomir@gmail.com.
level of environmental reporting in the annual reports.
Nearly all of this additional disclosure has focused on the
impact of environmental issues on a company’s financial
results and position, requiring separate inclusion of such
items as expenditures for pollution and prevention,
cleanup costs, actual and contingent liabilities for
environmental remediation from past operation, and
assets related to environmental protection. In addition,
some countries also require disclosures on resource
consumption and pollutant emissions in annual reports, in
managerial statement (Fleischman and Schuele, 2006).
Environmental accounting is not only part of a reporting
system. It is also a very effective communication tool,
since all environmental remedial strategies implemented
by the managers must be accompanied by disclosure to
have any effect on external parties. That is, information is
necessary to change perceptions. Remedial action which
is not publicized will not be effective in changing

11266 Afr. J. Bus. Manage.
perceptions (Cormier and Gordon, 2001). This perspective,
as provided by legitimacy theory, highlights the
strategic importance of corporate disclosures, such as
those made within annual reports and other publicly
available documents. The public disclosure of information
through annual reports can be employed by an organization
to counter or offset negative news, or may simply
provide information to stakeholders about attributes of the
organization which were previously unknown. In addition,
organizations may draw attention to their achievements,
for instance environmental awards won, or safety
initiatives that have been implemented, while sometimes
neglecting or down-paying information concerning
negative implications of their activities, such as pollution
or workplace accidents (Deegan, 2002).
The aim of this paper is to provide a comparative
perspective on the output of environmental accounting
systems. For the purpose of the analysis, the methodological
focus is set on monetary elements included in the
annual reports (that is, the notes to the consolidated
accounts and the environmental management report).
The second methodological choice shall be related to the
selected sample. Following the tradition of the accounting
literature, two accounting cultures will be selected for
their paradigmatic opposition: the French and the British
financial reporting systems. The largest listed companies
on the London and Paris stock exchanges, 50 companies
from each country, shall provide observations for the
four-year panel used in our analysis, after eliminating
those firms operating in industries with trivial
environmental impacts.
Using content analysis, the environmental elements
expressed in monetary terms shall be classified in
accordance with the relevant accounting policies, as to
create a comprehensive picture of financially quantifiable
environmental impacts disclosed in the corporate annual
reports for the 2008/2009 financial year. The comparative
outlook shall be subsequently extended to include the
previous three financial years (2005/2006, 2006/2007
and 2007/2008).
A phenomenon called “reporting inertia” shall be
documented through a series of statistical tests on the
proportion of environmental accounting elements present
in the annual reports of sample companies over the
whole period of analysis.
As the relevant regulatory benchmark, four separate
accounting frameworks shall be used: the French chart of
accounts, UK generally accepted accounting principles
(GAAP), the European directives and recommendations
on account preparation and corporate reporting, and the
International Financial Reporting Standards (IFRS).
Building on the traditional separation of accounting into
two paradigms – Continental European (French) versus
Anglo-Saxon (British) – we will test the hypothesis thatthe
quantity of environmental elements in the annual reports
of French companies does not significantly differ from the
quantity of environmental disclosure exhibited by UK
companies.
Finally, a detailed discussion shall complement the
presentation of results, along with a review of possible
limitations of our research. The final conclusions shall be
addressing issues such as the voluntary nature of
environmental reporting, the barriers to efficient standardsetting
in this area and the lack of uniformity regarding
the disclosure of financially quantifiable environmental
impacts, in the form of environmental accounting
elements.
PERSPECTIVES ON ENVIRONMENTAL ACCOUNTING
A general framework
Accounting provides a very selective yet powerful symbolic
representation of the corporate entity. The language
of “assets”, “liabilities”, “costs” and “profits” define the
operational and ontological limits of the enterprise and
provide a technique which configures the organizational
autonomy and sensitivity to environmental disturbances
(Gray et al., 1995).
The term “environmental accounting” has been used to
describe attempts to determine environmental costs and
benefits to the organization. The main focus is internal,
including the costing of energy use and waste disposal,
and quantifying the benefits from the sale of environmentally
benign products or from environmental subsidies.
External impacts on the natural environment relate to the
organization’s use of resources and generation of
emissions and waste. These impacts can be measured,
for example in terms of tones of carbon dioxide emitted,
but also in monetary terms, such as through the costs for
acquiring certificates for greenhouse gas emissions.
Environmental accounting is usually involved in several
areas, such as: energy accounting; waste accounting;
environmental criteria in capital expenditures; target
setting for efficiency improvements (Wycherley, 1997).
The entire concept of an accounting transaction is
bound to the notion of “private cost”. The result is that
many social costs in the form of polluted air, water and
soil, and the large palette of ecological damage are not
recognized by the accounting process (Bedford, 1970,
cited in Fleischman and Schuele, 2006). The environmental
accounting system is part of a larger corporate
environmental policy, which aims to prevent and reduce
environmental impact, through life-cycle analysis,
integration of environmental values into the supply chain,
eco-design of products and services and environmental
monitoring and auditing (Dragomir, 2008). Therefore, the
purpose of an environmental accounting framework is to
provide a general fit over the area regulated: (a) to raise
awareness of environmental issues; (b) to develop guidelines
to assist identification of environmental issues and
evaluation for reporting purposes; (c) to provide
education programs across disciplines focused on

environmental issues and their accounting treatment; and
(d) to develop practices of environmental accounting, with
recommendations on best practices.
There are clear limits to the use of environmental
accounting. There are practical difficulties in terms of
operations such as complex and highly interdependent
manufacturing processes or office locations. The costs of
collecting accounting information may outweigh its value
in some cases. Moreover, it is very difficult for accountants
to prepare meaningful estimates of the business
benefits of adopting a green strategy (particularly
concerning such intangibles as a good public image or
selling benign products) (Wycherley, 1997).
Technical aspects of environmental accounting
Environmental financial accounting integrates corporate
environmental and business policies designed to analyze
environmentally related costs and benefits, contributing to
the recognition of capital and operating expenses for pollution
control equipment, environmental taxes and fines,
environmental subsidies and other similar elements. A
complementary step in developing an environmental
financial accounting system is the setup of a dedicated
cost accounting. The latter is defined as the use of
accounting records to directly place costs on every environmental
aspect, as to determine the cost of all types of
related action. In this respect, environmental actions
include pollution prevention, environmental design and
environmental management. Past approaches on environmental
impacts were mainly based on environmental
cleanup costs and product disposal (Yakhou and
Dorweiler, 2004).
The main component to consider for environmental
accounting is that of environmental costs. The U.S.
Environmental Protection Agency (1996) defines environmental
costs as those costs that have a direct financial
impact on a company (internal costs), and costs to
individuals, society and the environment (external costs).
The type of costs included in an environmental
accounting system ultimately determines the scope of the
system. Environmental data can be captured using
generalized scientific models to estimate emission levels
and resource consumption. In cases where resources are
purchased from suppliers, direct measurement by technical
instrumentation is possible. For example water meters
record consumption at the source, as do electricity
meters. In many cases the sampling method is the only
cost effective method of data capture due to the
excessive cost of measuring all emissions and natural
resources consumed (Lamberton, 2005).
An environmental cost accounting system is a floworiented
system which is based on a systematic causeand-effect
analysis. Especially output-related costs, for
examole, emissions, waste disposal and waste water are
assigned correctly to the inputs which cause them.
Dragomir and Anghel-ilcu 11267
Environmental costing contributes to an internal pricing
system which evaluates inputs, processes and products
with their real costs. This procedure creates both a
decision-oriented information base for the environmental
management system and for the planning, control and
supervision of material and energy flows (Lethmate and
Doost, 2000).
Internal costs may include conventional costs,
potentially hidden costs, contingent costs and image or
relationship costs (Environmental Protection Agency,
1995). Conventional costs include costs of capital equipment,
raw materials and supplies. Hidden costs refer to
the results of assigning environmental costs to overhead
pools or overlooking future and contingent costs.
Contingent costs refer to environmental costs that are not
certain to occur in the future but depend on uncertain
future events, for example, the costs involved in
remediating future spills. Image and relationship costs
are less tangible costs because they are incurred to
affect subjective perceptions of management, customers,
employees, communities, and regulators. This category
can include the costs of annual environmental reports
and community relations activities and costs expended
voluntarily for environmental activities such as tree
planting. The costs themselves are not intangible, but the
direct benefits that result from corporate image expenses
often are (de Beer and Friend, 2006).
External costs include: (1) environmental degradation
for which firms are not legally liable and (2) adverse
impacts on human beings, their property and their welfare
that cannot always be compensated for through legal
systems (de Beer and Friend, 2006). External costs
usually arise from specific attributes of natural resources.
Some of these exhibit private good characteristics. Fossil
fuels, minerals, agricultural and some forested land would
be examples of such resources. Private markets for the
allocation of such resources tend to develop and function
reasonably well. Environmental problems, by contrast,
are often associated with resources which exhibit public
good characteristics, where markets are either
incomplete or nonexistent. For such resources (for
example clean air and water, ocean fisheries and natural
areas), incomplete markets create a danger of
exhaustion from misuse (Milne, 1991).
European perspectives on environmental accounting
Accounting can be seen to perform a role of providing an
organization with stability in the face of uncertainty and
rapid change. Sometimes, this stability may have
unwanted consequences, as illustrated by the case of a
company where the accountants were driven by the
accounting year to delay the planting of seeds by two
months, resulting in a complete failure of the management
aim to produce a recreational area. The inability of
accounting to be flexible enough to account for the seed

11268 Afr. J. Bus. Manage.
in the ground as an asset at year-end was a direct cause
of having to do the planting all over again the following
year (Wycherley, 1997).
The failures of accounting may be endogenous, as in
the aforementioned case, or exogenous, when the limitations
are attributable to the differences between national
accounting systems, traditions and techniques. Comparative
accounting research has attempted to capture those
elements of international convergence or divergence that
lead to incomparability between financial reports issued
by companies in different parts of the world. This lack of
comparability arises from the architecture of accounting
systems having uneven levels of development and
sophistication, or simply relying on incompatible paradigms.
Some of these elements will be discussed in the
following paragraphs, in the context of the European
Union accounting directives, national accounting systems
and the application of the international financial reporting
standards (IAS/IFRS) for listed companies.
In the European Union, the fourth directive (78/660/
EEC) of 25 July 1978 coordinates member states’
provisions concerning the presentation and content of
annual accounts and annual reports, the valuation
methods used and their publication in respect of all
companies with limited liability. Together with the fourth
directive, the seventh directive (83/349/EEC) of 13 June
1983 belongs to the family of “accounting directives” that
form the arsenal of community legal acts governing
company accounts. The latter defines the circumstances
in which consolidated accounts are to be drawn up. Any
company (parent company) which legally controls
another company (subsidiary company) is under a duty to
prepare consolidated accounts. Beginning January 1,
2005, all European Union companies having securities
listed on an EU exchange have been required to prepare
consolidated (group) accounts in conformity with the
International Financial Reporting Standards (IFRS),
issued by the International Financial Standards Board
(IASB). Thus, the analysis of how certain IFRSs can be
incorporated into national legislations represents an
important avenue for research, mostly because controversial
issues, such as environmental accounting, are not
recognized by many national standard setters and
professional accounting bodies.
The aforementioned European directives do not make
reference to any type of element to be associated with
environmental accounting. Thus, in the dawn of the IFRS
era in Europe, the European Commission acknowledged
two problems regarding environmental accounting
(European Commission, 2001): that any or all of the
different stakeholder groups (regulatory authorities,
investors, financial analysts and the public) could feel that
the disclosures were insufficient or unreliable and that
there was a low level of voluntary disclosure, even in sectors
which have a significant impact on the environment.
The lack of harmonized guidelines may also lead to
investors and other stakeholders not being able to
compare companies or adequately assess environmental
risks affecting the financial position of the company. In
spite of issues of sensitivity or confidentiality, users of
financial statements need information about the impact of
environmental risks and liabilities on the financial position
of the company, and about the company’s attitude
towards the environment.
The Commission (EC, 2001) further formulated the
complaint that the international accounting standards
board (IASB) had provided only little guidance directly
related to such matters and that no specific international
accounting standard was solely focused on environmental
issues. The recommendation took as a source of
reference several international accounting standards
(IAS), which were of specific relevance to environmental
issues, in particular IAS 36 on impairment of assets, IAS
37 on provisions, contingent liabilities and contingent
assets and IAS 38 on intangible assets. Balance sheets
should contain details of provisions and environmental
liabilities and the notes to the annual accounts and
consolidated accounts should contain details of valuation
methods applied to environmental issues, extraordinary
environmental expenditures, details relating to provisions
in the balance sheet as well as details about contingent
environmental liabilities and costs incurred as a result of
fines and penalties for non-compliance with environmental
regulations and compensations paid to third
parties.
Whereas “environmental costs” is a pivotal concept for
management accounting, the financial reporting systems
are usually concerned with such elements as “environmental
liabilities” and “environmental capital expenditure”.
According to the European Commission’s recommendation
on the recognition and disclosure of environmental
elements in the annual accounts (EC, 2001), liabilities
can be seen from a double perspective: either as a legal /
contractual obligation to prevent, reduce or repair environmental
damage, or as a constructive obligation arising
from the enterprise’s own actions, when the enterprise
has committed itself to protect the environment.
Environmental liabilities are strongly tied to specific costs,
since an environmental liability is recognized when a
reliable estimate of the costs derived from the obligation
can be made. The term “provisions” refers to environmental
liabilities which are uncertain either in terms of
their due date or in terms of their amount to be settled.
Finally, financial accounting, as well as capital
budgeting, is concerned with environmental expenditure
and associated procedures, such as depreciation and
impairment. Environmental expenditure should be
capitalized (that is, recognized as an asset for use on a
continuing basis) when that expenditure is intended to
extend the life, increase the capacity or improve the
safety or efficiency of other assets owned by the
enterprise. All the above elements should be disclosed in
the annual report to the extent that they are material to
the financial performance or the financial position of the

eporting entity.
Comparative perspectives: International financial
reporting standards (IFRS) and national accounting
standards
The debate surrounding international convergence is
often focused on the fundamental traits of accounting
systems. Explicitly, researchers eventually find themselves
discussing the implications of one national system
belonging either to the “Anglo-Saxon” or to the
“continental” paradigm (Alexander and Archer, 2000).
The most homogenous group is believed to be formed of
countries belonging to the continental system, mainly due
to the incorporation of EU directives; especially Germany,
France, Austria and Belgium can be looked upon as
representing the nucleus of the Continental group. On the
other hand, the term “Anglo-Saxon accounting” is used to
refer to an approach to financial accounting and reporting
that is supposedly common to the UK and Ireland, the
USA and other English-speaking countries including
Canada, Australia, and New Zealand (d’Arcy, 2001).
However, since the process of European accounting
harmonization started in 1978 with the fourth directive,
and culminated with the adoption of IFRS for all publicly
traded companies, the “Continental vs. Anglo-Saxon”
classification is increasingly losing ground (Ding et al.,
2007). Some authors even argue, with strong evidence,
that this classification is simply a myth: within each group
and between these groups there are similarities and
differences which should be treated on a piecemeal basis
(Street and Gray, 2002).
On the topic of environmental accounting, the IASB
considers that environmental reports presented outside
financial statements are not within the scope of IFRS,
even if many companies operate in industries in which
environmental factors are significant. However, there are
several international accounting standards (IAS) containing
guidelines on the recognition and measurement of
financial elements connected to environmental protection
(EC, 2008):
(1) IAS 16 recognizes items of property, plant and
equipment acquired for environmental reasons. Such
items qualify for recognition as assets because they
enable an entity to derive future economic benefits from
related assets;
(2) IAS 37 recognizes obligations in the form of penalties
or clean-up costs for unlawful environmental damage.
Similarly, an entity should recognize a provision for the
decommissioning costs of an oil installation or a nuclear
power station to the extent that the entity is obliged to
rectify damage already caused;
(3) IFRIC 5 recognizes that the purpose of decommissioning,
restoration and environmental rehabilitation
funds, is to segregate assets to fund some or all of the
Dragomir and Anghel-ilcu 11269
costs of decommissioning plant (such as a nuclear plant)
or certain equipment (such as cars), or in undertaking
environmental rehabilitation (such as rectifying pollution
of water or restoring mined land).
For the purpose of this paper, two countries have been
selected: France and the United Kingdom. They occupy
the second and the third place, respectively, in a
classification on nominal Gross Domestic Product (in
market prices) in 2008 in the European Union (Eurostat,
2010). In terms of market capitalization, Euronext Paris
(index Euronext 100) and the London stock exchange
(index financial times stock exchange (FTSE) 100)
occupy the first and the second place, respectively, on a
list of largest stock exchanges in the European Union for
January 2009 (WFE, 2010).
France is a EU Member State. Consequently, French
companies listed in an EU securities market have
followed IFRS starting with 2005. The national accounting
system is compliant with the European directives, and
relies on the French chart of accounts (plan comptable
général – CRC Regulation n°99-03, with subsequent
revisions). The chart of accounts has been amended to
include provisions inspired by IFRS, concerning the
recognition, measurement and recording of assets,
impairments, provisions, and similar elements (Stolowy
and Ding, 2003).
Regarding environmental accounting in the annual
accounts of French companies, the chart of accounts
(CRC, 2007) recognizes only the fixed assets acquired
for reason of environmental protection. Although not
directly increasing the future economic benefits related to
a particular existing asset, they are recognized as assets
if they are necessary for the entity to obtain future
economic benefits from its other assets. For example, a
chemical manufacturer may need to install some new
processes for handling chemicals to comply with
environmental regulations on production and storage of
hazardous materials. Improvements to facilities are
recognized as corresponding assets, because without
them the entity would be unable to manufacture or sell its
chemicals.
The United Kingdom is a EU Member State. Consequently,
British companies listed in an EU securities
market have followed IFRS starting with 2005. At a
domestic level, all accounting standards developed by
the financial reporting council (FRC) since 1990 have
been issued as financial reporting standards (FRS).
These standards are in compliance with the European
directives, but bear profound similarities with the
international accounting standards (IAS), mainly due to
their common ancestry and shared paradigm (Cairns,
2004; Christensen et al., 2007).
Regarding environmental accounting in the annual
accounts of British companies, the FRSs recognize
elements in a similar fashion to IFRS (that is the recognition
and measurement of capitalized environmental

11270 Afr. J. Bus. Manage.
expenditure). However, there are several points of
interest within this corpus of standards (ASB, 2010). For
example, FRS 12 considers that an entity should recognize
a constructive obligation to rectify environmental
damage, even if that particular UK Company operates in
a country where there is no environmental legislation. If
the entity has a widely published environmental policy in
which it undertakes to clean up all contamination that it
causes, the entity is bound to recognize this obligation in
its annual accounts. FRS 30 imposes the recognition of
“heritage assets”, which can have historical, artistic,
scientific, geophysical or environmental qualities. FRS 5
stipulates that, if the operator is obliged to meet any
liabilities as a result of a contract (for example,
environmental clean-up costs), these should be recorded
separately, within liabilities. SSAP 13 considers that the
outcome of a research and development project should
be examined for its ultimate commercial viability in the
light of factors such as, inter alia, consumer and
environmental legislation.
In the European Union, national accounting standards
are in compliance with the European Directives, which
are also compatible with IFRS (Dragomir and Ilcu, 2008).
Nevertheless, environmental accounting is largely outside
the scope of international accounting convergence, since
the national and international standard-setters have not
found a way to link environmental cost accounting with
the financial system of the enterprise. Even if environmental
accounting relies on the identification, allocation
and analysis of material streams and their related money
flows to fairly reflect environmental impacts, their
associated financial effects remain unidentified and
undisclosed to the public. Moreover, the Council
Recommendation on environmental accounting matters
(EC, 2001) has found little echo in the national regulatory
frameworks of France and the United Kingdom.
METHODOLOGICAL ASPECTS
The formulation of hypotheses
The present study uses a mixed design methodology. A mixed
model design is a research which uses both quantitative and
qualitative data in one or two stages of the research process, so
that the mixing of quantitative and qualitative approaches happens
in every stage of a research. From a qualitative perspective, the
main research hypothesis is based on the literature review, and is
formulated as follows: Since there are no regulatory constraints on
the financial recognition of environmental elements, the output of
environmental accounting is largely voluntary.
A corollary will be demonstrated using content analysis: the
voluntary disclosures regarding environmental impacts are quantitatively
reduced, and qualitatively obscure or insignificant. From a
quantitative perspective, a balanced panel (that is, the same
number of companies for the entire time span of four financial
years) will be used to conduct statistical tests on the following two
hypotheses:
(1) There is a certain “reporting inertia” leading to time invariability
in the reporting behavior of sample companies;
(2) National accounting culture exerts a certain influence over the
amount of environmental disclosure, in a direct relationship with the
degree of regulatory pressure exerted by national standards.
Sample selection
The present study uses hand-collected panel data on environmental
accounting elements disclosed over multiple time periods for
the same European corporations. The target sample was set to
include 100 companies: half British companies listed on the London
stock exchange, half French companies listed on the Paris stock
exchange. The tool available at Euroland.com was used to sort the
list of companies by their market capitalization (access date 31
December, 2009). A complete list, with an indication of primary
stock exchange and sector of activity, is presented in Appendix 1.
The sample collected for financial year 2008/2009 is considered
to be the reference sample, consisting of 100 observations (that is,
companies), each of them having issued at least the annual financial
report for that year. Our purpose was to extend the analysis to
include several prior years of environmental disclosure. Thus, we
took the list of companies from the reference sample, and collected
data for another three financial years (FY): 2005/2006, 2006/2007
and 2007/2008. Financial year 2005/2006 was considered to be the
first year of relevant data, because annual reports of European
companies have become comparable since January 1st, 2005, due
to the adoption of IFRS for all listed companies on European stock
exchanges.
However, the initial dataset was an unbalanced panel. Some of
the companies belonging to the reference sample did not issue
annual reports for periods prior to 2008; mainly because they were
formed in 2006 to 2007 after a merger between other companies
(for example this is the case of ArcelorMittal, GDF Suez and CGG
Veritas, which did not exist as such in 2005 to 2007). The
descriptive statistics in Table 1 describes the reference sample of
2008/2009 and the incomplete sample data collected for the prior
three financial periods, back to 2005, the year of passage to IFRS.
The financial years are expressed as 200X/200Y because each firm
is allowed to issue its financial statements for 12 months ending at
a certain date of choice (for example, for financial year 2008/2009,
72% of firms have 31 December 2008 as their fiscal year-end, 12%
have 31 March 2009, and the rest between 12 September 2008 and
30 June 2009).
The largest group companies were extracted from the indexes of
the respective stock exchanges, excluding those belonging to the
following sectors (Euroland’s denominations): banks, life and
general insurance, financial services, real estate investment trusts,
business support services, security and alarm services, internet
software and services, wireless communications services, computer
and consulting services, engineering and industrial software, internet
service providers, property investment and management, TV,
radio and diversified media, advertising, publishing, medical technology
and supplies, travel services, engineering and architectural
services.
It is apparent that the activities specific to the excluded sectors
have only trivial impact on the natural environment. For the purpose
of our analysis, it means that these economic activities, mostly
providing services to end consumers, do not interact with the
natural environment in a significant manner: specifically, they do not
have direct greenhouse gas emissions, they use only indirect
energy (that is, they do not produce electricity for their own needs),
and they have negligible quantities of waste. Furthermore, a pilot
study conducted on the annual reports of several such companies
revealed that no environmental accounting elements were
disclosed in the notes to the annual accounts. Hence, the inclusion
of these companies would have distorted the analysis.
The selected companies were grouped by industry to emphasize
the economic environment that has been targeted in the process of

Table 1. The reference sample and the comparative perspective for the prior financial years (FY).
County of incorporation
FY 2005/2006
(No. of firms)
FY 2006/2007
(No. of firms)
Dragomir and Anghel-ilcu 11271
FY 2007/2008
(No. of firms)
Reference sample
FY 2008/2009
(No. of firms)
United Kingdom 47 47 49 50
France 46 47 48 50
Excluded companies
Alcatel Lucent
ArcelorMittal
CGG Veritas
ENRC
Fresnillo
GDF Suez
Suez Environnment
ArcelorMittal
CGG Veritas
ENRC
Fresnillo
GDF Suez
Suez Environnment
Fresnillo
GDF Suez
Suez Environnment
Table 2. An overview of the reference sample, with the 100 companies grouped by country and industry.
Euroland industry UK France Total
Aerospace and defense 4 2 6
Autos and transport equipment 0 3 3
Chemicals 1 1 2
Construction and materials 0 6 6
Consumer products - food, beverages 6 2 8
Consumer products – non-food 1 4 5
Entertainment and leisure 3 2 5
Health and pharmaceuticals 2 3 5
IT, Information technology 0 1 1
Manufacturing 1 4 5
Mining and metals 11 3 14
Oil and gas 6 3 9
Retail 6 3 9
Telecom 3 4 7
Transportation 0 5 5
Utilities 6 4 10
Grand Total 50 50 100
sample selection. A detailed view of the target sample (financial
year 2008/2009) grouped by country and industry is presented in
Table 2. In this analysis, 16 main industries (Euroland’s denominations)
have been selected as to create a statistically significant
sample for those British and French companies which disclosed
financially quantifiable environmental elements in their corporate
annual reports. The data in Table 2 shows the distribution of the
companies in the respective industries; more than one quarter of
the sample (24 observations) is active in three environmentallysensitive
industries: Mining and Metals, Utilities and Chemicals.
This is convenient, since extractive industries (for example minerals
and precious metals, production of steel), utilities (for example,
electric, gas and water utilities) and chemicals (for example paints,
catalysts and technologies for chemical processes) usually have an
incontestable footprint on the natural environment.
RESULTS AND DISCUSSION
The heterogeneous data collected from the corporate
annual reports of the companies have been synthesized
in order to create a general classification of financially
quantifiable environmental elements. This classification
has been used to organize all the environmental elements
expressed in monetary form, in accordance with
the international financial reporting standards (IFRS). The
following would be dicussed: environmental assets and
investments (based on IAS 16), provisions for decommissioning
and restoration (based on IAS 16 and IAS 37),
other environmental provisions for environmental protecttion
and litigation (based on IAS 37), environmental
expenditure (based on regular disclosure in the income
statement), and environmental taxes, fines, donations
and sponsorship (based on regular disclosure in the
sustainability reports). The following aspects would be
discussed in this paper in relation to the results of the
content analysis regarding monetized environmental
-

11272 Afr. J. Bus. Manage.
disclosure in corporate annual reports, followed by a
statistical analysis of the reporting trend for our panel
data, and the national accounting differences between
the sample firms.
A presentation of environmental accounting
elements extracted from financial reports
Using content analysis, the environmental elements expressed
in monetary terms are hereafter summarized, to
create a comprehensive picture of financially quantifiable
environmental impacts presented in the corporate annual
reports for the 2008/2009 financial year. However, as
noted in the following; environmental assets and investments;
environmental provisions for decommissioning,
dismantling and restoration; other environmental provisions
and liabilities; environmental expenditure;
donations, sponsorship, taxes and fines.
These elements are quasi-identical for prior periods, as
there is a certain “reporting inertia” which influences the
presentation of environ-mental accounting assets. In
other words, even if the extracted elements have a crosssectional
appearance, the results can also be extrapolated
to other periods, due to a reporting pattern, with low
variability and poor informational content.
Environmental assets and investments
Environmental assets and investments are recognized in
accordance with IAS 16 property, plant and equipment,
indicating that some fixed assets may be acquired for
safety or environmental reasons. The acquisition of such
elements, even in the absence of future economic benefits,
may be necessary for the uncompromised use of
other operating fixed assets. In this case, it is clear that
the acquisition of environmental assets is outside the
scope of the general definition of an asset. This derogation
is based on the fact that future economic benefits
may be compromised in the absence of certain
environmental assets, even though the latter are only
accessories to the main operation. As an example, the
standard presents the case of a chemical plant which is
forced to introduce new substance manipulation processes,
in order to conform to current legal obligations;
the operational improvements are capitalized as
environmental assets, since the firm would not be able to
produce and sell its chemicals without these processes.
A selection of environmental assets:
(1) Anglo American: environmental rehabilitation trusts,
recorded in the balance sheet and recognized as longterm
assets;
(2) Antofagasta: investments incurred in the group’s
mining operations in reforestation, environmental monitoring,
archaeology and wildlife management plans;
(3) APPR: investments in operating motorways to reduce
environmental impacts and decrease risks related to
water, noise, rubbish, biodiversity and landscape;
(4) AstraZeneca: investments in laboratories to improve
the facilities for the evaluation of the environmental fate
and persistence of pharmaceuticals;
(5) BP: capital expenditure on the prevention, control,
abatement or elimination of air, water and solid waste
pollution;
(6) Imperial tobacco group: investments for reducing
energy consumption by replacing a steam-driven vacuum
chamber with an electrical-driven vacuum pump;
(7) Kazakhmys: commissioning of an acid plant for
reducing emissions; other investments on precipitators at
air turbines for improving the air quality;
(8) Scottish and south energy: development of an
offshore wind farm. It involves installation of wind turbines
and turbines in water depths; investments in refurbishing
and developing hydro-electric schemes;
(9) Severn Trent: investments to build modern sewer
network, to improve the infrastructure and to prevent
sewer flooding;
(10) Tesco: investments to install solar panels, wind
turbines and one store installation for solar generation;
(11) United utilities group: capital investments including
infrastructure renewals expenditure comprising water
services and waste water services;
(12) Vallourec: the group invested in projects directly
related for environmental compliance (fume and dust
filters and collection systems), safety improvements (fire
protection systems, gas systems), and improvements in
working conditions (lighting, heating and ventilation),
noise abatement and water recycling;
(13) Vedanta resources: capital expenditures to improve
operational efficiency, to modernize older plants to meet
company’s environmental goals; installation of a cleaner
tail gas treatment plant in order to reduce SO2 emissions
and results in zero waste;
(14) Xstrata: investments to develop improved methane
capture techniques.
Environmental provisions for decommissioning,
dismantling and restoration
This type of environmental provisions is recorded for
environmental long-term assets in accordance with IAS
16. The provisions for decommissioning and dismantling
are made for the value of costs relating to the
decommissioning of plant or other site restoration work,
and are incorporated into the value of the fixed asset.
Environmental restoration provisions are recorded when
the company has obligations to undertake restoration,
rehabilitation and environmental work, when environmental
disturbance is caused by the development or ongoing
production at the companies’ sites. These costs are
estimated at the beginning of the asset’s useful life, and
are assimilated to a provision in compliance with IAS 37.
Provisions for environmental clean-up and remediation

costs are based on current legal and constructive requirements,
technology, price levels and expected plans for
remediation. Actual costs and cash outflows can differ
from estimates because of changes in laws and regulations,
public expectations, prices, discovery and analysis
of site conditions and changes in clean-up technology.
The future expenses with dismantling and site restoration
may also be derived as a consequence of the continuous
use of an asset whose environmental impact is not
negligible. However, PriceWaterhouse Coopers (2004)
considers that, whenever environmental degradation is
outside the industrial parameters for the use of a certain
asset, the supplementary expenses should be incurred
immediately. Provisions for dis-mantling and clean-up
costs are persistent elements, that is, they are recognized
at one point in time and may be found unaltered for
several financial years in the balance sheet.
A selection of environmental provisions for decommissioning,
dismantling and restoration:
(1) Anglo American: obligations to undertake restoration,
rehabilitation and environmental work when environmental
disturbance is caused by mining property;
(2) ArcelorMittal: environmental provisions linked to
dismantling of steelmaking installations and soil treatment
of sites;
(3) BG Group: provision of decommissioning related to
the end of the producing lives of fields;
(4) EDF: provisions for long-term radioactive waste
removal and storage of radioactive waste resulting from
decommissioning of regulated nuclear installations; longterm
and direct storage of spent fuel that cannot be
recycled on an industrial scale in existing installations:
plutonium or uranium fuel derived from enriched
processing;
(5) Eramet: provisions for restoration of mining sites, for
dismantling facilities and replanting sites;
(6) France Telecom: provisions for decommissioning and
rehabilitation of sites for restoring mobile telephony
antennae, dismantling telephone poles, management of
waste electronic equipment;
(7) GDF Suez: provisions for dismantling nuclear facilities
and provisions for nuclear fuel reprocessing and storage;
provisions for rehabilitating land on which former gas
production plants were located: construction of infrastructure
(biogas recycling facility, installation of leachate
treatment facility) and demolition of installations used;
(8) Morrison Supermarkets: property provisions comprise
onerous leases provision, petrol filling station decommissioning
reserve and provisions for dilapidations on
leased buildings;
(9) Rio Tinto: close down and restoration expenditures
incurred at the end of the relevant operation.
Other environmental provisions and liabilities
This category is recorded in accordance with IAS 37
Dragomir and Anghel-ilcu 11273
Provisions, contingent liabilities and contingent assets.
IAS 37 proposes a definition for several elements which
are intimately linked with the prudence principle in
accounting. A provision is a liability whose value and date
of payment are uncertain and which is recognized whenever:
(a) the company has a current obligation (example
of an environmental nature) from a past event; (b) an
outflow of future economic benefits is to be expected in
this circumstance; and (c) a good estimate can be
provided for this obligation. Unlike ordinary liabilities, the
standard defines a constructive obligation as an uncertain
liability imposing the recognition of a provision. For
example, a company conducts its extractive operations in
a country with no environmental legislation. However, the
company has publicized its environmental policy, which
states that any remediation expenses arising from
polluting activities will be supported by the firm. In case
such incidents occur, the company has a constructive
obligation, and implicitly a provision, for the best estimate
of these future expenses. However, the standard does
not provide any details on the type and magnitude of an
event that is deemed to trigger a constructive obligation.
Environmental expenditures that relate to current or
future revenues are expensed or capitalized as appropriate.
Environmental liabilities are recognized when
environmental assessments or clean-ups are probable
and the associated costs can be reasonably estimated.
The amount recognized is the best estimate of the expenditure
required. Where the liability will not be settled for a
number of years, the amount recognized is the present
value of the estimated future expenditure. Environmental
provisions and liabilities are persistent elements, that is,
they are recognized at one point in time and may be
found unaltered for several financial years in the balance
sheet.
A selection of other environmental provisions:
(1) Air France-KLM: estimates of future costs related to
regulations concerning noise resulting in the alteration of
take-off and landing procedures and in flight path
diversions to avoid densely-populated areas around
Schiphol airport;
(2) ArcelorMittal: environmental provisions relating to
remediation of former coke plant sites and the capping
and monitoring of landfills or basins previously used for
residues and secondary materials; Environmental provision
to clean pond water and to meet the requirements
of the Luxembourg Environment Administration regarding
discharges in the water and also maintain sufficient cold
water reserves to permit the production of degassed steel
in warmer months;
(3) AstraZeneca: provisions for the estimated costs of
future environmental investigation, remediation and
operation and maintenance activity beyond normal
ongoing expenditure for maintaining the Group’s research
and development and manufacturing capacity and product
ranges; it is probable that such costs will be incurred

11274 Afr. J. Bus. Manage.
and can be estimated reliably.
(4) Renault: provisions that concern environmental compliance
costs for industrial land that the Group intends to
sell and expenses related to the EU directive on end-oflife
vehicles;
(5)Schneider: environmental provisions set aside to cover
reclamation costs;
(6) Vallourec: provisions for environmental risks that
cover the costs of soil treatment at industrial sites;
(7) Wolseley: provisions related to asbestos litigation
involving certain Group companies. Asbestos related
litigation is covered by insurance and accordingly an
equivalent insurance receivable has been recorded in
other receivables.
Environmental expenditure
This category comprises current operating expenditures
(immediately recognized in the income statement) carried
out by companies in relation with environmental protecttion
and amelioration. This type of expenditure includes
environmental insurance, R and D, studies, training, and
obtaining ISO 14001.
A selection of examples concerning environmental
expenditure:
(1) Aeroports de Paris: expenditures to reduce the negative
environmental impact and consists of land-scaping,
the treatment of surface runoff and collection of
elimination of non-hazardous and hazardous waste;
(2) Air France-KLM: environmental expenditures that
involve both soil and groundwater decontamination of
diverse traces of hydrocarbons, solvents and metal
deposits;
(3) Alstom: R&D programs for development of CO2
capture technologies and for the AGV, the last generation
of very high speed trains;
(4) APRR expenditure committed to mitigate the environmental
impact of the construction of a new motorway:
acoustic protection, water protection, waste processing,
landscaping;
(5) Bouygues: R&D programs that include energy efficiency
for both new and existing buildings, optimization of
the overall lifecycle cost, energy consumption commitments
based on thermodynamic and eco-neighborhoods;
(6) Danone: costs for reducing atmospheric emissions
and costs of waste treatment;
(7) EDF: R&D expenditures for environmental protection
relates to: nuclear, fossil-fired, hydro, energy ecoefficiency,
research into renewable energies, local impact
of climate change, other studies furthering knowledge of
environmental issues (biodiversity, water quality, noise
reduction);
(8) Legrand: Programs to raise employee awareness of
environmental issues;
(9) LVMH: Operation environment expenses related to
protection of the ambient, air and climate; waste water
management; protection and clean-up of the soil, underground
water and surface water; protection against noise
and vibrations; protection of biodiversity and the
landscape; protection against radiation;
(10) Saint-Gobain: salaries and other payroll expenses
for environmental officers; ISO 14001 and EMAS environmental
certification and renewal costs – this includes all
certification related expenses and charges for outside
consultants, internal and external training, the development
and upkeep of EMS and ISO 14001 systems,
audits, and meetings on the topic of certification
coordination and review;
(11) Veolia Environnment: compensation paid in execution
of legal decisions concerning the environment and
actions taken to repair environmental damage;
(12) Vinci: Expenditures for environmental protection: soil
remediation, cleaning and decontaminating structures;
maintenance of natural spaces; premiums for insurances
cover of environmental risks;
(13) Wolseley: expenditures for reducing emission from
copper and lead smelters.
Donations, sponsorship, taxes and fines
This category consists, on the one hand, of voluntary
environmental donations and sponsorship showing the
companies’ commitments toward the community and the
natural environment. On the other hand, the fines and
taxes paid for environmental purposes are disclosed in a
manner that demonstrates extreme attention for the
company’s public image. These payments are mandatory
for improving the companies’ public perception.
A selection of environmental donations, sponsorship,
fines and taxes:
(1) Accor: Environmental expenditures for organizing the
second Earth Guest Day, launching the Plant for the
Planet reforestation program, enhancing reporting
systems and supporting partnerships;
(2) Air France-KLM: Expenditures incurred to support
Good Planet/WWF project against deforestation in
Madagascar; tax paid to finance sound-proofing for
homes situated near airports and exposed to aircraft
noise;
(3) Associated British Foods: Nine environmental fines in
relation to failure to meet effluent standards, uncontrolled
releases to air and emissions of noise and dust;
(4) BT Group: a prosecution by the Environment Agency
resulted in fines and costs to erecting five telegraph poles
within a flood defense in UK;
(5) Cadbury: Expenditures incurred in respect with
charitable purposes: education and enterprise,
environment, health and welfare;
(6) Casino Guichard: eco-packaging tax and ecocontribution
on promotional brochures;
(7) Eurasian Natural Resources: The environmental
authorities in the Republic of Kazakhstan conduct regular

inspections at site operations; fines and penalties paid;
(8) GDF Suez: 53 complaints and eleven fines relating to
environmental damage;
(9) Johnson Matthey: a violation related to the selective
screening of wastewater samples for compliance analysis
ended with fines paid;
(10) Peugeot: the Group launched a host of initiatives
with local partners concerning topics as road safety, the
environment and assistance to victims of natural
disasters.
(11) Reckitt Benckiser: two environmental fines for
exceeding the wastewater discharge quality.
DISCUSSION AND LIMITATIONS
All aforementioned elements are a selection of a larger
quantity of monetary environmental disclosures within the
annual reports of British and French corporations. The
presentation of results has omitted irrelevant pieces of
information and identical elements to be found in the
financial notes of several companies. However, the
reader should be aware that the level of detail for the
extracted data is mostly the same level of detail relative
to the data source. That is, the content analysis in the
methodology was intended to capture the unaltered
wealth of information (or lack thereof) from the annual
accounts, in order to validate the two hypotheses of this
paper. Just as a reminder, the primary hypothesis was
referring to the voluntary aspect of environmental
accounting, while the corollary was addressing the
dubious quality of monetary environmental disclosures.
The voluntary aspect is in the middle of a theoretical
controversy. The philosophy behind IFRS induces the
idea that all reported elements should be material, in that
they should have an attached value above a certain
monetary threshold. The users of financial information will
never see the materiality principle in action, just its
effects. Therefore, no external user can be reasonably
certain that some environmental elements of particular
importance have not been left out due to the application
of the materiality principle. This reasoning applies to the
first two paragraphs of our analysis, concerning the
environmental assets and the provisions for dismantling
and decommissioning, which are usually included into the
cost of property, plant and equipment. For example, an
element such as “investments to develop improved
methane capture techniques” (Xstrata, FY 2008) cannot
be reasonably assessed by any external user, since there
is no additional information on the nature of the asset, its
useful life, its estimated benefits or the materiality
threshold for the recognition and disclosure of such
investments as long-term assets.
The dubious character of environmental disclosure in
monetary terms is particularly significant for such
elements as environmental protection expenditure and
other related costs. The very specific character of
Dragomir and Anghel-ilcu 11275
environmental contingencies, current expenditure and
related payments is a direct consequence of the implementation
of an environmental management system,
including the dedicated cost accounting. However, the
users of financial statements cannot be truly assured that
all relevant environmental expenditure was classified as
such, or that the environmental risks have been provided
with reasonable estimates based on past experience.
Moreover, there are several types of expenditure which
are not neutral from a reputational point of view: for
example, fines and taxes carry an inherent negative connotation,
while donations and sponsorship are perceived
as evidence of environmental responsibility. Prior literature
(Adams, 2004) has showed that some “qualitative”
monetary elements are usually omitted when reputational
costs are exceeding the benefits from the exercise of
transparency.
Reporting inertia – an overview of monetary
disclosures in environmental accounting
“Reporting inertia” is a phenomenon which accounts for a
lack of variability in the corporate disclosure quality and/
or quantity over longer periods of time. Our study proposes
a time span of four years, between financial year
2005/2006 and financial year 2008/2009. From a
regulatory point of view, the accounting disclosure
requirements for this period were relatively stable, but the
reader must be aware that the beginning of the period
was right after the introduction of IFRS in Europe.
Beginning with 1 January 2005, companies listed on
European Union stock exchanges were required to provide
a complete set of financial statements in accordance
with IFRS, besides their individual financial statements in
compliance with national accounting standards. For many
companies, this has been a first encounter with IFRS and
their disclosure requirements, and that is why we would
have expected an increasing trend in corporate reporting.
For the purpose of this statistical analysis, we have used
the following abbreviations for the five classes of
accounting elements, as determined by content analysis.
(1) Environmental assets and investments – EAS;
(2) Environmental provisions for decommissioning,
dismantling and restoration – DRP;
(3) Other environmental provisions and liabilities – OEP;
(4) Environmental expenditure – EXP;
(5) Donations, sponsorship, taxes and fines – DST.
As indicated in the methodological aspects, we are
interested in testing the following hypothesis: The quantity
of environmental disclosure pertaining to a specific
class of accounting elements has a low variability over
time, that is, the frequency of reported accounting
elements stays approximately the same over the four
financial years.

11276 Afr. J. Bus. Manage.
Table 3. The frequency of environmental accounting elements present in the annual reports of European companies.
Environmental accounting
elements (count)
FY 2005/2006
(93 companies)
FY 2006/2007
(94 companies)
FY 2007/2008
(97 companies)
FY 2008/2009
(100 companies)
EAS 29 31 32 35
DRP 26 25 25 32
OEP 31 29 32 29
EXP 35 37 37 42
DST 23 20 29 31
The presence of environmental accounting elements
expressed in monetary form was coded in five binary
variables, corresponding to the five classes of accounting
elements. In other words, the dichotomous variables do
not capture environmental reporting quantity, but the
mere presence of the five types of environmental
accounting elements in the annual reports. One company
may have several elements of environmental expenditure
in one year’s report, but their presence is coded only
once within the binary variable. For the entire sample, the
counts in Table 3 indicate the frequency of environmental
accounting elements, for each financial year. This crosstabulation
is used only for descriptive purposes, as the
quantitative procedures described hereafter were
conducted on a reduced dataset.
The statistical analysis employed here would only be
viable when applied on a balanced panel, which required
the elimination of 7 companies which had no annual
reports for at least one financial year. Thus, as mentioned
in the sample description, the final dataset contains 93
companies, with a total of 372 observations.
The “reporting inertia” hypothesis was tested using a
nonparametric test for related samples, Cochran’s Q,
which tests the hypothesis that several related dichotomous
variables have the same mean. The null hypothesis
for the Cochran's Q test is that there are no differences
between the variables (Sheskin, 2004). If the calculated
probability is low (p < 0.05) the null-hypothesis is rejected
and it can be concluded that the proportions in at least
two of the variables are significantly different from each
other. In our case, the related dichotomous variables are
observations of a certain type of environmental accounting
elements, for each financial year. This means that
there are four related variables (that is one for each
financial year), for each of the five types of environmental
accounting elements (that is environmental assets and
investment).
The descriptive statistics presented in Table 4 are
frequencies of environmental accounting elements present
in the annual financial and sustainability reports of
the sample companies. The last two columns present the
results of the nonparametric Cochran’s Q, where an
asymptotic significance larger than .05 indicates that we
cannot reject the null hypothesis that there is a “reporting
inertia”, that is a lack of variability in the environmental
reporting practices of our sample companies.
Overall, the results presented in Table 4 indicate that
we cannot reject the null hypothesis of pronounced
similarity between the reporting practices of sample
companies over the four financial years. Therefore, the
afore statistical analysis presented suggests one important
aspect of environmental accounting: there is no
significant temporal dimension of this type of disclosure.
Environmental reporting does not improve over time,
mainly because it possesses an “inertia” which firms are
reluctant to overcome. This inertia may be due to three
factors, which often work in conjunction:
(1) Firms have a fixed reporting pattern. Content analysis
has revealed that annual financial and sustainability
reports do not change in their informational content from
year to year. Each firm has developed its own reporting
format, which is used for a long period of time (more than
five years, as our sample suggests), updating only the
financial numbers and other numerical indicators. In
some cases, some vague assertions – for example “GSK
and its heritage companies have spent more than £100
million cleaning up more than 50 sites in the US over the
last 20 years” (GlaxoSmithKline, FY 2008) – are retained
for a number of years, in each subsequent report. For the
purpose of providing descriptive statistics regarding
environmental accounting elements, we have included
such financially quantifiable items in the count presented
above, even if some are only pseudo-examples of
environmental accounting.
(2) Corporate environmental protection activities are
scarce or the environmental management system does
not produce sufficient information to indicate otherwise.
Firms belonging to certain environmentally-sensitive
sectors are sometimes caught in a posture of weak environmental
reporters, mainly because their management
systems do not produce relevant financially quantifiable
environmental information. This is not surprising, since
we have demonstrated that environmental accounting
standards are not as well developed or reliable as they
should be, in order to force the creation of a specialized
environmental accounting department within each company.
In other cases, however, environmental protection
is a peripheral activity whose results are not deemed
worthy to be internally quantified, and which is only mentioned
briefly in some annual financial and sustainability
reports.

Table 4. Descriptive statistics and the results of Cochran’s Q for testing the “reporting inertia” hypothesis.
Dragomir and Anghel-ilcu 11277
Environmental accounting elements The frequency of environmental accounting elements for each financial year 2005/2006 2006/2007 2007/2008 2008/2009
Value of
Cochran’s Q
Asymptotic
significance
EAS 29 31 30 34 3.5 .321 > .05
DRP 26 25 25 29 5.16 .16 > .05
OEP 31 29 31 27 3.47 .324 > .05
EXP 35 37 36 40 1.78 .618 > .05
DST 23 20 28 28 7.48 .058 > .05
Note. Sample size: 372 observations for 93 companies. There are 93 observations for each financial year. The significance level for Cochran’s Q is 0.05. The tests are two-tailed.
(3) The persistent character of some accounting
elements, for example, provisions for decommissioning,
dismantling and site restoration. This
type of provisions is recorded at the beginning of
the useful life of an asset, and remains intact
throughout the whole period, which is presumably
long-term. These accounting elements may not
have increases or decreases for many years, yet
they are presented each year in the notes to the
annual accounts. The same applies to other
environmental provisions (example for legal risks),
which may not be used until definitive court sentences
have been given on environment-related
cases against the firm. These elements are also
part of the “reporting inertia” phenomenon,
although these elements are persistent by nature,
and cannot be altered until some external event
triggers an expenditure to settle an existing obligation
(example for environmental protection, or
for dismantling of assets).
A major limitation of this type of analysis – and
of the corresponding statistical results – is that the
researcher cannot actually determine which of the
above scenarios is actually explaining the scarcity
of environmental accounting information in corporate
annual reports. The researcher can at most
find proof of a “reporting inertia” which may last
even for a decade, but cannot pinpoint the exact
causes for this inertia. On the other hand, the
researcher cannot exclude items of environmental
disclosure from one year’s report, simply
because they were present in a quasi-identical
form in prior annual reports. Therefore, we can
conclude that the “reporting inertia” is distorting
the results of content analysis, whenever we try to
add a temporal dimension to environmental
accounting disclosure. In other words, the
temporal dimension appears to be irrelevant to
this type of qualitative analysis, even if it had
appeared promising in discovering the evolution of
corporate disclosure in connection with radical
changes in the international accounting context
(that is, the adoption of IFRS for use in the
European Union for listed companies).
An analysis of national differences relative to
environmental disclosures
The reviewed literature has proposed a discussion
on national differences regarding accounting cultures.
We have advanced the idea that the two
countries selected for our study also stand for two
contrasting accounting paradigms, that is, the
Continental (French) vs. the Anglo-Saxon (UK)
accounting culture. Therefore, a logical extension
of our research would be to seek whether these
differences actually exist when it comes to
environmental disclosure, and to quantify the
magnitude of these differences.
We are relying on the same classification of
environmental accounting elements: environmental
assets and investment (EAS); provisions
for dismantling and decommissioning (DRP);
other environmental provision and liabilities
(OEP); environmental expenditure (EXP); and
donations, sponsorship, taxes and fines (DST).
Using the same balanced panel of 93 companies
(372 observations), we simply split the sample
according to their country of incorporation (France
– 46 companies and 184 observations; UK – 47
companies and 188 observations), and used
these two samples for group comparisons.
The most appropriate statistical tool for
comparing two independent samples with binary
responses is the Mann–Whitney U test, which is
one of the best-known non-parametric significance
tests. The null hypothesis would be that
the quantity of environmental elements in the
annual reports of French companies does not
significantly differ from the quantity of environmental
disclosure exhibited by UK companies.
The test involves the calculation of a statistic,
usually called U, which can be approximated
using the normal distribution for larger samples
(such as the present one).
The results presented in Table 5 are based on

11278 Afr. J. Bus. Manage.
Table 5. The frequency of environmental accounting elements, with a comparison between the two countries
Environmental
accounting elements
The quantity of environmental disclosure within
the two groups, for the whole period (4 years)
France (184 obs.) UK (188 obs.) Statistic U Z
Non-parametric significance test:
Mann-Whitney U
Asymptotic
significance
EAS 66 58 16428 -1.025 0.305 > .05
DRP 38 67 14704 -3.206 0.001 < .05
OEP 66 52 15876 -1.699 0.089 > .05
EXP 95 53 13242 -4.612 0.001 < .05
DST 24 75 12652 -5.821 0.001 < .05
Note. Sample size: 372 observations for 93 companies. The significance level for Mann-Whitney U is 0.05. The tests are two-tailed.
the frequency of environmental accounting elements
present in the annual reports of companies from the two
countries. Whenever the significance of the U statistic is
below the 0.05 threshold, we can reject the null
hypothesis and confirm the fact that the two accounting
paradigms are divergent in respect to environmental
accounting.
As indicated by the results in Table 5, the differences
between national accounting cultures are not equally
visible for the whole spectrum of environmental accounting
elements. We will therefore discuss the magnitude of
these differences in relation to the previous presentation
of national standards regarding environmental accounting.
The national accounting regulations, both for France
and the UK, are the main drivers for the configuration of a
national accounting culture. The underlying assumption,
as proven by the literature (Feleagă et al., 2010), is that
national accounting culture has a significant influence of
the recognition and measurement of accounting
elements, even in supposedly uniform reporting
environments, such as the IFRS. In other words, national
standards exert a strong influence on the presentation of
annual financial statements and reports.
The following discussion will attempt to provide an
explanation for the differing patterns of environmental
disclosure with reference to the national accounting
standards regarding environmental reporting,
The recognition of environmental assets and investments
(EAS) appears not to be influenced by national
accounting culture, since we cannot reject the null
hypothesis (p = .305). This fact can be explained by an
overall compatibility between national regulation and
IFRS (that is, more specifically IAS 16) on the recognition
and treatment of environmental assets. The relevant
accounting policies regarding the capitalization of
environmental protection expenditure have been
implemented by the French and British standard-setters
in a similar manner to that of the IFRS, indicating a
successful accounting convergence process on this
specific topic.
The treatment of environmental provisions for decommissioning
and site restoration (DRP) is part of an
established accounting policy within the Anglo-Saxon
paradigm.
The results indicate that UK companies have
recognized significantly more elements of this type in
their financial accounts (p < 0.001), suggesting that, even
if French companies were also reporting in compliance
with IFRS, national accounting culture was a strong
barrier to the recognition of such elements. It is also
worth mentioning that several British accounting standards
are an almost identical version of the respective
International Accounting Standards (here including IAS
16 and IAS 37), and that the philosophy of IFRS has had
the best means to penetrate the British accounting
culture.
The frequency of other environmental provisions (that
is for environmental litigation and various environmental
costs) is slightly more reduced for British companies, but
not enough to generate a significant difference between
the two national accounting cultures (p > 0.05). However,
this phenomenon can be explained by the pronounced
conservatism within the Continental accounting paradigm
(Feleagă et al., 2010). The literature has proven that
companies from Continental European countries have a
propensity towards recognizing provisions for risks and
charges, even in excess of the demands of IAS 37.
Environmental expenditure (EXP) and sponsorship,
taxes and fines (DST) are accounting elements to be
found in the income statement, and which immediately
affect the financial performance of a firm (that is, the
bottom line figure). The results indicate that there are
significant differences between companies from the two
countries, but we cannot find a consistent pattern for the
recognition of these elements. We propose the following
explanation: all these expenditure elements (environmental
protection costs, taxes, fines, sponsorship and
donations) are explicitly or implicitly connected with the
regulatory environment. The government and nongovernmental
organizations may demand environmental
actions and commitments from these companies.
Companies may conform to these requirements, and
therefore record environmental protection expenditure, or
may be indifferent to such request, and thus be forced to
pay fines and taxes for non-compliance. In other words,
we suggest that these two categories of environmental

element may be complementary and in an inverse
relationship: more of one means the less of the other. A
certain balance regarding the recognition and occurrence
of such elements is mainly due to an external regulatory
context, which acts as an arbiter between the types of
environmental expenditure a company records in its
annual accounts.
A limitation of this type of analysis is that the profiles of
the companies from our sample are not perfectly
matched. The discussion so far has speculated on the
significant influence exerted by national accounting
culture. However, we have to admit that there are other
factors which could modulate this influence. For example,
the industrial sector of each company may impose
specific requirement regarding the type of environmental
expenditures, the optimal degree of conservatism
(through the recognition of provisions), or the existence of
environmental assets and capitalized environmental expenditure.
It is perfectly natural for firms in some sectors
not to record a certain type of environmental accounting
element (for example, provisions for decommissioning
and site restoration), while for others to be explicitly
required to recognize such elements (for example
respectively, for firms in the mining sector). On the other
hand, the maturity of a company in its sector may also
influence its sensitivity to the existence of such environmental
elements. Older and more prudent firms may find
it extremely useful to recognize and accurately measure
provisions for environmental litigation, while younger
firms may prefer to immediately expense such elements.
The accounting policies recommended by IFRS are
flexible enough to accommodate all these options, while
admitting that this regulatory regime does not necessarily
insure comparability between economic entities.
National accounting standards, which are compulsory
for all companies irrespective of size, are generally not
supportive of environmental accounting. In either France
or the UK, the implementation of European Commission’s
recommendation on environmental accounting elements
(EC, 2001) has been unsatisfactory. Moreover, the IFRS
do not specifically address such issues as environmental
expenditure or investments. A significant amount of
environmental accounting data (that is, specific costing
allocated to products and processes) is produced through
an environmental management system; the rest can be
extracted from financial accounting documentation,
especially regarding those elements under the auspice of
IFRS (that is, environmental provisions). However, these
two information systems are not decoupled from one
another. Environmental risks are also taken into account
by corporate policies, while environmental costing should
be eventually reflected into such indicators as operational
expenses or turnover. It is very difficult to assess the
separate influence of individual factors (such as the
national accounting standards, or the existence of a
specialized management system) on the quantity of
environmental disclosure present in the annual accounts
of European corporations.
Conclusions
Dragomir and Anghel-ilcu 11279
The contribution of this study is three-fold: firstly, we have
devised a qualitative assessment of environmental
accounting elements to be found in the annual financial
and sustainability reports of 100 European companies;
secondly, we have provided quantitative evidence on the
existence of a phenomenon called “reporting inertia”,
which produces a time-invariant quantity of environmental
accounting elements; thirdly, we have conducted statistical
tests aiming to describe the magnitude of the
influence national standardization has on the accounting
policies chosen by French and British companies.
The results of the qualitative inquiry through content
analysis of annual reports have revealed that companies
have low levels of environmental reporting in monetary
terms. Some elements of environmental accounting are
compulsory through the application of the international
financial reporting standards (IFRS), while others are not
in the least regulated by generally accepted standards. A
classification of environmental accounting elements was
provided based on the relevant international standards
and accounting policies: environmental assets and
investments; provisions for decommissioning and site
restoration; other environmental provisions;
environmental expenditure; and environmental donations,
sponsorship, fines and taxes. The qualitative inquiry
indicates that managers and accountants have a
significant amount of discretion when it comes to what
environmental elements to recognize and how to
measure and report them. Sector differences, maturity of
the firm and reputation issues may add to the complexity
of this picture, which can be eventually described as
lacking detail and relevance to external stakeholders.
The “reporting inertia” is a phenomenon which has never
been linked with environmental disclosure before.
However, both from the qualitative side of our inquiry and
from the quantitative evidence, this phenomenon appear
as extremely relevant for environmental accounting.
“Reporting inertia” refers to a certain approach to corporate
environmental reporting, where companies are using
prefabricated phrases and paragraphs to report almost
the same monetary elements year after year, for long
periods of time. Clearly, this type of environmental
reporting does not answer the informational demands of
stakeholders, since it is almost impossible for external
environmental challenges to be as immutable as corporate
reports present them. Using inferential statistics on a
balanced sample of 93 firms (372 observations), we have
proven that there are no significant differences in the
proportion of environmental accounting elements
extracted from the sample companies’ annual financial
and sustainability reports for a period of four financial
years.
In the last stage of our analysis, we wanted to test

11280 Afr. J. Bus. Manage.
whether the two contrasting accounting paradigms – the
Continental European versus the Anglo-Saxon paradigm
– are truly opposite when it comes to environmental
reporting in monetary terms. Using 46 companies from
France (a country implementing the former paradigm)
and 47 companies from the UK (representing the letter),
we used our previously created balanced sample with
observations for four years to conduct group comparison
tests on all five categories of accounting elements. With
regard to environmental assets and other environmental
provisions, we have found no significant differences
between the two groups of companies, therefore
suggesting that the international accounting convergence
process was successful. On the matter of environmental
provisions for decommissioning and site restoration,
which traditionally belongs to the Anglo-Saxon paradigm,
companies in the UK disclose significantly larger quantities
of information than their Continental counterparts,
while on the matter of diverse environmental costs, the
reporting patterns are divergent (that is, French
companies report significantly more environmental expenditure,
while UK companies recognize more elements
classified as donations, sponsorship, fines and taxes).
The contributions of this paper and the discussions
presented indicates that the comparative perspective on
environmental accounting should not be neglected.
Environmental reporting standards are more difficult to
develop than financial reporting standards, considering
that the external stakeholder groups (for example,
governmental agencies, ecological organizations, the
investors, the communities or the public) have diverse
information needs. In the current context, where most
environmental reporting is voluntary, companies are able
to experiment with format and content, with a goal of
providing information to satisfy the greatest number of
stakeholders. Furthermore, multiple reporting formats
allow for the identification of best practices which could
serve as useful input for the development of standards.
However, the voluntary nature of environmental reporting
leads to specific “window-dressing” and “greenwashing”,
when opting only for good news or misleading information,
such as by presenting cost cuts as reductions in the
use of resources. The introduction of standards into a
system of voluntary reporting would serve to level the
playing field. Finally, in the absence of relevant and
detailed standards, independent verification of
environmental reporting is problematic.
ACKNOWLEDGEMENTS
This article was funded by CNCSIS-UEFISCSU project
type PN II-RU code PD 640/2010.
REFERENCES
Adams CA (2004). The ethical, social and environmental reporting
performance portrayal gap. Account. Audit. Accountab. J., 17(5):
731-757.
Alexander D, Archer S (2000). On the Myth of Anglo-Saxon Financial
Accounting, Int. J. Account., 35(4): 539-557
ASB (2010). Financial Reporting Standards (FRSs) issued by the
Accounting Standards Board,
Bedford NM (1970). The future of accounting in a changing society.
Champaign, IL: Stipes Publishing Co.
Cairns D (2004). The implications of IAS/IFRS for UK companies, Int. J.
Disclosure Govern., 1(2): 107-118
Christensen HB, Lee E, Walker M (2007). Cross-sectional variation in
the economic consequences of international accounting
harmonization: The case of mandatory IFRS adoption in the UK, Int.
J. Account., 42: 341-379
Cormier D, Gordon IM (2001). An examination of social and
environmental reporting strategies, Account. Audit. Accountab. J.,
14(5): 587-616
CRC (2007). General Accounting Plan, 1999 – Plan Comptable
Général, Règlement n°99-03 du 29 avril 1999 du Comité de la
réglementation comptable – CRC, last amended 2007,
http://www.anc.gouv.fr/
D’Arcy A (2001). Accounting classification and the international
harmonization debate – an empirical investigation. Account. Organ.
Soc., 26: 327-349
De Beer P, Friend F (2006). Environmental accounting: A management
tool for enhancing corporate environmental and economic
performance, Ecol. Econ., 58: 548-560
Deegan C (2002). The legitimising effect of social and environmental
disclosures: a theoretical foundation, Account. Audit. Accountab. J.,
15(3): 282-311.
Ding Y, Hope O.-K, Jeanjean T, Stolowy H (2007). Differences between
domestic accounting standards and IAS: Measurement, determinants
and implications, J. Account. Pub. Policy, 26: 1–38
Dragomir VD (2008). Eco-management and the paradigm of selfregulation.
Environ. Eng. Manage. J., 7: 427-431
Dragomir VD, Ilcu ER (2008). Corporate governance in the European
Union: a timeline of regulatory transformations, Theoretical and
applied economics / Conference Supplement, pp.154-160, available
at: http://www.ectap.ro/documente/suplimente/
Finante_nov_2008_en.pdf.
EC (2001). Commission Recommendation of 30 May 2001 on the
recognition, measurement and disclosure of environmental issues in
the annual accounts and annual reports of companies, Brussels,
2001/453/EC, OJ L 156 of 13.6.2001.
EC (2008). Commission Regulation No 1126/2008 of 3 November 2008
adopting certain international accounting standards in accordance
with Regulation (EC) No 1606/2002 of the European Parliament and
of the Council, Brussels, OJ L 320, p.1.
Elkington J (1998). Cannibals with forks: The triple bottom line of 21st
century business. Stony Creek, CT: New Society Publishers.
EPA (1995). An introduction to environmental accounting as a business
management tool: key concepts and terms, United States
Environmental Protection Agency, Office of pollution Prevention and
Toxics, Washington, http://www.epa.gov.
EPA (1996). Valuing potential environmental liabilities for managerial
decision-making: a review of available techniques, United States
Environmental Protection Agency, Office of pollution Prevention and
Toxics, Washington, http://www.epa.gov.
Eurostat (2010). Gross domestic product at market prices.
http://epp.eurostat.ec.europa.eu.
Feleagă L, Dragomir VD, Feleagă N (2010). National Accounting
Culture and Empirical Evidence on the Application of Conservatism,
Econ. Comput. Cybern. Stud. Res., 3: 43-60
Fleischman RK, Schuele K, (2006). Green accounting: A primer. J.
Account. Educ., 24: 35-66
Gray R, Walters D, Bebbington J, Thompson I, (1995). The greening of
enterprise: an exploration of the (non) role of environmental
accounting and environmental accountants in organisational change.
Crit. Perspect. Account., 6: 211-239.
Gray R (1994). Corporate reporting for sustainable development:
Accounting for sustainability in 2000 AD, Environ. Values, pp. 17-45.
Lamberton G (2005). Sustainability accounting – a brief history and
conceptual framework, Account. Forum, 29: 7-26

Letmathe P, Doost RK (2000). Environmental cost accounting and
auditing. Manager. Audit. J., 15/8: 424-430
Milne MJ (1991). Accounting, environmental resource values, and nonmarket
valuation techniques for environmental resources: a review,
Account., Audit. Accountabil. J., 4(3): 81-109
PriceWaterhouse Coopers (2004). IFRS 2005. Divergences France/
IFRS Tous secteurs (y compris Banques et Assurances). Editions
Francis Lefebvre, pp. 741-742
Sheskin DJ (2004). Handbook of parametric and nonparametric
statistical procedures, 3rd ed. Boca Raton: Chapman & Hall /CRC
Stolowy H, Ding Y (2003). Regulatory flexibility and management
opportunism in the choice of alternative accounting standards: an
illustration based on large French groups. Int. J. Account., 38: 195–
213
Dragomir and Anghel-ilcu 11281
Street DL, Gray SJ (2002). Factors influencing the extent of corporate
compliance with International Accounting Standards: summary of a
research monograph, J. Int. Account., Audit. Taxation, 11: 51–76
WFE (2010). Domestic Market Capitalization January 2010, World
Federation of Exchanges. http://www.world-exchanges.org.
Wycherley I (1997). Environmental managers and accounting, J. Appl.
Manage. Stud., 6(2): 169-184
Yakhou M, Dorweiler VP (2004), Environmental accounting: an
essential component of business strategy. Bus. Strateg. Environ., 13:
65–77

11282 Afr. J. Bus. Manage.
APPENDIX
Appendix 1. The sample companies (100 items).
- - -
- - -
- -
- -
- - MM -
-
- - -
- - -
- - -
Antofagas - - -
- - -
-
- - -
-
- Imerys - -
AstraZeneca (LSE) - HP - -
- - -
- - Severn Trent (L -
- - -
- - -
- - -
- - Rt -
- - -
- - -
- - -
- L'Oreal ( - -
- - -
- - -
- - -
Casino Guichar - - -
- - -
- - -
- - -
-
- - -
- - -
- - -
-
-
Randgold Res. - -
The primary stock exchanges are: LSE – London stock exchange; PAR – Paris stock exchange; The abbreviations for the industries
are the following: Aerospace and defense – AD; Autos and transport equipment – ATE; Chemicals – Ch; Construction and materials
– CM; Consumer products - non-food – CP; Consumer products - food, beverages – CPf; Entertainment and leisure – EL; Health and
pharmaceuticals – HP; IT, Information technology – IT; Manufacturing – Mf; Mining and metals – MM; Oil and gas – OG; Retail – Rt;
Telecom – Tl; Transportation – Tr; Utilities – Ut.

African Journal of Business Management Vol. 5(28), pp. 11345-11351, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.061
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Factors affecting process orientation in IranianSocial
security organization’s hospitals
Somayeh Hessam* 1 , Shaghayegh Vahdat 1 and Shahaboddin Shamshirband 2
1 Department of Health Services Administration, Science and Research Branch, Islamic Azad University, Fars, Iran.
2 Department of Basic Science, Islamic Azad University (IAU), Chalous, Iran.
Accepted 16 August, 2011
Process orientation was one of the new approaches in managerial topics, which were recently
considered by the Iranian healthcare centers. The study’s aim is to identifies the main factors of
process orientation in health care management arena in Iranian hospitals. To achieve this objective, the
main variables were recognized and the questionnaire was determined. Iranian Social security
organization’s hospitals were chosen as a sample among all Iranian healthcare centers (n=48).The
validity of questionnaire were assured with expert judgment and the reliability was determined using
Cronbach’s alpha and Pearson correlation (1 st and 2 nd times). Cronbach’s alpha coefficient and Pearson
correlation was respectively as 0.819 and (p

11346 Afr. J. Bus. Manage.
(2002) further argue that, process management and its
associated set of managerial practices and programs (for
example, total quality management, Six Sigma, ISO
9000) is perhaps the most important managerial
innovation of the last 20 years.
A business process is a complete, dynamically
coordinated set of activities or logically related tasks that
must be performed to deliver value to customers or to
fulfill other strategic goals (Strnadl, 2006). Various
empirical researches indicate that there is a positive
correlation between process management and business
success (Skerlavaj et al., 2007; Trkman, 2010).
Customer satisfaction, quality issues and managing
change are crucial factors in the current ever-expanding
competitive business environment. For many organizations,
implementation of a process approach represents a
fundamental step in their management systems, which
means a fundamental change from a functionally oriented
organization to a process oriented system (Balzarova et
al., 2004).
Processes associated with twenty-first century products
and services are far more complicated than pins (Pin
factory of Smith), and require many more tasks.
Managing and coordinating these tasks are very difficult
(Kim and Ramkaran, 2004).
An effective and integrated management is even more
important in those fields where business justifications are
coupled with compulsory safety constraints, in order to
care for the end-users (Carmignani, 2008).
All activities within an organization can be described in
terms of processes. They have some stimulus, they bring
about some change, and they use some resources. The
inputs and outputs of the process can be described and,
to varying degrees, measured. The origins of the
process-based view of the organization (business
process management) are predominantly operational and
predominantly concerned with managing flows of material
people or information. Improvement of the processes has
focused on attempts to change practices to be more
responsive to customers and to improve performance in
quality, time, speed and reliability, while reducing
production costs (Armistead, 1999). Basically, this can be
seen as an industrial view on business processes, where
input (raw material) is transformed into output (finished
products) (Goldkuhl and Lind, 2008).
Business process is a field of knowledge at the
intersection of management and information technology,
encompassing methods, techniques and tools to design,
enact, control and analyze operational business
processes involving humans, organizations, applications,
documents and other sources of information. Business
process management follows a life cycle consisting of
four phases, namely: design (modeling), implementation,
enactment and analysis (Filipowska et al., 2009).
Improving organizational efficiency and effectiveness
inevitably involves process improvement. Over the last 25
years, a variety of business process improvement (BPI)
methodologies and frameworks has been proposed and
sometimes applied (Dalmaris et al., 2007).
Biazzo and Bernardi (2003) argued that, today
managers are enthusiastic and indeed impassioned
about processes because of the possibilities for change
that they offer in terms of both reducing the fragmentation
and compartmentalisation of work and improving the
capacity for lateral coordination and communication.
The competitiveness of a company is mostly dependent
on its ability to perform well in dimensions such as cost,
quality, delivery dependability and speed, innovation and
flexibility to adapt itself to variations in demand. While
alignment of operations with strategic priorities is core to
competitiveness, the continuous improvement of operation
processes plays a very important complementary
role in quest of competitiveness in the long run (Alam et
al., 2010). Continuous improvement has been defined as
a company-wide process of focused and continuous
incremental innovation (Carpinetti et al., 2003).
The ability of organizations to successfully deploy
appropriate business processes relies heavily upon 1- the
effectiveness of systems that support the management of
constantly evolving business processes that support the
current set of business needs, and 2- the ability of
process participants to understand and reason about the
constantly evolving business processes. This requires
that the fit between business processes (BP) and
systems that support the management of business
processes be continuously maintained and evolved
(Ramesh et al., 2005).
Over the years, process management has become
more important for manufacturing. Today and in the
future, the service sector (like healthcare centers) will
also increasingly use process management techniques
and technologies for health care, banking, government
and retail. The trend in coming years will be, for example,
lean and six sigma, process statistics, control principles,
and business process management underpinning
process management tasks (Reijers, 2006; Paim et al.,
2008).
Hamidi (2001) mentioned that, although numerous
hospitals have developed ongoing programs for the
implementing process orientation approaches, and many
process orientation surveys have been published in the
arena of health care management, few researches has
been devoted to factors that may affect process
orientation. Also, Nikniaz (2002) said that, peer-reviewed
health-related journals publish numerous articles in terms
of process orientation studies each year, but few of them
were attended to main factors of this approach in health
care management arena. Even though some of
healthcare centers are applying process orientation, but it
can be said that even though some of Iranian healthcare
centers are applying process orientation approach, the
factors of process orientation have not yet been studied
in these organizations and factors associated with the
process orientation in Iranian hospitals are not recognized

Table 1. Frequency distribution of research community in accordance with demographic characteristics.
Demographic factors Abundance (%)
Sex Female 44
Male 56
Age groups Less than 30 years old 6
30-39 54
40-49 37
50 years and older 3
Educational degree Associates degree 10
Bachelor degree 59
Higher 31
Acquaintance with process orientation topics Very high 14
High 47
Medium 33
Low 3
Very low 3
Participation in training courses in the field of
process management
now. Therefore, the present research was performed in
health care management arena with objective such as
the identification of factors having effects on process
orientation in the Iranian Social security organization’s
hospitals and the research question was “what were the
main factors that affect process orientation in the Iranian
Social security organization’s hospitals?”
MATERIALS AND METHODS
First, in order to collect data, the researchers developed a questionnaire
based on variables impacting process orientation in health
management arena in hospitals. To assess the validity of the
questionnaire, expert judgment and interview with expert method
was applied. So the designed questionnaire, along with explanations
regarding terms and concepts were presented to five
university professors, three managers in the ministry of health, and
two persons in charge of quality improvement in hospitals, and they
were asked to express their views about its construct, content,
formal appearance, and writing mode. The necessary amendments
were then made and it was finally confirmed by other experts, its
content validity and construct validity were assured. Iranian Social
security organization’s hospitals (as the greatest public nongovernmental
health institution in Iran) which had applied at least
one of the process orientated models, were chosen as a sample
among all Iranian healthcare centers (n=48).
To determine the reliability of the questionnaire, it was sent to all
the mentioned hospitals. The questionnaire was filled out by the
research community two times with an interval of 14 days. The
members of research community were in charge of quality improvement
of the social security organization’s hospitals. After the
Less than 10 h 7
From 10 to 20 h 17
From 20 to 30 h 14
More than 30 h 62
Tabibi et al. 11347
mentioned questionnaires had been filled out, the reliability of the
questionnaire was determined using Cronbach’s alpha and Pearson
correlation (1 st and 2 nd times). Cronbach’s alpha coefficient of the
component “process orientation”, was 0.819 and Pearson
correlation was (p

11348 Afr. J. Bus. Manage.
Table 2. Recycled matrix of factors.
Process orientation
Code Variable 1 st factor 2 nd factor 3 rd factor t-value R 2
F1 Identification of processes 0.640 4.70* 0.29
F2 Design processes 0.581 3.87* 0.31
F3 Identification of sequence and interaction of processes 0.692 4.17* 0.52
F4 Determination of indexes and their standards for control of
processes
0.438 0.545 3.51* 0.68
F5 Provision of resources to support the implementation of processes 0.559 3.99* 0.58
F6 Availability of information for the implementation of processes 0.439 4.59* 0.39
F7 Control, measurement and analysis of processes 0.668 4.01* 0.57
F8 Revision and improvement of processes 0.774 4.70* 0.87
F9 Identification of people in charge of improvement of processes 0.554 4.46* 0.41
F10 Flexibility of processes for consistency with the requirements and
requests of customers
0.999 3.16* 0.69
F12 Observance of standard service for designing of processes 0.972 -0.41 1.17
* t>1.96
than half of them have participated in training course of
process management for more than 30 hours (Table 1).
In the first step, the correlation of each identified
variables, and internal consistency of all variables were
calculated in the component ‘process orientation’. The
correlation of variable “objective copying the processes of
similar hospitals” was negative and the correlation of
variable “comparison of processes with those of other
similar hospitals” with all variables was small. Therefore,
these two variables were omitted.
In the next step and before explanatory factor analysis,
the Kaiser-Meyer-Olkin approach was used to determine
the sufficiency of sample size for the component and
Bartlet test of sphericity was used to establish whether
the correlation matrix has meaningful difference with zero
or not. The sufficiency of sampling and meaningfulness of
the correlation matrix for the component “process
orientation” were respectively: 0.741 and p

Figure 1. Path diagram of process orientation component.
Table 3. Fitness indexes calculated for the component.
Tabibi et al. 11349
Component/index RMSEA GF CFI NFI NNFI IFI RFI AGFI RMR X 2
P-valve
process orientation 0.050 0.92 0.94 0.90 0.92 0.94 0.87 0.88 0.034 44.18 p>0.05
same category as that of the variables of the 2 nd factor,
all forming a single factor and finally 2 factors were
extracted from the process orientation component.
The fitness indexes of GFI, CFI, NFI, NNFI, IFI and
RMR were respectively 0.92, 0.94, 0.90, 0.92, 0.94, and
0.034 for the component “process orientation’’ and Pvalue
was more than 0.05. The findings achieved from
the confirmatory factor analysis showed that these fitness
indexes calculated for the component “process orientation’’
of health management were desirable. The
indexes RMSEA, AGFI and RFI were respectively 0.050,
0.88 and 0.87. Nevertheless, other fitness indexes are
evidences of desirable and appropriate fitness (Table 3).
After the above stage, the approved factors were
named: The first and second factors of the component
“process orientation” were named respectively “control
and improvement of processes” and “design and
implementation of processes”.
DISCUSSION
Findings of this research showed that, two factors have
been identified regarding process orientation in health
care management arena in Iranian hospitals. First factor
has been called “control and improvement of processes”
and the second one was “design and implementation of
processes”. The confirmatory factor analysis too,
indicates that the structural model of these factors was a
proper one.
Dalmaris et al. (2007) have tried to identify the framework
of the factors influencing process improvement. In
this framework, processes are identified and then some
instructions are developed for process improvement and
finally processes improve based on developed tools. So it
may be mentioned that in respect to the result of this
research, the findings of the mentioned research
corresponded with the present research.
Balzarova et al. (2004) said that, in doing process
orientation, the organization must able to manage its
operations and tasks based on the monitoring and
analysis of its key processes (KPs). In their research,
Balzarova et al. (2004) stated that, for performing
process orientation, organizations should first try to
identify processes and activities related to them, and then
take measures to monitor and analyze main processes.
In fact it can be said that, the findings of this research
corresponded with the present one.
Harmon (2003) states that, ISO 9000 family of
standards and 6 Sigma methodologies emphasize first

11350 Afr. J. Bus. Manage.
identification of processes and their operation, and then
their improvement. In fact the result of Harmon’s research
corresponded with the research.
The results achieved from Dalmaris et al. (2007),
Balzarova et al. (2004) and Harmon (2003) research is
consistent with the results of the present research.
Paim et al. (2008) in their research said that, the
bibliographic review yielded a set of tasks that were
grouped conceptually into “designing processes”,
“managing processes from day to day” and “fostering
process-related learning”. In Paim et al. (2008) research,
managing process factor is in fact that process control
factor in this research and fostering process factor seems
to be similar to the first factor “process improvement”,
with the difference that are in Paim et al. (2008) research,
managing process factor and fostering process factor
have been identified separately, but in this research
these two factors have been recognized as one factor
titled as” control and improvement process”.
According to a research by Sanders (2008), the main
factors of process orientation are as follows: “process
design”, “process control”, and “process improvement”. In
this research, “process orientation” was divided into three
main factors of “process design”, “process control” and
“process improvement”. It could be said that the
researches of Sanders (2008) and Paim et al. (2008)
corresponded somehow with the present research.
Ko et al. (2009) and Sandhu and Gunasekaran (2004)
said that, the cycle of process management began with
“process orientation” and “identification of processes”.
Effective performance of process management is ensured
with the performance of continual improvement. In
this research, present research identification of process is
one of the main variables, and process improvement is
one of the main factors of process orientation in the
arena of health care management.
On the basis of the results achieved from the present
research, and taking into account that the factors “design
and implementation of process” and “control and
improvement of process” have been given high points by
the people in charge of quality improvement in the social
security organization’s hospitals, It may be concluded
that, these factors have substantial roles in the performance
of process orientation in health care management
arena and are considered to be main factors for the performance
of process orientation in these organizations.
ACKNOWLEDGEMENTS
Our sincere thanks go to all the authorities and staff of
the social security organization’s hospitals who kindly
helped us to collect the data for this research. The
authors declare that there is no conflict of interests.
REFERENCES
Alam GM, Hoque KE, Ismail L, Mishra PK (2010). Do developing
countries need education law to manage its system are ethics and
a market-driven approach sufficient? Afr. J. Bus. Manage., 4(15):
3406-3416.
Alexopoulos DS, Kalaitzidis I (2004). Psychometric properties of
Eysench Personality Questionnaire- Revised (EPQ-R) short scale
in Greece. Personal. individ. Differenc., 3(7): 1205-1220.
Anderson A, Hallberg N, Timpka T (2003). A model for interpreting
work and information management in process-oriented healthcare
organisations. Int. J. Med. Inform., 7(2): 47-56. Armistead C
(1999). Knowledge management and process performance. J.
Knowl. Manage. 3(2):143-154.
Alzarova MA, Bamber CJ, McCambridge S, Sharp JM (2004). Key
success factors in implementation of process-based management:
A UK housing association experience. Bus. Proc. Manage. J.,
10(4): 387-399.
Benner MJ, Tushman M (2002). Process Management and
Technological Innovation: A Longitudinal Study of the
Photography and Paint Industries. Adm. Sci. Q., 4(7): 676-706.
Biazzo S, Bernardi G (2003). Process management practice and
quality management standard. Bus. Proc. Manage. J., 9(2): 149-
169.
Carmignani G (2008). Process-based management: a structured
approach to provide the best answers to the ISO 9001
requirement. Bus. Proc. Manage. J., 14(6): 803-812.
Carpinetti LCR, Buosi T, Gero´lamo MC (2003). Quality management
and Improvement: A framework and a business-process reference
model. Bus. Proc. Manage. J., 9(4): 543-554.
Dalmaris P, Tsui E, Hall B, Smith B (2007). A framework for the
improvement of knowledge-intensive business processes. Bus.
Proc. Manage. J., 13(2): 279-305.
Filipowska A, Kaczmarek M, Kowalkiewicz M, Zhou X, Born M
(2009). Procedure and guidelines for evaluation of BPM
methodologies. Bus. Proc. Manage. J., 15(3): 336-357.
Goldkuhl G, Lind M (2008). Coordination and transformation in
business processes: towards an integrated view. Bus. Proc.
Manage. J., 14(6): 761-777.
Hamidi Y (2001). The evaluation of total quality management in
health system and optimized model for Iran. PhD dissertation,
Islamic Azad University Science and Research Branch, Tehran,
Iran.
Harmon P (2003). Editor, Business Process Change: A Manager’s
Guide to Improving, Redesigning, and Automating Processes.
Morgan Kaufmann: Amsterdam.
Hashim F, Alam GM, Siraj S (2010). Information and communication
technology for participatory based decision-making-E-management
for administrative efficiency in Higher Education. Int. J.
Physic. Sci., 5(4): 383-392.
Kim HM, Ramkaran R (2004). Best practices in e-business process
management extending a re-engineering framework. Bus. Proc.
Manage. J., 10(1): 27-43.
Ko R, Lee S, Lee E (2009). Business process management (BPM)
standards: a survey. Bus. Proc. Manage. J., 15(5): 744-791.
Neubauer T (2009). An empirical study about the status of business
process management. Bus. Proc. Manage. J., 15(2): 166-183.
Nikniaz A (2002). The Evaluation of Improved Processes in
Department Azerbayejen Province Health Center. PhD
dissertation, Islamic Azad University Science and Research
Branch, Tehran, Iran.
Paim R, Caulliraux HM, Cardoso R (2008). Process management
tasks: a conceptual and practical view. Bus. Proc. Manage. J.,
14(5): 694-723.
Ramesh B, Jain R, Nissen M, Xu P (2005). Managing context in
business process management systems. Require. Eng., 10: 223-
237.
Ranganathan C, Dhaliwal JS (2001). A survey of business process
reengineering practices in Singapore. Inform. Manage., 39(2):
125–134.
Reijers Hajo A (2006). Implementing BPM systems: the role of
process orientation. Bus. Proc. Manage. J., 12(4): 389-409.
Sanders J (2008). Discovering process management: One of the
least understood concepts in Operations Management. PhD
dissertation, Faculty of the graduate school of the University of

Minnesota, Minnesota, USA.
Sandhu M, Gunasekaran A (2004). Business process development
in project-based industry: A case study. Bus. Proc. Manage. J.,
10(6): 673-90.
Skerlavaj M, Indihar Stemberger M, Skrinjar R, Dimovski V (2007).
Organizational learning culture—the missing link between
business process change and organizational performance. Int. J.
Prod. Econ., 106(2): 346-367.
Strnadl CF (2006). Aligning business and it: The process-driven
architecture model. Inform. Syst. Manage., 23(4): 67-77.
Tabibi et al. 11351
Trkman P (2010). The critical success factors of business process
management. Int. J. Inform. Manage., 30:125–134.
Verigidis K, Tiwari A, Majeed B (2008). Business process analysis
and optimization:Beyond reengineering. IEEE. Trans Syst. Man.
Cybern. Part C, 38(1): 69–82.

African Journal of Business Management Vol. 5(28), pp.11335-11344, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1623
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
The three components of organizational commitment
on in-role behaviors and organizational citizenship
behaviors
Chun-Chen Huang 1 * and Ching-Sing You 2
1 Department of International Business, Asia University, Taiwan, ROC.
2 Department of Business Administration, Transworld University, Taiwan, ROC.
Accepted 6 May, 2011
There is strong theoretical support for organizational commitment impact on in-role behaviors and
organizational citizenship behavior performance. However, previous studies did not attain consistent
conclusions with respect to the influence of organizational commitment on organizational citizenship
behavior. The purpose of the study is to adopt the three components of organizational commitment
scale of Meyer and Allen (1991) and followed the suggestions of Williams and Anderson (1991) to
explore the influence of the three components of organizational commitment on in-role behaviors and
two dimensions of organizational citizenship behavior (OCBI and OCBO). In conclusion, this research
finds that the three components of organizational commitment have a considerably important influence
on in-role behaviors and two dimensions of organizational citizenship behavior (OCBI and OCBO).
Key words: Affective commitment, continuance commitment, in-role behaviors, normative commitment,
organizational citizenship behaviors.
INTRODUCTION
Organizational citizenship behaviors (OCB) have been
researched intensively over the recent years, and various
factors affecting OCB have been explored (Bateman and
Organ, 1983; O’Reilly and Chatman, 1986; Williams and
Anderson, 1991). Among these factors, organizational
commitment (OC) is regarded as one of the variables
drawing researchers’ attention (O’Reilly and Chatman,
1986; Paulin et al., 2006; Smith et al., 1983; Williams and
Anderson, 1991). Podsakoff et al. (2000) found that
attitude, such as OC, is positively correlated to OCB in
meta-analysis. Williams and Anderson (1991) suggested
that OC should be further explored because there is
strong theoretical support for its impact on OCB performance.
However, previous studies did not reach
consistent conclusions with respect to the influence of
OC on OCB.
On the other hand, through literature review, this
*Corresponding author. E-mail: cchuang1127@asia.edu.tw.
research found that OC and OCB study fields still
revealed the following insufficiencies:
1) Researches on segmented citizenship behavior into inrole
behaviors and extra-role behaviors are scanty.
Williams and Anderson (1991) suggested that a good
measurement of OCB should include items representing
IRB because such an analysis could clarify whether the
respondents differentiated between intra-role and extrarole
behaviors. Borman and Motowidlo (1993) and Organ
(1995) also suggested that distinction between contextual
performance (like OCB) and task (that is, in-role)
performance were both theoretically and practically
important because they were probably determined by
different antecedents. However, few studies so far have
adopted the suggestion of Williams and Anderson (1991),
among them was Cohen (2006) who examined the relation
between multiple commitments, ethnicity, and values
with OCB (OCB altruism vs. OCB organization) and inrole
performance. For example, Bolon (1997) examined
the relationships between the three components, as well

11336 Afr. J. Bus. Manage.
as job satisfaction and two separate forms of OCB (OCBI
and OCBO), but not including IRB. Singh and Singh
(2010) examined the effect of role overload and
perceived organizational support in OCB, and OCB was
distinct from OCB-I and OCB-O, but not including IRB. In
addition, Williams and Anderson (1991) suggested that
distinguishing OCBO and OCBI was important since
many past studies indicated that these two forms of OCB
activities could have different antecedents. However,
many studies did not include these two dimensions at the
same time, but evaluated by single dimension, called
overall OCB (Brief and Motowidlo, 1986; O’Reilly and
Chatman, 1986; Rego et al., 2010; Smith et al., 1983).
2) The scale of the three components of OC discussed in
Meyer and Allen (1991) was rarely involved in the
research framework. Most researches did not examine
the three components of OC, instead focused on unidimensional
representation of OC. O’Reilly and Chatman
(1986) used the following three aspects of OC as
antecedents of extra-roles. The dimensions of attachment
studied included: compliance (instrumental involvement
for specific, extrinsic rewards), identification (involvement
based on a desire for affiliation), and internalization
(involvement predicated on congruence between individual
and organizational values). Williams and Anderson
(1991) discussed the relationships among OC, two types
of OCB (OCBI and OCBO), and in-role behaviors (IRB).
OC was measured with the questionnaire from O’Reilly
and Chatman (1986), but based on factor analysis, the
internalization and identification scales were combined to
form an overall organizational commitment, and a single
aspect was used to measure OC.
This study was conducted in Taiwan where collectivism is
a cultural norm (Hofstede, 1997). This not only helped us
to compare the results to other similar studies conducted
in a collectivist culture (Chen and Francesco, 2003;
Cheng and Stockdale, 2003; Chang et al., 2006), but also
provided empirical evidence about the generalizability of
the three components of OC and OCB model outside
North America. Thus, this research adopted the three
components of OC scale of Meyer and Allen (1991) and
followed the suggestions of Williams and Anderson
(1991) to explore the influence of the three components
of OC on IRB and two dimensions of OCB (OCBI and
OCBO).
ORGANIZATIONAL CITIZENSHIP BEHAVIOR
Katz (1964) pointed out that in order to operate efficiently,
an organization must possess the following three basic
conditions pertaining to employees: (1) participating and
staying in the organization, (2) acting according to the
behavioral principles regulated by the organization; and
the most important condition, (3) automatic devotion to
the organization. Bateman and Organ (1983) followed the
third extra-role categorized by Katz (1964), and defined it
as “citizenship behavior”. Smith et al. (1983) later
conceptualized these contributions as “organizational
citizenship behavior” (OCB) which was a kind of nonorganizational
formal regulation and behavior, which
could not be assessed by formal reward and punishment
system. Organ further explained that “individual behavior
that is discretionary, not directly or explicitly recognized
by the formal reward system and that in the aggregate,
promotes the effective functioning of the organization”
(Organ et al., 2005).
In recent years, Western scholars have increasingly
emphasized the importance of OCB. The practical importance
of OCB is that it improves organizational efficiency
and effectiveness by contributing to resource transformation,
innovation, and adaptability in environments
demanding complex, ambiguous, and team-oriented work
(Organ, 1988; Organ et al., 2005). Examples of these
efforts include cooperation with peers, performing extra
duties without complaint, punctuality, volunteering and
helping others, using time efficiently, conserving
resource, sharing ideas and positively representing the
organization (Turnipseed and Rassuli, 2005).
There are several classifications with respect to OCB,
and the most common ones are: (1) analysis by single
dimension, generally called OCB (Bateman and Organ,
1983); (2) dividing OCB into altruism and generalized
compliance (Smith et al., 1983); and (3) divided into five
dimensional models: (a) altruism- the helping of an
individual coworker on a task, (b) courtesy- constructive
gestures that help prevent problems for coworkers, (c)
conscientiousness- carrying out one’s duties beyond the
minimum requirements, (d) sportsmanship- refraining
from complaining about trivial matters , and (e) civic
virtue- participating in the governance of the organization
(Organ, 1988). Among the three classifications discussed
earlier, the first method with single dimension was
rarely adopted by researchers and it only appeared in the
early studies (Bateman and Organ, 1983; Motowidlo,
1984) and two dimensions, revealing ambiguous
meanings. However, as for the five-dimensional model,
there is a great deal of conceptual overlap between the
constructs (Podsakoff et al., 2000; Coleman and Borman,
2000), perhaps in recognition that the constructs of
Organ’s (1988) OCB model, overlap with each other
(Organ, 1997; Coleman and Borman, 2000; Motowidlo,
2000). Several scholars have begun to consider whether
the dimensions should be combined into conceptually
distinct sub-groups. Williams and Anderson (1991)
divided OCB into three dimensions; (a) in-role behaviors
(IRB) – the responsibilities undertaken by the employees
(for example, works full 8 h day, completes assigned
duties on time, complies with rules and regulations), (b)
OCBI – behaviors that immediately benefit specific individuals
and, through this means, indirectly contribute to the
organization (for example, helps others who have been
absent, takes a personal interest in other employees), and

and (c) OCBO – behaviors that benefit the organization in
general (for example, gives advance notice when unable
to come to work, adheres to informal rules devised to
maintain order). Organ (1997) followed the lead of
Williams and Anderson (1991) and designated altruism
and courtesy as OCBI, whereas conscientiousness,
sportsmanship, and civic virtue as OCBO. Thus, this
research will follow the suggestions of Williams and
Anderson (1991) to explore the influence of the three
components of OC on IRB and two dimensions of OCB
(OCBI and OCBO).
THE THREE COMPONENTS OF ORGANIZATONAL
COMMITMENT
Organizational commitment is commonly defined as
employees’ interest in, and connection to an organization
(Hunt et al., 1989; Meyer and Allen, 1997; Mowday et al.,
1979). Employees who are committed to their firms tend
to identify with the objectives and goals of their organizations,
and wish to remain in their organizations (Hunt et
al., 1989). Porter et al. (1974) proposed that OC can be
characterized by: (1) a strong belief in, and acceptance
of, the organization’s goals and values; (2) willingness to
exert considerable effort for the organization; and (3) a
strong desire to remain a member of the organization.
WeiBo et al. (2010) reviewed the main studies of OC
from Becker (1960) one-side-bet theory, Porter (1974)
affective dependence theory, O’Reilly and Chatman
(1986), and Meyer and Allen (1984, 1990) threecomponent
till today’s Cohen (2007) two-dimension and
Somers (2009) combined theory. Although the scholars
do not seem to reach an agreement on organizational
commitment in terms of its classification and perspective,
the three components of organizational commitment
established by Meyer and Allen (1991) have generally
covered its content.
Meyer and Allen (1991) proposed a three-component
conceptualization of OC. Meyer and Allen (1984) initially
proposed a distinction be made between affective commitment
(AC) and continuance commitment (CC), with
AC denoting an emotional attachment to, and involvement
in, the organization, and CC denoting the perceived
costs associated with leaving the organization. Allen and
Meyer (1990) later suggested the third discrete component,
termed NC, which reflects a perceived obligation to
remain in the organization. The three components model
of OC proposed by Meyer and Allen (1991) has provided
the predominant framework for OC research during the
past decade because it is based on a exhaustive understanding
of OC. The three-components model includes:
(a) affective commitment (AC, emotional attachment to
one’s organization); (b) continuance commitment (CC,
attachment based on the accumulation of valued side
bets such as pension, skill transferability, relocation, and
self-investment that vary with organizational membership);
and (c) normative commitment (NC, attachment based on
Huang and You 11337
motivation to conform to social norms regarding
attachment).
CONCEPTUAL FRAMEWORK AND HYPOTHESES
In Weiner’s (1982) model, commitment was viewed as
the totality of these internalized beliefs and was responsible
for behaviors that; (a) reflect personal sacrifice
made for the sake of the organization, (b) do not depend
primarily on reinforcements or punishments, and (c)
indicate a personal preoccupation with the organization.
Because these are characteristics that could be used to
describe OCB, additional support is provided for
commitment being an antecedent of OCB.
The first component, AC, refers to the employees’ emotional
attachment to, identification with, and involvement
in the organization (Allen and Meyer, 1990). Many
studies proved that there is a positive correlation between
AC and intra-role performance (Allen and Meyer, 1996;
Mathieu and Zajac, 1990). AC was also regarded as an
important factor for predicting extra-role behaviors, such
as OCB (Scholl, 1981; Wiener, 1982). O’Reilly and
Chatman (1986) found that AC could significantly predict
OCBO. Both Steer (1977) and Angle and Perry (1981)
found similar results. From the empirical cases,
McFarlane and Wayne (1993) also found that there was
significant correlation between AC and OCB. However,
some studies attained different research results; e.g.
Williams and Anderson (1991) found that there was no
relationship between OC (including AC and NC) and
OCB. In addition, Shore and Wayne (1993) indicated that
there was a correlation between AC and OCB, but it was
insignificant. Although past studies did not have
consistent conclusions with respect to the relationship
between AC and IRB, OCBI, and OCBO, most of the
studies still believed that AC has a positive influence on
these three dimensions. Thus, the following hypotheses
are proposed:
H1: Affective commitment has a positive effect on IRB.
H2: Affective commitment has a positive effect on OCBI.
H3: Affective commitment has a positive effect on OCBO.
The second component, CC, refers to commitment based
on the costs that an employee associates with leaving the
organization (Allen and Meyer, 1990). It has been
suggested that CC may be negatively linked to certain
work behaviors (Meyer and Allen, 1997). One of the
explanations is that employees with strong CC believe
they are “trapped” in a “no choice” situation (that is, they
have to stay with the organization even though they do
not want to); as such, they react with anger toward the
situation and, accordingly, behave negatively (Meyer and
Allen, 1997). Both IRB and OCB are work related
behaviors; therefore, employees with stronger CC may
be more reluctant to perform IRB and OCB. Thus, it was
therefore hypothesized that:

11338 Afr. J. Bus. Manage.
Figure 1. Conceptual framework.
H4: Continuance commitment has a negative effect on
IRB.
H5: Continuance commitment has a negative effect on
OCBI.
H6: Continuance commitment has a negative effect on
OCBO.
The third component, NC, refers to the employees’
feelings of obligation to remain in the organization. NC
may be developed based on socialization experiences
provided by the family, culture, and employing organization.
A young person would learn about the general
appropriateness of organizational loyalty from one’s
family and the surrounding cultural environment (Meyer
and Allen, 1997; Wiener, 1982). NC may also be rooted
in feelings of indebtedness toward an organization for its
supply of certain benefits, for example, tuition reimbursement
or in-house training. The feelings of obligation may
continue until the employees feel that they have “paid
back” the debt (Meyer and Allen, 1991; Scholl, 1981). In
the study of O’Reilly and Chatman (1986), NC (referred
to as internalization) affected the employees’ OCB.
Pearce and Gregersen (1991) pointed out that employees
with a high degree of responsibility show the work behavior
of OCB. Since the sense of responsibility is the major
factor in NC, we can infer that NC has a positive
relationship with IRB and OCB. Thus, we hypothesized
that:
H7: Normative commitment has a positive effect on IRB.
H8: Normative commitment has a positive effect on OCBI.
H9: Normative commitment has a positive effect on
OCBO.
Based on the aforementioned literatures and research
hypotheses, the framework of this research is shown in
Figure 1.
RESEARCH METHODOLOGY
Sample and procedure
A pretest of the questionnaire was performed to ensure content
validity and reliability within the target context. Ten experts in the
organizational behavior areas were invited to assess wording
clarity, question item sequence adequacy, and task relevance.

Table 1. Sample characteristics.
Variable Demographic Number Valid percent
Gender
Male
Female
10
300
3.5
96.5
Age
Nursing role
Job experience
Education level
Average wage
Several minor modifications of the wording and the question item
sequence were done based on the comments collected from these
experts. The investigation targeted hospital nurses in the north,
middle, and south areas of Taiwan. Three hospitals were selected
for each area sampled, giving a total of nine hospitals. Fifty
questionnaires were distributed to each hospital, resulting in a total
possible sample of 450 nurses. The completed questionnaires were
returned to the principal investigator by mail. Incomplete questionnaires
such as missing values or double-checked items were excluded.
The overall response rate (310 completed questionnaires)
was 69%.
A summary of the sample characteristics is presented in Table 1.
The majority of the respondents were female (96.5%) and staff
nurses (77.4%). Most subjects (82.5%) were less than 35 years old,
and most of them (74.5%) had worked more than three years. The
predominant education level was college (53.9%), and their
average wage was NT$ 30,000 to 40,000.
Measurement instruments
The organizational commitment questionnaire used in this study
was developed by Meyer and Allen (1991). It contains 10 questions
examining the relationship of employees to the organization. Strong
evidence for the reliability and validity of the OC scale has been
Under 25 years 64 20.6
26-30 years 139 44.8
31-35 years 53 17.1
36-40 years 37 11.9
Over 41 years 17 5.5
Staff nurse 240 77.4
Administrator 29 9.4
Advanced practice nurse 41 13.2
Under 1 year 26 8.4
1-3 years 53 17.1
3-6 years 68 21.9
6-9 years 105 33.9
Over 9 years 58 18.7
High school or below 15 4.8
College 167 53.9
University 116 37.4
Graduate school or above 12 3.8
Under NT$30,000 49 15.8
NT$30,001-35,000 91 29.4
NT$35,001-40,000 104 33.5
NT$40,001-50,000 49 15.8
Over NT$50,000 17 5.5
Huang and You 11339
reported (Chen and Francesco, 2003; Cheng and Stockdale, 2003;
Meyer et al., 2002). A five-point Likert scale ranging from strongly
disagree (1) to strongly agree (5) was used. The three factors
identified were: (a) AC; (b) CC; and (c) NC.
The organizational citizenship behavior questionnaire followed
Smith et al. (1983), Williams and Anderson (1991), and Podsakff et
al. (1990). It contains 10 questions examining the relationship of
employees to the organization. Since the researchers did not have
consistent descriptions and classifications on OCB (Steers, 1977),
this research followed the scales of several studies for the design.
A five-point Likert scale ranging from strongly disagree (1) to
strongly agree (5) was used. The three factors identified were: (a)
IRB; (b) OCBI; and (c) OCBO. The questionnaire items measuring
constructs are listed in Table 2.
DATA ANALYSIS AND RESULTS
Measurement model
Data analysis is carried out in accordance with a twostage
methodology: the measurement model and the
structure model (McDonald and Ho, 2002). The first step
in the data analysis was to assess the construct validity

11340 Afr. J. Bus. Manage.
Table 2. Summary of measurement scales.
Construct Measure Mean SD Loading
Affective commitment (AC) Cronbach’s alpha = 0.8221
AC1 I do not feel a strong sense of belonging to my organization.* 3.19 0.88 0.73
AC2 I do not feel emotionally attached to this organization.* 3.15 0.88 0.78
AC3 I do not feel like part of the family at my organization.* 3.39 0.86 0.83
Continuance commitment (CC) Cronbach’s alpha = 0.8127
CC1 Too much of my life would be disrupted if I decided I wanted to
leave my hospital now.
2.74 1.04 0.80
CC2 I feel that I have too few options to consider leaving this hospital. 2.63 1.01 0.89
CC3 One of the few negative consequences of leaving this hospital
would be the scarcity of available alternatives.
2.52 0.86 0.64
Normative commitment (NC) Cronbach’s alpha = 0.7703
NC1 Even if it were to my advantage, I do not feel it would be right to
leave my hospital now.
2.65 0.96 0.67
NC2 I would feel guilty if I left this organization now. 2.43 0.97 0.72
NC3 This hospital deserves my loyalty. 2.85 0.87 0.72
NC4 I would not leave my hospital right now because I have a sense of
obligation to the people in it.
2.90 0.93 0.61
In-role behaviors (IRB) Cronbach’s alpha = 0.7628
IRB1 Adequately completes assigned duties. 2.59 0.87 0.67
IRB2 Fulfills responsibilities specified in job description. 2.56 0.90 0.65
IRB3 Performs tasks that are expected of him/her. 2.86 0.89 0.76
IRB4 Meets formal performance requirements of the job. 2.59 0.80 0.60
OCBI behaviors (OCBI) Cronbach’s alpha = 0.7648
OCBI1 Goes out of way to help new employees. 3.96 0.66 0.70
OCBI2 Helps others who have heavy work loads. 4.03 0.63 0.78
OCBI3 Helps others who have been absent. 4.30 0.67 0.69
OCBO behaviors (OCBO) Cronbach’s alpha = 0.6602
OCBO1 I make suggestions to improve the organization. 3.64 0.68 0.70
OCBO2 I volunteer for tasks that are not required. 3.15 0.91 0.78
OCBO3 I attend functions that are not required, but that help the
university’s image.
3.66 0.68 0.69
for the six measurement elements (that is, AC, CC, NC,
IRB, OCBI, and OCBO) with LISREL confirmatory factor
analysis.
As shown in Table 2, reliability was tested using the
Cronbach’s alpha values. All of these are above 0.66,
well above the common acceptance level of 0.60
(Bagozzi and Yi, 1988). Convergent validity assesses the
extent to which varying approaches construct measurements,
and yielded the same results (Campbell and
Fiske, 1959). Convergent validity was evaluated for the
measurement scales using two criteria suggested by
Jöreskog and Sörbom (1989): (1) all indicator factor
loadings (λ) should be significant and exceed 0.45, and
(2) average variance extracted (AVE) by each construct
should exceed 0.50. As shown in Table 3, all λ are higher
than the 0.45 benchmark, and most AVEs are greater
than 0.5, except for the IRB and OCBO scales with AVE
slightly below the required minimum criteria of 0.5 (0.46,
0.43, respectively). Therefore, the measurement of the
convergent validity was acceptable.
Discriminant validity assesses the extent to which a
concept and its indicator differ from another concept and
its indicators (Bagozzi and Phillips, 1991). Discriminant
validity of the resulting measures was assessed using the
guidelines suggested by Fornell and Larcker (1981): the
square root of AVE for each construct should exceed the
correlation between that and any other construct. Table 3
lists the correlation matrix, with correlation among

Table 3. Correlations and AVE.
Construct AVE CR AC CC NC IRB OCBI OCBO
AC 0.61 0.82 0.78
CC 0.55 0.78 -0.11 0.74
NC 0.50 0.75 0.11 0.61 0.71
IRB 0.46 0.77 0.19 -0.02 0.24 0.68
OCBI 0.52 0.77 0.27 -0.26 -0.09 0.06 0.72
OCBO 0.43 0.70 0.36 0.04 0.25 0.13 0.08 0.66
Table 4. Model fit index summary.
Huang and You 11341
Fit index Score Recommended value
Absolute fit measures
χ test 322.28
df 137
GFI 0.90 >0.9 (Bentler, 1983, 1988; Browne and Cudeck, 1993; Hayduk, 1987)
RMSEA 0.06 0.9 (Bentler, 1983, 1988; Browne and Cudeck, 1993; Hayduk, 1987)
CFI 0.93 >0.9 (Bentler, 1983, 1988; Browne and Cudeck, 1993; Hayduk, 1987)
Parsimonious fit measures
2 /df 2.35

11342 Afr. J. Bus. Manage.
Table 5. The results of SEM analysis of research model.
Hypothesis Path β t value Results
H1: Affective commitment has a positive effect on IRB. AC � IRB 0.13 1.75 H1 not supported
H2: Affective commitment has a positive effect on OCBI. AC � OCBI 0.24 3.19** H2 supported
H3: Affective commitment has a positive effect on OCBO. AC � OCBO 0.32 3.83** H3 supported
H4: Continuance commitment has a negative effect on IRB. CC � IRB -0.23 -2.24* H4 supported
H5: Continuance commitment has a negative effect on OCBI. CC �OCBI -0.26 -2.55* H5 supported
H6: Continuance commitment has a negative effect on OCBO CC � OCBO -0.09 -0.87 H6 not supported
H7: Normative commitment has a positive effect on IRB. NC � IRB 0.37 3.26** H7 supported
H8: Normative commitment has a positive effect on OCBI. NC � OCBI 0.04 0.37 H8 not supported
H9: Normative commitment has a positive effect on OCBO. NC � OCBO 0.27 2.41* H9 supported
and that they had to do to meet the basic requirements of
work. Thus, AC does not have significant
influence on IRB. Therefore, when the employees identify
themselves with the organizational goals, values, and
mission, and are willing to make their best efforts in the
organization, they not only would help, and communicate
with, their colleagues, but also try their best to devote
their efforts to the organization. People who feel that they
are treated well by an organization, such as receiving fair
treatment (Allen and Meyer, 1990) or participating in
decision-making (Rhodes and Steers, 1981), are more
likely to develop AC. Meyer and Allen (1991) found that
work attitudes/perceptions, namely: organizational
dependability, peer cohesion, role clarity, personal
importance, job challenge, participation, goal clarity, goal
difficulty, management receptivity equity, and feedback,
were the best predictors of AC.
From a practical perspective, firms should increase employees’
emotional attachment to the organization, such
as providing the employees diverse tasks through
assignments, empowerment, supportive work environment,
proper career management with partners, and work
rotation.
Secondly, this study finds that CC has significant and
negative influences on IRB and OCBI; however, it does
not have significant influence on OCBO. It shows that
when the employees have stronger CC, they are less
willing to accomplish their duties and actively help others
in the organization.
Although the influence of CC on OCBO is insignificant,
it is still a negative effect. As proved by many past
studies, CC is expected to be related negatively to these
desirable work behaviors. Meyer and Allen (1991)
suggested that the two most important antecedents of CC
are employees’ accumulated side bets, or organizationdependent
investments (for example, lack of skill
transferability, relocation concerns, and pensions), and
the availability of job alternatives.
From a practical perspective, firms should avoid
treating “seniority” as the condition for promotion and
should reduce CC by strengthening AC and NC CC. As
such, this study suggests that hospital leaders can
encourage participation, fully empower staff, increase
educational training, and promote the developmental
growth of nurses.
Finally, our findings show that NC has significant
influence on IRB and OCBO; however, it does not have
significant influence on OCBI. The influence of NC on
intra-role behaviors is greater than that on OCBO and
OCBI.
Thus, when employees are restricted by morality or
stay in the organization because of the sense of
responsibility, they not only would fulfill their duties, but
also devote themselves to the organization. Allen and
Meyer (1996) indicated that NC is developed by
socialization experiences provided by family, culture, and
employing organization.
Employees with strong NC may feel a more deepseated
obligation “to act in a way which meets
organizational goals and interests” (Wiener, 1982). Meyer
et al. (1993) proposed two antecedents of NC-
socialization toward loyalty that emphasize the
appropriateness of remaining loyal to one’s employer,
and receipt of benefits (not side bets) that make the
employees feeling a sense of obligation to reciprocate
until the debt has been repaid (for example, employer
paying college tuition or providing training; Meyer and
Allen, 1991; Meyer et al., 1993).
Hofstede (1992) categorized the social level of China
as collectivistic.
In addition, Moorman and Blakely (1995) found that
collectivistic subjects are more likely to perform OCB, and
they have higher level of NC. From a practical
perspective, managers can set up the rights of work,
societal norms of obligations, and responsibility in the
name of the group instead of individual, or the norms of
performance which could be an influence on NC.
Limitations and suggestions for further research
Although our findings provide meaningful implications for
OCB, our study has some limitations. First, from literature
review, we can find that many studies did not include two

dimensions of OCB (OCBI and OCBO) at the same time.
In addition, many researches did not distinct the
difference of OCB and in-role behaviors, but evaluated
OCB by single dimension.
Furthermore, most researches did not examine the
three components of OC, instead focused on
unidimensional representation of OC.
Therefore, the chief limitation of this investigation lies in
the insufficiency of direct literatures to prove the impact of
the three components of OC on in-role behaviors, OCBI
and OCBO. However, this investigation has tried to
provide such a proof through reasoning based on
literature review.
Secondly, Podsakoff et al. (2000) examined the
literatures related to OCB and indicated that almost 30
potentially different forms of citizenship behavior had
been identified. However, this research only explores
IRB, OCBI, and OCBO. Future studies can explore the
influences of the three components of OC on more
dimensions of OCB.
Thirdly, this research did not include the antecedents of
the three components of OC, including individual or workrelated
variables, or consider the outcomes of OC or
OCB, such as absence or turnover intention, or whether
the three components of OC on different dimensions of
OC are affected by other moderating variables. Therefore,
it would be interesting to examine the antecedents
of OC and the influence of OC or OCB on work related
outcomes, and explore if there are moderating variables
in the relationship between OC and OCB.
Finally, this research was only managed in one country.
Future researches can focus on cross-cultural studies,
and explore the influence of individualism and
collectivism on the relationship between OC and OCB.
In conclusion, this research finds that the three components
of OC have a considerably important influence
on IRB and OCB. However, as for the influence of the
three components of OC on IRB and OCB, there is still
no consistent conclusion which is worthy to be explored
in future studies. Practically, our results suggest that
managers can strengthen the employees’ performance
through a better understanding of the nature of OC.
REFERENCES
Allen NJ, Meyer JP (1990). The measurement and antecedents of
affective, continuance and normative commitment to the organization.
J. Occup. Psychol., 63: 1-18.
Allen NJ, Meyer JP (1996). Affective, continuance, and normative
commitment to the organization: An examination of construct validity.
J. Vocat. Behav., 49: 252-276.
Angle H, Perry J (1981). An empirical assessment of organizational
commitment and organizational effectiveness. Adm. Sci. Q., 26: 1-14.
Bagozzi R, Phillips L (1991). Assessing construct validity in
organizational research. Adm. Sci. Q., 36(3): 421-458.
Bagozzi R, Yi Y (1988). On the evaluation of structural equation models.
J. Acad. Mark. Sci., 16(1): 74-94.
Bateman TS, Organ DW (1983). Job satisfaction and the good soldier:
The relationship between affect and employee “citizenship”. Acad.
Manage. J., 26: 587-595.
Huang and You 11343
Becker HS (1960). Notes on the concept of commitment. Am. J. Sociol.,
66: 32-40.
Bentler PM (1983). Some contributions to efficient statistics in structural
models: Specification and estimation of moment structures.
Psychometrika, 48: 493-517.
Bentler PM (1988). Theory and Implementation of EQS: A Structural
Equations Program. Sage, Newbury Park, CA.
Bolon DS (1997). Organizational citizenship behavior among hospital
employees: A multidimensional-analysis involving job satisfaction and
organizational commitment. Hosp& Health Serv. Adm., 42: 221-241.
Borman WC, Motowidlo SJ (1993). Expanding the criterion domain to
include elements of contextual performance. In Schmitt N, Borman
WC(Eds), Personality Selection. San Francisco: Jossey-Bass.
Brief A, Motowidlo SJ (1986). Prosocial organizational behaviors. Acad.
Manage. Rev., 11: 710-725.
Browne MW, Cudeck R (1993). Alternative ways of assessing model fit.
In: Bollen, K.A. and Ling, J.S. (Eds.), Testing Structural Equation
Models, Sage, Newbury Park, CA.
Campbell DT, Fiske DW (1959). Convergent and discriminant validation
by the multitrait-multimethod matrix. Psychol. Bull., 56(2): 81-105.
Chang HT, Chi NW, Miao MC (2006). Testing the relationship between
three-component organizational/occupational commitment and
organizational/occupational turnover intention using a non-recursive
model. J. Vocat. Behav., 70: 352-368.
Chen ZX, Francesco AM (2003). The relationship between the three
components of commitment and employee performance in China. J.
Vocat. Behav., 62: 490-510.
Cheng Y, Stockdale MS (2003). The validity of the three-component
model of organizational commitment in a Chinese context. J. Vocat.
Behav., 62: 465-489.
Cohen A (2007). Commitment before and after: An reconceptualization
of organizational commitment. Hum. Res. Manage. Rev., 17: 336-
354.
Cohen A (2006). The relationship between multiple commitments and
organizational citizenship behavior in Arab and Jewish culture. J.
Vocat. Behav., 69: 105-118.
Coleman VI and Borman WC (2000). Investigating the underlying
structure of the citizenship performance domain. Hum. Res. Mange.
Rev., 10(1): 25-44.
Fornell C, Larcker DF (1981). Evaluating structural equation models with
unobservables and measurement error. J. Mark. Res. 18(1): 39-50.
Hayduk LA (1987). Structural Equation Modeling with LISREL:
Essentials and Advances. Johns Hopkins University Press, Baltimore,
MD.
Hofstede GH (1992). Cultural constraints in management theories.
Paper presented at the annual meeting of the Academy of
Management, Las Vegas, NV.
Hofstede GH (1997). Cultures and organizations: software of the mind.
New York: McGraw-Hill.
Hunt SD, Wood VR, Chonko LB (1989). Corporate ethical values and
organizational commitment in marketing. J. Mark., 53: 79-90.
Jöreskog KG, Sörbom D (1989). LISREL 7: A guide to the program and
applications (2nd ed.). Chicago, IL: Scientific Software International.
Jöreskog KG, Sörbom D (1993). LISREL 8: Structural Equation
Modeling with the SIMPLISTM Command Language. Scientific
Software International, Chicago.
Katz D (1964). The motivational basis of organizational behavior.
Behav. Sci., 9: 131-133.
Mathieu JE, Zajac D (1990). A review and meta-analysis of the
antecedents, correlates, and consequences of organizational
commitment. Psychol. Bull., 108: 171-194.
McDonald RP, Ho MR (2002). Principles and practice in reporting
structural equation analyses. Psychol. Methods, 7(1): 64-82.
Meyer JP, Allen NJ (1984). Testing the “side-bet theory” of
organizational commitment: some methodological considerations. J.
Appl. Psychol., 69: 372-378.
Meyer JP, Allen NJ (1991). A three-component conceptualization of
organizational commitment. Hum. Res. Manage. Rev., 1: 61-89.
Meyer JP, Allen NJ (1997). Commitment in the Workplace: Theory,
Research, and Application. Sage, Thousand Oaks.
Meyer JP, Allen NJ, Smith CA (1993). Commitment to organizations and
occupations: extension and test of a three-component

11344 Afr. J. Bus. Manage.
conceptualization. J. Appl. Psychol., 78: 538-551.
Meyer JP, Stanley DJ, Herscovitch L, Topolnytsky L (2002). Affective,
continuance, and normative commitment to the organization: A metaanalysis
of antecedents, correlates, and consequences. J. Vocat.
Behav., 61: 20-52.
Moorman RH, Blakely GL (1995). Individualism -- collectivism as an
individual difference predictor of organizational citizenship behavior.
J. Organ. Behav., 16(2): 127-142.
Motowidlo SJ (1984). Does job satisfaction lead to consideration and
personal sensitivity? Acad. Manage. J., 27: 910-915.
Motowidlo SJ (2000). Some basic issue related to contextual
performance and organizational citizenship behavior in human
resource management. Hum. Res. Mange. Rev., 10(1). 115-126.
Mowday RT, Steers RM, Porter LW (1979). The measure of
organizational commitment. J. Vocat. Behav., 14: 224-247.
O’Reilly C, Chatman J (1986). Organizational commitment and
psychological attachment: The effects of compliance, identification,
and internalization on prosocial behavior. J. Appl. Psychol., 71: 492-
499.
Organ DW (1988). Organizational citizenship behavior: The good soldier
syndrome. Lexington, MA: Lexington Books.
Organ DW (1995).A meta-analytic review of attitudinal and dispositional
predictors of organizational citizenship behavior. Pers. Psychol., 48:
775-802.
Organ DW (1997). Organizational citizenship behavior: It’s construct
clean-up time. Hum. Perform., 10(2): 85-97.
Organ DW, Podsakoff PM, Mackenzie SB (2005). Organizational
citizenship behavior: Its nature, antecedents and consequences.
Sage, Thousand Oaks, CA.
Paulin M, Ferguson RJ, Bergeron J (2006). Service climate and
organizational commitment: The importance of customer linkages. J.
Bus. Res., 59: 906-915.
Pearce JL, Gregersen HB (1991). Task interdependence and extra-role
behavior: A test of the mediating effects of felt responsibility. J. Appl.
Psychol., 76: 838-844.
Podsakoff PM, MacKenzie SB, Moorman RH, Fetter R (1990).
Transformational leader behaviors and their effects on followers’ trust
in leader, satisfaction and organizational citizenship behaviors.
Leadersh. Q., 1: 107-142.
Podsakoff PM, MacKenzie SB, Paine JB, Bachrach DG (2000).
Organizational citizenship behaviors: A critical review of the
theoretical and empirical literature and suggestions for future
research. J. Manage., 26(3): 513-563.
Porter LW, Steers RM, Mowday RT, Boulian PV (1974). Organizational
commitment, job satisfaction, and turnover among psychiatric
technicians. J. Appl. Psychol., 59: 603-609.
Rego A, Ribeiro N, Cunha MP (2010). Perceptions of organizational
virtuousness and happiness as predictors of organizational
citizenship behaviors. J. Bus. Ethics, 93:215-235.
Rhodes SR, Steers RM (1981). Conventional vs. worker-owned
organizations. Hum. Relat. 12: 1013-1035.
Scholl RW (1981). Differentiating commitment from expectancy as a
motivation force. Acad. Manage. Rev., 6: 589-599.
Shore LM, Wayne SJ (1993). Commitment and employee behavior:
Comparison of affective commitment and continuance commitment
with perceived organizational support. J. Appl. Psychol., 78: 774-780.
Singh AK, Singh AP (2010). Role of stress and organizational supprt in
predicting organizational citizenship behavior. IUP J. Organ. Behav.,
IX(4): 7-19.
Smith CA, Organ DW, Near JP (1983). Organizational citizenship
behavior: Its nature and antecedents. J. Appl. Psychol., 68: 655-663.
Somers MJ (2009). The combined influence of affective, continuance
and normative commitment on employee withdraw. J. Vocat. Behav.,
74: 75-51.
Steer RM (1977). Antecedents and outcomes of organizational
commitment. Adm. Sci. Q. 22: 45-56.
Wiener, Y (1982). Commitment in organizations: A normative review.
Acad. Manage. Rev., 7: 418-428
Williams LJ, Anderson SE (1991). Job satisfaction and organizational
commitment as predictors of organizational citizenship and in-role
behaviors. J. Manage., 17(3): 601-617.
WeiBo Z, Kaur S, Jun W (2010). New development of organizational
commitment: A critical review (1960-2009). Afr. J. Bus. Manage., 4(1):
12-20.

African Journal of Business Management Vol. 5(28), pp. 11309-11315, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1177
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Employees’ perceptions regarding social health
insurance: A case of Kinshasa, Democratic Republic of
Congo
T. Kayiba and E. M. Rankhumise*
Department of Management and Entrepreneurship, Tshwane University of Technology, Private Bag X680,
Pretoria 0001, South Africa.
Accepted 30 December, 2010
Sustaining splendid health has always been a wish for every employee of any formal organisation. If health is
not excellent, employees are likely not to function as expected hence it is imperative to have social health
insurance. This article reports on the findings derived from a research conducted in Kinshasa, Democratic
Republic of Congo. The researcher personally distributed structured questionnaires among employees in 15
organisations. Findings show that the majority of the respondents experience problems in organizing their
health care where it emerged that, 1) the majority of the employees from public sector are not assisted in
organizing their health care, 2) they use out-out-pocket financing means for their health care, 3) in general,
employees from public sector are not aware of health insurance and interestingly employees from mix
companies and private sector are knowledgeable on health insurance, 4) respondents with post matric
qualifications prefer to use private hospital when they are sick, 5) employees choose health facility based on
good quality service provided. In general, it emerged from the findings that there is willingness to pay
contribution should the social health insurance be introduced.
Key words: Social health insurance, Health financing systems, Democratic Republic of Congo (DRC), Out-of-pocket,
Mixed –company.
INTRODUCTION
Good health status is imperative for the good life, wellbeing
and development of nations. If health is not
excellent, workers cannot do their jobs properly, children
can not go to school and in fact, this situation will affect
the whole family even more if no social insurance is in
place. Furthermore, good health is needed for economic
and social development.
An efficient and equitable health care system is
therefore an important intervention in breaking the vicious
cycle of poverty, vulnerability and ill health caused by
non-provision of social health insurance (Jutting, 2005).
Developed countries often use social health insurance
(SHI) to mobilise funds and pool risks, but low- and
middle-income countries rarely use this approach and
*Corresponding author. E-mail: Rankhumiseem@tut.ac.za.
rely mostly on general revenues and direct out-of-pocket
payments as sources of health care financing (Hsiao et
al., 2007).
As mentioned by the World Health Organisation (WHO)
(2007), the most common ways of financing health care
are:
1. Tax funding, where the money to pay for health services
comes from the general revenue of the government
(sales taxes, income taxes, import and export taxes). Tax
is usually imposed on public health facilities, but also
increasingly on private provision. Taxes may be
progressive (the better-off pay more than the poor) or
regressive (favouring the better-off).
2. SHI, where members pay a contribution based on their
income to a health insurance agency, which purchases
health services from either public or private facilities. The
contribution is proportional to income, so that within the

11310 Afr. J. Bus. Manage.
pool of SHI members, the better-off subsidise lower
income groups. Also, the healthy and young subsidise
the sick and elderly. To avoid a high-risk pool, SHI is
usually compulsory.
3. Private health insurance, where people buy health
insurance for themselves from private, for-profit
insurance companies.
These companies pay providers of health services for
their members and charge their members’ premiums
according to their health risk status. As a consequence,
the poor usually cannot afford private health insurance.
4. Community-based health insurance entails local
insurance schemes that raise money from their members
to pay for their health services.
5. Out-of-pocket spending is not a health financing
scheme in itself but rather the way money is spent on
health in the absence of a system. In this case, people
buy health services straight from health providers and
pay the full price for the services.
Out-of-pocket spending is very problematic as it causes
people to fall into poverty because of medical expenses.
SHI pools the health risks of its members and the
contributions of organisations. The contributions from the
households and organisations are normally based on
income and government contributions are financed
mostly through taxes (Carrin, 2002).
In principle, SHI involves compulsory membership for
all formal workers, and in this way, it clears the assumption
of voluntarism. SHI depends on the economy of the
country. The more people are employed, the higher the
contributions will be from the members of the insurance.
There are a number of reasons for compulsory
participation. Firstly, it actually avoids the exclusion of
certain population groups, such as the poorest and most
vulnerable. Exclusion in a voluntary scheme can arise
due to lack of political interest in vulnerable groups. The
rationale is that the poorest may simply not be interested
in opting for membership due to a lack of capacity to pay
the proposed health insurance contributions. Secondly,
compulsory insurance by its nature inhibits adverse
selection. The latter occurs in a voluntary framework
when people in good health judge health insurance as
too expensive, and therefore opt not to join the scheme.
According to Carrin (2002), SHI is recognised to be a
very powerful measure for granting the population access
to health services in an equitable manner. For the
implementation of SHI to be effective, the scheme should
assure their members that they will receive the promised
health insurance benefits.
If the health services cannot be delivered as promised,
the trust of the covered population could easily fade away
and this may lead to members no longer paying the
contributions as expected.
The other important issue is that sometimes the
services are available, but the service providers as
appointed to deliver the services simply do not comply
with the SHI principles. This could be as a result of the
service providers being uncertain about their expected
incomes. In the Democratic Republic of Congo (DRC),
during the colonial period in 1885 to 1960, health
financing was supported by the colonial government, civil
societies (religious confessionals) and the private sector.
Of these three groups, the colonial government played an
important role, which led to the control of several
illnesses such as variola (DRC, Ministère de la Santé,
1999:3). Since independence, this organisation has been
ravaged by political troubles, socio-economic crises and
an interruption of bilateral cooperation within the country
(DRC, Ministère de la Santé, 1999).
Financing of the health sector in the DRC was led by
three principal actors:
1. The government by means of the budget (DRC,
Ministère de la Santé, 1999).
2. The Institut National Securité Sociale (INSS), or
National Institute of Social Security (DRC, Ministère de la
Santé, 1999), which deals with health affairs of workers in
the formal sector related to illness and accidents
concerning professionals.
3. The public and private enterprises, where public
enterprises are allowed to deal with the health mission
(DRC, Ministère de la Santé, 1999:5) and private
enterprises are obliged to organise health services for
their employees (DRC, Ministère de la Santé, 1999).
In striving to improve the health sector, DRC authorities
adopted the objective of “health for everybody for 2000”.
This objective involved primary health care as a strategy,
where the financing of health would allow equity, quality
and efficiency (DRC, Ministère de la Santé, 1999).
Despite adherence to the principles and requirements
of the “health for everybody” objective, the widespread
socio-economic crises and reduced commitment of
government led to the diminution of the resources
allocated to the health sector and deterioration of health
indicators. The following illustrates this situation (WHO,
2008):
1. Per capita health expenditure in US dollars: 5 (2005)
2. Life expectancy at birth for males/females: 46/49
(2006)
3. Infant mortality for every 1,000 live births: 129 (2006)
4. Proportion of population with sustainable access to an
improved water source: 46% (2004)
5. Human poverty index rank out of 108 countries: 88
(2007)
6. Gross national income per capita: 140 (2007)
7. When compared to the colonial period, the role of
public authority decreased.
Currently, the DRC context has changed significantly,
with several years of civil war and ongoing conflict in
some provinces within the country which have worsened
the health indicators. In addition, as stated in the
foregoing, the public and private enterprises which are
supposed to provide health care for their employees do

not in practice accomplish this mission (Dibwe et al.,
2005).
In November 2005, in the DRC, the government
created the National Programme of Social Protection
(DRC, Cabinet du président de la République, 2005). The
original intention was that the programme would involve
nine projects, including compulsory health insurance
(DRC, Cabinet du président de la République, 2005).
However, this initiative has not been implemented yet.
This paper intends to explore the employees’ perceptions
regarding the introduction of SHI.
Based on the information in the foregoing, this paper
specifically addresses the issue of the feasibility of SHI
and determines the challenges faced by employees in
formal organisations regarding non-provision of SHI in
Kinshasa, in terms of their sector of employment and
qualifications. Suggestions are also offered for improving
the current situation.
Specific objectives
1. To determine how employees organise and feel about
their health care.
2. To determine the challenges experienced by
employees in the formal sector.
3. To recommend possible ways of addressing the
challenges.
RESEARCH DESIGN AND METHODS
In this study, the quantitative approach was used with the intention
to identify the current situation of health care sponsorship of
employees in Kinshasa (DRC) and to determine the factors that can
explain the choice of SHI by employees. In order to achieve this, a
survey was conducted among employees in Kinshasa. As the
objective of the study was to get opinions about SHI, this survey
method was regarded as appropriate.
The population of this study consisted of employees working in
the formal sector within Kinshasa, as this was where most of the
government departments and companies are situated. The
researcher personally distributed questionnaires to 312 employees,
representing 15 companies/organisations. A simple random sampling
was used, where each individual in the population has an equal
chance of being selected to be a part of the study (Creswell, 2003).
Ethical consideration
Prior to data collection, the researcher obtained permission from
each institution selected in Kinshasa and was aware of their code of
ethics. Informed consent was obtained from the participants by
means of an information leaflet outlining the purpose of the study.
The participants were further informed of their rights that they had a
right to participate and that if they were not interested, they were at
liberty to inform the researcher of their intentions. Participants were
also informed that anonymity would be maintained as no personal
identifiers were used in the research.
Data collection and analysis
Primary data for the study were collected by means of a structured
Kayiba and Rankhumise 11311
questionnaire. The researcher developed the questionnaire as
there was no existing relevant questionnaire that could be used for
this particular study. The steps used by Muheki (1998) were
adopted and adjusted to be in line with the objectives of this
particular research. The questionnaire was self-administered and
contained closed questions. The questionnaire was constructed in
English and translated into French, which is the official language in
DRC. It was divided into four sections. The first section comprised
questions relating to the biographical details of the respondents.
The second section contained questions associated with the sector
of employment. The third section included questions concerning
health care behaviours and household. In the first three sections,
respondents were asked to choose their answers from different
statements. The fourth section included 23 questions regarding the
SHI plan, where a 5-point Likert scale was used, ranging from
“strongly agree”, “agree”, “uncertain” and “disagree” to “strongly
disagree”. Respondents were requested to provide their answer to
each statement concerning the SHI plan by selecting one of the five
choices. However, the researcher collected the data for a period of
two months, and no follow-up research was done due to the fact
that most of the questionnaires were returned.
The collected data were analysed by using SPSS. Descriptive
statistics, which present quantitative descriptions of data in
manageable form (Babbie, 2007), were used. Furthermore, these
quantitative data were analysed at an inferential statistical level with
a computation of chi-square tests to ascertain variations in terms of
educational qualification and sector of employment in relation to
various statements on SHI.
Validity and reliability
To ensure reliability and validity of the instrument, Cronbach’s alpha
test was employed. This is a measure of internal reliability for multiitem
summated rating scales. According to Kent (2001), the coefficient
varies between zero for no reliability to unity for maximum
reliability and it is recommended that a value of 0.7 is achieved. It is
assumed that if the alpha for any scale is greater than 0.7, it reflects
an acceptable directive. For this specific research, the Cronbach’s
alpha coefficient yielded 0.895, which reflects high reliability of the
measuring instrument used for the research.
FINDINGS
Sample realisation
The results were analysed and interpreted based on the
240 returned questionnaires out of the 312 questionnaires
that were distributed to the public and private
sector employees. This constitutes a response rate of
77.2%. The overall number of respondents in the survey
consisted of 192 males (80%) and 48 females (20%).
Some 20.7% of the respondents were in the age group of
24 to 34 years, 34.0% ranged from 35 to 45 years, 31.1%
were in the age group of 46 to 56 years and 14.1% were
in the age group of 57 years and more. Furthermore, of
the 240 responses, 49.2% of the respondents had a
bachelor’s degree or postgraduate qualification, 27.5%
had a national diploma, 22.9% had a matric qualification
and only 0.4% had primary school education. In terms of
the sector of employment, 66.5% of the respondents
were in the public sector, which is a major provider of
employment, 30.1% were in the private sector and only
3.3% were in mixed companies.

11312 Afr. J. Bus. Manage.
Table 1. Highest educational qualification distribution by statements.
Statement
Primary school level
(%)
Matric level
(%)
National diploma
(%)
Bachelor’s/Post-graduate
degree (%)
Choice of service provider when sick
1 Private hospitals 0.0 41.2 51.6 68.0
2 Public hospitals 0.0 37.2 31.3 17.4
3 Traditional healers 0.0 2.0 1.6 0.0
4 Self-medication practice 0.0 9.8 10.9 2.8
5 Church/missionary 100* 9.8 4.6 13.6
Chi square
Reason for choosing service provider 0.000
1 Cheaper price 0.0 45.1 22.2 10.2
2 Good quality of service 100.0* 45.1 42.9 57.4
3 My culture/behaviour 0.0 0.0 0.0 7.4
4 Reputation of the service provider 0.0 5.9 22.2 11.1
5 Other 0.0 3.9 12.75 13.9
Means of payment of medical bills 0.004
1 The company/organisation pays 0.0 13.0 28.1 41.1
2 Out-of-pocket 100.0* 87.0 67.2 51.4
3 Medical insurance pays 0.0 0.0 1.6 0.9
4 Other 0.0 0.0 3.1 6.5
Knowledge about health insurance plan 0.000
1 Yes 0.0 19.6 53.1 68.7
2 No 100.0* 67.4 23.4 17.4
3 Somewhat 0.0 13.0 23.4 13.9
Regarding the type of employment status in the
company/organisation, 96.2% of the respondents
had full-time or permanent employment and 2.5%
had part-time or casual employment.
DISCUSSION
Here, this study focuses on the discussion of the
two tables which present the statements relating
to SHI and inferential statistics for highest
qualifications and sector of employment.
Table 1 reflects the percentages of respondents
relating to their highest educational qualification
by statements and the chi-square P-value of
statements. The findings from Table 1, regarding
the choice of service provider when sick, showed
that 68.0% of the respondents with a bachelor’s
degree/postgraduate qualification went to private
0.005
hospitals when they were sick and 100% with
primary school level education went to church/
missionary hospitals. The chi-square P-value
associated with this statement yielded 0.005.
In terms of service provider, the respondents
attested that they made their choices on the basis
of good quality of the service provided. This represents
57.4% of respondents with a bachelor’s
degree/postgraduate qualification and 100% of

Table 2. Sector of employment by statements.
Statement
Public
sector (%)
Mixed
companies (%)
Kayiba and Rankhumise 11313
Private
sector (%)
Chi
square (%)
Means of payment of medical bills 0.000
1 The company pays 10.6 62.5 71.2
2 Out-of-pocket 83.4 37.5 25.8
3 Medical insurance pays 0.7 0.0 1.5
4 Other 5.3 0.0 1.5
Knowledge about health insurance plan 0.001
1 Yes 44.5 75.0 70.0
2 No 37.7 25.0 12.9
3 Somewhat 17.8 0.0 17.1
respondents with primary school level education.
The results show that despite the level of qualification
of respondents, the majority used out-of-pocket financing
as a way to pay their medical bills. Of these, 100% had
primary school level education, 87% did matriculation,
67.2% had a national diploma and 51.4% had a
bachelor’s degree/postgraduate qualification.
It emerged from the statement “knowledge about health
insurance plan” that 100.0% of respondents with primary
school education and those that did matriculation (67.4%)
did not know about the health insurance plan. 68.9% of
their counterparts with a bachelor’s degree/postgraduate
qualification did know about the health insurance plan.
The chi-square test P-value for this item is 0.000; this
shows a highly significant association between this item
and the highest qualification. The findings also show that
a high percentage (100.0%) amongst the respondents
with primary school level education preferred going to
church missionary hospitals when they were sick.
The percentage of the respondents’ sector of
employment jointly by statements and chi-square P-value
is presented in Table 2.
In terms of the method of payment of medical bills, it
emerged that a greater percentage (83.4%) of the
respondents in the public sector used out-of-pocket
financing to pay their medical bills. 71.2% of their
counterparts in the private sector indicated that their
companies/organisations paid their medical bills. The chisquare
test P-value is 0.000, which shows a high
statistically significant association between this item and
the sector of employment.
Fundamentally, 75.0 and 70.0% of those in mixed
companies and private companies knew about the health
insurance plan, respectively, while only 44.5% of those in
the public sector knew about it.
There seems to be less interest by public sector
employees in SHI. The rationale could be that there is no
hope that they would ever be given support by their
organisations or even the government, in that the chisquare
test P-value represents 0.001, which means a
statistically significant association.
Limitations of the research
Since this research was conducted in Kinshasa only, this
may limit the generalisability of the findings to other
settings and populations. Thus, further research should
attempt to replicate and extend these findings to different
areas of the DRC.
Conclusions
The primary objective of this research was to address the
issue of the feasibility of SHI and to determine the
challenges faced by employees in formal organisations
regarding non-provision of SHI in Kinshasa, DRC. The
findings on which the article is based suggest that there
are challenges faced by employees in the formal sector.
These challenges are as follow:
1. Employees from the public sector are not assisted in
organising their health care.
Consequently, they use the out-of-pocket system to
pay their health bills. This in fact leads to poverty and
vulnerability of the society.
2. Employees (71.2%) in the private sector highlighted
that their companies/organisations paid their medical
bills.
3. The majority of employees indicated that when they
were sick they preferred to go to private hospitals rather
than public hospitals. This is as a result of better services
provided by the private sector. Contrary to this,
employees with primary school level education (100%)
went to the church/missionary hospital when they were
sick.
4. Employees choose the health facility based on good
quality service provided.
5. There is a need to revitalise public hospitals so that

11314 Afr. J. Bus. Manage.
people will use them and thus SHI will be successful.
6. It noted that employees in mixed companies and
private companies know about the health insurance plan;
whilst on average, employees in the public sector have
little knowledge about it.
RECOMMENDATIONS
The following recommendations are made:
1. It is imperative to improve the infrastructure of public
hospitals. As shown in the findings, the majority of the
respondents indicated that they opted for private
hospitals when they were sick and that this choice was
made on the basis of quality service provided by private
hospitals.
2. It is recommended that the public sector should assist
their workforce to pay their medical bills on a pro rata
basis.
3. It is imperative to educate employees about the
introduction of SHI across the workforce spectrum.
4. Once the scheme is implemented, it is essential to
monitor it to ensure that there is no abuse of the system
by both members and service providers and as such
government should build managerial capacity amongst
those who will be monitoring the system.
5. The DRC should consider implementing SHI to ensure
that employees and their families’ health problems are
addressed, especially since the surveyed employees
indicated that if the scheme was introduced, they would
be willing to pay their contributions.
6. Should the government consider introducing the social
health system, it is essential to market the scheme so
that every employee will be conversant with it, since the
scheme would be compulsory.
The discussion in the article provides valuable
information on which employers and the government can
develop a way of addressing the realities of health care
matters in the DRC to ensure that the livelihood of the
employees and their families is improved.
Above all, the introduction of SHI would ensure
accessibility to affordable and high quality health care for
all Congolese communities, irrespective of their wealth
status. In conclusion, based on the responses from
participants, there is an indication of willingness to pay
their contribution should SHI be introduced. Thus, the
government of DRC should consider its position of
“health for everybody for 2000” which was initiated in
2005. This can address the challenges the employees in
the formal sector are experiencing, particularly those in
the public sector, since they are using mainly their own
funds to pay their medical expenses.
REFERENCES
Babbie E (2007). The practice of social research. 11 th ed. Belmont:
Thomson/Wadsworth.
Barnighausen T, Sauerborn R (2002). One hundred and eighteen years
of the German health insurance system: are there any lessons for
middle and low income countries? Soc. Sci. Med., 54: 1559-1587.
Bayarsaikhan D, Kwon S, Ron A (2005). Development of social health
insurance in Mongolia: success, challenges and lessons. Int. Soc.
Sec. Rev., 58(4).
Carrin G, Desmet M, Basaza R (2000). Social health insurance
development in low income developing countries: new roles for
government and non-profit health insurance organizations [Online].
Available from:
http://www.concertation.org/gimi/concertation/resource.do?page=/con
certation/publications/carte/autrespublications/Monographies_323711
7967_2189.PDF [Accessed: 23/04/2010].
Carrin G (2002). Social health insurance in developing countries: a
continuing challenge. Int. Soc. Sec. Rev., 55(2):57-69.
Carrin G, James C (2005). Social health insurance: key factors affecting
the transition toward universal coverage. International Soc. Sec.
Review, 58(1).
Creswell JW (2003). Research design, qualitative, quantitative and
mixed approaches. 2 nd ed. Thousand Oaks: Sage.
Democratic Republic of Congo (2005). Cabinet du président de la
République. 2005. Journal officiel, Déc. No 23, 46e année [Online].
Available from: http://www.radiookapi.net/files/file_file_128.pdf
[Accessed: 13/05/09].
Democratic Republic of Congo (1999). Ministère de la Santé. 1999.
Politique et plan directeur de financement de service de santé
[Online]. Available from:
http://www.minisanterdc.cd/Articles/Reformesanitaire/Politique%20de
% 20financement%20des%20soins.pdf [Accessed: 13/05/09].
Dibwe D, Pochet R, Petit P (2005). La gestion des soins de santé dans
les entreprises de Lubumbashi [Online]. Available from:
http://www.congoforum.be/upldocs/Gestion%20des%20soins%20de
%20sant%C3%A9%20dans%20les%20entreprises%20de%20Lubum
bashi.pdf [Accessed: 23/04/09].
Hsiao CW, Shaw PR, Fraker A, Hanvoravongchai P, Jowett MJ, Pinto
D, Ramachandra S (2007). Social health insurance for developing
nations. Washington: World Bank.
Jutting PJ (2005). Health insurance for the poor in developing countries.
Aldershot: Ashgate.
Kent R (2001). Data construction and data analysis for survey research.
New York: Palgrave Macmillan.
Mcintyre D, Doherty J, Gilson L (2003). A tale of two visions: the
changing fortunes of social health insurance in South Africa. Health
Pol. Plan., 18(1):47-58.
Muheki C (1998). Willingness to pay for social health insurance: a case
study of Kampala (Uganda). Master’s dissertation, University of Cape
Town.
Office of United Nations High Commissioner for Human Rights s.a.
Special rapporteur on the right of everyone to the enjoyment of the
highest attainable standard of physical and mental health [Online].
Available from:
healthchttp://www2.ohchr.org/english/issues/health/right/[Accessed:
11/05/2009].
Preker AS, Scheffler RM, Bassett MC (2007). Private voluntary health
insurance in development, friend or foe? Washington: World Bank.
Polit DF, Beck CT (2008). Nursing research. Generating and assessing
evidence for nursing practice (8 th ed). Wolters Kluwer: Lippincott
Williams & Wilkins.
Saltman, BR, Busse R, Figueras J (2004). Social health insurance
systems in Western Europe. European observatory on health
systems and policies series.
Sherlock LP (2006). When SHI goes wrong: lessons from Argentina and
Mexico. Soc. Pol. Adm., 40(4).
Smith MJ, Solanki GC, Lalloo R, Myburgh NG, Cornell JE (2008).
Support for social health insurance in South Africa. Journal of Health
Management, 10(1):1-8.
World Health Organization s.a (2009). WHO definition of health
[Online]. Available from:
http://www.who.int/about/definition/en/print.html [Accessed:
11/05/2009].
World Health Organization (2007). Report of the technical support

mission for the feasibility assessment and financial projection results
for SHI scheme in Lesotho [Online]. Available from:
http://www.who.int/health_financing/documents/LSO_SHI-
Feasibility_Assessment_Final_Report.pdf [Accessed: 13/05/2009].
World Health Organization (2008). Democratic Republic of Congo,
health actions in crisis [Online]. Available from:
http://www.who.int/hac/crises/cod/drc_profile_dec08.pdf [Accessed:
11/05/09].
Kayiba and Rankhumise 11315

African Journal of Business Management Vol. 5(28), pp. 11476-11486, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.759
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
High-tech companies’ readiness assessment for
alternative workplaces
Jun Ha Kim 1 and Yi-Kai Juan 2
1 Department of Housing and Interior Design, Kyung Hee University, Seoul, South Korea.
2 Department of Architecture, National Taiwan University of Science and Technology (NTUST), 43, Section 4, Keelung
Road, Taipei 106, Taiwan.
Accepted 10 August, 2011
The aim of this research is to provide an understanding of the assessment of the initial readiness for
alternative workplace arrangements (AWA) to assist the process of decision-making for its adoption
from an organizational perspective. A set of readiness level assessment indicators (RLAI) with three
main categories of relative advantage, compatibility and complexity was developed to help decision
makers assess the extent of an organization’s readiness for the adoption of an AWA. Using the RLAI, a
total of 64 real adoption cases were collected from 19 large high-tech companies that had already
adopted any of the six AWA types: hoteling, group address, shared office, home office, and virtual
office. RLAI proposed by this research established a comprehensive and systematic approach and
provided decision makers with helpful guidelines for the AWA readiness assessment. The results
documented in this paper provided many other organizations facing similar decision problems with
insightful strategies and useful implications.
Key words: Alternative workplace arrangements (AWA), innovation attributes, readiness level assessment
indicators (RLAI), rank correlation analysis.
INTRODUCTION
A growing body of evidence shows that globalization and
advances in information and communication technology
(ICT) have prompted a revolution in the way work is
produced. One of the most notable changes is the
establishment of the alternative workplace arrangement
(AWA), in which workers have more freedom in their work
hours and workplaces. As more and more businesses
have begun to adopt AWAs, the number of employees
who are working away from a permanently assigned
office space and those who are geographically and
virtually distributed has been increasing throughout the
world (Venezia et al., 2007; AT&T, 2003). At the same
time, real estate costs, air pollution, and traffic congestion
*Corresponding author E-mail: yikaija@gmail.com.
Abbreviations: AWA, Alternative workplace arrangements;
IRLA, readiness level assessment indicators; ICT, information
and communication technology; TBL, triple bottom line; CVR,
content validity ratio.
resulting from mass commuting have worsened while
companies seek to retain talented, knowledgeable
workers in order to remain competitive (Roper and Kim,
2007).
Nemertes’s benchmark study 2007 discloses that 83%
of the participating organizations consider their workplace
structure as virtual where 91% of participating organizations’
employees work outside of headquarters and about
96% of them utilize some forms of real-time collaboration
tools such as Web conferencing, instant messaging and
video conferencing. Thus, the trend of AWA is likely to
continue in the future, leading to greater reductions in the
traditional ratio of workers to workspace, particularly in
large companies. To successfully respond to the various
demands of the competitive business environment
(Martinez-Sanchez et al., 2008) and to adapt to the
growth of AWAs, such organizations are moving away
from their physical headquarters and entering larger networks
across cities and countries. All of these changes
have forced them to reevaluate their goals and find
solutions to the challenges they face by adopting AWA,
ICTs, and other workplace practices (Roper and Kim, 2007).

The purpose of this research is to provide an
understanding of the assessment of the initial readiness
for AWA to assist the process of decision-making for AWA
adoption from an organizational perspective. The specific
objective is to develop Readiness Level Assessment
Indicators (RLAI) for assessing the extent of an organization’s
readiness for the adoption of an AWA. RLAIs can
be used as to predict the potential successfulness of AWA
adoption. To achieve this, at the outset, with particular
emphasis on Roger’s innovation attributes (Roger, 1995)
and the Leavitt’s model of organizational subsystems
(Leavitt, 1965), surrounding factors and relevant
attributes that can be used to assess the organizational
readiness for AWA adoption were identified and selected,
and based on these parameters, RLAI was developed.
Using RLAI, a total of 64 AWA adoption cases were
collected from high-tech companies for this research.
In exploring various types of AWA, this research was
limited to only primary places for work. If employees work
in an alternative workplace only part of the time and still
retain the permanently assigned workspace at the central
office, organizations cannot expect to reduce their
business cost because they have not actually implemented
an AWA. Therefore, only primary place for work
and full-time AWAs were considered in this research.
Other types of AWA, such as mixed (for example, hoteling
and home office), part-time, and supplemental work-athome
AWAs were not examined in this research. In a literature
review focusing on a primary place of work, AWAs
were limited to six recurring types: on-site workplaces,
such as hoteling; group addresses; shared offices; off-site
workplaces, such as satellite offices; home offices, and
virtual offices. Off-site types were selected based on the
place of work. On-site types could be differently classified
by their space configuration (for example, group office)
and usage (for example, hoteling and shared office).
LITERATURE REVIEW
Much scholarly work has been done on the topics of
AWA, especially on significant factors and characteristics
to be considered for the AWA adoption. Venkatesh and
Vitalari (1992) have provided three factors in a conceptual
model for AWA study: organization/work, ICT, and
household including worker’s characteristics. All three
independent variables are related to a fourth factor:
supplemental work at home which is the dependent
variable in their study. Characteristics surrounding AWA
are organizational, individual, work, household and
technology characteristics (Belanger and Collins, 1998)
and the AWA environments are separated into four
components: social environment, technical environment,
resource environment, and organizational structure
environment (Venezia et al., 2007).
Numerous studies of AWA adoption have focused on
organizational factors (Ruppel and Harrington, 1995),
Kim and Juan 11477
worker and task attributes (Kayworth and Leidner, 2002),
and technological supports (Gupta and Somers, 1995).
Kowalski and Swanson (2005) provide a framework of
critical success factors including support, communication
and trust that are instrumental for organizations looking to
develop an AWA program.
Much research has attempted to find solutions for AWA
adoption issues. Fritz et al., (1994) proposed a method
for determining the suitability of an AWA implementation
framework. (Bui et al., 1996) have described various
types of AWAs and discussed the suitability of each AWA
type from the organizational perspective. Shin et al.
(2000) have depicted a conceptual model of intraorganizational
adoption of telework at four stages: initiation,
adoption, implementation and institutionalization.
Higa and Wijayanayake (1998) have delineated the
adoption patterns of different AWA types by different
areas and the size of organizations in Japan. Higa and
Shin, (2003) have examined and compared five successful
AWA adoption cases and four not-so-successful
adoption cases in terms of four AWA adoption phases:
inception, testing, implementation and future planning.
However, none of the decision models has shown clear
evidence regarding its applicability to the AWA adoption
decision process (Clark, 1998). A literature review reveals
that there is little guidance about which organization,
work types, workers and workspaces are compatible for
AWA programs (Belanger and Collins, 1998), and no
research about how the decision should be made to
adopt a particular type of alternative workplace
arrangement (Fritz et al., 1996). Today’s enterprises need
assistance in assessing their readiness for AWA and
developing a distributed workplace strategy (Harrison,
2002). The problem is that decision makers have no
established tools to assess their readiness for AWA or to
select among the most appropriate AWA type considering
their organizations’ business reasons of adoption and the
current readiness conditions.
ASSESSMENT METRICS
Organizational readiness assessment needs to be
performed to provide decision makers with a reliable,
quantifiable assessment of the organization’s potential
readiness to successfully making the transition to
alternative workplaces (Grantham et al., 2007). Major
business reasons, surrounding factors and relevant
attributes were identified based on Roger’s innovation
attributes and Leavitt’s model of organizational
subsystems as shown in Figure1.
Assessment indicators were finally determined by two
different stages. First, significant factors, relevant
attributes and assessment indicators were initially
selected based on the literature review. Next, the validity
of the selected assessment indicators was additionally
evaluated by the panel of experts formed for this research

11478 Afr. J. Bus. Manage.
Figure 1. Development process for the assessment indicators.
research in order to build up the RLAI, which measure
the initial readiness of high-tech companies for adopting
an AWA.
Innovation attributes
Innovation
Category
Relative
Advantage
Compatibility
Complexity
AWA
Category
Business Reasons
Appropriateness
Challenges
Many researchers describe innovation as a new idea,
policy, process, product or program. An adoption of AWA
as an alternative form of workplace arrangement would
create some type of innovation in the organization. There
are five perceived innovation attributes influencing
adoption intention: relative advantage, compatibility,
complexity, observability and trialability in innovation
diffusion theory (Rogers, 1995).The innovation theory can
be served as a foundation for decision makers to start
considering the adoption of AWA as an organizational
innovation. Among these attributes speeding the adoption
of organizational innovation, which is AWA in this study,
relative advantage, compatibility and complexity are
found more important in influencing adoption decision
(Karnowski and White, 2002; Sia et al., 2004). Therefore,
relative advantage, compatibility and complexity were
used to investigate factors in each of the three AWA
categories: business reasons, appropriateness and
challenges.
From a readiness assessment standpoint, business
reasons were represented as objectives and how clearly
objectives of AWA adoption are identified and selected
were assessed. Next, appropriateness was measured as
appropriateness level and how appropriate in general the
AWA adoption is with existing conditions was evaluated.
Finally, challenges were measured as overcoming level. It
measured whether or not managerial control practices
exist, and if they do exist, evaluated how actively they are
Objectives
Appropriateness
level
Overcoming
level
practiced. All the factors and attributes were extracted
from each AWA category and readiness assessment
areas to build up the RLAI as shown in Figure1.
AWA category
Business reasons
RLAI Category
Economic
Environmental
Social
Organization
Work
Employee
Facilities
Managerial
issues
Relative advantage can be described as the degree to
which an organizational innovation is considered as being
beneficial over the existing practice (Roger, 1995). The
business reasons, objectives or expected benefits of
adopting AWA can be substituted for relative advantage in
adopting AWA. Organizational objectives, for instance,
could be focused on office space cost savings and
improved productivity. Its objectives should match for the
expected benefits of establishing AWA (Roper and Kim,
2007). Adopters of AWAs have reported clear benefits
such as reduced operating costs by office space savings
and requirement parking spaces, improved productivity,
ease in staff recruitment and retention of skilled
knowledge workers, reduced turnover and absenteeism,
improved customer satisfaction, reduced traffic congestion
and environmental impacts, improved employees’
work-life balance, provision of working opportunities for
the elderly and handicapped (Belanger and Collins, 1998;
Kurland and Bailey, 1999; Cascio, 2000; Higa and Shin,
2003; Peters et al., 2004). David Elkington (1998) coined
the concept of the Triple Bottom Line (TBL), which he
introduces to emphasize that a single dimension of economic
value alone cannot fully explain various benefits.
Thus, reporting on social and environmental performance
is also necessary to explore its various benefits. The TBL
concept provides with convincing approach to appreciate

Table 1. Reasons for AWA adoption in the TBL concept.
Kim and Juan 11479
Aspect Adoption reason Variable
Retention/attraction of skilled workers A1
Reduced office space costs A2
Economic
Improved productivity A3
Reduced turnover and absenteeism A4
Improved customer satisfaction A5
Environmental Reduced traffic congestion, better air quality A6
Social
business reasons of AWA adoption from three aspects.
Various business reasons for AWA adoption can fall into
three categories as classified in the triple bottom line
(TBL) as depicted in Table 1.
Appropriateness
When the adoption of an AWA is seen as suitable given
an organization’s existing conditions, it can be concluded
that the organization is ready to adopt the AWA. The
existing conditions can be represented as significant
factors surrounding the AWA. Leavitt selected four
elements for describing an organization: technology,
structure, task and people (Leavitt, 1965), and management
scholars added organizational culture to the four
elements selected by Leavitt (Gordon and Olson, 1985).
These elements gave an outline at the beginning to
identify the important factors for AWA adoption when
measuring appropriateness for a given organization. As
work patterns and structures evolve faster than
workplaces are able to adapt, business patterns are also
dramatically changing. AWAs can be attractive to large
organizations due to their potential benefits, but
organizations need to consider whether or not their goals,
objectives, and surrounding conditions, as well as
characteristics of their work, employees, and facilities,
are appropriately suited for readiness assessment.
Challenges
At the organizational level, control, coordination and
supervision of distributed workers are much harder than
they are for workers on-site. Most challenges in adopting
AWA are found in the area of managerial issues,
including performance evaluation and coordination from
the organizational perspective. It can be assumed that
well-prepared managerial actions including a wide range
of activities dealing with challenges in AWA settings can
positively influence the success of AWA adoption. Areas
in which AWA can lead to difficulties are performance
Employment opportunities for aging and handicapped people,
better employee work-life balance
evaluation, supervision, coordination, policy and guideline
provision and learning opportunities for distributed
workers (Fritz et al., 1996; Fritz et al., 1998; Apgar, 1998;
Kurland and Bailey, 1999; Cascio, 2000; Felstead et al.,
2003; Roitz and Jackson, 2006).
A7
Readiness level assessment indicators (RLAI)
In addition to the final selections of significant factors and
attributes, assessment items for each attribute were
added to complete the RLAI. RLAI was developed to
provide decision makers with an understanding of how to
assess the initial organizational readiness AWA adoption.
The survey questions were designed to get RLAI
validated by experts. According to a three-point likert
scale (“1”= not necessary, “2”=important but not
essential, “3”=essential) each assessment item was carefully
rated by the 15 experts and finally selected for this
research using the content validity ratio (CVR) method to
ensure the adequacy of indicators items. The CVR, which
is an item statistic, is helpful in the retention or rejection
of specific items (Lewis et al., 1995). According to the
CVR table published by Lawshe, a CVR of 0.49 is
minimally required for each item to prove its validity for
AWA readiness assessment metrics when there are 15
people on the content evaluation panel. Among all items
identified from the literature review and finalized through
the analysis of the results on the first survey, items with
CVR values higher than 0.49 were retained, and a list of
18 assessment items was finalized for RLAI as shown in
Table 2.
DATA COLLECTION AND ANALYSES
Through telephone interviews, conference calls and email
questionnaires, the RLAIs are used to collect a total of 64 real
adoption cases from 19 large high-tech companies such as
computer, consumer electronics, engineering, IT, networking and
telecom companies that had already adopted any of the six AWA
types: hoteling, group address, shared office, satellite office, home
office, and virtual office. Most of the companies provided more

11480 Afr. J. Bus. Manage.
Table 2. Readiness level assessment indicators (RLAI).
Innovation
Attribute
Relative Advantage
Compatibility
RLAI
Category
Factor Attribute Variable Assessment indicator Readiness assessment
Please mark top three important objectives of AWA adoption for your company with an X Check your objectives
Objectives
Triple Bottom
Line(TBL)
A1 Retention/attraction of skilled employees
A2 Reduced office space costs
Economic
A3 Improved productivity
A4 Reduced turnover and absenteeism
A5 Improved customer satisfaction
Environmental A6 Reduced traffic congestion and environmental impacts
Social A7 Employment opportunities for aging and handicapped people and employees’ work-life
balance
What is the appropriateness level of your company for each assessment indicator? Please rate each item for your case (“1”= Relatively low, “2” = medium, “3” = relatively high)
Appropriateness level
Organizational
culture
Work
Employee
Facilities
Support X1 AWA is supported at all levels of organization
Fairness X2 The degree of equal promotional opportunity for distributed workers
Trust X3 The level of trust between managers and their employees
Type X4 The level of interaction/communication needed to perform the work
The degree of sequential work process vs. reciprocal work process
Process X5
(The degree of transactional vs. open ended work process) Close to reciprocal
process:1, close to sequential process:3, middle or both: 2
Autonomy X6 The degree of autonomy for work (work scheduling, decision prerogatives, etc.)
Deliverables X7 The level of clarity of defined deliverables
Physical presence X8 Required physical presence at the office for work to be able to access specific
technology, equipment or live interpersonal response(location dependency)
Preference X9 Employees’ level of preference for AWA
Sufficiency X10 Employees’ level of self-sufficiency to work
Familiarity X11 Employees’ level of familiarity with ICT
Experience X12 Employees’ work experiences with flexible work style
ICT X13 The provision of ICT support
Premises services X14 Building maintenance, cleaning, alternative workplace services, etc.
Business support
services
X15 Utilities, furniture, business equipment, office set-up, etc.
What is the overcoming level (establishment capacity) for each assessment indicator? Please rate each item for your case (“1” = if not exist, “2” = if exist but not actively in practice, “3” = if exist
and actively in practice)

Table 2. Contd
Performance
evaluation
X16 Results-based performance evaluation method in practice
Managerial issues Coordination
(Teamwork)
X17 Virtual teamwork in practice within the organization
Policy/Guideline X18 Clear policy/guideline provision for AWA
Outputs Y1 Please select only one type with an X for Hoteling Group
your case
address
Complexity
Overcoming level
provided more than one adoption cases since most of the
cases are documented by different years. The number of
cases documented in 2005 was three; in 2006, six; in
2007, eleven; and in 2008, 22. The number of ongoing
cases was 22.
Case description on objective of AWA
Adoption (A1-A7)
Major objectives of the actual adoption cases from
high-tech companies were tabulated as shown in
Table 1 and differences between responses from
the experts without focusing on technology
companies and the ones from the actual adoption
cases from technology companies were identified.
The most important objectives of AWA adoption
from both experts (26.7%) and actual adoption
cases from technology companies (25.5%) were
“reduced office space costs”. The second most
important objectives selected by the experts
(22.2%) were “retention/attraction of skilled
employees,” whereas the second most important
objectives selected by the actual cases (25.0%)
were “improved productivity.” Finally, the third
most important objectives selected by the experts
(17.8%) were “improved productivity,” whereas the
third most important objectives selected by the
actual cases (14.6%) were “retention/attraction of
Shared
office
Y2 Please rate your overall satisfaction with the adoption (“1”=less satisfied, “2”=satisfied,
“3”=highly satisfied)
skilled employees”. More than half of the
technology companies felt that reduced office
space costs and improved productivity are important.
Therefore, it can be concluded that there
is not much difference between selecting general
objectives and specific objectives of high-tech
companies for AWA adoption. Most technology
companies felt that reduced turnover and
absenteeism, improved customer satisfaction,
reduced traffic congestion and environmental
impacts, and employment opportunities for aging
and handicapped people and employees’ work-life
balance (A4-A7) are not important.
Case description on type selection (Y1) and
satisfaction level (Y2)
As shown in Table 3, among on-site types,
hoteling was selected by eleven cases
representing 17.2% followed by shared office
(14.1%) and group address (12.5%). Among offsite
types, satellite office was selected by thirteen
cases representing 20.3%, followed by virtual
(18.8%) and home office (17.2%). Over 55% of
the technology companies allowed off-site working
situations. As suggested by respondents in the
pre-testing stage, only 20.3% of the cases were
Kim and Juan 11481
Satellite
office
Home
office
Virtual
office
categorized as “less satisfied,” and 28.1% of the
cases were marked as “satisfied”. More than half
of all the cases were categorized as “highly satisfied”.
About 80% of all the cases were marked
as “satisfied” or above with their satisfaction level,
which means that adoption cases collected by
RLAI, which was expected to effectively capture
the readiness level of the organizations, could
represent best practices for AWA adoption.
Case description on readiness level (X1-X18)
Most of the high-tech companies seemed to have
rated the readiness levels, named as X1-X15,
“medium.” However, a substantial number of
companies rated X2, X6, X9, and X13 “relatively
high,” as shown in Table 4. Among managerial
issues measuring the establishment capacity of
managerial practices, results-based performance
evaluation methods and virtual teamwork in
practice within the organization existed in most
technology companies, but they were not actively
in practice. However, most technology companies
not only had a clear policy/guideline provision for
AWA, but actively followed it. Among the 18
variables measuring the readiness level of each
case, 16 variables were positively correlated with

11482 Afr. J. Bus. Manage.
Table 3. Outputs of AWA adoption.
High-tech company
Type selection (Y1) N %
Hoteling 1 11 17.2
On-site
Group address 2 8 12.5
Shared office 3 9 14.1
Off-site
Satellite office 4 13 20.3
Home office 5 11 17.2
Virtual office 6 12 18.8
Satisfaction level (Y2) 64
Less satisfied 1 13 20.3
Satisfaction level Satisfied 2 18 28.1
Highly satisfied 3 33 51.6
Table 4. Readiness levels of collected cases.
X1-X15
Organization
Work
Employee
Facilities
X16-X18
Managerial
issues
High-tech Company
Relatively low(1) Medium(2) Relatively high (3)
N % N % N %
Support X1 7 10.9 39 60.9 18 28.1
Fairness X2 7 10.9 34 53.1 23 35.9
Trust X3 11 17.2 42 65.6 11 17.2
Type X4 12 18.8 42 65.6 10 15.6
Process X5 11 17.2 35 54.7 18 28.1
Autonomy X6 10 15.6 34 53.1 20 31.3
Deliverables X7 15 23.4 31 48.4 18 28.1
Physical presence X8 25 39.1 30 46.9 9 14.1
Preference X9 16 25.0 23 35.9 25 39.1
Sufficiency X10 9 14.1 39 60.9 16 25.0
Familiarity X11 14 21.9 32 50.0 18 28.1
Experience X12 24 37.5 28 43.8 12 18.8
ICT X13 5 7.8 32 50.0 27 42.2
Premise X14 17 26.6 35 54.7 12 18.8
Business X15 10 15.6 37 57.8 17 26.6
Does not
exist (1)
Exist but not
active (2)
Exist and
active (3)
N % N % N %
Performance evaluation X16 12 18.8 26 40.6 26 40.6
Coordination(Teamwork) X17 18 28.1 32 50.0 14 21.9
Policy/guideline X18 29 45.3 17 26.6 18 28.1
the satisfaction level of AWA adoption, and among the 16
readiness variables showing a positive rank correlation
with Y2, 13 variables were significant at the .01 level
(99% level) for a two-tailed prediction.
DISCUSSION
While describing AWA adoption cases collected from
high-tech companies, some important features of

Figure 2. Overcoming level comparison between different AWA types.
successful AWA adoption cases were revealed. One was
the relatively more important objectives by different AWA
type, and the other feature was that there are different
readiness levels expressed in appropriateness and
overcoming levels as reported for on and off site types.
First, relatively more successful cases were extracted
from the case data. Only 33 cases with the satisfaction
level “3”, highly satisfied with AWA adoption, were
selected for more analysis to determine suggested
objectives by different AWA types. For each AWA type,
the main objectives of each case have been identified
based on the higher frequency of selection from the
respondents. According to the frequency of selection, the
main objectives for adopting each AWA type are the
following:
(i) Hoteling: “reduced office space costs” and “improved
productivity”
(ii). Group address: “improved productivity” and “reduced
turnover and absenteeism”
(iii) Shared office: “reduced office space costs” and
“employee’s work-life balance”
(iv) Satellite office: “improved productivity” and “improved
customer satisfaction”
(v). Home office: “reduced office space costs” and
“improved customer satisfaction”
(vi) Virtual office: “reduced office space costs” and
“improved customer satisfaction”
Next, the average overcoming levels were calculated
from successful cases to compare differences in overcoming
levels between on-site and off-site types. Among
three overcoming measurement areas, levels of off-site
types were higher than the ones of on-site types in all
three areas as shown in Figure 2 This confirms the previous
research findings (Kurland and Bailey, 1999) that
managerial challenges become greater as distributed
Kim and Juan 11483
workers are further from the central office in terms of
physical distance so that higher overcoming levels are
needed for successful AWA adoption.
Finally, the average appropriateness levels were
calculated from successful cases to compare differences
in the levels between on-site and off-site types. Among
the 15 appropriateness measurement areas, levels of off-
site types indicated higher than the ones for on-site types
in 11 areas. However, the appropriateness levels for onsite
types found to be higher in four areas are as follows
(Figure 3):
(i) The level of interaction/communication needed to
perform the work
(ii) The degree of sequential work process vs. reciprocal
process (The degree of transactional vs. open ended
work process) “close to reciprocal process”: 1, “close to
sequential process”: 3, “middle or both”: 2
(iii) Required physical presence at the office for worker to
be able to access specific technology, equipment or live
interpersonal response (location dependency)
(iv) The level of premise supports
The areas in which obvious distinctions between on-site
and off-site types revealed were work types, process and
physical presence. These attributes are all under “work”
factor.
This indicated that the level of interaction needed to
perform the work and the level of required physical
presence at the office are found to be lower in off-site
types. Surprisingly, sequential work processes were
found more in on-site types, whereas it was assumed that
sequential work processes would be found more in offsite
types. This indicated that work process is not a
critical attribute in selecting an appropriate AWA type
because work process itself doesn’t seem to be a
significant issue anymore with the development of

11484 Afr. J. Bus. Manage.
sophisticated ICT.
Figure 3. Appropriateness level comparison between different AWA types.
Figure 4. Spearman and Kendall’s Correlation between X’s and Y2.
Rank correlation analysis results
Rank correlation analysis was conducted to measure the
association between two ordinal variables: the readiness
level and the satisfaction level. Two rank correlations,
Spearman’s and Kendall’s, show similar results. Among
the 18 variables measuring the readiness level for each
case, the 16 variables were positively correlated with the
organization’s satisfaction level with AWA adoption as
depicted in Figure 4. Among the 16 variables showing a
positive correlation with Y2, 13 of them were significant at

the 0.01 level (99% level) for a two-tailed prediction.
Assuming that X4 and X8 are less significant variables
measuring the readiness, it is concluded that a positive
correlation exists between organizational readiness level
for AWA adoption and organization’s satisfaction with
AWA.
CONCLUSION AND FUTURE RESEARCH
This research resulted in the development of RLAI that
decision makers can use when measuring their
organization’s readiness for AWA adoption. Secondly,
three important features were revealed from analyzing
only 33 best practice cases. First, more important
objectives were found in each of six AWA type adopted by
participating high-tech companies. Another finding was
that the average overcoming levels of off-site types are
higher than those of on-site types in three measurement
areas such as “results-based on performance evaluation
method in practice”, “virtual team work in practice within
the organization” and “clearly written policy, guideline and
procedure provision for AWA”. The final feature was that
among the 15 appropriateness measurements, the
average appropriateness levels of off-site types indicated
higher incidence than the ones of on-site types in 11
areas.
However, appropriateness levels for on-site types were
higher in the four features of “the level of interaction/
communication needed to perform the work”, “the degree
of sequential work process vs. reciprocal process (The
degree of transactional versus open ended work process)”,
“required physical presence at the office for work
to be able to access specific technology, equipment or
live interpersonal response” and “the level of premise
supports including building operation and maintenance
and cleaning.
Finally, of special note, X1, X3, X7, X12, X13, X15 and
X18 were highly correlated with the satisfaction level
expressed in how much an adoption meets the initial
objective of the AWA adoption(correlation is significant at
the .01 level). Therefore, it is concluded that a positive
rank correlation exists between organizational readiness
level for AWA adoption and organization’s satisfaction
with AWA.
The scope of this research was limited to only the
initiation stage and adoption stage. In the future, it will be
necessary to extend the scope to the next implementation
stage, where a detailed feasibility study including
cost estimation and risk analysis for the final adoption
decision is conducted. In future research, based upon a
larger sample of AWA adoption cases from other
industries, more efforts could be researched to develop
decision support systems which can provide even more
accurate and solid predictions regarding AWA adoption
decision issues as well as measure the performance of
distributed workers.
REFERENCES
Kim and Juan 11485
Apgar M (1998). The alternative workplace: changing where and how
people work. Harv. Bus. Rev., 76: 121-136.
AT&T (2003). Remote working in the net-centric organization. Retrieved
November 17, 2010, from
http://jobfunctions.bnet.com/abstract.aspx?docid=317586.
Belanger F, Collins RW (1998). Distributed work arrangement: a
research framework. Inf. Soc., 14: 137-152.
Bui T, Higa K, Sivakumar V, Yen J (1996). Beyond Telecommuting:
Organizational Suitability of Different Modes of Telework in System
Science. Proceedings of the Twenty-Ninth Hawaii International
Conference IEEE 2nd to 6th January 1996, Hawaii, USA.
Cascio WF (2000). Managing a virtual workplace. Acad. Manage. Exec.,
14(3): 81-90.
Elkington J (1998). Cannibals with Forks: The Triple Bottom Line of 21 st
Century Business. New Society Publishers, Jackson, TN.
Fritz MB, Higa K, Narasimhan S (1994). Telework: Exploring the
Borderless Office in System Science. Proceedings of the Twenty-
Seventh Hawaii International Conference. IEEE 3rd to 7th January
1994, Hawaii, USA.
Fritz MB, Narasimhan S, Rhee H (1996). Adoption of Remote Work
Arrangements: An Initial Analysis. Proceedings of the Twenty-Ninth
Hawaii International Conference IEEE 2nd to 6th January 1996,
Hawaii, USA.
Fritz MB, Narasimhan S, Rhee H (1998). Communication and coordination
in the virtual office. J. Manage. Inform. Syst., 14(4): 7-28.
Gilleard JD, Rees D (1998). Alternative workplace strategies in Hong
Kong. Faculty, 16(5/6): 133-137.
Gordon BD, Olson MH (1985). Management Information Systems:
conceptual foundations, structure and development. 2nd ed. New
York, NY: McGraw Hill.
Harrison A (2002). Accommodating the new economy: the SANE space
environment model. J. Corp. Real Estate, 4(3): 248-265.
Higa K, Wijayanayake J (1998). Telework in Japan: Perceptions and
Implementation in System Science. Proceedings of the Thirty-First
Hawaii International Conference IEEE 6th to 9th of January 1998,
Hawaii, USA.
Higa K, Shin B (2003). The telework Experience in Japan. Commun.
ACM, 46(9): 33-242.
Karnowski A, White BJ (2002). The role of facility managers in the
diffusion of organizational telecommuting. Environ. Behav., 34(3):
322-334.
Kayworth TR, Leidner DE (2002). Leadership effectiveness in global
virtual teams. J. Manage. Inform. Syst., 18(2): 7-40.
Kowalski KB, Swanson JA (2005). Critical success factors in developing
teleworking programs. Bench., 12(2): 236-249.
Kurland NB, Bailey ED (1999). The Advantages and challenges of working
here, there, anywhere, and anytime. Organ. Dyn., 28(2): 53-68.
Lawshe CH (1975). A Quantitative Approach to Content Validity. Pers.
Psych., 28: 563-575.
Lewis CH, Snyder CA, Rainer KR (1995). An empirical assessment of
the information resource management construct. J. Manage. Inform.
Syst., 12(1): 199-223.
Martinez-Sanchez A, Perez-Perez M, Vela-Jimenez MJ, de-Luis-
Carnicer P (2008). Telework adoption, change management, and firm
performance. J. Organ. Chang. Manage., 21(1): 7-31.
Nemertes benchmark. Building a successful virtual workplace 2007.
Retrieved March 15, 2010, from
http://www.nemertes.com/networkingelecommunications/nemertes
benchmark building a successful virtual workplace.
Peters P, Tijdens KG, Wetzels C (2004). Employees opportunities,
preferences, and practices in telecommuting adoption. Inform.
Manage., 41(4): 469-482.
Rogers EM (1995). Diffusion of Innovations. New York, NY: Free Press.
Roitz J, Jackson E (2006). AT&T adds business continuity to the long
list of telework’s advantages. J. Organ. Excell., (Spring): 3-12.
Roper K, Kim J (2007). Successful distributed work arrangements: a
developmental approach. J. Faculty Manage., 5(2): 103-114.
Ruppel C, Harrington S (1995).Telework: an innovation where nobody is
Getting on the bandwagon?. ACM SIGMIS Database Arch., 26(2/3):
87-104.

11486 Afr. J. Bus. Manage.
Shin B, Sheng OR, Higa K (2000). Telework: Existing Research and
Future Directions. J. Organ. Comp. Elect. Com., 10(2): 85-101.
Swan B, Belanger F, Watson-Manheim M (2004). Theoretical
Foundations for Distributed Work: Multilevel, Incentive Theories to
Address Current Dilemmas in System Science. Proceedings of the
Thirty-Seventh Hawaii International Conference. IEEE 5th to 8th
January 2004, Hawaii, USA.
Venezia C, Allee V, Schwabe O (2007). Designing productive spaces for
mobile workers: Role insights from network analysis. Inform. Knowl.
Syst. Manage., 5: 1-15.
Venkatesh A, Vitalari NP (1992). An emerging distributed work
arrangement. Manage. Sci., 38(12): 1687- 1706.

African Journal of Business Management Vol. 5(28), pp. 11442-11449, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.586
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Exploring agency problems in corporate governance
from the perspective of economic ethics of the
capitalist market
Hsiang-Yi Lin 1 * and Chih-Wen Huang 2
1 Graduate Institute and Department of International Business, Ching Yun University, 229 Chien-Hsin Rd., Jung-Li 320,
Taiwan R.O.C.
2 Graduate Institute and Department of International Business, Tamkang University,151 Yingzhuan Rd., Danshui Dist,
New Taipei City 25137,Taiwan R.O.C.
Accepted 16 August, 2011
Moral hazard is reflected in the information asymmetry between the agent and the principal, as well as
under the assumption that humans are egotistic with limited rationality, often risk-averse and
antagonistic towards each other’s goals. The agent could thus hide the truth from the principal, not
abide by their mutual agreement, and tamper with the investment objectives and plans. From the
perspective of Taiwan’s external market mechanisms to corporate governance, shareholders
(principals) can exploit situations such as poor sales of company products or corporate managers
failing to yield satisfactory performance, etc., to monitor and keep the managerial hierarchy (agents) in
check through capital market and corporate control market. A practical way to avoid such moral hazard
is for the agents to follow the Golden Rule advocated by Hans Küng “Do unto others as you would have
others do unto you”, and to live up to the ethical principle of “commitment to a culture of tolerance and
a life of truthfulness”. Relatively, the agency costs required by other methods to reduce information
asymmetry and moral hazard problems seem comparatively higher than the former.
Key words: Agency problem, corporate governance, business ethics, moral hazard information asymmetry,
economic ethics of the capitalist market.
INTRODUCTION
Transaction costs in market activities can be divided into
search and information costs, negotiating and contracting
costs, enforcement and monitoring costs. If the
transaction information of both parties appears unequal
and asymmetric, that is, one party has more information
or information of higher value, whereas the other party is
unable to obtain information of the same quality and
quantity, the implication is that the search and information
costs of the transaction costs are too high, or that one
transaction party has valuable private information, this is
known as information asymmetry (Kuo, 1996).
*Corresponding author. E-mail: orthodoxianikita@gmail.com.
Tel: 886-937-525-357.
If one party of the transaction is the principal and the
other the agent, after the negotiating and contracting
costs are processed and the contract is signed, yet after
the signing, the agent exhibits dereliction of his administrative
duties, which results in an increased risk and
subsequently an increase in enforcement and monitoring
costs, this is called “moral hazard”. Moral hazard will also
be reflected in the information asymmetry between the
agent and the principal, as well as under the assumption
that humans are egotistic with limited rationality, often
risk-averse and antagonistic towards each other’s goals,
the agent could thus hide the truth from the principal, not
abide by their mutual agreement, and tamper with the
investment objectives and plans; or the agent has access
to information that is prone to internal and external
changes, and this information would compromise the

interests of the principal who is obviously disadvantaged
(without this information). Accordingly, since the agent's
self-interest is placed above the interests of the principal,
and the agent also will not advise the principal, thus
leaving the interests of the principal damaged. Although
principals and agents appear to be in cooperative relationship,
their conflicting interests may cause a number of
problems such as moral hazard, adverse selection, and
risk aversion. Therefore, the occurrence of moral hazard
could result in information asymmetry, and it is the main
cause of the agency problems. The following chapter will
further discuss issues of transaction costs, information
asymmetry and agency problems, and their intricate
relationship.
Transaction costs, information asymmetry and
agency problems
The traditional neoclassical theory of the firm does not
consider the existence of market transaction costs. So,
under such framework, the information costs market
participants face not only equate to zero, but the
opportunities to access the same information are also
equal, and there is no moral hazard or agency problems
caused by the prior or subsequent information asymmetry.
However, the reality of the economic world is closer to
that of Coase's transaction cost theory of the firm, in
which transaction costs in market activities are everywhere.
Founder of the New Institutional Economics, and
also the 1993 Nobel Laureate in Economics, D.C. North
(1990, 3-16), recognized the profound influence of
Coase’s two papers “Problem of Social Cost” and “The
Nature of the Firm” in his exploration of institutional
factors of the economic development process. As previously
discussed in the first chapter, Coase clarified the
basis of how firms exist, and Adam Smith emphasized
that the omnipotent yet invisible hand of the market would
automatically reach a market equilibrium, but it does not
consider transaction costs in the actual economic activities,
making market participants often use organizations
such as a firm rather than rely on market mechanisms.
After Coase examined the system and the organizational
factors of economic activities, he believed that when
market transaction costs become too high, the establishment
of firms can minimize parts of the transaction costs,
thus justifying the existence of firms. So Coase believed
that the existence of firms is to avoid some of the
transaction costs. This belief further inspired him to
propose the institution model that would reduce the
transaction costs.
The core of information economics lies in incomplete
information and information asymmetry (Wang and Chen,
2003), and both are reasons in the increased transaction
costs. The issues of profits, risks and uncertainties discussed
by Knight (1985) are in fact closely related to the
abovementioned incomplete information and information
Lin and Huang 11443
asymmetry, and inevitably made a profound impact on
the later development of information economics. In the
case of information asymmetry, the buyer wants to know
the seller’s thought, and the seller would also like to know
the buyer’s upper limit. So the Game Theory developed
by Von Neumann and O. Morgenstern is widely applied in
information economics. In information asymmetry, when
the principal entrusts the agent with a task, moral hazard
may occur when the agent lacks strong ethical and moral
self-control. This occurrence is what Arrow (1984)
referred to as “hidden action” and “hidden information”.
When a firm is established, all the activities in this organization
are a series of principal and agent relationship,
this is where the Agency Theory is developed, which
focuses on dealing with the relations of division of labor
and contract (Perrow, 1986; Eisenhardt, 1989).
The rise and development of the Agent theory is closely
related to the studies of economics system. So Coase’s
Theory of the firm did affect the economists of The
Institutional School, such as the 1986 Nobel Laureate in
Economics, Buchanan (1983), who was the first to explicitly
consider politics a concept of transactions, as well as
use the ‘economic man’ model to observe political
exchange and the institutional structure derived from
transactions, and was the man behind expanding the
domain of Constitutional Economics and Public Choice
Theory (Buchanan, 1983; Gan and Huang, 1994). G.
Tullock shared equal fame with Buchanan in the Public
Choice Theory, but his untimely death failed to garner him
the Nobel Prize in Economics. They both were deeply
influenced by the transaction Cost Theory of Coase,
especially his paper “The Nature of the Firm”, which led
the economists to think about the role firms play in
market activities, and prompted Buchanan and Tullock to
reconsider the function of the government in the political
market, they even regarded the government as a super
company (Buchanan, 1983; Gan and Huang, 1994).
North (1990) developed transaction costs from the political
process to accentuate a close relationship between
the merit and defect of the system and the economic
growth. Gilpin (1987), on the other hand, noted that the
New Institutionalism and the Hegemonic Stability Theory
of the international political economics and were all
inspired by the exposition of the transaction cost theory.
When Williamson (1975, 1986, 1996) examined the
relation between the issues of transaction costs and
information asymmetry, he also proposed six factors that
would affect transaction costs: bounded rationality, opportunism,
uncertainty (arisen from risks), small-number
bargaining, information impactedness and atmosphere.
He also divided transaction costs into seven categories:
searching (information) costs, negotiating costs,
contracting costs, monitoring costs, enforcement costs,
inspection costs and service costs. Consequently, information
asymmetry in transactions inspired him to develop
the concept of moral hazard. Williamson (1975) further
combined the concept of moral hazard with Simon’s

11444 Afr. J. Bus. Manage.
(1964) bounded rationality of the Economic Man Theory
and advocated that information asymmetry causes
behaviors such as negotiating, uncertainty, opportunism
and others, which disrupt market operations, and these
behaviors can be used to explore the fragility, unreliability
and its derived agency problem in the contracts of market
transactions.
In fact, agency problems were first identified by Berle
and Means (1932) when they observed modern corporations
separating the ownership from the managerial
authority, and concluded that agency problems were
inevitable between the shareholders and the managers.
Jensen and Meckling (1976) also observed that when a
principal employs an agent through contract to perform
certain task on his (the principal’s) behalf, the principalagent
relationship thus exists, and agency problems will
inevitably occur. This theory emphasizes that the principal
and the agent have their own rationalized self-interest
and preferences, so both are inclined to maximize their
personal interests, but, when conflicts of interest between
both of them occurs, especially in the situation of
information asymmetry, then the possibility of the agent
deceiving the principal or not performing his commissioned
duty would arise. This refers to the so-called
agent cost, and the agent cost is deemed the major
cause of the agency problem noted in this research.
In addition, the 1982 Nobel Laureate in Economics,
Stigler (1988) first proposed the Coase Theorem in his
autobiography (Memoirs of an unregulated economist), a
term he used to express his admiration towards Coase’s
works. However, Stigler also noted that Coase’s two
papers, “The Problem of Social Cost” and “The Federal
Communications Commission”, relate that information is
a commodity, a valuable product that can be purchased.
Stigler further added that market transaction costs can be
lowered without the existence of a single owner. However,
in the situation of a single owner, the information is no
longer regarded as valuable commodity, thus problems
like information asymmetry, moral hazard and agency
problems will not occur. The 2001 Nobel Laureate in
Economics Akerlof’s “Lemons” model states that information
asymmetry arises because experienced sellers of the
used car market in the U.S. want so desperately to sell
their used cars to used car buyers that they conceal the
condition of the vehicle, thus resulting in moral hazard
(Akerlof, 1970). Similarly, if stock market investors are
clueless about high-risk or low risk listed companies and
cannot distinguish between mines stocks and blue chip
stocks, then this stock market is volatile and unbalanced.
Thus, the above discussion reveals that in market
transactions, the party with more information should be
honest and open with full disclosure of information to
eliminate any information asymmetry, which means, he
should possess ethical values of honesty and transparency
so that markets can achieve equilibrium.
Rasmusen (1989) broadly defined agency problems as
principal-agent problems, so adverse selection and moral
hazard are a kind of agency problems. He believed that
information asymmetry can cause three agency
problems, namely hidden action, hidden information, and
adverse selection, all these three agency problems
jeopardize the principal’s interests. Therefore, the
imperfection in markets impedes the formation of an
optimal contract the principal and agent, thus causing an
incomplete contract drawn between the two, this means,
that although both parties agree to abide by the rights
and obligations in a market contract, in an incomplete
contract, the agent’s hidden action or hidden information
inevitably leads to deception. This is the main cause in
the outbreak of the Enron accounting fraud scandal at the
end of 2001, and also the dilemma that corporate
governance wishes to overcome.
Ethical considerations and agency problems
As mentioned earlier, the main reason in agency problems
arises from moral hazard that is caused by information
asymmetry between the principal and the agent.
Eisenhardt (1989), who had conducted in-depth
discussions on agency problems and their implications,
particularly talked about the agents’ tendency towards
selfish motives and risk aversion in his assumptions of
human behavior, and also noted that the occurrence of
moral hazard is expected because the objectives
between principals and agents are in conflict, and their
risk preferences not aligned. For a detailed discussion,
please see Table 1.
Therefore, minimizing agency problems and moral
hazard is closely related to the compliance of corporate
ethics. Barnea et al. (1981) divided the sources of agency
problems into three types namely information asymmetry,
debt financing and minority shares held by internal
stakeholders. One of the aforementioned sources, debt
financing, is what the academia called the debt agency
problem. The third source, minority shares held by internal
stakeholders, is referred to as equity agency problem.
Fama (1978), Smith and Warner (1979), Stulz and
Johnson (1985) all noted that when corporate creditors
face information asymmetry and shareholders and
corporate managers lacking in capability and integrity,
they are unable to get access to the necessary
knowledge, thus resulting in agency problems between
corporate shareholders and creditors. This debt agency
problem can be further divided into four sub-problems,
namely asset substitution, claim dilution, perquisite
consumption, and underinvestment. The following is a
detailed explanation:
1. Asset substitution problem occurs when firms borrow
money from creditors, corporate managers often engage
in high-risk investment plans to pay off the debts and the
interest due, which may cause the creditors to lose everything,
resulting in their interests being compromised.

Table 1. Theoretical implications of agency problems.
Lin and Huang 11445
Main ideas Determine the most efficient information organization and risk-sharing costs
Specify the agency relationship between principals and agents
Unit of analysis The contract signed by principals and agents
Assumptions on human behavior Selfish motives
Bounded rationality
Risk aversion
Assumptions on organizations Conflict of objective between the organization members
Information asymmetry problem between principals and agents
Efficiency is the indicator of measuring organizational effectiveness
Assumptions on information Information is a commodity
Contracting problems Moral hazard
Adverse selection
Risk sharing
Scope of the problem Agency problems arise when there is a conflict of objectives between principals
and agents, as well as an increase in costs required to monitor the behavior of the
agents
Source: Eisenhardt (1989) cited in Feyjin (1996).
2. Claim dilution problem occurs when there is no
increase in corporate assets; corporate debtors face both
unresolved and new debts, resulting in corporate
creditors’ claims being diluted.
3. Perquisite consumption problem occurs when corporate
debtors, under unchanged corporate assets, reduce
company's savings and increase consumption of nondurable
goods, thus resulting in a diminution of creditors’
future protection.
4. Underinvestment problem occurs in loan contracts,
when the benefits are credited to the creditors in limited
investment plans or projects, companies would deliberately
give up investment cases with positive cash flow,
as to refute the validity of the aforementioned loan
contract.
The other type of agency problems between shareholders
and corporate managers is the equity agency
problem, of which the main cause is still moral hazard.
Contrary to the scenario painted by the traditional Neo-
Classical Theory of the firm, which describes that firms
are the major pursuer of corporate profits and they will
pursue and maximize their own benefits, when a
company’s ownership and its managerial authority are
separated, the corporate and managerial hierarchy loses
their shares in the remaining claims, as well as in
business risk, inevitably resulting in moral hazard, and
The risk preferences of principals and agents are not aligned, thus resulting in risk
preference problem
consequently agency problems (Fama and Jenen, 1983).
Lambert (1983) also pointed out that there are three
types of moral hazard caused by conflicts of interest
between corporate shareholders and corporate
managers:
1. Corporate and managerial hierarchy pursuing
privileges and non-monetary benefits instead, such as
purchasing luxury cars as the company’s official cars for
their own use, at the expense of shareholders.
2. Corporate shareholders and managerial hierarchy
have different attitudes when facing risk, so the business
decisions taken by the managerial hierarchy would help
consolidate its own position and status, but also
compromise corporate shareholders’ interests.
3. Corporate shareholders and managerial hierarchy
have different views on company’s long-term decisions,
so managerial hierarchy would carry out short-term
investments for its own benefit, thereby affecting
corporate shareholders’ interests.
Agency problems and control mechanisms
The previous four debt agency problems namely asset
substitution, claim dilution, perquisite consumption and
underinvestment, all arise from subsequent information

11446 Afr. J. Bus. Manage.
Table 2. Control mechanisms to debt agency problem.
Authors Control mechanisms
Barnea et al. (1981) Apart from employing capital market functions, complex contracts are also necessary to
control the agency problems
Merge both the interests of shareholders and creditors:
Each shareholder buys some corporate bonds, and each bondholder buys company
stocks
Perform informal restructuring: When a breach of contract is imminent, the administration
authority can issue shares or bonds through the existing capital market to conduct an
informal corporate restructuring, thus avoiding the related agent costs. If the administration
authority is powerless, external investors could take over the company and conduct
informal restructuring to gain arbitrage
Issuance of environment-dependent securities: Issuing warrants or convertible securities to
ensure the company perform its planned investment strategy and maximize the value of
corporate securities, thus avoiding agent costs
Berkovitch and Kim (1990) Proposed: Drawing up financial contracts, minimizing debt agency problem
Source: Feyjin (1996).
asymmetry that leads to moral hazard. This moral hazard
jeopardizes the interests of creditors because corporate
shareholders who only consider their own welfare take
advantage of the information asymmetry between themselves
and the creditors, resulting in creditors unable to
acknowledge the situation and therefore intervene.
Barnea et al. (1981), and Berkovitch and Kim (1990)
have advocated drawing up a more complex and updated
contract to avoid the debt agency problem, but such
proposal would involve an increase in the debt agency
cost, which is not a good approach after all. A detailed
description on control mechanisms to debt agency
problem is listed in Table 2.
In the equity agency problem, Dewing (1953), Lewellen
(1971), Mork et al. (1988), Oviatt (1988) and Williamson
(1983), have all talked about the board monitoring
functions. However, in recent years, several failed cases
of corporate governance in prominent asset stripping
scandals in Taiwan, such as the former chairwoman and
general manager Sophie Yeh of PROCOMP Informatics
and former director and executive vice president Hongjo
Hu of Pacific Electric Wire and Cable, both were corporate
board of directors as well as senior administrative
authorities. Therefore, not only are the corporate ownership
and its managerial authority in the modern corporate
Design debt covenant in advance, including secured debt terms, rental terms, restrictive
dividend payout terms, asset-backed terms
Design different methods to allow the company to first settle old debts before making new
investment plans
When drawing up new contracts, refer to the original/old contracts to mitigate conflicts
structure not separated, the senior managerial authorities
who possess corporate ownership even go so far as to
control the board, resulting in the board completely failing
to fulfill its monitoring functions. Consequently, even the
control mechanisms to equity agency problem proposed
by Dewing et al. (1953) are unable to solve this agency
problem.
Moyer and Sisneros (1989) stated that securities
analysts can play the role of external monitoring. However,
in many cases, even certified public accountants
(CPAs) were prosecuted, including the world-renowned
Arthur and Andersen which was involved in its nonindependent
audits of Enron’s financial statements and
subsequently dissolved. In addition, the constant outbreak
of corporate scandals seems to fully demonstrate
the disappearance of market efficiency. Therefore, while
Fama (1980) believed that using market mechanisms to
solve equity agency problems seems to have limited
effect; Oviatt (1988) claimed that agents' values and
ethics are an effective tool in solving the equity agency
problems. A detailed description is listed in Table 3.
Conclusion
This paper explores the theoretical implications of the

Table 3. Control mechanisms to equity agency problem.
Lin and Huang 11447
Authors Control mechanisms
Dewing et al.(1953) Proposed: The possibility of administrative authority being dismissed or replaced by
shareholders
Lewellen( 1971) Proposed: Design management compensation contracts
Merge the interests of managers and shareholders, such as giving the administrative
authority performance shares, stock options
(Fama, 1980) Proposed: Create an integral system by administrating labor market mechanisms
In this market, the current and future wage standards of administrators will be
determined by their performance, so administrators will actively take action to satisfy
the interests of shareholders in order to improve their personal wealth management
Hart (1993) Proposed: Performance through product market competition
The outcome of product market competition will affect company’s stock price, so
administrative authority is committed to enhancing efficiency and reducing idleness at
work
Williamson (1983) Proposed: The substitution hypothesis between the board of directors and the
alternative external governance
Emphasize on the substitution effects between the board and the external governance
mechanisms. Stronger mechanisms for the board appear in periods or regions where
capital and labor markets are dysfunctional and ill-operated. The higher the ownership
percentage held by external directors, the lower the agency problems
Morck et al. (1988) Proposed: The board managerial and monitoring functions
The main function of the board is to monitor the opportunistic behavior of the
administrative management, any incompetent managers will be replaced by the board.
(Oviatt, 1988) Proposed ten approaches to to achieve alignment of the interests of shareholders and
managers:
capital market incentives
management compensation incentives
stock options incentives
threat of replacement
mutual monitoring among managers
monitoring of institutional investors
monitoring of divisional structure
monitoring of the board
various kinds of market competition
agents’ values and ethics
(Moyer and Sisneros,1989) Proposed: the function of securities analysts
Securities analysts play the role of external monitoring for the company, and their
monitoring activities are related to the ownership percentage held by managers,
corporate lifecycle and corporate debt ratios
Source: Modified by Feyjin (1996).
agency problem, such as the analytical units, assumptions
on human behavior, assumptions organizations,
assumptions on information, issues of drawing up contracts,
control mechanisms to agency problems, and
their relationship with the ethical factors of markets. It
also examines market transaction costs, where principals
and agents hold discrepant internal corporate information,
thus resulting in information asymmetry. It finally
looks into the market agency problem, which arises when
the interests of the principals and agents are not aligned
and the agents often work to maximize for their own
benefit. This paper illustrates the agency problem through

11448 Afr. J. Bus. Manage.
two important cases of economic crime in Taiwan,
PROCOMP Informatics and Pacific Electric Wire and
Cable. The main cause of the crime arose when their
company leaders Yeh and Hu compromised their
corporate values and ethics, thus resulting in agency
problems. A lesson to learn from this is that, in market
transactions the party with more information should be
honest and open with full disclosure of information in
order to eliminate information asymmetry, which means,
he should hold ethical values of corporate honesty and
transparency so that markets can attain equilibrium.
On the other hand, apart from corporate ethics, this
paper also explores the possibility of an alternative solution
to reduce market transaction costs and information
asymmetry problem. Although the agents may face the
control mechanisms of the equity agency problem, moral
hazard may still occur when they hide their action and
information. A practical way to avoid such moral hazard is
for the agents to follow the Golden Rule advocated by
Hans Küng “Do unto others as you would have others do
unto you”, and to live up to the ethical principle of
“commitment to a culture of tolerance and a life of truthfulness”.
Relatively, the agency costs required by other
methods to reduce information asymmetry and moral
hazard problems seem comparatively higher than the
former.
From the perspective of Taiwan’s external market
mechanisms to corporate governance, shareholders
(principals) can exploit situations such as poor sales of
company products or corporate managers failing to yield
satisfactory performance, etc., to monitor and keep the
managerial hierarchy (agents) in check through capital
market and corporate control market. However, under the
hypothesis that the market system is built on information
transparency, so, if the agents use information
asymmetry to manufacture false financial and accounting
information and subsequently hire non-independent
accountants to audit and endorse the financial statements,
thus resulting in market failure, this is considered
one of the mains reasons in corporate governance
failure. On the other hand, Taiwan’s internal control
mechanisms to corporate governance can be divided into
soft control and hard control. If the purposes of these two
controls are mutually exclusive, that is, if the power of the
soft control which governs the managerial conduct and
behavior, ethical values, management style and administrative
philosophy within the corporate internal control
system, is greater than the power of the hard control that
is positively guided by legal norms, corporate rules and
regulations, etc., then it will lead to embezzle-ment of the
agency problems, which is also another important reason
in corporate governance failure.
In theory, corporate governance belongs to the equity
agency problem, and equity agency problem has its root
in the modern corporate structure where ownership and
managerial authority are separated. In this structure, the
interests and objectives of the corporate owners (principals)
and the corporate managers (agents) are often not
aligned. Furthermore, the assumptions of humans in the
agency problem theory also claim that both principals and
agents have their own self-interests and motives, as well
as their own risk aversion, even members of the same
organization can have opposing goals. Coupled with
information asymmetry, there exists a severe moral
hazard between principals and agents. In addition to the
agents exerting their ethical spirit of completing the task
entrusted by the principals, the principals may also need
to spend more on monitoring costs, especially the aforementioned
hard control costs, in order to lower moral
hazard and equity agency problem.
REFERENCES
Akerlof G (1970). The Market for “Lemons”. J. Econ., 84: 175-187.
Arrow KJ (1984). The Economics of Information, Cambridge, Belknap
Press, pp. 106-117.
Berle A, Means G (1932). The Modern Corporation and Private
Property, New York, Macmillan Inc.
Barnea A, Haugen RA, Senbet LW (1981). Market Imperfections,
Agency Problems, and Capital Structure: A Review. Finan. Manage.,
Summer: 7-21.
Berkovitch E, Kim H (1990). Financial Contracting and Leverage
Induced Over-and-Under-Investment Incentives. J. Finan., July: 765-
794.
Buchanan JM (1983). The Public Choice Perspective, Economia delle
Scelte Publiche. January: 7-15.
Coase R (1937). The Nature of Firm. Econ., 4(16): 386-405.
Dewing K, Lehn AS (1953). Foundations of Finance, New York, Basic,
pp. 42-47.
Eisenhardt KM (1989). Agency Theory: An Assessment and Review.
Acad. Manage. Rev., 14 (1): 38, 57-74.
Fama EF (1978). The Effects of Firm’s Investment and Finan Decisions
on the Welfare of its Securityholders. Am. Econ. Rev., June: 272-284.
Fama EF (1980). Agency Problems and Theory of the Firm. J. Polit.
Econ., 88 (2): 288-307.
Fama EF, Jensen MC (1983). Agency Problems and Residual Claims. J.
Law Econ., 26(June): 327-349.
Gan S, Huang C (1994). Principles of Economics - Prosperity and
progress, Taipei, Shinlou, pp. 472-536.
Gilpin R (1987). The Political Economy of International Relations, New
Jersey, Princeton UP, pp. 65-117.
Jensen MC, Meckling WH (1976). Theory of the Firm: Managerial
Behavior, Agency Costs and Ownership Structure. J. Finan. Econ., 3:
305-360.
Knight FH (1985). Risk, Uncertainty, and Profit, Chicago, University of
Chicago Press, pp. 68-83.
Kuo M (1996). The Impact of Information Asymmetry on Cost of Debt
Capital – An Empirical Study using Bank Loan, Taipei, National
Chengchi University – Department of Business Administration, PhD
Dissertation, p. 18.
Lambert RA (1983). Long-Term Contracts and Moral Hazard. Bell J.
Econ., 14 (Autumn): 441-452.
Lewellen W(1971). The Ownership Income of Manage, New York,
NBER, pp. 35-50.
Mork R, Shleifer A, Vishny RW (1988). Manage Ownership and Markets
Valuation: An Empirical Analysis. J. Finan. Econ., January/March:
293-316.
Moyer RC, Chatfield RE, Sisneros PM (1989). Security Analysts
Monitoring Activity: Agency Costs and Information Demands. J. Bus.
Finan. Account., Summer: 385-398.
North DC (1990). Institutions, Institutional Change and Econ
Performance, Cambridge, Cambridge UP, pp. 3-16, 85-96.
Oviatt BM (1988). Agency and Transaction Cost Perspectives on the
Manager-Shareholder Relationship. Acad. Manage. Rev., 13(78):
214-225.
Perrow C (1986). Complex Organizations: A Critical Essay, 3rd ed, New

York, McGraw- Hill, pp.105-110.
Rasmusen E (1989). Games and Information: An Introduction to Game
Theory. Acad. Manage. Rev., 25(1): 77-87.
Simon. HA (1964). Administrative Behavior, 2 ed, New York, Macmillan,
pp.55- 60.
Stigler GJ (1988). Memoirs of an Unregulated Economist, New York,
Basic Books, pp. 84- 87.
Smith CW, Warner JB (1979). On Financial Contracting: An Analysis of
Bond Covenant. J. Finan. Econ., 7: 117-161.
Stulz RM, Johnson H (1985). An Analysis of Secured Debt. J. Finan.
Econ., 14: 501-521.
Tsen F (1996). A Study of Control Tools of Agency Problems and
Business Performance- Listed Companies in Taiwan, Taipei, National
Chengchi University – Department of Business Administration, MD
Dissertation.
Lin and Huang 11449
Wang C, Chen S (2003). The Economics of Information, Taipei, National
Open UP, p. 3.
Williamson OE (1975). Market and Hierarchies, New York, Free Press,
pp. 55-61.
Williamson OE (1983). Credible Commitments: Using Hostages to
Support Exchange. Am. Econ. Rev., 73(4): 32-38.
Williamson OE (1986). The Economic institutions of Capitalism, New
York, Free Press, pp. 115-120.
Williamson OE (1996). The Mechanisms of Governance, Oxford, New
York, Oxford University Press, pp. 95-111.

African Journal of Business Management Vol.5 (28), pp. 11425-11434, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.305
ISSN 1993-8233©2011 Academic Journals
Full Length Research Paper
Performance evaluation of open end and close end
mutual funds in Pakistan
Bilal Nafees, Syed Muhammad Amir Shah and Safiullah Khan
University of Central Punjab, Lahore, Pakistan.
Accepted 30 May, 2011
The study is about evaluating the performance of close and open end mutual funds in Pakistan. It
provides guidance to the investors on how risk-adjusted-performance evaluation of mutual funds can be
done and how they can use performance analysis at the time of investment decision making. Different
researches had been conducted on mutual fund industry of Pakistan to evaluate the performance but the
focus of majority of researches was on close end funds. This research considered both close and open
end funds. The risk adjusted performance of both types of mutual funds has been measured through
traditional measures such as Sharpe measure, Sortino measure, Treynor measure, Jensen differential
measure and information measure. Secondary data has been used for performance evaluation. The
results through Sharpe measure and Sortino measure are negative of sample data. It shows risk adjusted
negative return to investors. Treynor measure results of few funds are better; however, overall result of
Treynor measure is also negative. The Jensen differential measure and information measure results have
documented negative performance, whereas, market portfolio result of all measures is positive which
shows positive return per unit of risk. The results of all measures indicate that mutual fund industry is
below as compared to market portfolio performance. Risk adjusted performance results of mutual funds
depict negative risk adjusted returns to investors. The probable reason for negative risk adjusted returns
of mutual fund industry can be setback by global financial crisis to the market during sample period.
Key words: Portfolio, mutual funds, risk adjusted performance, open and close end funds.
INTRODUCTION
Mutual fund industry of Pakistan has worth of multibillions.
Mutual fund industry nowadays, one of the emerging
industry of Pakistan. Mutual fund industry has shown a
phenomenal growth over the last few years. The growth is
bi-directional, besides the growth in Net Asset Value
(NAV), the number of mutual funds has also increased.
Despite huge financial setback in 2008, mutual fund
industry suffered less by the global and domestic financial
recession as compared to some other financial sectors.
Pakistan, being an Islamic country, mutual funds are
developed on Islamic based concept, that is why people
have more tendency to invest in the mutual funds.
The basic concept behind mutual funds is to generate a
Pool of money that is collected by the professionals
*Corresponding author. E-mail: bilalnafees_5929@hotmail.com.
Tel: +92-42-5880007.
and then invested in profitable activities to maximize the
investor’s wealth. The management just charges their
operating expense for managing the fund. There are
several other benefits to invest in the mutual funds, like
Liquidity, Diversification, Variety and Convenience. By
liquidity, which mean investing in mutual funds provides
benefit to the investor to sell shares/units on any business
day, by law, the company is bound to buy back at NAV
price. Diversification defines investing in mutual fund
which provides the opportunity for investors to automatically
diversify the investment in such a way that all the
money is pooled then invested into many different
investment opportunities that allow the investors to
automatically diversify them.
Variety implies that the mutual fund industry is emerging
that is why numerous varieties of mutual funds are
available to the objective investor who has a choice of
investment. Convenience implies that while investing in
mutual funds, units/shares can be purchased and sold

11426 Afr. J. Bus. Manage.
Table 1. Net asset value of the mutual funds industry of Pakistan (2001 to 2010).
Years 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
NAV of funds (PKRMillions) 21,070 25,341 50,455 93,819 1,25,058 1,59,798 3,00,841 3,35,226 2,04,826 1,99,699
Source: MUFAP (Mutual Munds Association of Pakistan).
Table 2. Open end and close end equity based funds returns (2006 to 2010).
Variable
2006 2007
Years
2008 2009 2010
Returns of Open End Equity Based Funds (%) 27.66 42.93 -3.88 -38.12 19.83
Returns of Close End Equity Based Funds (%) 9.2 24.24 -1.62 -32.16 13.49
Source: MUFAP (Mutual Funds Association of Pakistan).
through broker, investment bank, insurance agent, by email,
over the internet via online buying and selling or over
the telephone. Companies also send monthly, quarterly,
semi annually and annual statement to investors of their
account information. All companies are bound to follow the
regulations that are oversight by the security exchange
commission of Pakistan (SECP). Therefore, it is
mandatory for all the funds to give information about their
objectives, their investment strategy, Information about
how investor can purchase units/shares and how mutual
fund would redeem units, risk of investment and
information about fund fees and expenses.
The mutual fund industry of Pakistan has worth of
multibillions, and the history of the mutual funds industry
starts from 1962. At first, an open end mutual fund was
introduced by an institute called NIT (National Investment
Unit) which was regulated by the government. Substantial
development in mutual fund industry took place in late
2000, when the government decided to windup the ICP
(Investment Corporation of Pakistan) which was the
regulatory authority of NIT and decided to privatize all the
funds managed by NIT. The mutual funds industry griped
rapid growth when private entities were allowed to
maintain mutual funds.
Statistics of 2010 shows the total net worth of mutual
funds industry is near two hundred million (Table 1),
indicating huge development in the industry as compared
to year 2001 (89.44%). The mutual fund industry of
Pakistan should try to earn more trust of the investors to
increase the interest of investor for investment in mutual
funds because it is a longstanding issue if mutual funds
operate for the interest of Asset Management Company or
for the interest of the investors.
The performance of open end funds is better than close
end funds as far as return on equity is concerned. On the
contrary, the open end equity funds faced more loss as
compared to close end equity funds during 2008 and
2009. In 2009, open end funds faced 5.96% more loss as
compared to close end funds. In 2010, open end fund
earned 6.32% more return on equity as compared to close
end funds (Table 2).
LITERATURE REVIEW
Sharpe (1966) conducted research to develop such technique
that can help the investors to evaluate mutual fund
performance. He concluded that performance can be
determined by considering risk of returns and dividing
average excess return by risk, which is a meaningful and
theoretical measure. If return of fund’s portfolio is volatile,
then it means that fund carry risky portfolio.
Jensen (1967) conducted a research and determined
the technique, Jensen alpha, to measure the risk adjusted
performance of mutual funds. To provide better returns to
the investor, the cost and benefits analysis of the decision
should be done more closely before making investment
decision.
Daniel et al. (1997) who did a research to measure the
performance of mutual funds based upon benchmark,
concluded that when a strategy is proposed by the
managers based on fundamental analysis, then he/she
should expect that strategy will outperform simpler.
Mechanical nature of strategies can be implemented at a
cost which is substantially lower. So, if the active portfolio
is unable to beat the performance of market than
mechanical strategies, that mean managers may be
wasting their time.
Carhart (1997) conducted a research to determine the
persistence performance of the mutual funds, where he
explored that the funds expense and all the charges of
any type have significant impact on the performance of the
mutual funds, and as long as the expense increases, the
fund return decreases. Further, having a look over the net
asset value (NAV) of the mutual funds (Figure 1), it can be
determined easily that the load funds performance is not
better than no- load funds and investors should not invest
in such funds whose performance is not constantly

Value
Year
Figure 1. Net Asset Value of All Mutual Funds in Pakistan.
Figure 2. Return of open and close end equity based mutual funds in Pakistan.
positive (Figure 2).
Shah and Hijazi (2005) who conducted a research to
evaluate the performance of mutual fund industry of
Pakistan concluded that the funds which underperform
usually face diversification problem. In the annual report,
the risk associated with the fund should also be stated, so
that investors can compare risk with expected returns to
before making investment decision. Further, to enhance
the investor interest to invest in the mutual funds can be
made possible through the offer of new mutual funds
which should be distinctive on the base of objectives.
Bauer et al. (2005) conducted a research on ethical
perspective of the mutual funds and determined that the
funds which have unprecedented growth is due to ethical
run of mutual fund markets. After keeping the constant to
different variables such as size of the funds and
transaction cost, they determined that statistically results
are insignificant. On the contrary, statistically returns of
mutual funds are less as compared to mutual funds, which
operate through conventional manner. Bauer et al. (2005)
conducted a research on New Zealand mutual funds, and
Nafees et al . 11427
concluded that the balanced funds underperformed the
market, whereas the funds whose objective is to invest in
the equities of such mutual funds is usually excess than
the market because of active management style. They
concluded that the persistence performance of the mutual
funds is due to the passive investment strategy and not
the active management strategy. Further, the
management expense ratio of the equity based funds and
load charges are negatively correlated. As long as the
fund size increases, the funds return also increases. It
helps to achieve the economies of scale and led the basis
of more actively managed portfolio. Using statistical
technique, they argued that funds whose alpha value is
negative significantly, their future performance is likely to
be risky. So the investors should avoid investing in such
funds.
Keswani and Stolin (2006) conducted a research to
determine the performance of the mutual fund sector and
concluded that sector competitiveness affect the mutual
fund performance because if number of players are small,
then the competition will be less intensive and mutual

11428 Afr. J. Bus. Manage.
funds will not be able to perform well. The persistent performance
of mutual funds depends on the competitiveness
of the sector. Moreover, performance of mutual funds
sector can be determined through the use of total number
of funds available in the sector and total number of
matured mutual funds in the sector.
Sipra (2006) conducted a research to evaluate the
performance of mutual fund industry of Pakistan for 1995
to 2004 and concluded that performance of funds is not
better, relative to the market performance, only small
number of funds performance was above the market
performance and that performance was not consistent in
nature. Further, results denotes semi strong form of
market efficiency.
Afza and Rauf (2009) who conducted a research to
evaluate the performance of open end mutual funds
explored that returns which are risk adjusted is positively
correlated with the age, turnover and expenses of the
fund. Further, using regression analysis, they found no
significant difference in the results of funds which have
load charges and which do not have load charges. The
size of assets cannot be used as a measure to distinguish
the superior and inferior fund. Moreover they stated that
investor should have a look over the past performance of
funds while selecting superior and inferior fund.
Crespo (2009) who conducted a research on Spanish
mutual fund to determine the ethical implication of the
fees, used Logit model to determine the relationship
among the fees or charges of the mutual funds and
investor behavior, explored that funds which contain no
front load, investors tendency to invest in such mutual
funds is subject to price sensitivity. That mutual funds
which charge high fee and redemption charges, investors
of such funds expects more return because of imposition
of high charges charged by the fund management. High
charges made by the fund imply that the management is
earning more and it also indicates high expense; so the
ultimate benefit obtained by the management rather gives
benefit to the investors. Although the load funds charged
high fee due to lack of sophistication of the investors,
investors usually own small capital that is why they avoid
spending their search cost.
Joop and Verbeek (2009) for research used multifactor
model to evaluate the performance of mutual funds,
explored that proxies of the model use to evaluate the
performance of mutual funds return provides better result.
Proxies to evaluate the performance of mutual funds are
based on the portfolio of hypothetical stock, which do not
take into account the cost of a transaction, impact of trade,
and restriction of trading, further that with use of multi
factor model it can also be determined that whether
managers were able to take in account the effect of value
premium, size premium and premium of momentum, for
this purpose the excess return of the funds were
determine. Moreover, they claimed that if the alpha value
comes zero, it means that fund manager run the fund with
the value factor and if the value comes non-zero it means
that manager run according to the market factor.
Khalid et al. (2010) conducted a research to evaluate
the performance of close end mutual funds using two new
ratios which have not been used earlier to evaluate the
performance of mutual funds, concluded that the close
end mutual are not performing well due to fluctuations in
the capital market. The managers need to be more efficient
to earn good returns and use such strategies which
can ensure better returns. Budiono and Martens (2010)
did a research on how an investor can make decision
while investing in mutual funds, explored that considering
ability ratio and expense ratio as variables along with the
traditional variables, like determining the excess return by
using tools that help to determine the risk adjusted returns
can be much more helpful to determine the real performance
of the mutual funds in longitudinal way. The use of
past performance to determine the alpha value is not
suitable because this technique does not predict the
significant alpha while using the other information along
with the past performance data like fees, load charges
increases the value of alpha significantly.
Hartzalli et al. (2010) who conducted a research to
evaluate the performance of real estate mutual funds
concluded that funds which are actively managed by the
managers due aggressive and timing market strategy are
more likely to generate the higher return for the investors.
Moreover, funds whose tendency is not to invest in single
segment, for them, other benchmark is selected based on
the preferred horizon of investment. Moreover, fund can
earn superior return on the basis of information available
to the fund manager because the manager can use this
information to come up with the best diversified portfolio to
earn more return.
RESEARCH METHODOLOGY
Population
Population of all mutual funds operating in Pakistan has been taken
because research mainly focuses on the performance of mutual
funds industry of Pakistan. Data can be obtained conveniently; this is
another reason of selecting the population of mutual fund industry of
Pakistan.
Sample data
A total of eight close end and eleven open funds from the mutual
fund industry of Pakistan have been selected for performance
evaluation from the population and all funds are equity based and
balanced funds. Moreover, all those funds are selected whom annual
reports from 2006 to 2010 are available. The objective of equity and
balanced funds is to invest in the equities and balanced funds also
prefer to invest most of the portion of investment in the equities. That
is why market performance has been selected as relative benchmark
performance to determine the excess returns. Panel data has been
used for the analysis which, stacked by the unit, such as, after
selection of the mutual fund, data from 2006 to 2010 has been taken
for each fund. The data is a balanced pool.

Data collection sources
All the reports have been downloaded from the respective web sites
of asset management companies of mutual funds ranging from 2006
to 2010. Market data has been collected from Business Recorder
web site. Treasury bills data has been collected from Economic data
of the state of Pakistan that is available on web site of state bank of
Pakistan.
Variables selections
Profit after taxation amount has been obtained from the income
statement, and net asset value amount has been taken from
statement of assets and liabilities to calculate the returns. Treasury
bills data has been used as risk free rate or as bench mark rate. For
beta calculation, fund returns have been used on y-axis and market
returns on x-axis. To calculate the market return, KSE-100 index
values have been used. For information measure, high average
return has been used as benchmark that reflects highest average
performance.
Measures for analysis
Fives measures have been used for performance evaluation of
mutual funds for both open end funds and close end funds: 1)
Sharpe measure; 2) Sortino measure; 3) Treynor measure; 4)
Jensen differential measure; 5) Information measure.
Sharpe measure
This measure was developed by William F. Sharpe in 1966 to
determine risk adjusted performance such as the average excess
return earned by funds over per unit of risk, during time span taken
for performance evaluation. It tells whether returns of portfolio are
due to smartness of management or due to taking excess risk. The
formula of Sharpe measure is as follow:
Sharpe measure =
Rp-Rf
StdvRP
Where: Rp= average return of portfolio; Rf= average risk free;
StdvRP= standard deviation of portfolio returns.
Sharpe measure is divided into three components: average return
of funds, average risk free return and standard deviation of portfolio
returns. Average return is calculated by geometric mean of portfolio’s
return. Average excess return is calculated by subtracting average
fund return from average risk free return and then average excess
return is divided by the standard deviation of fund returns. If answer
is positive and more than one that means fund has performed well
and indicates smartness in decisions, higher measure result
represent better risk adjusted performance. The negative Sharpe
measure represents that performance of risk free assets is better
than the performance of the securities selected for the investment.
Sortino measure
Sortino measure was introduced by Frank Sortino in 1944. Sortino
measure also measures risk adjusted performance of funds. It is
modified form of Sharpe measure. Down side risk is taken for
calculation of Sortino measure to divide excess returns of portfolio
instead of standard deviation that is the major difference between
Nafees et al . 11429
Sortino and Sharpe measures. The Sortino measure ensures that
risk, more realistically, has been taken into account for performance
evaluation because negative values of the excess returns are used
to calculate downside risk, whereas in Sharpe Measure calculation
down side and upside both risk are used to calculate standard
deviation. The formula of Sortino measure is as follow:
S=(R-T)/DV
Where; R= average portfolio return; T= average (MAR) minimum
acceptable return; DV= downside-volatility.
Sotino measure is divided into three components: average
portfolio return, average minimum acceptable return and down side
volatility. After calculating excess return of each year which is the
difference of portfolio return and MAR (risk free return), if value of
excess return is positive then zero is allocated to that value and if
the excess return is negative then the same value is used with
negative sign to calculate standard deviation. The standard deviation
of all values is taken over a time span and taken into account for
calculation. In such a way, the down side risk is calculated. The
average return of portfolio is calculated by geometric mean over the
time span taken into account for evaluation which is of five years.
Average (MAR) represents average risk free return, and it is also
calculated by geometric mean and is used as benchmark return. The
difference of average return of portfolio and average minimum
acceptable return is taken to calculate average excess return. The
average excess return is divided by the down side risk to calculate
Sortino measure. If its answer is positive and higher, it indicates
better performance of fund.
Treynor measure
Treynor measure was developed by Jack Treynor. It measures risk
adjusted performance of fund over per unit of systematic risk.
Treynor’s risk adjusted performance indicates how much excess
return has been earned by over per unit of systematic risk. This
measure is also known as reward to volatility. The formula of Treynor
measure is as follow:
Teynor measure =
Rp - Rf
β
Where Rp = the observed average fund return; Rf = the average risk
free return; β = coefficient as a measure of systematic risk.
β =
∑(rm − rf ) * (ri − rf ) − n * ∑ (rm − rf ) / n * ∑ (ri − rf ) / n
∑ (rm − rf ) 2 − n * ∑ ((rm − rf ) / n)) 2
Where rm = market return; ri = portfolio return; rf = risk free return; n
= number of observations.
Three different values are used for calculation of Treynor
measure; average return of portfolio, average risk free return and
beta. Average return of portfolio and average risk free return is
calculated by geometric mean of fund’s return over the time span
taken into account for performance evaluation. The average return of
the portfolio is subtracted from average risk free return to determine
excess return. Beta is the value which represents degree of volatility
of mutual fund returns against the returns of financial market. Beta
value can be determined through regression, assuming the returns
of the fund as Y-axis values and market returns as X-axis values.
The average excess return is divided by the beta of each fund to
determine the Treynor measure. If the answer of Treynor measure is

11430 Afr. J. Bus. Manage.
high, it means that fund management has earned high yield over per
unit of market risk. Treynor measure tells the performance of mutual
fund in relation to volatility of fund against market, not in relation to
its own volatility. It is the most important advantage of Treynor
measure.
Jensen differential measure
Jensen measure was developed by Michael Jensen in 1967. It is
also known as Jensen alpha. Jensen measure is used to measure
risk adjusted performance. It is used to compare the average excess
return of the portfolio with predicted return determined through
CAPM (Capital Asset Pricing Model) for given beta of portfolio and
market return. To evaluate the performance of the mutual fund
manager, investor should not look at overall portfolio return but also
look at the risk attached with the portfolio. If two mutual funds have
same level of return on equity but the risk of both portfolio is different
than investor will prefer to invest in such fund which carry low risk.
Formula of Jensen measure is as follow:
Rp – Rf = αp + βp [ Rm – Rf] + €p
Rp = average return of portforlio; Rf = average risk free return; Rm =
average market return; €p= error term; α p and β p = both are
parameters of the model.
Three different values are used to calculate Jensen alpha average
return of portfolio, average risk free return and average market
return. Excess returns of the portfolio are determined by taking the
difference of portfolio returns and risk free returns each year and
values of this difference are considered as Y-axis values. Excess
market returns are determined by taking the difference of market
return and risk free returns and values of the market excess returns
are considered as X-axis values. By applying regression on Y and X
values, alpha value and significance (t-value) value is considered for
analysis. Positive value of alpha denotes that fund has earned
excess return and if value of alpha is negative that means fund has
unable to outperform against market performance. Positive alpha
also denotes that manager has outperformed the market with better
stock picking skills.
Information measure
Information measure was introduced by Thomas Goodwin in 1998.
Information measure determines alpha component over per unit of
standard deviation of excess returns. Alpha return is attributable to
manager skills regarding stock picking option. It deducts the effect of
movements of market in returns and adjusts it for the risk taken. It
gives pure figure that is used to evaluate good portfolio
management. The formula of information measure is as follow:
Information measure =
(Rp – Rf)
Sp-1
Where Rp = average return of portfolio; Ri= return of benchmark; Spi
= average tracking error (standard deviation of the excess returns
determined by taking the difference of portfolio returns and
benchmark returns).
Three different values are required to calculate information
measure average return of portfolio, average bench-mark return and
average tracking error. Average return of the portfolios is determined
by geometric mean over the time span taken into account for
analysis, after that, highest average return is selected as benchmark
return. The difference of portfolio returns and benchmark returns is
taken to determine excess return for each year. Standard deviation
of excess returns is calculated. Average excess return is calculated
by taking the difference of average portfolio return and average
benchmark return after this average excess return is divided by
standard deviation of excess returns. In such way, information is
calculated. If the answer of information measure is positive, then it
means fund performance is better over per unit of risk of excess
returns. If the funds have earned higher return than information
measure, result will be high.
RESULTS AND INTERPRETATION
Sharpe measure
Market performance is better and above as compared to
close and open end mutual funds having Sharpe measure
of 0.513. Atlas Stock Mutual Fund performance is better
over the last five years because it has the highest Sharpe
measure which is 0.33 and Pakistan stock market fund
performance is lowest over last five years and its Sharpe
measure result is -1.36, on average, Sharpe measure
result is -0.51 as far as all open end mutual funds are
concerned in the sample. First capital mutual fund
performance is better because of having highest Sharpe
measure which is 0.46 and Meezan Balanced Fund
performance is lowest and its Sharpe measure result is -
1.39, on average Sharpe result of Sharpe measure is -
0.29 as far as all close end funds are concerned in the
sample. Average performance of the industry including
close end and open end mutual funds is -0.42 over the
last five years calculated through Sharpe measure (Table
3).
Sortino measure
Performance of market portfolio is better and above the
performance of open and close end mutual funds, the
result of market performance calculated through Sortino
measure is 1.02. Atlas Stock Market Fund performance is
better and its Sortino measure result is 0.42 which is
better as compared to other mutual funds in the sample.
Pakistan Stock Market Fund performance is lowest with -
1.69, average Sortino measure answer is -0.61 of all open
end mutual fund in the sample. First Capital Mutual fund
performance is above over the performance of all close
end funds in the sample which is 0.62 and Meezan
Balanced Fund performance is lowest (-1.86) as compare
to other mutual funds in the sample, average value of
Sortino measure result is -0.38 of all close end funds in
the sample. The overall result of the industry calculated
through Sortino measure for sample data is -0.52
including open and close end funds (Table 4).
Treynor measure
The beta of most open end mutual funds is less than one,

Table 3. Sharpe measure results.
Nafees et al. 11431
Mutual fund name Average returns Excess returns Standard deviation Sharpe measure
Market KSE 100 29.06 18.35 35.78 0.513
Open end mutual funds
Pakistan Capital Market Fund -15.23 -25.94 24.52 -1.06
Atlas Stock Market Fund 21.53 10.82 32.33 0.33
Pakistan Stock Market Fund -21.39 -32.10 23.62 -1.36
Pakistan Strategic Allocation 14.50 3.80 27.26 0.14
Crosby Fund -29.31 -40.01 45.22 -0.88
JS Islamic Fund 22.31 11.60 42.86 0.27
JS KSE30 Index Fund 21.46 10.75 59.30 0.18
JS Large Cap Funds -20.29 -31.00 50.83 -0.61
Unit Trust of Pakistan -23.77 -34.48 33.23 -1.04
AKD Index Tracker Fund -9.91 -20.61 25.59 -0.81
National Investment Trust Unit -16.07 -26.78 29.84 -0.90
Close end funds
AKD Golden Fund -17.64 -28.35 31.89 -0.89
Meezan Balanced Fund -10.59 -21.30 15.33 -1.39
Asian Stock Fund 10.37 -0.34 26.20 -0.01
First Capital Mutual Fund LTD 22.40 11.69 25.53 0.46
Pakistan Premier Fund Limited 19.75 9.04 33.57 0.27
PICIC Growth Fund 14.05 3.34 23.92 0.14
PICIC Investment Fund 15.86 5.15 28.02 0.18
Meezan Islamic Equity Fund -23.63 -34.34 30.08 -1.14
which means that managers’ strategy against market risk
is defensive and those whose beta is greater than one
means that fund was being managed through aggressive
strategy. Atlas Stock Market fund has highest result of
Treynor measure (12.2), Pakistan Stock Market fund
performance is lowest because of having low Treynor
measure (-56.6) and the average result of Treynor
measure of all open end funds of sample is -22.77. Beta of
all close end funds is less than one which means all close
end funds had defensive strategy against market
movement. First Capital Mutual Fund performance is
better, while on the contrary, Meezan Balanced Fund
performance is lowest at .94 and -49.78 respectively;
average performance result of all close end funds of
sample is -4.27. Average result of Treynor measure
including all open end and close end funds of sample is -
17.1 (Table 5).
Jensen differential measure
JS KSE30 fund performance is better amongst all open
end funds because its alpha value is positive (0.96), while
all other funds in the sample has negative alpha value, Js
Islamic fund performance is lowest in the sample of open
end mutual fund (-16.85). The average result of alpha of
all mutual funds is -8.32. No fund among close end funds
in the sample has positive alpha, the Asian Stock Fund
performance is lowest (-20.53). The average value of
alpha is -7.39 of all close end funds of sample. The overall
average value of alpha is -7.93 including open and close
end funds (Table 6).
Information measure
The result of market portfolio measured through information
measure is 0.21, which means that market has
earned excess return of 0.21 over per unit of volatility of
excess returns. Js Islamic Fund performance is better as
compared to all funds in the sample and its average return
has been used as benchmark return to calculate
information measure of all open end funds (22.63). The
performance of Js Large Capital Fund is lowest and result
of its information measure is -3.32. The average value of
information measure of all open end mutual funds is -1.53.
The First Capital Mutual Fund performance is above over
the performance of all close end funds (0.46) and its
average return value has been used as benchmark return.
Meezan Balanced Fund performance is low amongst all
close end mutual funds (-1.39). The information measure
result of all close end mutual funds is -0.29. The average

11432 Afr. J. Bus. Manage.
Table 4. Sortino measure.
Mutual fund name Average returns Excess returns Down side risk Sortino measure
Market KSE 100 29.06 18.35 18.07 1.02
Open end mutual funds
Pakistan Capital Market Fund -15.23 -25.94 17.04 -1.52
Atlas Stock Market Fund 21.53 10.82 25.73 0.42
Pakistan Stock Market Fund -21.39 -32.10 18.97 -1.69
Pakistan Strategic Allocation 14.50 3.80 24.17 0.16
Crosby Fund -29.31 -40.01 37.93 -1.05
JS Islamic Fund 22.31 11.60 39.01 0.30
JS KSE30 Index Fund 21.46 10.75 37.94 0.28
JS Large Cap Funds -20.29 -31.00 44.75 -0.69
Unit Trust of Pakistan -23.77 -34.48 29.41 -1.17
AKD Index Tracker Fund -9.91 -20.61 25.08 -0.82
National Investment Trust Unit -16.07 -26.78 25.28 -1.06
Close end funds
AKD Golden Funds -17.64 -28.35 25.93 -1.09
Meezan Balanced Fund -10.59 -21.30 11.46 -1.86
Asian Stock Fund 10.37 -0.34 26.36 -0.01
First Capital Mutual Fund LTD 22.40 11.69 18.79 0.62
Pakistan Premier Fund Limited 19.75 9.04 29.10 0.31
PICIC Growth Fund 14.05 3.34 20.30 0.16
PICIC Investment Fund 15.86 5.15 23.01 0.22
Meezan Islamic Equity Fund -23.63 -34.34 23.83 -1.44
value of information measure of industry is -1.01 including
all open and close end funds of the sample (Table 7).
SUMMARY AND CONCLUSION
This study evaluates risk adjusted performance of mutual
funds over five years ranging from June 2006 to June
2010. After evaluating the risk adjusted performance of
open and close end funds, the following results have been
inferred. Sharpe measure result of nine funds is positive
including market portfolio but less than one, which
indicates over per unit of portfolio risk the excess return is
less than one. The result of Sharpe measure reflects bad
performance of the industry. Sortino measure results of
eight funds are positive but less than one, which means
over one unit of down side risk the excess return is less
than one and the average result of the industry is
negative.
Treynor measure depicts that beta value of most mutual
funds is less than one that means defensive strategy had
been adopted, only few funds whose beta is more than
one against market movement that were being managed
aggressively. Performance of eight funds from the sample
of twenty portfolios is positive, which is better and average
result of the industry give a picture of low as well as
negative performance.
The Jensen alpha measure results show that only one
fund has positive alpha but t-value of that fund is not
significant and all the other funds in the sample has
negative alpha value that means no fund except one has
performed well. The results of information measure also
represent such situation which is similar to the other
measures. Performance of many funds is substantially low
as compared to benchmark performance.
The average result of information measure represents
negative performance of the industry over per unit of risk
calculated through excess returns. The KSE-100 index
portfolio results calculated through all measures of risk
adjusted performance are positive, which means that
market portfolio performance is better as compared to
mutual fund performance. Mutual fund industry faced huge
set back due to recession of financial year 2008 and due
to which performance of the industry sharply went down
but the performance in 2010 is better and positive as far
as terminal performance is concerned.
There are few limitations of this research such as Fama
French Measure which has not been applied. This
research is only on the equity and balanced mutual funds,
irrespective of whether open or close end fund.

Table 5. Treynor measure.
Nafees et al. 11433
Mutual fund name Average returns Excess returns β=Slope of funds Treynor measure
Market KSE 100 29.06 18.35 1 18.35
Open end mutual funds
Pakistan Capital Market Fund -25.94 -15.23 0.54 -47.9
Atlas Stock Market Fund 10.82 21.53 0.89 12.2
Pakistan Stock Market Fund -32.1 -21.39 0.57 -56.6
Pakistan Strategic Allocation 3.8 14.5 0.73 5.2
Crosby Fund -40.01 -29.31 0.96 -41.8
JS Islamic Fund 11.6 22.31 1.14 10.2
JS KSE30 Index Fund 10.75 21.46 1.45 7.4
JS Large Cap Funds -31 -20.29 1.23 -25.2
Unit Trust of Pakistan -34.48 -23.77 0.79 -43.9
AKD Index Tracker Fund -20.61 -9.91 0.6 -34.5
National Investment Trust Unit -26.78 -16.07 0.75 -35.6
Close end funds
AKD Golden Funds -17.64 -28.35 0.84 -33.86
Meezan Balanced Fund -10.59 -21.3 0.43 -49.78
Asian Stock Fund 10.37 -0.34 0.54 -0.63
First Capital Mutual Fund LTD 22.4 11.69 0.38 30.94
Pakistan Premier Fund Limited 19.75 9.04 0.91 9.93
PICIC Growth Fund 14.05 3.34 0.62 5.36
PICIC Investment Fund 15.86 5.15 0.73 7.06
Meezan Islamic Equity Fund -23.63 -34.34 0.79 -43.42
Table 6. Jensen differential measure.
Mutual fund name Alpha value t -value
Open end mutual funds
Pakistan Capital Market Fund -12.21 -1.36
Atlas Stock Market Fund -6.02 -1.33
Pakistan Stock Market Fund -2.94 -0.37
Pakistan Strategic Allocation -9.74 -1.85
Crosby Fund -0.73 -0.04
JS Islamic Fund -16.85 -1.78
JS KSE30 Index Fund 0.96 0.06
JS Large Cap Funds -16.13 -0.91
Unit Trust of Pakistan -5.89 12.78
AKD Index Tracker Fund -12.26 -1.28
National Investment Trust Unit -9.74 -1.09
Close end funds
AKD Golden Funds -5.19 -0.91
Meezan Balanced Fund -1.64 -3.45
Asian Stock Fund -20.53 -2.30
First Capital Mutual Fund LTD -2.25 -0.20
Pakistan Premier Fund Limited -9.93 -2.37
PICIC Growth Fund -9.52 -2.05

11434 Afr. J. Bus. Manage.
Table 6. Contd.
PICIC Investment Fund -8.88 -1.65
Meezan Islamic Equity Fund -1.20 -0.23
Table 7. Information measure.
Mutual fund name
Average
returns
Average excess
returns
Standard deviation of
excess returns
Information
measure
Market KSE 100 29.06 6.6 31.05 0.21
Open end mutual funds
Pakistan Capital Market Fund -15.23 -37.54 27.06 -1.39
Atlas Stock Market Fund 21.53 -0.78 13.02 -0.06
Pakistan Stock Market Fund -21.39 -43.70 20.85 -2.10
Pakistan Strategic Allocation 14.50 -7.81 15.73 -0.50
Crosby Fund -29.31 -51.62 20.19 -2.56
JS KSE30 Index Fund 21.46 -0.85 34.89 -0.02
JS Large Cap Funds -20.29 -42.60 13.83 -3.08
Unit Trust of Pakistan -23.77 -46.08 13.90 -3.32
AKD Index Tracker Fund -9.91 -32.22 20.95 -1.54
National Investment Trust Unit -16.07 -38.38 15.06 -2.55
JS Islamic Fund* 22.31 11.60 42.86 0.27
Close end funds
AKD Golden Funds -17.64 -40.04 20.99 -0.89
Meezan Balanced Fund -10.59 -32.98 21.49 -1.39
Asian Stock Fund 10.37 -12.03 29.26 -0.01
Pakistan Premier Fund Limited 19.75 -2.64 26.31 0.27
PICIC Growth Fund 14.05 -8.35 17.68 0.14
PICIC Investment Fund 15.86 -6.54 18.60 0.18
Meezan Islamic Equity Fund -23.63 -46.03 25.61 -1.14
First Capital Mutual Fund LTD* 22.40 11.69 25.53 0.46
*Performance of these funds has been used as bench mark performance.
REFERENCES
Afza T, Rauf A (2009). Performance Evaluation of Pakistani Mutual
Funds. Pak. Econ. Soc. Rev., 199-214.
Bauer R, Koedijk K, Otten R (2005). International evidence on ethical
mutual fund performance and investment style. J. Bank. Financ.,
1751-1764.
Budiono DP, Martens M (2010). Mutual Funds Selection Based on
Funds Charactristics. J. Financ. Res., 249-265.
Carhart MM (1997). On Persistence in Mutual Fund Performance. J.
Financ., 57-82.
Crespo RM (2009). Spanish mutual fund fees and less sophisticated
investors examination and ethical implications. Eur. Rev. J., 224-
240.
Daniel K, Grinblatt M, Titman S, Wermer R (1997). Measuring Mutual
Fund Performance with Characteristics Based Benchmark. J.
Finan., pp 1035-1058.
Hartzalli JC, Muhihofer T, Titman SD (2010). Alternative Benchmarks
for Evaluating Mutual Fund Performance. J. Real Estate Econ., 121-
154.
Jensen CM (1967). The Performance Of Mutual Funds. J. Financ.,
389-416.
Joop H, Verbeek M (2009). On the Use of Multifactor Models to
Evaluate Mutual Fund Performance. J. Financ. Manage., pp 75-
102.
Keswani A, Stolin D (2006). Mutual Fund Perfomance Persistence and
Competition a Cross-Sector Analysis. J. Financ. Res., 349-266.
Khalid SI, Abbas MZ, Shah DS (2010). Performance Evaluation of
Close-ended Mutual Funds by Investment Objectives in Pakistan’s
Economy. Iinterdisciplinary J. Contemp. Res. Bus., 193-213.
Rauf A, Talat A (2009). Performance Evaluation of Pakistani Mutual
Funds. Pakistan Econ. Soc. Rev., 1-10.
Shah SA, Hijazi ST (2005). Performanc Evaluation of Mutual Funds in
Pakistan. Pak. Dev. Rev., 863-876.
Sharpe WF (1966). Mutual Funds Performance. J. Bus., pp. 119-138.
Sipra N (2006). Mutual Fund Performance in Pakistan, 1995-2004.
Centre for Management and Economic Research (CMER), pp. 1-14.

African Journal of Business Management Vol. 5(28), pp. 11323-11334, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1493
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Networking in the Kenyan informal sector: An attempt
to manage the market failures
Ongong'a J. O. 1 * and Abeka E. O. 1
1 School of Business and Legal Studies, Bondo University College, Box 210-40106, Bondo, Kenya.
2 Department of Economics and Business Studies, Maseno University, Box 333-40100, Maseno , Kenya.
Accepted 26 May, 2011
This paper examines the role of informal personal networks in determining micro small enterprises
(MSE’s) success in Kenya. It adopts the network perspective theoretical approach. Empirically, the
paper finds that MSE’s in Kenya get around market failures and lack of formal institutions through
entrepreneurial personal network as a copying strategy in the process of global transformation to
bridge the entrepreneurial global divide. General hypothesis predicting the ‘likelihood of MSE’s with
better network performing better’ is supported by performance models though pro-poor growth is
evident with an average business performance. Network strategies to promote small enterprises are
recommended to policy makers, donors and actors in the field against those of the failed traditional
strategies. However, there are few empirical studies available in this area particularly in less developed
countries; therefore, further research is necessary in this direction.
Key words: Kenya, networks, MSE’s performance, environment.
INTRODUCTION
It is no doubt that the role of entrepreneurship in the
emerging economies such like Kenya can not be
undermined as a number of research in this field has
pointed out (Gok, 1999; McCormick, 2009; McPherson,
1996). In Kenya, MSE’s plays a crucial role in the
process of development as findings from the 1999
National MSE Baseline Survey show that MSE’s activities
are contributing to at least 18.4% of country’s gross
domestic product (GDP) and 25% of non-agricultural
GDP; employing approximately 17% of the total labour
force from which 64% were in the urban employment in
2002 (Karekezi and Majoro, 2002). In terms of income
contribution, workers in the MSE sector earn an average
income per month, which is two and a half times more
than the minimum statutory wages in the formal sector.
Employment creation in the formal private sector
decelerated by 67.7% (from 74.0 thousand new jobs in
2007 to 23.8 thousand new jobs in 2008) but employment
in the informal private sector is estimated to have
expanded from 7.5 million in 2007 to 7.9 million in 2008.
*Corresponding author. E-mail: rojejaksm@yahoo.com.
New jobs created generally in the whole country declined
from 485.5 thousand in 2007 to 467.3 thousand in 2008
(GoK, 2008). Given the importance of this sector in areas
of employment creation, growth and poverty alleviation, it
is important that it is efficiently managed for effective
results within the broader over all objectives. Efficient
management has been lacking also due to external
factors that are beyond the owner-manager’s control.
These factors are inherent in the institutional environment
of Kenya which favours larger firms.
In addition, ongoing changes in the business
environment with regard to globalization of markets act
as a further challenge to firms’ growth prospects in
Kenya. In addition, liberalization of markets has made
competition real among firms and only those with a
competitive edge can survive. Policy recommendations of
the government of Kenya as contained in its 7th National
Development Plan on Divestiture and subsequently in
Sectional paper No. 2 of 2005, advocates for the
government to take the leading role by providing an
enabling environment for MSE’s market operations. This
will require the establishment of infrastructure for access
to markets, provision of work site structures,
dissemination of market information through networks

11324 Afr. J. Bus. Manage.
and innovation amongst other well-known strategies.
With all the above in mind, MSE’s are expected to add
value to their owners and to the society in general but this
has not been the case in Kenya. This is further proved by
considering the number of MSE’s that manage to grow in
terms of sales, profit and assets as well as the number of
people it employs. The MSEs’ churning rate has been
worrying for this sector and as such needs a quick
redress by all the stakeholders both in government and
private sector (McPherson, 1996). While these
challenges and drawbacks are real, MSE’s ownermanagers
need not to sit back in the short run as these
problems persist but should network and come up with
various strategic options to address unfavorable
conditions so as to secure a more conducive, stable
working economic environment within this sector in
Kenya.
Empirical research has shown that the economic
success of MSE’s in many countries depends on informal
personal networks. Long term business relationships
emerge as a result of failures in both market and
institutional settings which is characteristic of least
developed countries (LDC) countries. MSE’s in Kenya get
around such market failures and lack of formal institutions
by developing relations with the outsiders through a
support mechanism provided either by friends, family
members, relatives or neighbours. In other cases
cooperative relations among groups of MSE’s organized
in business networks and in associations or local
community organizations perform these functions.
Prominent examples of such private orderings have been
found in the support networks and informal relationships
in Europe, America and Asia (Birley, 1985; Bryson et al.,
1993; Burt, 2009; Curran et al., 1993, Goodman and
Bamford, 1990; Rabelloti, 1995; Steier and Greenwood,
2000; Uzzi, 2004).
The evidence that private institutional arrangements
among the MSE’s facilitate their performance in many
countries fits a theory of the firm that views the enterprise
as a collection of contracts and relationships between the
firm and various stakeholders from the external
environment (Coarse, 1937, 1988; Alchian and Demsetz,
1972; Williamson, 1985). Institutional gap left by the
government of Kenya has proactively made the MSE’s to
circumvent the risks involved through informal
institutional settings of social networks. Risks are as well
inherent in such arrangements but it is the ideal
Mechanisms through which the MSE’s can operate under
such environment (Birley et al., 1991).
In the next section a theoretical concept is developed
which features the choice of network variables as drawn
from the dynamic network perspective theories or
literature on marketing, organizational and sociological
economics to shed light on how exceptionally high level
of poverty can be overcome in Kenya. This recapitulates
into the description of the hypothesis then methodology.
Lastly the results are discussed on how the network
structure impact on MSE’s business performance and
sustainability and their policy implications.
THEORETICAL CONCEPT
To understand network requires a deep understanding of
dynamic pattern of networks given that they do not evolve
overnight (Venkataraman, 1989). With respect to the
instrumental role of social capital, the study adopt a
Marketing Network Model developed by Hakansson and
Johanson (1988), which reconciles both social network
perspective (Aldrich Zimmer, 1986; Birley, 1985, 1990;
Birley Cromie, 1988; Granovetter, 1976, 1985;
Johannisson, 1986, 1987a, 1988, 1995b; Uzzi 1996,
1999; Veciana Clarke, 1999) and Resource Dependency
Theory (Butler and Sohod, 1995; Pfeffer and Salancik,
1978, 2003). The marketing network model is an
amalgamation of these two distinct theoretical
perspectives. The study uses the integrated network
theoretical approaches of marketing network model on
the argument that, small firms cannot perform better
without direct or indirect network relationships hence the
hypotheses is formulated on this basis. Researchers
have used different types of theoretical approaches in
order to analyze and understand networking and small
business performance as there is no single general
theory of small business networks.
Economic functions can be performed either within the
boundaries of hierarchical firms (within the organization)
or by market processes that cut across these hierarchical
boundaries; either hierarchies or markets. For small
firms, the economic functions and transactions within the
boundaries of hierarchical firms are either impossible or
extremely difficult because small firms, being small and
alone, are inherently lacking in resources thereby causing
higher production costs.
Market mechanism is also not a better solution
because perfect competition is far from reality especially
in developing countries like Kenya. Perfect competition
causes higher transaction costs. Hence, it is clear those
small firms find it difficult to perform their economic
activities either at the level of hierarchical firm (or
bureaucracy) or market. Given this, small firms in
developing countries need support to compete and
survive in their businesses. Networking is one of the best
solutions given in the literature for the development of
small firms in LDCs because networking lies between the
hierarchy (or bureaucracy) and the market (Borg, 1991;
Jarillo, 1988; Thorelli, 1986). Hierarchies and markets are
regarded as being the polar ends of a variety of
governance options (Butler, 1991; Williamson, 1985).
In the network, the logic of exchange differs from the
economic logic of market and hierarchy. The logic of
exchange of networks considered in this study is that of
‘social embeddedness’ because ongoing social ties
shape actors expectations and opportunities in ways that

differ from the economic logic of market behavior
(Granovetter, 1985; Uzzi, 1996). A small firm without
networking with its external actors is bound to fail.
Networking is the best solution for small firm
development (Borg, 1991; Donckels and Lambrecht,
1995; Gibb, 1993; Johannisson, 1990b; Szarka, 1990). At
one hand of firm’s hierarchy level, firms are too small and
thus growth may be hindered by lack of resources. At the
market level, on the other hand, transaction costs to
obtain necessary resources are extremely high.
Therefore, small firms have to obtain resources and
support from ‘outsiders’ or external actors. Thus, small
firms are dependent on other external actor, which is
called ‘interdependence’. Hence, to study small firms and
entrepreneurship, this research views focal firms within
their external environment. Within this integrated model,
resource dependency approach examines the behavior of
a firm within its environment on the basis of resource
dependence meanwhile social network approach looks at
how network relationships influence small business
performance and its application to the economic
phenomena.
In addition, Social network approach views
entrepreneurship as an act of creation and small
business as a way of life that is different from the rational
economic behavior. As with social network approach,
actors and their exchange relationships are very
important for small firm development. In this framework,
entrepreneurship is seen as an ongoing process of
venturing forth through personal networking in which
actors, resources, exchange relations and activities are
the major network elements. On the whole, there are two
major arguments behind the concept of networking.
Firstly, since market transactions tend to become costly,
firms attempt to overcome transaction costs by
networking.
Secondly, in order to perform, firms need various kinds
of resources. Small firms, in particular, do not have all
these resources fully at their disposal. Firms gather these
resources from ‘outsiders’ or, in other words ‘external
actors’. As most resources are controlled by external
actors, a small firm always depends on its outside actors.
Therefore, in order to perform economic activities, firms
have to enter into relationships with these external or
outside actors thereby forming entrepreneurial networks.
Firms being heterogeneous in nature, they face different
problems and requirements in different phases of their
development. Therefore, the firms need different
resources and support in different stages of business
growth.
At the start –up phase the business needs resources
but an entrepreneur does not have all the necessary
resources needed to start the business. This he can
acquire through personal networks (Birley and Cromie,
1988; Curran et al., 1993; Ostgaard and Birley, 1996).
Networks are not static; they are dynamic (Birley and
Cromie, 1988, Butler and Hansen, 1991) as relations are
Ongong and Abeka 11325
continuously constructed and reconstructed during
interactions (Grabher, 1993). It is very common for small
entrepreneurs in Kenya to follow evolutionary network
model to meet different needs of different phases of
entrepreneurship as other writers suggest. At the
entrepreneurial stage or phase, the entrepreneurs
discuss with friends, relatives and formal co-workers
before they practically start their businesses. Besides,
these networks also encourage new entrepreneurs. This
is the stage where businesses are developed and social
support is sought (Butler and Hansen, 1991; Bridge et al.,
1998; Larson and Starr, 1993). Professional and
organizational actors play a very small role in the case of
Kenya’s MSE’s when compared to other phases as at the
second stage they never engage professionals but make
use of friends and relatives to do the professional work
for them if any.
Development of the hypothesis
The study brings forth its three major hypotheses out of
which eight sub-hypothesis are developed to help us
understand fully the impact of networks on micro small
enterprises in Kenya. The study seeks to test the
following hypotheses:
H1: Small firms engage in local, homogeneous networks
among themselves to cope with uncertainty and risk.
H2: Heterogeneous networks which include non-local
partners stabilize performance outcomes.
In this second hypothesis the study deal with how the
entrepreneur having acquired the resource moves up the
hierarchy and his/her problem now is extending his/her
market through network thereby he/she gets into by
subcontracting with outsiders in the second phase of the
businesses. He makes use of the elements of network to
carry on his activities by extending his network.
Dependent variable here is performance and
sustainability measured in terms of the market expansion
or as well profit and sale can be used. But the
independent variables to be measured here is the
network density of activities of business focused network
with regard to money, information and nonmaterial
support.
H3: Networking with interest groups influences their
agenda and actions and therefore benefits small
businesses. In this hypothesis it is assumed that an
enterprise with more networks relations benefits more
than the one with low network density through resource
support from outside actors which ultimately help improve
their business performance.
Eight sub-hypothesis is set for their analysis and as
drawn from the 3 main hypotheses by linking them to the
dependent variable of performance measurements. The
dependent variable growth is dichotomous (growth, no

11326 Afr. J. Bus. Manage.
growth). Growth is used here as a performance indicator
given that performance is a relative term.
Sub-hypothesis (a): Owners' membership in various
support groups or clubs and societies has a positive
impact on the business’ performances;
Sub-hypothesis (b): Consultation with family members
has a positive impact on the performance of the
business;
Sub-hypothesis (c): Consultation with friends has a
positive impact on the performance of the business;
Sub-hypothesis (d): Use of external consultants is
positively related to the performance of the business;
Sub-hypothesis (e): Attendance at seminars’ has a
positive impact on the business performance;
Sub-hypothesis (f): Participation in trade fairs is positively
related to business performance;
Sub-hypothesis (g): Trade, exhibition and fare are
positively associated with growth of the firm;
Sub-hypothesis (h): Advertisement has appositive impact
on the business performance;
At the same time, the study also expect; hypothesis: The
relations with other entrepreneurs (no contact/ immediate
neighbourhood, local surroundings for example
village/small town/wards/quarter, district, regional, Kenya
wide, International) are boosted by other network
elements (memberships in various support groups, clubs
and societies, consultation with relative, consultation with
friends, external consultation, attending seminars, and
participating trade fairs/exhibition; Advertisement); (i and
j).
Dependent variable
Performance and sustainability measured in terms of
profits terms, sales made and expansion of the market.
Note that these financial indicators are used for growth of
the small firms and by extension satisfy the ‘if’ condition
for performance in the small business case. The firm
sustainability is achieved when correlation between
performance and networks is positive and significant at a
given level of significance. Whereby from the good
performance an entrepreneur can climb the hierarchy of
net works and back forth in a circular manner.
Independent variables
The independent variables are: Social networks,
supporting networks, and inter-firm networks. For the
correlation measures, the study used network densities
for social, support, and inter-firm networks. In the case of
probability analysis, the study used networks as dummy
variables; dummy (social): 1 = if the entrepreneur had
social network relations (yes), 0 = otherwise (no); dummy
(supporting):1 = if the entrepreneur had support network
relations (yes), 0 = otherwise (no); and dummy (interfirm):
1 = if the entrepreneur had inter-firm network
relations (yes), 0 = otherwise (no).
Control variable
Before testing the hypotheses, it was important to ensure
that the potential effect of the other factors was
minimized. Several other enterprise related factors (such
as firm and market locations, number of employees, and
types of businesses) and entrepreneurial related factors
(such as gender, age, place of birth, education, and work
experience) were therefore statistically controlled for in
the estimations.
METHODOLOGY
The population and sample
The population of this study is micro small enterprises in Kenya
estimated to be 1.3 Million based on the MSEs Baseline Survey
carried out by the government of Kenya in 1999 once and has not
been carried out again (CBS, K-Rep/ICEG 1999 pp. 17,105). The
population frame which targets those small enterprises in the big
towns, peri-urban, urban and rural areas was selected on the basis
of this research framework and comprised the micro small
enterprises in four districts of Kenya based on their location, size
and region. Then the research sample was selected from the
population frame by using a standard sampling method.
The sampling method
A total of 400 firms were sampled through a multi-stage cluster
sampling method. Four strata were chosen from eight clusters
covering areas; for example, cities; of which Nairobi was chosen to
be representative of all the major cities in Kenya, towns; of which
Kisumu town was chosen to be representative of all the major town
in Kenya having a population above 10,000 people, urban; of which
Eldoret was chosen to be representative of all urban areas in Kenya
with a population of between 2000 people to 10,000 people, and
lastly rural; of which Kakamega was chosen to be representative of
all the rural areas in Kenya and the choice of Kakamega was made
as informed by the BIOTA4C project and the other geographical
activities taking place in Kakamega.
From these stratified clusters, 400 MSE’s were chosen based on
their demographic and economic characteristics with each stratum
producing 100 MSE’s. A bigger percentage of the total MSE
populations of the small enterprises (61%) are concentrated in the
rural areas and the type of industry in which most of them are
involved in are service industry (40%) followed by manufacturing
(23.2%). The response rate of the MSE’s owner or managers was
impressive with 99% response rate (Table 1). Due to practical
difficulties (money, time, and transport), the study were restricted to
this particular number of the sample size despite the immense
cooperation received among the entrepreneurs.
Variables and variable measurement
Determining the variables to use for such kind of a study is an
upheaval task because most of the variables are social
relationships. The measurement of social relationships has always
been a nagging and unresolved problem (Hall et al., 1977). For this

Table 1. Type of enterprise and sampling area (Clusters).
Ongong and Abeka 11327
Sampling area of the respondents
Type of enterprise/ sampling area
Town Urban Peri-urban Rural Total
n % N % n % n % n %
Manufacturing 45 11.2 10 2.5 23 5.8 15 3.8 93 23.2
Service (Incl. Repair, health and Beauty ,I.T) 32 8.0 58 14.5 41 10.2 29 7.2 160 40.0
Trade 9 2.2 16 4.0 16 4.0 18 4.5 59 14.8
Agricultural Processing 3 0.8 1 0.2 2 0.5 4 1.0 10 2.5
Handicraft 10 2.5 8 2.0 3 0.8 25 6.2 46 11.5
Food and beverage/Restaurant. 1 0.2 7 1.8 15 3.8 9 2.2 32 8.0
Total 100 25 100 25 100 25 100 25 400 100
Source: Survey Data (2008-2009).
study purpose the following general questions were asked to the
entrepreneurs about their networking activities; for instance- how
many business partners do you have? Where they are mostly
situated? For how long have you been cooperating with your
partner firms? For which purpose do you cooperate with other
firms? Are you a member of any support group? Who is your main
source of input? Who is your main customer? Do you have any
subcontracting arrangements for inputs or orders received from
clients? How do you set your prices? What are the main methods of
advertisement of your product and services? Have you sought and
received any formal assistance for any of the above problems for
your business in the last 2 years? In order to obtain a better and
deep understanding about the external actors and their roles,
respondents were given five choices of answers; not important,
fairly important, average, important, and very important. Besides,
they were also given a choice of two sets of six persons to whom
they could turn to most likely for business related advice or any
other help. The main aim of this questionnaire was to collect
information on the relationships between respondents and these
two set of persons.
Dependent variables of the study
Performance and sustainability
Based on a review of the literature (Donckels and Lambrecht, 1995;
Hansen, 1995; Ostgaard and Birley, 1996) pertinent to the
measurement of performance, two objectives of measures of
growth were included; sales growth and increase in profitability over
a given time period. In addition, market expansion (Local, regional
and national) is used as a business performance measure. Studies
(Johanson and Mattson, 1993) in the field of marketing and
international business have identified a positive relationship
between network formation and market expansion of small
businesses.
Growth of sale = [{(sale in current season – sale in previous
season)/sale in previous season}/2]* 100 ……………….
………………. (i)
Growth of profit = [{(profit in current season – profit in previous
season)/profit in previous Season}/2]* 100 ……… ……………... (ii)
However, the big challenge facing many MSE’s is that the
entrepreneurs do not properly keep business records related to
their daily business operations due to ignorance, therefore,
obtaining financial details for sales and profit is foolhardy. To
overcome this agony for the MSE’s in Kenya, the study tried to get
the relevant data on sales and profits by asking respondents their
perception with respect to last year business performance to that of
the current one as expected for next year. To capture this
categorically the respondents were asked about whether their sales
or profits vary over time on a seasonal or monthly basis. The figures
got were compared to the performance and sustainability
parameters for those particular firms who provided the required
information.
Independent variables of the study
Network density
Network density is a very important indicator measure in evaluating
entrepreneurial networks in the firms three different phases. It is
generally measured as the proportion of ties present out of allpossible
ties (Burt, 1992; Greve, 1995; Duysters, 1995). Network
densities also can be obtained by dividing the number of existing
alliances among actors in the network by the total number of
possible links between those actors. For all the practical reasons,
network density is very difficult to exhaustively measure due to egocentricism
of human beings (Greve, 1995) where only relations that
are directly connected to ego are visible as networks are defined
from a focal person’s perspective.
Network size
The larger the network, the greater is the number of network
members who provide emotional support, goods and services.
Entrepreneurs with large networks may win both ways; not only do
they have more potential providers of support in their networks, but
also each number of their network is more likely to be supportive
(Wellman and Gulia 1993). Network size was obtained by asking
respondents to estimate the number of people or organization with
whom they dealt with in business activities, resource support and
discussions of their business, information on market, technology
and group membership. Therefore, the size of entrepreneurial
networks may be one of the most important variables to explain the
success of a small enterprise (Aldrich et al., 1987; Hansen, 1995;
Johannisson, 1986; Greve, 1995). Entrepreneurs identify product or
service ideas, access to markets, information, money and other
resources in their environments, and they also gain access to these
resources through various members of their networks.
The importance of size is recognized by almost all writers, but
there have been significant shifts recently in how the term is used.

11328 Afr. J. Bus. Manage.
Table 2. Performance of small enterprises.
in profitability term % in sales term % Major market location %
Growth 58.75 57.00 National 20.75
Neutral 31.50 40.50 Local 45.75
Decline 9.75 2.50 Regional 33.50
Source: Survey Data (2008-2009). The firms were divided into three groups (growth, neutral, and decline firms) on the
basis of the respondents’ answers and data availability.
One usage of size focuses upon the number of ties or links
between an organization and outside contacts. These approaches
converge on the basic idea that what matters is the number of links
between an organization and its context is that; the greater the
number of links, that is the more extensive the network, the better
for the organization, irrespective of whether the links are direct or
indirect (Larson, 1992).
Control variables of the study
Control variables help us to minimize the potential effect of the
other factor that may be considered to affect the outcome of the
other variables in a relationship therefore, they should be controlled
in the estimation. Previous researches (Donckels and Lambrecht,
1997; Sarder et al., 1997) have suggested several enterprise- and
entrepreneurial –related factors that affect growth. Based on the
same, the following enterprise-related factors were included as
control variables in this study:
1. Service sector are known to be growth oriented and solid in
network therefore it was found prudent to include them (Donckels,
1995; Lambrecht, 1997) than Manufacturing and trade.
2. Firm’s size is used as a control variable because previous
network studies have found that the larger small enterprises to be
more in the growth league (Donckels and Lambrecht, 1995; Mohan-
Neill, 1995). It is therefore, important to ensure that the potential
effect of the size of a firm is minimized before testing our
hypotheses.
3. Firms’ location is important for network formation and business
performances. Pervious studies found that firms that are inside
industrial estates are more in the growth league. Those firms have
a better potential for networking (Grabher, 1993b; Lomi and Grandi,
1997).
4. When one analyses the performance of small enterprises and
network formation, one can not overlook the possible impact of
family workers in the business. Family influence is very strong in
small Businesses (Chu, 1996; Johannisson, 1990a). Mostly they
are used during peak and off seasons as unpaid in cash to ease the
work pressure. Therefore, the impact of family worker has also to
be minimized before testing our hypotheses.
5. The mentioned entrepreneur -related factors have an impact on
the growth orientation of a small enterprise and network formation
(Donckels and Lambrecht, 1995, 1997). The quality of the network
is highly dependent upon given personal skills and attributes
(Johannisson 1988).
6. Entrepreneurs who have lower education and highly trained are
more likely to be in the growth league (Donckels and Lambrecht,
1995) .The same research on network formation points out that
there is a causal relationship between network formation, growth,
and level of education.
7. Gender composition of networks is significantly different for men
and women (Singh and Reynolds, 2001). The present study uses
these variables as control factors since it is necessary to make sure
that the potentially moderating effect of those factors is minimized.
Model specification
Given the nature of the data which is, qualitative, binary and
categorical, a logit or regression techniques was used to analyze
the data. Drawn from logit, an empirical model used to test the
effects of network strategies on firm performance and sustainability
as we control for other firm and entrepreneurial characteristics is
stated as:
Where is the growth (yes=1) or no growth (No= 0) of firm i th
as measured by the financial performance indicators of
denoting sales made by firm i th and profit
made by firm i th respectively between the high and low seasons.
While D_Strat.j are the network relationship dummy variables of
strategies adopted with j=1, 2 and 3 to represent social network,
support networks and business networks respectively and ‘s
representing the explanatory variables of the network elements and
the is the error term to capture for all the unobserved and
control variables and with ,
, , being the network coefficients.
RESULTS AND ANALYSES
The empirical results of the regression models for the
dependant, independent and control variables are
presented here as empirical evidence. Their relationships
are traced on how they relate with each other on building
the networks for MSE’s in Kenya. This is followed by a
detailed discussion and conclusions of these findings as
to whether the relations exhibited consequently has an
impact on the performance, growth and sustainability of
the MSE’s in Kenya under the given institutional
environment. As mentioned before, the dependent
variable of the first hypotheses was identified as firm
growth and performance (in terms of profitability and in
terms of sales) and market expansion (National, regional
and local). The firms were divided into three groups
(growth, neutral and decline firms) on the basis of the
respondents’ answers and data availability as seen in
Table 2. From Table 2, 58.8% of firms report growth
category, while 9.8% of them are reported ‘not growth’.
31.5% of them are in neutral. In sales term, 40.5% are in
the neutral growth category as sales increase in 57.0 %
of firms. 2.5% recorded sales declining during the two

Table 3. Ordered logit regression analysis of business performance.
Independent
variable
(a)Network Elements:
Dependent
variable
Growth models †
(Ordered Logit) Financial
Profit
Model 1
Sale
Model 2
Ongong and Abeka 11329
Market expansion ‡
Model 3 (Logit)
Local Regional National
(i) Membership of a support group (Memb.) -0.5391474 -0.412117 -0.6257845 1.127626 -4.306764*
(ii) Consult with Relatives (RltvC.) 0.1768815 0.3213644 -0.0035988 0.2077015 4.688457
(iii) Consult with friends (FrndC.) 0.4204207 0.5413413 -0.7643241* -2.67127*** 2.188995*
(iv) Sponsor (Spo.) -0.6301558* 0.4360656 -0.7658631* 1.562125 3.946916
(v) External Consultancy (Excon.) -0.2912984 0.2297336 0.6087752** -1.048016* -0.620859*
(vi)Training attendance (Trainat.) 1.062539*** -0.8131616** 0.900166** -1.385168** -4.237001***
(vii)Trade Fairs/exhibitions (Exhb.) -0.8757981* 0.1488198 -0.5060612 0.7765897 -4.314818***
(viii) Advertisement linkages (Advert.) 0.876145** -0.4422695 0.9287112** -1.136924* -4.018183***
Local Contacts (LC) 1.092227*** 0.2776574 - - -
Regional contacts (RC). 0.0145981 0.4474843 - - -
National Contacts (NC) -1.356309 0.5181947 - - -
(b)Entrepreneur-related:
Age (Log form) -0.2574903 -0.3141566 -0.8465496 0.4008095 5.111433**
Gender 0.4416784* -0.0293623 -0.2337811 -0.1055492 -4.050044***
Location of the Respondent -2.06825*** 0.2434624 0.4514045 2.635399*** -4.077537
Educational level 0.0827355 0.0479231 0.1825082 0.5934178 0.5322168*
Owner's Period of experience (log form) 0.1612917 -0.1033626 -0.5007559** -0.0989244 5.111433**
(c)Enterprise-related:
Manufacturing Industry (S1) 0.1445633 0.6296169** -0.2698701 0.1677356 0.8007031*
Service Sector (S2) -0.077937 -0.4589423* -0.4939045* -1.022068* 1.368797
< 5 Employees (SE1) 0.544956 -0.8335374 -0.5937174 0.632742 0.674099
> 5 Employees (SE2) 0.2237928 -0.1952878 -0.1158605 0.3893944 -4.110874*
Firm's life time (Log form) -0.0351872 0.3125358* 0.1420394 0.493515 -1.291059
Regular Employees (RE) 0.0521155 0.0197024 0.0444308 -0.0241193 0.056759
Seasonal Employees (SE) -0.055761* -0.0009008 0.0250711 0.0475733 0.6124792
Intercept - - 4.056091* -8.055703* -56.09511*
Pseudo R 2 0.1296*** 0.0692*** 0.1580*** 0.2014*** 0.4893***
Source: Survey data (2008 - 2009) Note: z-values are in parentheses; N = 386; † Baseline (comparison category) is non-growth group ‡ Baseline
(comparison category) is regional market;*** P- value < 0.01-statistically significant at 1%** P- value < 0.05- statistically significant at 5%;*P -value <
0.10- statistically significant at 10%.
season’s periods of high and low (Growth is in financial
terms of which in sale terms it is measured by using
available records of which most of the small firms do not
own record therefore the entrepreneurs' point of view is
taken into consideration).
In the major market location, 20.8% of the small
business represented growth and 33.5% represented a
decline with a higher percentage of 45.8 stagnating at a
neutral state. The situation can further be understood by
considering the market segment in which these
enterprises operate be it at the local, regional or national
level. The models of growth in financial terms (Model 1),
in sale terms (Model 2) and market expansion (Model 3)
are presented in Table 3. Entrepreneur-related and
enterprise-related factors were used as control variables
in all models.The dependent variable of model 1 and 2
are binary choice as 1 for growth, and 0 for otherwise
(decline). Model 1 is statistically significant with a
moderate goodness of fit as indicated by the value of chisquare
(p-value < 0.01, Pseudo R 2 = 0.1296). The model
tests the impact of network elements on growth. In this
model, growth is defined in financial terms of profits (1 = if
growth, 0 = otherwise). Model 2 also tests the same
impact, but in terms of sales. The second model is also

11330 Afr. J. Bus. Manage.
Table 4. Predicted probabilities.
Independent
Variable
Dependent
variable
Growth models † (ordered logit)
financial
Profit
Model 1
Sale
Model 2
Local
Market expansion ‡
Model 3 (Logit)
Regional
National
(i) Membership of a support group (Memb.) -0.1219823 -0.0955673 -0.1224285 0.020988 -
(ii) Consult with Relatives (RltvC.) 0.041908 0.075337 -0.0007909 0.0053108 0.0050787
(iii) Consult with friends (FrndC.) 0.0968764 0.1235845 -0.1817392* 0.0280143** 0.0004007
(iv) Sponsor (Spo.) -0.1427058** 0.1075258 -0.1494425** -0.0272787* -0.0014332
(v) External Consultancy (Excon.) -0.0704618 0.05534 0.1302733** -0.0763014 -0.0000402
(vi)Training attendance (Trainat.) 0.2595442** -0.1806918** 0.2135369** 0.0356637 -
(vii)Trade Fairs/exhibitions (Exhb.) -0.2155145* 0.0355592 -0.1183603 0.034191 -
(viii) Advertisement linkages (Advert.) 0.2133669** -0.1048577 0.2129096** -0.0445727 -
Source: Survey data (2008 - 2009), *** P- value < 0.01; ** P- value < 0.05; * P- value < 0.10. Note: † baseline/ comparison category for growth models
(profit and sale) is ‘non-growth group’. ‡ baseline or comparison category for market expansion is ‘regional market’.
significant at 0.01 levels (p-value < 0.01, Pseudo R 2 =
0.0692). Positive relationship between network formation
and market expansion of small businesses has been
identified by international business and marketing
scholars (Johanson and Mattsson, 1993). Consequently,
in addition to the growth measures (profit and sale) the
study used market expansion within the seasonal periods
as a dependent variable to test the hypothesis. Most of
the small enterprises mainly serve the local market. In the
multinomial logistic model, model 3, the study therefore,
used regional market as the baseline (comparison
category). The baseline (regional market) is very
important when the results are interpreted. The
multinomial logistic model is also statistically significant
(p-value

Table 5. Partial correlations matrix 2.
Ongong and Abeka 11331
Mean S.D Memb RltvC. FrndC. Spo. Excon Trainat Exhb Advert
Memb 0.94 0.2378 1.0000
RltvC. 0.055 0.2283 0.0610 1.0000
FrndC. 0.0725 0.2596 -0.0106 0.1440 1.0000
Spo. 0.865 0.3422 0.0542 0.0632 0.0540 1.0000
Excon 0.40 0.4905 0.1633 -0.1298 -0.0708 -0.1105 1.0000
Trainat 0.8775 0.3283 0.0661 0.0232 0.0457 -0.1476 0.1027 1.0000
Exhb 0.0525 0.2233 0.0123 -0.0568 -0.0226 0.0602 -0.0778 -0.0488 1.0000
Advert 0.7075 0.4555 -0.1162 -0.0859 0.0526 -0.2219 0.2333 0.1956 -0.1197 1.0000
Index:(i) membership of support group (Memb.), (ii) consult with relatives (RltvC.), (iii) consult with friends (FrndC), (iv) sponsor (Spo.), (v)
external consultancy (Excon.), (vi) seminar and training attendance (Trainat.), (vii)trade fairs/exhibitions (Exhb.), (viii) advertisement
linkages (Advert), contacts with entrepreneurs (EntpC)-regional Contacts (RC)-both regional and national contacts (RC and NC),-national
contacts (NC)‡For control variables refer to 5.1.3 Note: p-values (two-tailed significance) are in parentheses. N = 386,*p-value < 0.01,**pvalue
< 0.05,***p-value < 0.10,† contact with other entrepreneurs (EntpC) has four categories: 0 = no contact; 1 = only local contact; 2 =
regional contact; and 3= only national contact.
on sales by 18% at 5% level of significance. The local
entrepreneurs have a 21% probability at 5% level of
significance of expanding their markets if they attend
seminars and training. In contrast to this is that the MSE’s
or entrepreneurs who attend trade fairs or exhibition has
a 22% probability of realizing decline in profit levels at
10% level of significance and an increase in market
expansion by 12% decrease but is statistically
insignificant. For advertisement linkages, those MSE’s
which advertise for their services and products has a
21% probability of registering growth in profits with similar
percentage in terms of market expansion locally at five
per cent level of significance. Meanwhile advertisement
has a 10% negative impact on level of sales for these
MSE’s even though it is statistically insignificant.
Important to note in this discussions is that marginal
impacts on growth on these variables were pro-poor as
the details above can indicate which is an attendant
problem for the MSE’s in Kenya. In addition to the
probabilities, partial correlations for network formation
variables were estimated as shown in Table 5.From
Table 5, 94% of the firms had membership to support
groups, 86.5% had a sponsor, 87.8% had attended
training and 70.8% had advertisement linkages.
Frequency of contacts on an average by relatives through
consultations was 5.5% in building the social networks,
7.3% for consultations with friends, 40% for external
consultations and 5.3% for trade fairs or exhibitions.
Looking at the value of the correlations which are below
30%, the study is assured there is problem of collinearity
of the variables.
Discussion
Network relations are vital and important for small
business, in particularly to the small firm as it does not
have all resources such as raw materials, capital,
machinery, etc. Therefore, small business network
researches (Donckels and Lambrecht, 1995; Ozcan,
1995; Szarka, 1990; Uzzi, 1999) suggest networking as a
necessary strategy in obtaining resources such as
gathering information, technology, finance, etc. Besides,
building contacts through networks are the fundamental
factor in determining the success of any firm (MacMillan,
1993) because through entrepreneurial networks, the
entrepreneur can gather information, look for customers
and suppliers, and obtain the other resources he needs.
As regards contacts with entrepreneurs, network
literature suggests that inter-firm linkages may span
various levels of aggregation: Firms may be linked only
locally, sometimes, interregional or globally (Stabber,
1996a).
The purpose of this chapter has been to explore the
impact of network formation on small business
performances. The study predicted the positive impact of
network formation on business performance. Logistic
regression technique was used to analyze the data. The
first hypothesis which includes seven sub-specific
hypotheses is about the impact of the formation of
networks on growth. The study tested this hypothesis by
using three separate dependent variables. Entrepreneurs
with only local contacts (LC) are significantly less likely to
be in the growth group. But those who have national level
connections are more likely to belong to the growth
group. In the case of the market expansion, the formation
of networks is positively related to the market expansion.
The results conclude that when the market expands
beyond the regional border, the influences of the network
connections are vital and important for the small
entrepreneurs. The second hypothesis is about the
network elements and the network relations with regional
and national entrepreneurs. The study expected the
relations with other entrepreneurs to be promoted by the

11332 Afr. J. Bus. Manage.
network elements and they are positively related with the
formation of networks. However, the study fails to identify
considerable network relations with international
entrepreneurs. At the same time, the study found that the
small entrepreneurs do not have direct export
opportunities. They deal with export market through some
link-agents or firms. Although the study expected the
second hypothesis that all of the network elements
influence network formation, the contact with other
entrepreneurs is not significantly influenced by external
consultancy. One reason for the lack of significant
relationship could be that the relationship between
education and contact with other entrepreneurs is
positive and significant. Meanwhile, we found that small
entrepreneurs who attend seminars and training and
participate in trade fairs have a higher chance of
developing relations with other entrepreneurs.
Consultation with relatives is also very critical as family
ties occupy an important role in entrepreneurial networks
in Kenya in which social relations are largely built around
the family. In such a society, family members work
together in their businesses as well as at home. The
family relationship is stronger in rural areas. The study
found that the rural-entrepreneurs consult and discuss
their business matters with relatives more than the
entrepreneurs in urban areas do. However, when the
study defined consultation and discussion with relatives
we omitted very close family members if they were
partners of their business. In most cases, the close family
members are also a part of the businesses. Future
research should be conducted in this direction. Tribal
variables should also be included into the overall model.
It is also important to study how the other enterprise-
and entrepreneur- related factors such as gender,
education, firms’ location etc. separately influence on
each of the network formation elements. The study found
that there are some significant relationships between the
network formation elements and the enterprise-and
entrepreneur -related factors, though they are not very
strong relationships. The results show that educated
entrepreneurs are more likely to attend seminars,
training, advertise and attend trade fairs, join professional
and other societies, and contact other entrepreneurs,
while they are less likely to discuss their business matters
with relatives and friends. Meanwhile, female
entrepreneurs discuss their business matters with
relatives and friends more than their male counterparts.
By contrast, compared to female owners, male counterpartners
are looking for more external consultants,
attending more seminars, and training, advertise and
attend trade fairs. The male entrepreneurs also have
more contacts with other entrepreneurs as pointed out
above. In conclusion, this chapter analyzed the impact of
network formation on the growth of small enterprises in
Kenya. The study found that network formation is an
essential aspect of small business development. Hence
networking, therefore, becomes an important element in
the growth and performance of small enterprises.
However, networking is time-consuming, experiencebased,
and does not evolve over night. Therefore, the
policy makers, small entrepreneurs, donors and others,
who deal with the development of small enterprises in
developing countries, can use the network formation
approach apart from their traditional supporting approach.
For instance, supporting institutions should organize
network activities for small businesses. Small business
owners should also realize the importance of constructing
Networks. However, there are few empirical studies
available in this area particularly in less developed
countries. Therefore, further research is necessary in this
direction. Researchers should also deeply consider
enterprise- and entrepreneur -related factors when
studying networking and small businesses.
Conclusion
The purpose of the study has been to analyze the role
and impact of networks on small business performance
and sustainability in Kenya. However, the concept of
networks and network analysis cannot easily be
explained due to an array of different definitions of
network found in the literature and on the other hand,
network analysis has been used in different areas of
studies by different researchers in different perspectives.
In this study, networking has been seen as an effective
vehicle for obtaining necessary resources for small
enterprises from the outsiders or external environment.
The study found that small entrepreneurs who maintain
regular relationships with external actors are more likely
to be successful in their respective businesses because
such relationships provide a constant and reliable source
of resources and effective influence on firms. These
external relationships are identified as entrepreneurial
networks in this study. This study is different from the
other studies in the field of small business networking in
four ways. First, current studies largely focus on formal
business networks such as alliance. In contrast, the focus
of this study is on the entrepreneurial informal network
relationships in a less developed country. Second, most
current studies are largely focused on the experiences of
developed countries (for example Birley, 1985 (USA),
Bryson et al., 1993 (UK), Curran et al., 1993 (UK);
Goodman and Bamford, 1990, (Italy). Therefore, there
was a gap in the understanding of small business
networks in developing countries. In particular, small
business networks in Kenya have not been studied and
some studies which have been done focus on the
possibilities of emerging clusters and subcontracting in
the industrial estates (McCormick and Pedersen, 1996).
Thirdly, this approach also differs from others in respect
of the unit of analysis. For example, the industrial estate
(holistic approach) has been widely used in the field of
small business development in developing countries. This

study has employed an individualistic approach (the egocentered
firm) to study small business development
within the context of entrepreneurial networks. Fourth,
entrepreneurial networks are always regarded as
advantageous for small business success. Apart from
various case studies, however, a critical approach was
needed in the network analysis in order to assess the
importance of networks for small business performance.
This study has filled this gap.
The study believe that this approach is necessary for
advancing research on the field of entrepreneurial
informal networks beyond general descriptions of the
advantages of networks of single case studies. In this
regard, the study contributes to network studies in four
ways. Firstly, the study analyzed entrepreneurial informal
network relationships.
Secondly, the recent studies in this area are largely
focused on the experiences of developed countries. A
very few or no such a study has been available in the
field of entrepreneurial networks in developing countries,
particularly in Africa.
Thirdly, the study used survey research approach to
test a number of hypotheses. Overall, this study
contributes to the literature by showing how small firms
use network relationships to overcome their business
bottle-necks, identify new market opportunities and finally
to perform their business successfully.
The findings of this study will without doubt be useful to
the policymakers, business community, researchers,
public institutions, financial organizations, donors and
supporting organizations of small firms, and social
workers particularly in Kenya and the other countries as
well.
To sum up, there are some conclusions from the study,
but the major conclusion is that entrepreneurial
networking can create a successful small firm sector by
helping to overcome the lack of resources, the
managerial and professional weakness of small firms
within a broader supportive external environment. Owing
to lack of resources, small enterprises always need to
maintain contacts with their external actors to obtain
necessary resources.
The actors of social networks and supporting networks
are very important for small enterprises particularly in
developing countries such as Kenya. Before a new
entrepreneur starts his venture, his social network
relationships work as an opportunity set. Then gradually
the entrepreneur develops his network relationships with
supporting agencies and other firms as well. The study
emphasizes the fact that, in order to really succeed in
business, small business entrepreneurs must use their
own personal networks as well as the inter-organizational
networks. To reach the conclusion, we analyzed informal
networks of small enterprises in Kenya. The study also
believes that the results have significant policy
implications. This empirical study has further
recommended the need for more in-depth comparative
studies before generalizing the results.
REFERENCES
Ongong and Abeka 11333
Abeka EO (1995). Role of State Cooperative Banks and Regional Rural
Banks in Supporting Micro and Small Enterprises in Agra District
(U.P):Their Perceived Relevance to Non- Financial Business
Development Services Offered. India (U.P) Agra: Unpublished M.A
Dissertation at RB Ambedkar University.
Aldrich H, Rosen B, Woodward BL (1987). "The Impact of Social
Network on Business Founding and Profit: A longitudinal Study" In
M. C. Wellesley, Frontiers Entrepreneurship Res., pp. 154-168.
Aldrich H, Zimmer C (1986). Entrepreneurship through Social Network.
In P. a. Nystrom, Handbook of Organization design, Oxford University
Press, 1: 385-408.
Alchian H, Demsetz (1972). " The Property Rights Paradigm". J. History,
1(1). pp. 39-77
Birley S (1985). " The Role of Networks in Entrepreneurial Process". J.
Bus. Vent., 1(1): 107-117.
Birley S, Cromie S (1988). " Social Networks and Entrepreneurship in
Northern Ireland". Enterprise in Action Conference, September.
Belfast.
Birley S (1990). Entrepreneurs Networks: Their Creation and
Development in Different Countries. Cranfield: Cranfield School of
Management.
Birley S, Cromie S, Myers A. (1991). " Entrepreneurial Networks: Their
Emergence in Ireland and Overseas". Int. J. Small Bus., 9(4): 56-74.
Borg E (1991). Problem Shifts and Market Research: The Role of
Networks in Business Relationships. Scand. J. Manage., 7(4): 285-
295.
Bryson J, Wood P, Keeble D (1993). "Business Networks, Small Firm
Flexibility and Regional Development in the U.K Business Services".
Entrepreneurship Reg. Dev., 5: 265-277.
Burt R (1992). " Structural holes: The Social Structure of Competition".
Cambridge: Havard University Press.
Butler R (1980). Control through Markets, Hierarchies and Collectives.
Paper Presented to European Group for Organization Studies
Conference. London: Imperial College.
Butler R (1991). Designing Organizations: A Decision-Making
Perspective. London: Routledge.
Butler J, Hansen GS (1991). " Network Evalution, Entrepreneurial
Success, and Regional Development". Entrepreneurship Reg. Dev.,
3: 1-16.
Butler R, Sohod S (1995). Joint-Venture Autonomy: Resource
Dependence and Transaction Cost Perspectives. Scand. J. Manage.,
11(2): 159-175.
Bridge S, O'Neill K, Cromie S (1998). Understanding Enterprise,
Entrepreneurship and Small Business. London: McMillan.
Donckels R, Lambrecht J (1995). " Networks and Small Business
Growth: An Explanatory Model". Small Bus. Econ., 7: 273-289.
Duysters G (1995). The Evolution of Complex Industrial Systems: The
Dynamics of Major IT Sectors. PhD Thesis, University of Maastricht.
Chu P (1996). Social Network Models of Overseas Chinese
Entrepreneurship: The Experience in Hong Kong and Canada".
Canad. J. Adm. Sci., 13(4): 358-365.
Curran JR Blackburn RA, Black S (1993). " Networks and Small Firms:
Construct Methodological Strategies and some Findings". Int. J. Small
Bus., 11(2): 34-45.
Coarse R (1937). "The Nature of the Firm". Economica.
Gibb AA (1993). Key Factors in the Design of Policy Support for the
Small and Medium Enterprise (SME) Development Process: An
OVerview. Entrepreneurship Reg. Dev., 7: 1-24.
GoK /CBS/ ICEG/ K-EP (1999). National Micro and Small Enterprise
Baseline Survey Results. Nairobi: Government Printer Press.
Goodman E, Bamford J (1990). Small Firms and Industrial Districts in
Italy. London: routledge.
GoK (2008). Economic Survey of Kenya. Nairobi: Government Printer
Press.
Grabher G (1993a). The Embedded Firm: On the Socioeconomics of
Industrial Networks. London and New York: Routledege.

11334 Afr. J. Bus. Manage.
Grabher G (1993b). " Rediscovering the Social in Economics of Interfirm
Relations". In G. Grabher, The Embedded Firm: On the
Socioeconomics of Industrial Networks". London and New York:
Routledge.
Granovetter M (1976). Network Sampling: Some First Steps. Am. J.
Sociol., 81: 1287-1303.
Granovetter M (1985). Economic Action and Social structure: The
Problem of Embeddedness. Am. J. Sociol., 91(3): 481-510.
Greve A (1995). Networks and Entrepreneurship: An Analysis of Social
Relations, Occupational and Background, and Use of Contacts
During the Establishment Process. Scand. J. Manage., 11(1): 1-24.
Hakansson H, Johanson J (1988). Formal and Informal Cooperation
Strategies in International Industries Networks. In FJ Contractor,
Cooperative Strategies in International Business. Mass: Lexington
Books.
Hall RH, Clark JP, Giordano PCJ, Van PV, Roekel M (1977). Patterns of
Inter-organizational Relationships. Adm. Sci. Q., 22: 457-474.
Hansen E (1995). Entrepreneurial Networks and New Organizational
Growth. Entrepreneurship Theo. Pract., pp. 7-19.
Jarillo J (1988). On Strategic Networks. Strat. Manage. J., 9: 341.
Johannisson B (1988: 85). Business Formation; A Netrwork Approach.
Scand. J. Manage., 31(3/4), 83-99
Johannisson B (1990b). Economics of Overview-Guiding the External
Growth of Small Firms. Int. Small Bus. J, 9(1): 32-44.
Johanson J, Mattson L G (1993). 'Internationalization in Industrial
System: A Network Approach'. In B. P. (eds)., The
Internationalization of The Firm: A Reader. Harcourt: Academic
Press.
Johannisson B (1987a). Beyond Process and Structure; Social
exchange Networks. Int. Stud. Manage. Org., 17: 49-63.
Johannisson B (1986). "New Venture Creation: A network Approach". In
RR (eds), Frontiers of Entrepreneurship Research. Wellesley, M.A:
Babson College. 236-38.
Johannisson B (1990b). Economics of Overview-Guiding the External
Growth of Small Firms. Int. Small Bus. J., 9(1): 32-44.
Johannisson B (1996). The dynamics of entrepreneurial networks.
Frontiers Entrepreneurship Res., pp. 253-267.
Kallinikos J (1995: 122). Cognitive Foundations of Economic
Institutions: Markets, Organizations and Networks Revisited. Scand.
J. Manage., 11: 119-137.
Karekezi S, Majoro L (2002). Improving Modern Energy Services for
African's Urban Poor. Energy Pol., 30: 1015-1028.
Larson A (1992). "Network Dyads in Entrepreneurial Settings: A study of
the Governance of Exchange Relationships". Adm. Sci. Q., 37: 76-
104.
Larson AA, Starr JA (1993). " A Network Model of Organization
Formation". Entrepreneurship Theory and Practice, Winter, pp. 5-15.
Lomi A, Grandi A (1997). The Network Structure of Inter-Firm
Relationships in the Southern Italian Mechanical Industry. In M.
Ebers, The Formation of Inter-organizational Networks. Oxford:
Oxford university Press.
MacMillan I (1993). The Politics of new Venture Management. Havard
Business Review .
McCormick D (1996). Small Enterprise: Flexibility and Networking in an
African Context. Nairobi, Kenya: Longman.
McPherson (1996). Growth of micro and small enterprises in Southern
Africa. J. Dev. Econ., 48: 253-277.
Mohan-Neill SI (1995). The Influence of Firm's Age and Size on its
Environmental Scanning Activities. J. Small Bus. Manage.,
Murphy TJ (2000). "Network, Trust and Innovation in Tanzanian's
Manufacturing Sector". World Dev., 30(4): 591-619.
Ostgaard TA, Birley S (1996). New Venture Growth and Personal
Network. J. Bus.Res., 36(1): 37-50.
Ozcan GB (1995). " Small Business Network and Local Ties in Turkey".
Entrepreneurship Reg. Dev., 7: 265-282.
Pfeffer J, Salancik GR (1978). The External Control Organizations: A
Resource Dependence Perspective. New York: Harper and Row
Publishers.
Pfeffer J, Salancik GR (2003). The External Control of Organizations: A
Resource Dependence Perspective. Stanford, Stanford University
Press.
Powell WW (1990). Neither Market nor Hierarchy: Network forms of
Organiztion. In BM (eds), Research in Organization Behaviour
Greenwich, CT: JAI Press. 12: 295-336.
Rabelloti R (1995a). Is There an " Industrial district Model"? Footware
Districts in Italy and Mexico compared. World Dev., 23: 29-41.
Sarder JH, Ghosh D, Rosa P (1997). The Importance of Support
Services to small Enterprises in Bangladesh. J. Small Bus. Manage.,
35(2): 26-36.
Staber U (1996a). The Social Embeddedness of Industrial District
Networks. In U. S. Staner, Business Networks: Prospects for
Regional Development. Berlin, New York: Walter de Gruyter.
Steier L, Greenwood R (2000). Entrepreneurhip and the Evolution of
Angel Financial Networks. Org. Stud., 21(1): 163-192.
Singh SP, Reynolds RG (2001). A Gender-based Performance Analysis
of Micro and Small Enterprises in Java, Indonesia. J. Small Bus.
Manage., 39(2): 174- 182.
Szarka (1990). " Networking and small Firms". J. Int. small Bus., 8(2):
10-22.
Thorelli H (1986). Networks: Between Markets and Hierarchies. Strat.
Manage. J., 7: 37-51.
Uzzi B (1996). 'The Socurce Consequences of Embeddedness for the
Economic Performance of Organization: The Network Effect. Am.
Sociol. Rev., 61: 674-698.
Uzzi B (999). 'Embeddedness in the Making of Financial Capital: How
Social Relations and Network Benefit Firms Seeking Financing". Am.
Sociol. Rev., 64: 481-505.
Uzzi B (1997). 'Social Structure and Competition in Interim-firm
Network: The Paradox of Embeddedness'. Adm. Sci. Q., 42(1): 35-
67.
Uzzi B, Lancaster R (2004). Embeddedness and price formation in the
corporate law market. Am. Sociol. Rev., 69: 319-344
Veciana JM , Clarke AM (1996). Theoretical Approaches to
Entrepreneurship: The Social network Approach. European Doctoral
Programme in Entrepreneurship and Small Business Management .
Venkataraman S (1989). Problems of Small Venture Start - up, survival
and Growth:A Transaction set Approach". PhD Dissertation,
University of Minnesota.
Wellman B, Gulia M (1993). Which Types of Networks Provide What
kinds of Social Support? Paper Presented at the International Sunbelt
Social Network Conference. Tampa.
Williamson OE (1985). The Economic Institutions of Capitalism: Firms,
Markets, Relationa Contracting. New York: The Free Press.

African Journal of Business Management Vol.5 (28), pp. 11399-11412, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.266
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
The change of consumers’ behavior in crisis
conditions: A psychological approach to the empirical
evidence from Romania
Amalia Pandelica 1 * and Ionut Pandelica 2
1 Faculty of Economics, University of Pitesti, Romania.
2 Faculty of Low and Economics, Agora University of Oradea, Romania.
Accepted 24 August, 2011
The purpose of this article is to analyze the causal relation between the psychological factors of
behavior in risk-generating situations and the change of consumers’ behavior in the context of
nowadays economic crisis. In order to test the conceptual model of behavior change in uncertain
conditions developed, the empirical data were collected according to a sample of 527 Romanian
consumers during April, 2010. The results of the study emphasize the starting premise, that is, in
nowadays economic crisis, there is a direct causality relation between the perception of the risks, the
risk-generating situation aversion and the change of consumers’ behavior. The results reveal, though,
the fact that the psychological factors considered within the conceptual model do not influence on the
same scale the change of behavior. The limits of the research arise from the perishable topic itself,
because the dimensions of the identified segments as well as the profile of the behavioral change vary
according to many identified factors. This study brings its contribution to the understanding of the
change of consumers’ behavior in economic crisis conditions by using a new perspective unexplored
by the literature in the field until now.
Key words: Economic crisis, risk perception, risk aversion, consumer’s behavior change.
INTRODUCTION
As Stegaroiu and Stegaroiu (2010) argue that currently
the world economy is facing a disaster whose futures
remain a mystery, the nowadays economic crisis began
as a financial crisis in the United States in 2008. Most of
the economic analysts identified the speculative bobble
and the greed of the companies as the generating causes
of the crisis.
An interesting aspect in the context of present global
economic recession is the fact that a certain type of crisis
generated the emergence of another type of crisis. If at
the end of 2008, the majority of economic experts
provided pessimistic forecasts regarding the evolution of
national economies in 2009, they did not suggest
anything about the social crisis generated by the effects
*Corresponding author. E-mail: pandelica.amalia@yahoo.com.
JEL classifications: L11, M31, M39.
of the recession, for example.
Within the conference “Crisis of Confidence: The
Recession and the Economy of Fear”, organized by the
Psychiatry and the Psychoanalytic Center, in 2009, the
following aspect was pointed out: the emotion did not
drive America into recession but it could contribute
essentially to its duration.” Thus, consumers’ emotional
response to the effects of the financial crisis conducted to
a lower level of confidence into brands, companies,
industries, etc. In other words, the emotion generated the
appearance of the lack of confidence crisis. This is
advocated, for example, by the decrease of Consumer
Confidence Index in the entire world at the end of 2008,
according to Nielsen Global Consumer Confidence Index
Report (2009). The report emphasizes that some
domestic markets achieved an absolute record as the
lowest CCI at the end of 2008. At the same time, Quelch
and Jocz (2009) point out that, in general, consumers’
behavior was determined by emotional reactions against
the recession.

11400 Afr. J. Bus. Manage.
The effects of the financial crisis, doubled by the lack of
confidence crisis had as a result, the consumption
decrease, people’s choice for savings being a correct
reaction to uncertainty – the perspective of domestic
economic evolution. Consumption decrease engendered
markets’ contraction and emergence of the classical
overproduction crisis. In this way, the emotion played an
important part in the transformation of the financial crisis
into a global recession. According to some experts, the
lack of confidence crisis could cause a prolongation of
the recession. On the other hand, the income shortage
has both economic and social outcomes affecting the
social relations as well as the individuals’ behavior and
inducing the social crisis (Walton an Mannelyan, 1998).
Thus, the number of those professing that they can no
longer afford almost anything has significantly increased
during the last two years on almost all domestic markets.
At the same time, the number of poor people was
augmented, causing important social upshots.
Last but not least, as Ronn Anderson points out,
unemployment generates what he calls “the loss of social
capital”. Social capital represents an individual’s capacity
to be part of the society and interact with the members of
the group he lives in. According to the results of some
empirical research, between unemployment, depression
and anxiety, there is a direct causal relation. A person
laid off not only loses his/her position within the
community, but also the capacity of interaction with the
members of the respective community. The loss of the
social capital affects both the individuals and the entire
community (Brand et al., 2008).
LITERATURE REVIEW
According to Dutt and Padmanabhan’s (2009) study,
there were 435 currency crises episodes across 195
countries over the period 1960 to 2006. Thus, at an
international level, there are a large number of studies
achieved in the context of various crisis periods, many of
them having as a purpose, the identification of core
changes occurred in consumers’ behavior and in spending
schemes as a result of the exposure to economic
impact generated by the crisis. The researchers had
various manners of approaching the topics.
One of the first relevant studies was made by Kelly and
Schewe (1974), which analyzed the reaction of American
consumers towards stagflation during 1973 to 1974. The
main directions pursued within this analysis were consumption
vs. savings, postponing important purchases,
extensive credit usage, and change of lifestyle. Shama
(1980) analyzed this change for New York consumers
and emphasized the fact that the recession determined
the change of cosumers’ motivations, values, attitudes
and expectations. The core changes identified by him at
the level of consumers’ behavior were: the desire to buy
less (the decrease of consumption desire), the
postponing of long-lasting product purchase, the focus on
comparing the products and an extension of purchase
duration, the change of purchase habits and waste
riddance.
Many researchers examined the consumers’ reaction to
economic impacts generated by the Latin American
crisis. Friszbain et al. (2003) identified more types of
strategies adopted by households in Argentina during
2001 to 2002, the most difficult period in the history of
this country. The strategies identified by them were
separated in groups according to the type of household
response to the economic crisis. The first group of strategies
called adaptive strategies, covers in fact, a reactive
response of the household with a view to quantitative and
qualitative consumption. The second group of strategies
called active strategies, engender a proactive response
of the household, focusing on home goods production for
selling, entrance on the labor market of a new member of
the household, at least one member of the family begins
to work longer hours, at least one member of the family
emigrated or relocated permanently in another city,
another province, etc. The third group of strategies called
social network, consists essentially in searching for living
support provided by people outside the household. The
aforementioned authors analyzed if between the
strategies identified and the type of economic shock
experienced by the household, there is a direct
connection and if the change of household behavior vary
according to the wealth and number of members of the
respective household.
Robles et al. (2002) identified within the context of the
same crisis episode, the following changes in
Argentinean consumers’ behavior: the avoidance of longterm
financial commitments by leaving out major
purchases: cars, houses, holidays, etc., the re-evaluation
of consumption mix by increasing expenses for basic
products, the change of purchase habits by guiding the
consumers toward self-service, discount outlets and
hypermarkets, the search for a favorable quality-price
balance. In broad lines, the same main directions in
consumers’ behavior change were identified by Zurawicki
and Braidon (2005) in a research made with the purpose
to identify middle-class Argentinean consumers’ reaction
between 2001 and 2002.
The Asian crisis that affected the entire region came
after a very long boom period, taking by surprise all
consumers, represented the research theme for many
researches. Ang et al. (2000) identified the following
behavioral changes within the Asian crisis: a lower consumption
for all product categories and waste riddance,
the search for extra information, the substitution of
products, the buying of home products rather than foreign
products, the choice of discount and neighborhood
shops.
The nowadays recession without precedent in the
entire world, called for all home consumers’ attention
upon revising their behavior and budget allotting. Quelch

and Jocz (2009) pointed out that in nowadays context
and during recession times in general, market segmentation
as per sociodemographic criteria can be less
relevant than the psychological segmentation that considers
the emotional reaction to economic environment.
Urbonavicius and Pikturnien (2009) emphasize that in
nowadays context, consumers’ behavior is emotional,
and identified six types of responses: 1) to continue with
the same behavior without any change – this type of
behavior does not imply changes at the level of high
income consumers who do not undergo but very scarcely
the effects of the crisis; 2) to reduce spending in order to
survive – this type of response implies significant
alterations of consumers’ behavior by a blunt reduction of
the quantity and quality of the products consumed as it
characterizes the consumers who are fully affected by the
recession; 3) to reduce spending in order to make some
savings – is a feature of the consumers whose budgets
were not significantly affected but choose to become
cautious for purchases that are not essentially necessary
and they prefer to save money; 4) to concentrate on
short-term increase of life quality, as long as it can be
afforded – this type of response characterizes the
category of young people free of financial and social
obligations; 5) to improve life quality by consuming more
products and services – this response implies the
increase of consumption of certain categories of products
and services, increase stimulated by the decrease of
prices for different products; and 6) improve life quality by
consuming better quality products and services – this
response means that consumers are driven towards
better quality products.
As one can notice, at international level, there are
many studies that provide a series of answers to the
following question: “how does the consumers’ behavior
change in crisis conditions?” But no study has analyzed
the causal relation between the psychological factors and
the change of consumers’ behavior in recession episode,
although many researchers point out that consumers’
response in such a context is rather emotional than rational.
Regarding this issue, Goldmen (2003) emphasized
the effects of the destructive emotions.
The development of the conceptual model
We started from a broader frame in order to determine
the core factors that generate the change of consumers’
behavior in nowadays global hectic context. We analyzed
several research studies made in the context of various
crises: economic crisis (Kelley and Schewe, 1975;
Shama, 1978, 1980; Shipchandler, 1982; Ang et al.,
2000; Ang, 2001; Zurawicki and Braidot, 2005; Dutt and
Padmanabhan, 2009; Garling et al., 2009; Fiszbein et al.,
2003; Qelch and Jocz, 2009; Robles et al., 2002;
Kittiprapas, 2002; Stegaroiu and Stegaroiu, 2010;
Urbonavicius and Pikturnien, 2010), food security crisis
Pandelica and Pandelica 11401
(Miller and Reilly, 1994; Pennings et al., 2002; Jin and
Koo, 2003; Lusk and Coble, 2005; Kalogeras et al., 2008;
Wansink, 2004), terrorism crisis (USA, 2001), public
health security crisis (Saad, 2009). The purpose of this
analysis was to discover to what extent these situations
with negative impact on people (risk-generating
situations) display some common aspects regarding the
change of behavior. The results of the analysis pointed
out that obviously, no crisis is similar to another as no two
similar crisis are alike, considering generating factors,
evolution, outcomes, severeness, etc. Still, an interesting
aspect of the analysis was the fact that in all riskgenerating
situations, the psychological factors play an
important role in determining the change of human
behavior.
Risk-concept is very often used in present days in
various circumstances: health, investments, terrorism,
economic trends, food security, strategy, and long lasting
business). In psychology, risk is defined as a subjective
construct influenced by how the event is interpreted
(Weber and Milliman, 1997). As Hillson and Murray-
Webter (2007) point out, there is a great range of
definitions for risk-concept in the academic literature.
Nevertheless, there is consensus within various approaches
as to the fact that risk is associated with uncertainty
and generates consequences. The current crisis is
labeled as uncertain and risk generating situation (economic
shocks) with significant effects on consumers on all
national markets. Zurawicki and Braidot (2004) defined
the economic crisis from consumers’ perspective as the
most traumatizing event that affects family’s life and
brings a sudden and substantial deterioration of
economic situation.
Akerlof and Shiller (2009) emphasized that a growing
number of economists recognized that a psychological
perspective is necessary in economic analysis. Thus,
considering that the crisis psychology is a main part of
the present global economic crisis and that the psychological
factors play an important role in the change of
consumers’ behavior we developed a conceptual model
of behavior change in uncertainty conditions. This
concepttual model presents how panic works in uncertain
situations.
Unemployment, inflation raise, the freezing or decrease
of salaries, the decrease of purchasing power, and the
decrease of deposits are only a few of the economic
shocks that consumers currently have to cope with, risks
respectively, as long as such evolutions represent “uncertainties
that matter” (Hillson, 2002; Hillson and Murray-
Webter, 2007), having important effects on individuals’
lives. Within our model, the economic shocks represented
stimuli that we labeled as risks. Each risk is perceived
and interpreted (represented) differently by each and
every individual, which is the assessment of the degree
of situational uncertainty, controllability of the uncertainty,
and confidence in these estimates (Sitkin and Weingart,
1995). Risk perception is the interpretation that an

11402 Afr. J. Bus. Manage.
Figure 1. Conceptual model regarding the change of consumers’ behavior in risk-generating
situations (uncertainty) (source: authors point of view).
individual makes with a view to the chances to be exposed
to risk content (Pennnings et al., 2002) and to the
estimated capacity to control the exposure, for example,
the extent to which the individual considers himself liable
to unemployment and the control degree of this situation.
This personal interpretation generates emotions such as
panic, anxiety, stress, fury, etc. On the other hand, each
individual likes/ dislikes in a certain measure the riskgenerating
situation, reflecting each person’s risk-attitude
that leads to certain feelings such as panic, confidence/
lack of confidence in brands, companies, government,
media, future. Risk-attitude is a hypothetical construction
reflecting whether the individual likes or dislikes riskgenerating
situation and risk aversion. Thus, risk aversion
is a mental projection of a certain situation (Hillson and
Murray, 2007). Finally, in uncertain situations, the change
of consumers’ behavior is determined by risk perception
and risk generating situations aversion (Figure 1).
But not all individuals are alike and they do not react
identically in a risk-generating situation, such as the
recession. That is why the change of consumers’
behavior is not expected to have the same intensity and
follow the same directions. Thus, it has been empirically
proven that people make appreciations about the chance

Pandelica and Pandelica 11403
Figure 2. Conceptual model regarding the psychological segmentation of the market. (source: Authors personal
contribution using Pennings et. al (2002), Lusk and Coble (2005) models).
of being exposed to the content of the same risk. Starting
from the models presented by Pennings et al. (2002) and
Lusk and Coble (2005) as to the consumers’ reaction in
risk conditions, we decomposed comsumers’ behavior in
two psychological dimensions considering the manner in
which they interact. We tried to obtain a clearer image of
the intensity and change directions at the level of
behavior in nowadays economic crisis (Figure 2).
The dynamic of the segments
When people interpret the content of a risk in order to
evaluate how bad a present situation is, they use their
previous experiences. When such a previous experience
exist in the recent history of a national economy, the
consumers’ response will be built on a learning lesson as
they tend to assess the gravity of the situation more
correctly. Should such an experience be absent, the
consumers will have the tendency “to project the worst of
their fears” (Anderson, 2009) in order to make this evaluation.
Thus, their response will be an emotional one
considering that there is no past previous context to allow
them to place such events. This is emphasized by
Zurawicki and Braidon (2004) in their research paper
regarding middle-class consumers’ reaction in the context
of the crisis in Argentina (2001 to 2002). They make a
clear point that in the case of crisis in Asia (1997 to
1998), the consumer was taken by surprise, considering
that nobody anticipated the crisis which occurred after a
long period of economic development; therefore, the
consumers had no previous context to rely on. On the
contrary, Argentinean consumers accumulated
experience regarding the “correct” reaction in recession
conditions considering the economic reforms of the
1990s envisaging the liberalization of the markets that led
to a decrease of life standard and change of consumers’
behavior. Therefore, they were in a much better position
for a correct evaluation of the gravity of the situation in
the context of 2001 to 2002 crisis.
On the other hand, the dimension of the four segments
alters according to the evolution of the economic crisis –
entering into recession, the bad situation of the national
economy, the climax of the recession, the signs of
recovery and total overcome of the crisis. Thus, risk
perception and risk-generating situation aversion alters in
time according to this evolution. Also, the dimensions of
the segments vary from one national market to another
according to the severe effects of the economic crisis
(how badly the national economy was affected).
Not least, considering that in economic crisis
conditions, the pessimism grows in accordance with the
reaction of the government through anti-crisis measures,
the dimensions of the four segments vary according to
the moment when the austerity measures were taken and
to the severeness of these measures. A good example in
such a case is Greece, where the austerity measures
adopted conducted to general strikes and violent riots. At
the same time, Romania’s case is a good example considering
that by the end of May, 2010, the government
adopted the most severe austerity measures in the
European Union which contributed to the lowest
consumers’ confidence index recorded in July, 2010, that
represented the highest diminishing during the last ten
years according to a study made by GfK (2010). In spite
of the fact that the austerity measurements were more
severe in the Romania case compared with Greece, the
Romanians reaction was moderated. This can by
explained by the uncertainty avoidance dimension that
characterizes the two cultures. According to the
Hofstede’s (2004) study, Greece is the most risk
avoidance culture recording a uncertainty avoidance

11404 Afr. J. Bus. Manage.
index (UAI) of 112, while Romania has a UAI of 90. As
Gärling et al. (2009) emphasized, the way people
behave, their attitudes and values, and the way they
perceive themselves, are functions of cultural, historical,
and political influence. Considering the austerity
measures as stimuli which generate people reaction, consumers’
response will be determinate by the individual
capacity to cope with unpredictability, and as Hofstede
and McCrae (2004) demonstrated, this capacity varies
from one culture to another. Thus, we can conclude that
the dimensions of the four segments vary to one national
market to another function to cultural factors.
In conclusion, the dimensions of the segments vary
from one national market to another as per the severeness
of the economic crisis effects (S), experience/ lack
of experience of some previously experienced economic
crisis in the relatively recent history of the national
economy (E) the moment of the crisis (T) the moment
when the government adopted the austerity measures
and the severeness of the measures (AM) and cultural
factors (CF):
Ms = f (S; E; T, AM, CF)
Establishing hypotheses
The present study is an exploratory one, based on the
premise that psychological factors play an important role
in the change of consumers’ behavior in nowadays
economic crisis context (uncertain conditions). Therefore,
its purpose is to examine the causal relation that develops
between the two psychological factors considered
within the conceptual model developed and the change of
consumers’ behavior:
H1: There is a direct relation of causality between risk
perception and the change of consumers’ behavior in
recession;
H2: There is a direct relation of causality between riskgenerating
situation and the change of consumers’
behavior in recession.
RESEARCH METHODOLOGY
Starting from the previous models, the diagnosis analysis for
identifying the main changes of consumers’ behavior focused on
the directions in Table 1. For data collecting, a questionnaire was
used as work instrument. It had a number of 26 questions adjusted
to the established diagnosis directions of the analysis. 25 of them
are close questions and one is open (word association). For the
purpose of measuring the psychological and behavioral variables a
five steps scale was used: 1 - to a low extent, 3 - to an average
extent, 5 - to a great extent. For the purpose of analyzing the
evolution of the expenses for the main categories of products and
services in the last six months, compared to the previous period, a
seven steps scale was used where 1 - much less, 4, in-between, 7 -
much more. The data were collected during April, 2010, at a level of
a 527 person’s sample.
In this research, validity was achieved by reviewing the academic
literature in the field about economic crisis, economic psychology,
consumer behavior, etc. A workshop was also conducted, and thus,
expert opinions were consulted. In order to check the internal
consistency of the measurement scale, we used Cronbach’s alpha.
The Cronbach’s alpha assessed was 0.76, greater then acceptable
standard value of 0.70.
RESULTS
Despite the optimistic forecasts made by different
analysts regarding Romania’s economic evolution in
2010 as well as Romania’s government optimism,
Romanians are more pessimistic regarding this topic,
according to the results of the research. This is pointed
out by the fact that a great majority (89.4%) of respondents
acknowledge that Romania will not overcome the
recession in 2010; 90.3% think that there will be no
recover of the national economic situation in the next six
months.
Considering the forthcoming six months, a great
number of respondents (61.1%) acknowledge that they
will be exposed to the consequences of the economic
crisis, to a great extent. This is emphasized by the
average (3.58) and the median (4.00) perception of
depression outcomes (risks). At the same time a great
majority of respondents (81.0%) acknowledge that the
evolution of the national economy is highly
dissatisfactory.
At present, the obvious pessimism is an important
factor that adjusts Romanian consumers’ behavior. This
is pointed out by the words used by the respondents to
define the situation of the present national economy; out
of 1028 words, 998 represent negative associations, such
as: ”prospectless”, “instability”, “disorientation”, “disaster”,
“delicate situation”, dramatic situation, “decrease”,
“chaos”, and only 30 words represent neutral or positive
associations such as: ”recover”, “increase”, “acceptable
situation”, “I do not believe in depression”, etc. Negative
associations emphasize not only respondents’ pessimism
but also the panic feeling that was fueled by the economic
shocks that the respondents experienced during the
last six months: unemployment, the decrease of their
income, the decrease of savings value, the decrease of
purchasing power, etc.
Considering the volume of data basis, we used Kmeans
cluster analysis in order to establish the segments.
The number of initial clusters (settled according to
the conceptual model of psychological segmentation of
the market) was 4. The initial centroids of the clusters
were a random choice made by SPSS after which within
each reiteration the grouping of the cases was made
according to the closest Euclidian distances to the
centroid of the recalculated clusters. Practically, within
this algorithm, one focuses on the minimization of the
variation inside the cluster and the maximization of the
differences between the clusters. After six reiterations,
the final convergent value was reached according to

Table 1. Diagnosis for identifying changes in consumers’ behavior.
Directions for accomplishing the diagnosis analysis Variables measured
The evaluation of the perception regarding the evolution of
the national economy in 2010
The evaluation of the economic socks experienced by the
population
The evaluation of risk perception over the exposure to the
effects of the economic crisis and of risk aversion – the
evolution of the national economy
The evaluation of the behavior change directions in the last
six months, compared to the previous period
The analysis of expense trends for the main group of
products and services in the last six moths compared to the
previous period
Pandelica and Pandelica 11405
i. Getting Romania out of the economic crisis in 2010
ii. The recover of the national economy within the next
six months
i. Unemployment
ii. Decrease of the incomes
iii. Freezing of the incomes
iv. Decrease of purchasing power
v. Decrease of the savings/ investments value
i. Risk perception – the exposure to economic socks
within the next six months
ii. Aversion against the evolution of the national
economy
i. Consumption vs. savings
ii. Migration towards the low demand curve
iii. Elimination/ postponing of major purchases
iv. Aggressive search of options
v. Product choice according to the price
vi. Product choice according to the quality
vii. Approach of new innovative products
i. Basic food products
ii. Other food products
iii. Alcoholic drinks and tobacco
iv. Personal care products
v. House maintenance and improvement
vi. transport
vii. water, gas, electricity
viii. magazines, newspapers, books
ix. garments and shoes
x. long-usage products
xi. voyages, holidays, leisure
xii. pharmaceutical products and medical care
The evaluation of population’s confidence degree i. in trademarks/ products
ii. in companies/ business environment
iii. in government and anti-crisis measures
iv. in the manner in which media reflects the evolution
of the recession
the results in Tables 2 and 3.
The psychological and sociodemographic profile of
the clusters
Using the final results of the reiteration process as well as
cross tabulation, we provided the psychological and
sociodemographic profile of each cluster:
Cluster 1: the ‘panicked’ are those consumers thinking
that within the next six months, they will be highly
affected by the consequences of the economic crisis and
the evolution of the national economy is extremely dissatisfactory
from their point of view. This cluster is made up
of employees with an income below $ 250(53.5%); retired
persons considering that 65.7% of members of the
sample belong to this cluster; unemployed persons
considering that out of 67 of the respondents 49 are

11406 Afr. J. Bus. Manage.
Table 2. The centroids of the final clusters generated by SPSS.
Psychological factor
Perception on the outcomes of the depression
generated by the evolution of the national economy
Clusters
1 2 3 4
4.38 2.42 3.70 1.67
Aversion to the evolution of the national economy 4.70 4.64 2.43 2.33
Table 3. The number of cases grouped in each cluster.
Cluster Number of cases Percent Profile
1 282 53.51 The Panicked
2 145 27.51 The Concerned
3 67 12.72 The Cautious
4 33 6.26 The Rational
Total 527 100 100%
‘panicked’ who acknowledged that they were affected by
unemployment in the last six months. They are
approximately the same number of men and women, the
proportion of men being a little higher (51.5%). The
‘panicked’ are first of all, individuals with a low level of
education, 81.5% of the total respondents graduated
secondary schools and 76.2% vocational schools and
they both are concentrated on this segment, they come
from rural area considering that 67.0% of the respondents
coming from rural area on the entire sample are
concentrated on this segment. An interesting aspect is
that 20.3% from the respondents professing that they
were not affected by the consequences of the depression
are ‘panicked’.
Cluster 2: the ‘concerned’ are those consumers thinking
that in the next six months they will be scarcely affected
by the consequences of the economic crisis and the
evolution of the national economic situation is deeply
dissatisfactory. This cluster encompasses firstly,
employees with a monthly income under $ 250 (28.3%)
or ranging from $ 251 to 500 (37.9%), students (17.9%),
entrepreneurs/ freelancers (10.3%), retired persons
(11%). The majority of the ‘concerned’ graduate highschool
is 41.4%, a higher education institution (41.4%) or
post-graduate courses (6.2%). An interesting aspect is
the presence of respondents acknowledging that they
were not affected by the consequences of the
depression. Thus, 36 respondents belong to this cluster
out of 79 of the entire sample.
Cluster 3: the ‘cautious’ are those consumers thinking
that within the next six months they will be highly
exposed to the consequences of the economic crisis and
the evolution of the national economic situation scarcely
displeases them (high risk aversion). This segment
encompasses employees (67.3%) with a monthly income
ranging from $ 200 to 500 (47.8%) or over $ 500 (19.4%)
as well as entrepreneurs, considering that 18.4% from
the total number of entrepreneurs of the sample belong to
this cluster. The presence of entrepreneurs shows the
impact of the recession on business environment and the
difficult problems they have to cope with for surviving in
the context economic crisis. The ‘cautious’ are both men
(49.3%) and women (50.7%) coming mostly from urban
areas (65.7%). Most of them graduated high-school
(55.2%), and higher education institutions (28.4%).
Cluster 4: the ‘rational’ are those consumers thinking
that within the next six months they will be scarcely
affected by the consequences of the economic crisis and
have a low aversion for the evolution of the national
economy. The respondents belonging to this cluster are
employees (63.6%) with a monthly income ranging from $
500 to 700 or more (46.4%), as well as those who
professed that they were not affected by the
consequences of the recession. Most of the ‘rational’ are
men (57.8%) and come from urban areas (95.6%). Those
who graduated highs-school were 40.0% and higher
education institution was 48.9%.
The change of behavior at the level of clusters
The profile of behavioral change for each cluster was
made with the help of cross-tabulation (Table 4).
i. The ‘panicked’: during the period analyzed (the last six
months), compared to the previous period, the main
tendency within this cluster was to reduce the
consumption. This is revealed by the fact that 69.86%
from the ‘panicked’ acknowledged that although they
consume less, they do not succeed to save almost
anything, therefore, the decrease of consumption was

Table 4. Descriptive statistics – the mean value of the behavior change at the level of clusters.
Pandelica and Pandelica 11407
Directions of evaluating behavior change
The Panicked
Mean value at the level of clusters
The Concerned The Cautious The Rational
Migration on demand curve (MDC) 3.7801 2.4069 3.1045 2.2424
Elimination/ postponing of major purchases (EPMP) 4.3617 3.1862 3.5373 2.8485
Aggressive search for options (ASO) 4.0567 3.4000 3.6269 2.8182
In the process of choosing products the price
comes first (PC)
In the process of choosing trademarks/product
quality comes first (QC)
4.2199 3.1724 3.6418 2.7576
3.3723 3.9793 3.9701 4.2121
A tryout of new innovative products (TNP) 2.1418 2.3793 2.4478 2.9394
was made for the purpose of surviving; only 14.54% of
them stated that, in the last six months, compared to the
previous period, they consumed less and saved more
money, therefore, the decrease of consumption was for
the purpose of saving money. The fact that most of the
respondents, although they consumed less, they did not
manage to save money almost at all, is due to a higher
inflation and prices during the period analyzed as well as
to a freezing or decrease of incomes as a consequence
of the measures taken by the Romanian employers. In
the last 6 months, the majority of ‘panicked’ consumers
changed the favorite products/trademarks with some
cheaper ones (67.02%) and eliminated/postponed most
of their important purchases (90.78%). At the same time,
they allotted more time to gathering information during
purchasing and buying products process (80.50%),
searching aggressively for comparative options and thus,
for the best choice, most of them consider first of all the
price (87.23%) but also, the quality (54.96%). It is
obvious that within this segment, there is a decrease of
consumption from both a quantity and quality point of
view. At the same time, an important part of the
‘panicked’ consumers approached a more rational
behavior not only by eliminating the products that are not
strictly necessary but considering a deeper analysis of
the quality-price balance. In other words, although they
are willing to pay less, they want the highest usefulness
for the price. Only a minority of the ‘panicked’ (8.16%)
was willing to try new innovative products in the last six
months.
ii. The ‘concerned’: as to the behavior of this cluster
regarding consumption vs. savings, there are two
directions: on the one hand 50.34% of its members
acknowledged that, in the last six months compared to
the previous period, they had a lower consumption for
most of them with the purpose of saving money
(28.77%); on the other hand, 44.66% of the concerned
consumers preserved the level of consumption by
decreasing their savings, the remaining part of them
having the same level of consumption and savings. Only
a minority (11.03%) of the members of this cluster
migrated to the lower level of the demand curve; a great
number of them kept on buying their preferred products/
trademarks or they changed them only in some circumstances
(categories of products). Only 43.45% of the
concerned consumers eliminated/postponed the purchase
of important items in the last six months compared
to the previous period, the rest of them kept on buying
the same. Unlike the ‘panicked’, only 48.9% of the
‘concerned’ allotted more time for information and
comparison of offers when making choices. Within this
process, quality comes first (79.31%) and then comes the
price (40.69%). One can notice that the decrease of
consumption was made through means of eliminating the
strictly necessary products, preserving though the quality
of the products and services consumed. The ‘concerned’
consumers are not willing to try new innovative products
during this period considering that only 13.10% tried such
products in the last six months.
iii. The ‘cautious’: this cluster also displays two major
directions relatively balanced regarding consumption vs.
savings. On the one hand, 47.70% of the cautious
acknowledged that they reduced consumption for the
purpose of surviving, in most of the cases, considering
that only 11.9% of them succeeded to save more in the
last six months. 52.30% of the members of this cluster
preserved their consumption, 22.4% of them decreasing
their savings in order to achieve that. Only a minority of
the members of this cluster migrated to the lower curve of
demand (32.4%) in the last six months, the remaining
kept on purchasing the favorite products and trademarks
or they changed them with cheaper ones only in some
circumstances. The majority of the ‘cautious’ eliminated/
postponed major purchases (50.75%) and gathered more
information for purchasing process (55.22%) searching
for the best choice options. For the ‘cautious’, the quality
comes first (71.64%) and then the price (59.70%) when
making choices for products. If we compare the ‘cautious’
with the ‘concerned’, a more rational behavior is more
intense at the level of this cluster. The ‘cautious’ reduced
their consumption by eliminating the unnecessary
purchases and by reducing the quantity of the products

11408 Afr. J. Bus. Manage.
Table 5. Descriptive statistics – mean value of the expenses at the level of clusters based on COICOP classification.
Categories of products and services
Mean value of the expenses at the level of clusters
The Panicked The Concerned The Cautious The Rational
Basic food products (meat, diary products, bread and
pastry products, fruits, vegetables, etc.)
3.8546 4.1724 4. 1045 4.3939
Other food products (sweets, soft drinks, coffee, etc.) 2.8759 3.5793 3.5522 3.6364
Alcoholic drinks and tobacco 2.0248 3.0621 2.9851 3.0606
Personal care products 3.3688 4.1310 3.7313 4.3636
House maintenance and improvement 2.6348 3.4966 3.1493 3.8788
Transport (gas, tickets, seasonal tickets) 3.3582 4.0069 3.4030 4.1515
Water, gas, electricity 3.9433 4.2828 3.9552 4.2121
Magazines, newspapers, books 2.2447 3.1655 2.4925 33939
Garments and shoes 2.7128 3.6000 3.3284 3.8788
Appliances and tools (household appliances,
electronics, furniture, etc.)
1.8369 2.9517 2.8060 3.0606
Journeys, holidays, leisure 1.7482 2.8897 2.6119 3.2727
Pharmaceutical and medical care products 3. 8723 4.2000 3.8507 4.1515
and services consumed. Only 10.45% of the concerned
tried new innovative products in the last six months.
iv. The ‘rational’: the main tendency at the level of this
cluster is preserving the consumption, considering that
63.70% of them preserved their level of consumption in
the last six months compared to the previous period;
27.3% of them decreasing their savings. The majority of
the ‘rational’ kept on purchasing favorite products and
trademarks migrating to the lower curve of demand only
in some situations. At the same time, the majority of the
members of this cluster made big purchases during the
period analyzed (69.70%). Generally, the ‘rational’ did not
allot more time for information when making choices as
they relied more on quality (84.85%) than on price
(39.39%). One can notice that within this segment, there
are no important changes in consumers’ behavior. There
is, still, a slight difference or rationalization through a
decrease of unnecessary purchases. The ‘rational’
represent the category of consumers who are willing to
try new innovative products considering 24.4% of them
expressed their willingness for trying new products in the
period analyzed.
The evolution of expenses on categories of products
and services at the level of clusters in the last six
months compared to the previous period
The analysis of the evolution of the expenses in the last
six months compared to the previous period at the level
of the clusters was made for the main categories of products
and services according to COICOP classification
(Table 5).
At the level of all clusters, we can notice the tendency
of rationalization of expenses in the last six months compared
to the previous period, by keeping the expenses at
the same level for strictly necessary products (basic food
products) and decrease of expenses for unnecessary
products (appliances, journeys, holidays, leisure). The cut
in expenses as well as rationalizing process vary in
intensity from one cluster to another.
The ‘panicked’ spent less in the last six months for all
categories of products in accordance with the main
tendency of decreasing the consumption. The ‘panicked’
spent the most on water, gas, electricity, basic food
products, pharmaceutical and medical care products. On
the contrary, they spent the least on journeys, holidays,
leisure, appliances, alcoholic drinks and tobacco. This
confirms the central tendency of eliminating/postponing
major purchases at the level of this cluster.
The ‘concerned’ spent almost the same amounts on
water, gas, electricity, pharmaceutical and medical care
products, basic food products, transport and personal
care products. The ‘concerned’ spent the least on
journeys, holidays, leisure, and appliances. The evolution
of the expenses during the period analyzed uphold the
change of the behavior on all directions measured. An
interesting aspect is the fact that the ‘concerned’ spent
more on pharmaceutical and medical care products
compared to other clusters.
The ‘cautious’ compared to the ‘concerned’, reduced
their expenses more for all categories of products. The
evolution of the expenses on categories of products and
services confirm the change of behavior at the level of
this cluster on all directions evaluated.
The ‘rational’ consumers compared to other clusters
spent, during the period analyzed, the same or a little
more for some categories of products, basic food products,
personal care products, transport, pharmaceutical
and medical care products. Within the frame of this
cluster, there is a slight tendency to reduce expenses for
some categories of products and services which are

Table 6. Descriptive statistics – mean value of the confidence at the level of clusters.
Pandelica and Pandelica 11409
Directions of evaluating the degree of confidence
The Panicked
Mean value at the level of cluster
The Concerned The Cautious The Rational
The degree of confidence in brands/ products 3.1099 3.6345 3.5970 4.0000
The degree of confidence in companies/ business
environment
The degree of confidence in the government and anticrisis
measures
The degree of confidence in the manner in which
media reflected the evolution of the depression
2.4716 2.9724 3.1493 3.7273
1.5851 2.1724 2.4627 2.8485
3.0638 3.3241 3.4328 3.6364
Table 7. Pearson’s correlation coefficients between independent and dependent variables.
Variable RP RA MDC EPMP ASO PC QC TNP
RP 1.0000
RA 0.248** 1.0000
MDC 0.612** 163** 1.0000
EPMP 0.519** 0.197** 0.590** 1.0000
ASO 0.406** 177** 0.402** 0.443** 1.0000
PC 0.524** 0.151** 0.550** 0.475** 0.459** 1.0000
QC -0.292** -0.173** -0.355** -0.267** -0.233** -0.373** 1.0000
TNP -0.197** 0.002 -0.112** -0.224** -0.195** -0.222** 0.232** 1.0000
**The correlation is significant at the level 0.01. RP – risk perception; ASO – aggressive search for options; RA – risk
aversion; PC – prices comes first; MDC – migration on the demand curve; QC – quality comes first; EPMP –
elimination/postponing of major purchases; TNP – tryout of new innovative products.
not strictly necessary – appliances, journeys, holidays,
leisure.
Measuring the degree of confidence
The measurement of the degree of confidence, at the
beginning of April, was made according to the following
directions: trademarks/products, companies/business
environment, the government and anti-crisis measures
taken by the government and the manner in which the
press reflected the evolution of the economic crisis. Using
cross-tabulation, we determined the degree of confidence
in the directions evaluated at the level of clusters (Table
6).
As expected, the ‘panicked’ are the most pessimistic
consumers, their confidence in companies/business environment,
government and anti-crisis measures has the
lowest values. On the contrary, the ‘rational’ are the less
pessimistic; nevertheless, their confidence in the government
and anti-crisis measures has decreased very much.
For both the ‘panicked’ and the ‘cautious’, there is the
same hierarchy regarding the degree of confidence: 1)
trademarks/products, 2) media, 3) companies/business
environment, and 4) government and anti-crisis measures.
As to the ‘concerned’, the hierarchy has slightly
changed, as they profess the highest degree of
confidence in the manner in which the press reflected the
evolution of the depression, the confidence in the
companies/ business environment coming after it. The
‘rational’ consumers seem to have less confidence in the
manner in which Romanian press reflected the evolution
of the economic crisis, their higher confidence being in
trademarks/products.
The analysis of correlations and hypotheses testing
In order to determine if between the independent
variables - the perception of the crisis effects generated
by the evolution of the national economy, the aversion to
the evolution of the national economy – and the
dependent variables (the change of behavior) measured
there are significant associations (a direct relation of
causality), we decided to use the Pearson’s correlation
analysis. The results of the analysis made for the entire
data base are presented in Table 7.
Almost all correlations presented in Table 7 (exception
is the one between the trial of new innovative products
and the approach of the national economy evolution) are
statistically significant at the level of 1%. The negative
statistic correlations may be noticed between the
independent variables and quality as well as between
them and the trial of new products. At the same time, the

11410 Afr. J. Bus. Manage.
negative statistic correlations may be noticed between
quality, the trial of new innovative products and all other
dependent variables, except the relation between quality
and the trial of new innovative products which is positive.
According to the correlation coefficient, some may note
as well, a significant statistic association between
behavior variables measured which emphasizes the consistence
of the scale used for determining the directions
of change at the level of consumers’ behavior.
H1 forecast that perception of the outcomes of the
recession (risks) and the change of consumers’ behavior
there is an association statistically significant. According
to the results and the correlation analysis, there was
identified a statistically significant association between
risk perception and all dependent variables used for
measuring the change of behavior. Thus, these results
sustain H1.
H2 forecast that between the aversion to the evolution
of national economy (risk-generating) and the change of
consumers’ behavior there is a statistically significant
association. The correlation coefficients obtained uphold
the fact that between an independent variable and almost
all dependent variable measured is such a relation except
the relation between the aversion to the evolution of the
national economy and the trial of new innovative
products. This conducts to a partial confirmation of H2.
CONCLUSIONS AND IMPLICATIONS
The nowadays economic crisis is an unprecedented
situation for the entire world if we consider its global
dimension and the severeness of its outcomes. The
negative economic evolutions of all national markets
conducted to important changes in people’s way of
thinking, or behaving as well as in their system of values
(things they value). The question that generated this
research study was to what extent the psychological
factors play an important role within this change. No
previous study analyzed the relation existing between the
psychological factors and the change of consumers’
behavior in recession periods. The empirical results
uphold the starting premise that there is a statistically
significant association between risk perception, aversion
to risk-generating situations and directions of behavior
change measured as: migration to the low demand curve,
the postponing/elimination of the major purchases, the
aggressive search of options in choosing products, in the
process of product choice the price comes first, in the
process of product choice the quality comes first, the trial
of new innovative products.
An interesting aspect relevant for the study is that, from
the two psychological factors considered within the conceptual
model, risk perception influences the change of
behavior to a greater extent, a fact upheld by the
coefficients of calculated correlation as well as by the
profile of behavior change at the level of clusters. Thus,
the most significant changes were identified at the level
of clusters for which the perception of the outcomes of
the economic crisis acquired the highest values. Another
interesting aspect pointed out by the results of the
research study is that from the two psychological factors
considered, the attitude to the risk-generating situations
has a greater influence than the perception of the confidence
degree risks. Thus, it has been proved that the
degree of confidence in all measured directions had the
lowest values at the level of clusters for which the risk
aversion had the highest values.
About the particularities of Romanian consumers’
behavior change in crisis conditions, our research emphasized
the existence of a dual tendency regarding the
consumption vs. saving. Even if most of the Romanian
consumers were hit by the economic crisis being affected
by the economic shocks, and the general sentiment in
April, 2010 which was that the worst that was not over,
they tended to sustain spending through diminishing their
saving. On one hand, part of the Romanian consumers
diminished saving in order to survive. This is the case of
the Panicked consumers. On the other hand, almost half
of the ‘caution’ and of the ‘concerned’ consumers
sustained their spending most of them diminishing their
saving. According to the empirical findings of the
researches presented in specialized literature, there is a
general tendency in different crisis episodes towards
saving, consumers considering saving as a response to
the uncertainty. In the first quarter of 2010, according to
The Nielsen Global Consumer Confidence and Spending
Report, at the global level, the consumers were still
cautions regarding their spending. But the concern varied
to one country to another. For instance, the UK
consumers were more optimist then other European
consumers taking in to account that in Q1 2010 they
started to make frequent shopping and they didn’t feel
constrained to shop at discount retailers. On the other
hand, Italian consumers had as top priority increasing
savings because of the uncertainty generated by the
unemployment increase in the same period. Also, Greek
consumers were under pressure of diminishing the
spending because of the economic instability.
In spite of the lack of recovery signs in April, 2010, and
of the pessimistic forecasting for the Romanian economy
evolution during 2010, an important part of the Romanian
consumers reduced saving in order to sustain spending.
This particularity, can be explained by some remaining
mentalities from the communist period, mentalities that
are still integrated in the Romanian culture. After 1990,
when the Romanian market became open, the Romanian
consumers faced with a large offer. They started to buy in
some cases more than they needed, after over 45 years
when their possibilities were limited because of the
penury of products and of the rationalization of basic
goods. They started to consume more than they produced
and to consume in advance, having a high degree
of indebtedness. We can note that, then but also now,

part of their spending represent waste. Anyway we have
to also note that our research pointed out a rationalization
of consumption and spending allocation in present
condition. This is a feature of consumers’ behavior
change in all national markets. But, we can state that the
present economic crisis is a lesson for many Romanian
consumers regarding the elimination of waste in the last
20 years.
The results of the study provide a new perspective for
approaching consumers’ behavior in crisis conditions providing
an empirical support for this perspective, opening
new directions for research. On the other hand, if in
conditions of economic growth the companies focus their
marketing efforts to identify consumers’ needs and
desires for building, communicating and delivering upper
value to their clients (Kholi and Jaworski, 1990), at
present, this approach could be an unjustified marketing
effort. What the companies should understand is how
their clients react, in particular and the consumers’ in
general, and how their behavior changed. Such an understanding
should represent the starting point in planning
the response of the organization in such conditions.
Thus, marketers should “penetrate” their clients’ minds.
For this reason the study offers them a clearer image
about the manner in which consumers react and behave
in economic crisis conditions.
LIMITS OF THE RESEARCH AND FUTURE
RESEARCH DIRECTIONS
The most important limit of this study comes from the
“perishability” of the topic itself, that is, from the fact that
there is a certain dynamics of the four segments as to
their dimension as well as to the intensity of the behavior
change according to the moments within the depression.
That is why, if the measurement had been made during
June (after the government of Romania imposed austerity
measures), the results would have probably shown more
intense changes at the level of Romanian consumers’
behavior.
As a further research direction, we intend to extend the
empirical measurement in other national markets in order
to test the generalization degree of the developed model.
But also, the model can be adopted by other researchers
with the same purpose.
The present research study focused on finding steady
empirical answers to the following research question: “to
what extent the psychological factors influence the
change of consumers’ behavior in the context of present
economic crisis?” But at present, many questions have
become very important: how durable are these changes?
How will the consumer be after the economic crisis?
REFERENCES
Akerlof G, Shiller R (2009). Animal Spirit: How Human Psychology
Drives the Economy, and Why It Matters for Global Capitalism.
Pandelica and Pandelica 11411
Princeton Univerdity Press, New Jersey.
Ang SH (2001). Personality Influences on Consumption: Insight from
the Asian Economic Crisis. J. Int. Consum. Mark., 13(1): 5-21.
Ang SH, Leong SM, Kotler P (2000). The Asian apocalypse: crisis
marketing. Long Range Plann., 33(1): 97-119.
Brand JE, Becca L, William TG (2008). Effects of Layoffs and Plant
Closings on Depression among Older Workers. Res. Aging, 30(6):
701-721.
Brand JE, Burgard SA (2008). Job Displacement and Social
Participation over the Life Course: Findings for a Cohort of Joiners.
Soc. Forces, 87(1): 211-242.
Dutt P, Padmanabhan V (2009), When to Push the Panic Button?
Impact of Currency Crisis on Consumer Behaviours.
http://faculty.edu/dutt/Research/Paddy_Pushan.pdf
Fiszbein A, Giovagnoli PI, Aduriz I (2003). The Argentine crisis and its
impact on household welfare. CEPAL review 79.
http://www.ceclac.org/publicationes/xml/0/20140/lcg2200i-
Fiszbein.pdf.
Garling T, Kirchler E, Lewis A, Fred van Raaij (2009). Psychology,
Financial Decision Making, and Financial Crisis. Psychol. Sci. Pub.
Interest, 10: 1-47.
Global Consumer Confidence, Concerns and Spending – a global
Nielsen consumer report (2009).
http://pt.nielsen.com/documents/tr_0905NelsenGlobalConsumerConfi
denceReport1stHalf09.pdf.
Global Consumer Confidence, Concerns and Spending – a global
Nielsen consumer report (2010).
http://lk.nielsen.com/site/documents/CCI1stQuater10.pdf.
Goleman D (2003). Destructive Emotions. Published by Bloomsbury
Publishing plc, London UK, ISBN 0-7475-5393-9.
Hillson DA (2002). What is risk? Towards a common definition. J. UK
Inst. Risk Manage., April, pp. 11-12.
Hillson DA, Murray-Webster R (2004). Understanding and managing
risk attitude. Proceedings of 7 th Annual Risk Conference, held in
London, UK, 26 November.
Hofstede G (2004). Hofstede Dimension Data Matrix.
http://www.geerthofstede.nl.
Hofstede G, McCrae RR (2004). Personality and culture revisited: linking
traits and dimensions of culture. Cross-Cult. Res., 38(1): 52-88.
Hope, Greed and Fear: The Psychology behind the Financial Crisis
(2009). http://knowledge.wharton.upenn.edu.
Jin HJ, Koo WW (2003). The effects of the BSE Outbreak in Japan on
Consumer Preferences. Eur. Rev. Agric. Econ., 30(2): 173-192.
Kalogeras N, Pennings JME, Koert VI (2008). Consumer Food Safety
Risk Attitudes and Perceptions over time: The Case of BSE Crisis.
12th Congress of the European Association of Agricultural
Economists.
Kelley EJ, Schewe LR (1975). Buyer behavior in a stagflation-shortages
economy. J. Mark., 39: 44-60.
Kittiprapas S (2002). Social Impacts of Financial and Economic Crisis in
Thailand, EADN Regional Project on the Social Impact of the Asian
Financial Crisis.
http://www.eadn.org/Social%20Impacts%20of%20Financial%20and%
20Economic%20Crisis%20in%20Thailand.pdf.
Kohli AK, Jaworski BJ (1990). Market Orientation: The Construct,
Research proposition, and Managerial Implications. J. Mark., 54: 19-
25.
Lusk JL, Coble KH (2005). Risk Perceptions, Risk Preference and
Acceptance of Risky Food. Amer. J. Agric. Econ., 87(2):393-421.
Miller D, Reilly J (1994). Food scares in the media, Glasgow: Glasgow
University Media Group Pearson C, Clair J (1998). Reframing Crisis
Management, Acad. Manage. Rev., 23(1): 59-76.
Pennings JME, Wansink B, Meulenberg M (2002). A Note on Modeling
Consumer Reaction to a Crisis: The Case of the Mad Cow Disease.
Int. J. Res. Market., 19: 91-100.
Quelch JA, Jocz KE (2009). How to market in a Downturn, Harvard
Business Rev., 87(4): 52-62. http://hbr.harvardbusiness.org/
2009/04/how-to-market-in-a-downturn/ar/1.
Robles F, Simon F, Haar J (2002). Winning strategies for the new Latin
markets, Financial Times/Prentice Hall.
Ron A (2008). Social Loose from Job Loss.
www.contexts.org/eye/tag/economic-crisis.

11412 Afr. J. Bus. Manage.
Saad L (2009). U.S. Flue Fear Abates, Precautionary Steps Still
Sparse. http://www.gallup.com/poll/118156/Swine-Flu-Fear-Abates-
Precautionary-Seps-Still-Sparse.html.
Shama A (1980). Marketing in a slow growth economy: the impact of
stagflation on consumer psychology. Praeger Publishers, New York.
Shama A (1978). Management and Consumers in Era of Stagflation. J.
Mark., 42(3): 43-52.
Shipchandler ZE (1982). Keeping down with the Joneses. Business
Horizons, 25(6): 32-38.
Sitkin SB, Weingart LR (1995). Determinants of risky decision making
behavior: A test of mediating role of risk perception and propensity.
Acad. Manage. J., 38: 1573-1592.
Stegaroiu CE, Stegaroiu V (2010). The algorithm for the development of
global financial crisis. Urbonavicius S, Pikturnien I (2010). Consumer
in the face of economic crisis: Evidence from two generations in
Lithuania. Econ. Manage., 15: 827-834.
Wansink B (2004). Consumer Reaction to Food Safety Crisis. Adv.
Food Nutrl. Res., 48: 103-150.
Weber EU, Milliman RA (1997). Perceived risk attitudes: Relating risk
perception to risky choice. Manage. Sci., 43: 123-144.
Zurawicki L, Braidot N (2005). Consumer during the crisis: responses
from the middle class in Argentina. J. Bus. Res., 58 (8): 1100-1109.

African Journal of Business Management Vol. 5(28), pp. 11413-11424,16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.280
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
A holistic application of process capability indices
Nyamugure Philimon 1 , Maposa Daniel 2 , Sigauke Caston 2 , Chiyaka Edward 1 and
Denwick Munjeri 1
1 Department of Applied Mathematics, National University of Science and Technology
P. O. Box AC 939, Ascot, Bulawayo, Zimbabwe.
2 Department of Statistics and Operations Research, School of Mathematical and Computer Sciences, University of
Limpopo, Turfloop Campus, Private Bag X1106, Sovenga 0727, South Africa.
Accepted 28 June, 2011
Different capability measuring techniques have been proposed and are being used in industry today.
Each one gives a certain portion of the quality picture, leaving out some equally important details about
the process. There is no single index which addresses the whole quality of a production process on its
own, hence the need to look at all the indices holistically. Each index has its own merits and demerits.
In this paper, we consider a case study of a company which manufactures belts, and has been using
only one index, Cpk . Its quality checks were indicating that the process was under control. On the other
hand, customers have been complaining that the belts they are manufacturing are not strong and tend
to breaking easily. This paper concentrates on addressing the production process of belts by looking at
different capability indices and come up with a method or algorithm that addresses this problem
holistically. Strengths and weaknesses of each capability index are analysed and sets of indices which
address the full picture of the production process are used to check the capability of the process.
Results show that customers were justified in their complaints as the new quality checks indicated that
the production process of these belts was incapable of producing belts which meet customer
satisfaction. Corrective measures were recommended to the company.
Key words: Holistic, capability index, control limits, production process, belts.
INTRODUCTION
Capability indices are tricky to interpret, controversial to
apply and often misunderstood by many practitioners.
Unless the properties of an index are clearly understood,
making major capital improvements may not be the most
prudent way to fix an unacceptable capability.
Understanding the meaning of a particular index can
have a profound impact on the cost of manufacturing.
Process improvement must be driven by more than the
need to improve an index number, otherwise
management may be wasting time and resources.
One process capability index C pk , is widely used to
determine whether manufacturing processes are capable
*Corresponding author. E-mail:pnyamugure@gmail.com.
Abbreviations: USL, Upper specifications limits; LSL, lower
specifications limits; T, target value; SPC, statistical process
control; PCI, process capability indices; PPI, process
performance index; QLF, quadratic loss function.
of meeting specifications or not. A company in Southern
Africa is currently using one process capability index pk
C
to determine whether their belt manufacturing process is
capable of meeting specifications. Collected data
indicates that the process is capable, but customers
complain that the belts are not strong and tend to break
easily. In this paper, we take a critical look at what pk
C
measures together with various other indices and also
investigate the inter-relationships between the indices.
Most literature would simply suggest that management
must choose the ‘correct’ index for their application or
process. A company that measures process capability
using only one index such as the pk
C to monitor all
quality characteristics of a process, may face problems
since the index may be effective only in measuring a
particular characteristic which does not fully define the
quality of a product under consideration. We suggest a
need to look at other indices to monitor other
characteristics of the process under this scenario. Each

11414 Afr. J. Bus. Manage.
index tells something different and a procedure is
suggested that will harmonise all these index values and
determine which indices should be used for measuring
quality characteristics of a process. The procedure will be
tested on data from the belt manufacturing company.
Whilst C pk is good at measuring certain characteristics
of a manufacturing process, it is not suitable for
assessing all characteristics of the process that may
influence quality.
The physical processes that manufacture the part are
generally subject to many sources of variation, starting
from the quality of raw material to the aging and wear-out
of the manufacturing equipment. Consequently, Y is a
random quantity (or a random variable), whose
distribution is often assumed to be Gaussian with mean,
2
say, � , and a variance, say�
. In manufacturing
parlance, the variance is referred to as the “natural
tolerance” of Y. When working with the process capability
indices, it is common practice to assume that both � and
2
� does not change with time, that is, the process is
stable, or is in statistical control.
The question which arises is as to whether the design
engineer’s compromise in going from the ideal target
value (T) to the upper and lower specifications limits
(USL and LSL respectively), is matched by the
manufacturer’s ability to meet such a compromise vis-à-
2
vis the assumed � and � mentioned earlier. The
process capability indices were introduced to address this
matter. The quantity (USL-LSL) is known as the
specification interval (or tolerance); denoted by 2d, where
d is the half length of the specification interval. The
midpoint of the specification interval, which will is denoted
by M, is equal to (USL+LSL)/2 (Figure 1).
Capability indices, similar to coefficients of variation,
are dimensionless measures of relative variability. It is a
ratio, or a number without units of measurement, that
compares process spread to tolerance spread and results
in a single number. That number is then judged
acceptable or unacceptable by some arbitrary standard.
An index can also be used to compare one process to
another or set a minimum acceptable quality standard for
processes. A capability index should be computed using
data from a stable process. Typically, process stability is
assessed by collecting sub-samples at regular intervals
and plotting sub-sample statistics on control charts. Once
the charts show a reasonable degree of stability, process
capability can be assessed.
Capability analysis is used in many facets of industrial
processes and is beginning to be used as well in
business processes. Capability analysis and thresholds
for capability indices are used in the qualification of
processes, acceptance of equipment, purchase parts
approval activities, continuous improvement efforts,
problem solving activities and for many other purposes. It
is the backbone for measuring processes ability to
produce product that falls within a desired specification
through the enumeration of variation. Capability indices
provide a yardstick for measuring improvement. The
accuracy of capability indices is dependent on proper
understanding of the theory behind the indices as well as
an understanding of variation.
PROCESS CONTROL AND PROCESS CAPABILITY INDICES
Statistical process control (SPC) and quality improvement methods
are generally based on control charts which are used for monitoring
relevant process characteristics, like process capability indices
(PCI) which were developed for measuring uniformity of the
process. The main goal of SPC consists of keeping small process
variation around a given target value and thus guaranteeing a small
number of nonconforming items produced and a large PCI-value.
Process capability analysis includes substantially more than just the
computation of any index. After process control has been
established, capability is assessed.
The use of classical univariate PCI is based on the following
assumptions:
i. There is only one quality characteristic considered.
ii. The distribution of the quality characteristic is approximately
normal.
Lack of normality may provide a misleading interpretation of the
result. For example, if a population distribution is uniformly
distributed over the interval from 0.5 - 2.5 with LSL = 0.5, USL =
2.5, and nominal (Target) = 1.5, then the mean of a uniform
1
� � � 0.29. Hence,
12
�USL�� ��LSL�
�2.5�1.51.5�0.5� � min � , ��min
� , ��1.
� 3� 3� � � 3(0.29) 3(0.29) �
distribution is � � 1.5 and
C
pk
15.
We have bad parts but the C pk index suggest that we are just
capable.
The quality characteristics of different items are stochastically
independent.
The process is under statistical control, that is, the process mean
and process variability are constant.
The sample size is large enough that calculations for standard
deviation are rational.
Assessment is essentially the act of comparing the distribution of
data, or a model, to the engineering requirements, typically in the
form of engineering specifications. If the process is deemed
capable, then the process will be maintained using statistical
process control methods. If, on the other hand, the process is
deemed not capable that is it is producing an acceptable level of
non-conforming product, then the process will undergo a process
improvement stage and work toward an acceptable level of
capability and control. Previous researchers (Kane, 1986; Chan et
al., 1988; Choi and Owen, 1990; Pearn et al., 1992; Greenwich and
Jahr-Schaffrath 1995) addressed different process capability
indices for providing measures for process potential and process
performance. The initially proposed PCI is C p and it was
proposed by Juran (1974).

Product
Bad
LSL USL
Good Product
Target/Midpoint (M)
Figure 1. Upper specification limit and lower specification limit.
C p is a powerful index that provides a quick observation to
determine whether the process is capable of meeting specification.
One could also say that C p is the ratio between what you want the
process to do (management’s hope or allowable spread) versus
what the process is actually doing (reality).
C p =
Hope
Re ality
It was initially known as the capability ratio (Kotz and Johnson,
2002). It is a measure of tolerance spread to process spread
(Figure 2) and is calculated as:
C p
USL � LSL d
�
6�
3�
� (1)
Where, USL and LSL are the upper and lower specification limits
respectively and d = (USL-LSL)/2, � is the subgroup standard
deviation.
C � c
It is often required that for acceptance we should have p
with c=1, 1.33, 1.5 or 1.67 corresponding to USL-LSL= 6 � , 8 � ,
9 � , or 10 � . Large values of C p are desirable and small
values undesirable (because a large standard deviation is
undesirable).
C p compares one process spread to another. It does not for
instance evaluate where process average is or if it is centered with
respect to the nominal (target) of the specifications. It is actually
possible to have a process producing product that is 100% out of
specification but associated with an acceptably high value of the
index, as shown in Figure 2.
Therefore, C p has its limitations, but it can serve as a powerful
tool once one understands its strengths and weaknesses. Despite
its common use in industry, enhancements and refinements of p
have been proposed. Kane (1986) proposed C pk as a PCI.
C
C pk
�
�USL � � � � LSL�
min�
, �
� 3�
3�
�
Philimon et al. 11415
Product
Bad
d � � � M
� (2)
3�
where � is the process mean and M = (USL+LSL)/2. Notice that
C pk is made up of two indices namely Cpu and Cpl, where
C
pu
C
pl
USL � �
3�
� (3)
� � LSL
3�
� (4)
Therefore, it can be written as C pk min �C pu, C pl �
� .
Negative values of C pk occur when the process average is
positioned outside of the specification interval. Whenever C p is
“large” and C pk is “small,” then � is not centered at the
middle of the tolerance.
In situations where both C p and C pk are “small,” � is centered
near the middle of the tolerance but the process spread is too wide.
If Cpk � 1,
it can be shown that M � d � � � M � d.
In 1991, Boyles pointed out that “the C p and C pk do not say
anything about the distance between process mean and target
value” and “are essentially a measure of process potential only”.
Boyles showed that C pk becomes arbitrarily large as σ
approaches 0, irrespective of where the process is centered and
this characteristic makes C pk unsuitable as a measure of process
centering. The same is true for C p .

11416 Afr. J. Bus. Manage.
Proportion
0.025
0.02
0.015
0.01
0.005
0
Product is 100% non-conforming
200 250 300 350 400 450 500 550 600 650 700
Measurement Y
Figure 2. Production from a potentially capable process which is currently producing product that is 100%
nonconforming. LSL=200 and USL=400.
Herman suggests that a different index, the ‘process performance
index’ (PPI) , p P might ‘have more value to a customer than C p ’.
The index P p is defined as:
P
USL � LSL
6�
p � (5)
total
An analogy to C pk is:
P
pk
�
�USL � � � � LSL�
�
� ,
�
�
� 3�
total 3�
total �
min (6)
P pk is also referred to as the preliminary process capability. It is
used whenever a new process is started or a major revision to an
existing process is resumed. This is why some practitioners
mistakenly assume P pk is for short-term data and is to be used on
an unstable process. Both assumptions are false. P pk is an initial
production run of a new process (less than 30 production days),
and C pk is everything thereafter.
One variation of C pk is a relatively new index called Cp T , in
which the T represents a target value. It allows one to select a
target dimension and calculate capability from the target. T
C p
calculations are the same as C pk calculations, except that one
substitutes a target dimension for the process average.
T
C p
�
�USL �T
T � LSL �
min�
, �
� 3�
3�
�
d � T � M
� (7)
3�
Like the C pk index, both parts of the Cp T index are calculated,
but only the minimum is used. The target dimension is usually the
nominal of the specification, and some call it the true process
centering of an index. In reality, however, the Cp T index is the
same as the C p index and has nothing to do with process
centering. If the target T is set as the midpoint of the specification
interval, that is T = M, Cp T yields the same ratio as C p .
The concept of variation has undergone paradigm shift recently
in industry. This shift has occurred in the interpretation of the quality
of product varying within the allowable process specification. All the
indices discussed so far have used the historical perspective of
variation. A historical perspective of variation is that product had the
same quality, that is to say that the product is equally good,
regardless of where it fell within the specification limits. Product is
considered bad or has less quality, only if it falls outside of the
specification limits. Engineers are comfortable with this notion of
variation, which is sometimes referred to as “Goal post mentality”
and is displayed graphically in the following figure 3.
The problem with the goal post mentality is the step function that
occurs directly at the specification limits. In regard to a process, the
quality of a part falling just within the specification limit has little
practical difference from the quality of a product falling just outside
the specification limit. This model of quality variation has little
relevance to industry. Figure 3, 4 and 5 shows a model that was
proposed by statisticians. This model is more practical in that the
loss in quality and thus value loss to an organization increases as
the quality varies from a process target.

Bad
Product
LSL USL
Figure 3.Goal post mentality.
Product
Bad
Product Equally Good
Target
LSL USL
Figure 4.Goal post mentality.
Value
Loss
Product Equally Good
Target
Loss Function Curve
LSL USL
Figure 5. Loss function mentality.
This notion of variation referred to as “loss function mentality”,
states that there is a quadratic relationship between the loss and
the distance from the target and it were proposed by Taguchi
(1985). This function is called the loss function curve and it ties
variation to the loss in a process. This notion is what capability is
based on. Capability indices enumerate a process ability to
minimize the loss function curve. Hsiang and Taguchi (1985) and
also Chan et al. (1988) developed the index C pm in order to take
Target
Bad
Product
Philimon et al. 11417
Product
Bad
into account the process centering and defined it as follows:
d
C pm � (8)
3 L(
Y)
where
L( Y)
�T
2
� E(
Y ) is the loss function. � �
LY is the loss
associated with a characteristic X not produced at the target. This

11418 Afr. J. Bus. Manage.
implies the loss is zero when the process is on target and positive
for any deviation from the target.
Boyles (1991) showed that for fixed μ, the index C pm is bounded
above when σ tends to 0 and furthermore, that
and hence
d
�T
�
3C
� .
pm
C pm
�
d
�3 � �T
�
Therefore, given a C pm index of 1.00, we know that
d d
M � � � � M � . This interval is much smaller than
3 3
the one for
M � d � � � M � d.
C pk
equal to 1.00 which is equal to
Parlar and Wesolowsky (1998) noted that if T = M, then the three
basic PCIs Cpk , Cp, C pm
are connected by the relationship
1 � C � p
Cpk � Cp�
� �1
3 �
�
C �
� pm �
2
Whereas the index C pm has the attractive features that it
incorporated the parameters d, � , T, and � , it has an important
omission, namely, the parameter M. The index C pmk rectifies this
deficiency. To devise an index that is more sensitive to departures
of � from T, Pearn et al. (1992) introduced another process
capability index, C pmk . The index takes its numerator from C pk
(9)
and its denominator from C pm , hence it is a hybrid.
C pmk
�
2
min( USL � �,
� � LSL)
�
3 � �
d � � � M
� �2 � �
2
3 � � T
� �2 � � T
(10)
When � is equal to M, C pmk is equal to C pm , when � is
equal to T, C pmk is equal to C pk . C pmk is certainly worse than
C pk for being associated with a certain percentage of nonconforming
product, but again, one should not choose this index if p
is the main interest. C pmk (and usually C pm ) is much more
sensitive than other capability indices to movements in the process
average relative to M. If � moves away from M, however, pmk
decreases more rapidly than does C pk (although both are zero
when � equals one of the specification limits). Conversely, when
C
� is brought closer to M, C pmk increases much faster than does
C pk . C pmk reveals the most information about the location of the
process average and the least about the proportion non-conforming
p.
Vannam (1995) showed that among all the indices presented
thus far, C pmk is the most sensitive to departures of �
from T. The ranking of the following four basic indices discussed
thus far in terms of sensitivity to departure of the process mean
from the target value, from the most sensitive to the least sensitive
are (1) C pmk , (2) C pm , (3) C pk and (4) C p .
Unified approach
The unified approach was proposed by Kerstin Vannman (1995).
Vannman constructed a superstructure class to include the four
basic indices, C p , C pk , C pm and C pmk as special cases. By
varying the parameters of this class, we can find indices with
different desirable properties. The proposed, new, indices depend
on two non-negative parameters, u and v, as:
C p
�u, v�
2
�
d � u � � M
3 � � v
It is easy to verify that:
� �2 � � T
(11)
C p (0,0) = C p ; C p (1,0) = C pk ; C p (0,1) = C pm ; C p (1,1) =
C pmk From the study of C p (u,v), large values of u and v will
make the index C p (u,v) more sensitive to departures from the
target value. A slight modification gives the even more general
*
index class which includes C pm as a special case as well.
C p
�u , u , v�
1
2
�0, 1,
1�
C p =
C
2
d � u1
� � M � u2
T � M
�
3 � � v
*
pm
� �2 � � T
The five C p , C pk , C pm , C pmk and
μ = T = M, but differ in behavior when
(12)
*
C pm , are equal when
� � T . By plotting the
four indices as surfaces, we can get a feeling for the sensitivity with
regards to departure of the process mean, µ, from the target value,
T, assuming that T = M. We note that, for fixed σ, when µ moves
away from T, then C p does not change, pk
C changes, but
slowly, C pm changes somewhat more rapidly than C pk , but
C pmk is the one that changes most rapidly (Vannman, 1993).

Normative approach
The normative approach for the control of quality is based on
decision-theoretic considerations. It provides a vehicle for
accomplishing both, the retroactive function of assessment and
monitoring, and the proactive function of prediction and control.
Furthermore, the normative approach is able to integrate the three
tasks of assessment, prediction and control within an interactive
and unifying framework. Here, one monitors the observable Y
(rather than the unobservable μ), and make a decision to continue
production, to modulate it or to stop it, based on the consequences
of the deviation of the Y from T. The decision is proactive and is
dictated by the predictive distribution of Y and the utilities
associated with a control of the process.
According to Singpurwalla (1998) the work of Jose and Telba
(1996) appears to be first to have introduced the normative
approach in the context of process capability indices.
Bayes capability index
A Bayesian index is proposed to evaluate process capability which,
within a decision-theoretical framework, directly assesses the
proportion of future parts which may be expected to lie outside the
tolerance limits.
The proposed capability index is a direct function of the data,
whose value is sufficient to solve the relevant decision problem.
The Bayes capability index B C (D) (Bernardo and Irony, 1996), is
given by:
1 �1
CB �D � � � �Pr � y � A � D��
(13)
v
where v will be set equal to 3 or 6 and A is the tolerance region,
�is the distribution function of the standard normal distribution,
and D the available data.
Accept that the process is capable if and only if:
B
� � 0
C D c
� (14)
where c 0 is a threshold value.
Mean square error
MSE embodies long-term and short-term variation around the
process mean, m, as well as the deviation of the process mean
from the target (that is, the process bias). In fact, the MSE can be
expressed directly as:
� �2 m T
2
2
MSE � � LT ��
ST � � (15)
2
MSE = Long-term variance component [ � LT ]
2
+ Short-term variance component [ � ST ]
m � T ]
+ The square of process bias [ � � 2
It reflects all variation and deviation from target directly and is
proportional to Taguchi’s quadratic loss function (QLF), so it directly
approximates the cost of quality associated with any process for
which QLF is appropriate.
Incapability index
Philimon et al. 11419
Greenwich and Jahr-Schaffreth (1995) considered a simple
transformation of the index C pm called C pp which was defined
as:
C PP
2
� � �T
� � � �
� � � � � �
(14)
� D � � D �
d�
D
3
T � LSL
where � , �USL � LSL�
d �
2
� , � USL�
T
D L
D U
They also defined the inaccuracy index as:
2
� ,
, � � D d , min �
C ia
L U D
2
� � �T
�
� � �
� D �
Table 1 summarises the main indices discussed in this study.
Belt manufacturing process
Firstly the company manufactures the rubber compounds which are
used for the top and bottom covers of the belt. The manufactured
rubber compounds are then tested for elongation and breaking
strength. The rubber compounds which pass the tests are then
pressed and mixed with fabric pliers to produce the final belt. Two
processes are used to produce the final belt.
In the first process, the rubber compounds and fabric pliers are
heated and pressed in an oven to produce the aged belt as a final
product. In the second process, the rubber compounds and fabric
pliers are simply pressed without heating to produce the un-aged
belt as a final product. The final belt (both aged and un-aged) is
then tested for elongation, breaking strength and adhesion, these
are the quality variables of interest. Belts which pass these tests
are then passed on to the customers. Belts which fail the test are
re-processed. Tabulating the approaches against the quality
characteristics showed that the approaches can cover up for each
other (Table 2). A 1 means the approach addresses the parameter
and a 0, means the approach does not take into account the
parameter. This is made possible by coming up with sets that
contain the least number of approaches that can effectively give the
total quality position of the process.
The algorithm
Step 1: Sum all the 1s and 0s for each approach
Step 2: Take the approach with the highest number to be the
seed.
Step 3: For the corresponding 0s on the seed approach, choose
the approach that covers most of the 0s.
Step 4: Choose the approach that covers most of the remaining
0s.
Step 5: Repeat Step 4 until all the 0s (parameters) are covered.
Step 6: The approaches chosen here make up a set.
For different set of approaches that completely address the quality
parameters, go through the above algorithm with a different seed
and corresponding parameters. For coming up with the sets, an
algorithm was developed and it uses Table 2.
Implementation of proposed procedure
The holistic approach procedure was tested on the belting

11420 Afr. J. Bus. Manage.
Table 1. Summary of the main indices.
Approach Formula
Process capability indices
C
C
C
C
p
pl
pu
pk
T
C p
C
pm
C pmk
*
C pm
USL � LSL
�
6�
� � LSL
�
3�
USL � �
�
3�
�USL�� ��LSL�
� min � , �
� 3� 3�
�
�
�
6
Unified approach �u, v�
Process performance index
production at general beltings limited. The system has got two
processes, compounding and belting. Compounding is the process
of producing rubber mixtures which would form the top and bottom
covers of the belt. The quality characteristics considered were the
elongation at break, measured in kN/m, and break strength,
measured in Mpa. Two samples of each compound are taken, one
is measured without aging and the other one is first aged in the
oven (high temperatures). So we had results of aged and unaged
compound. The second part of the system is the formation of the
belt by pressing the rubber compound to the fabric plies. The
quality characteristic considered here is the elongation at break
(kN/m), break strength (Mpa) and adhesion (Newtons).
The compounds are classified into two categories, PMB 68 and
C p
P
P
p
pk
�USL �T
T � LSL �
min�
, �
� 3�
3�
�
USL � LSL
2
� �
� � 2
T
� �
2
min( USL � �,
� � LSL)
�
3 � �
� �2 � � T
2
min( USL � T,
T � LSL)
�
3 � �
� �2 � � T
2
d � u � � M
�
3 � � v
USL � LSL
�
�
total
� �2 � � T
�USL �� ��LSL�
� min � , �
� 3�total3�total� PMB 50/67, and therefore, the belts produced from these
compounds were also considered under PMB 68 and PMB
50/67.
Verification of the set of approaches
Two of the many sets obtained, from the above mentioned
algorithm, are taken. Each set gives its own quality picture of the
process and verified whether it is really holistically addressing the
concerns of quality engineers. After this, they are compared
to each other to check whether they give the same quality of
information for the same belting process.

Table 2. Approaches versus quality characteristics.
Parameters
Inherent variation
Total variation
Bias
Normal distance
Stability
Target value
Symmetric tolerance
Asymmetric tolerance
Can be average
Sensitivity variation
Proactive [predictive and control]
Philimon et al. 11421
Cp 1 0 0 1 1 0 1 0 0 0 0 0 1 5
Cpk 1 0 0 1 1 0 1 0 0 0 0 1 0 5
Cpm 1 0 0 1 1 1 1 0 0 1 0 1 0 7
Cpmk 1 0 0 1 1 1 1 0 0 1 0 1 0 7
Pp 1 1 0 1 0 0 1 0 0 0 0 0 1 5
Ppk 1 1 0 1 0 0 1 0 0 1 0 1 0 6
MSE 1 1 1 0 0 1 0 0 1 1 1 1 0 8
Incapability 1 0 0 1 1 1 1 1 0 0 1 1 0 8
Desirability 1 1 0 0 0 1 1 1 0 0 0 1 0 6
Unified 1 0 0 1 1 1 1 0 0 1 0 1 0 7
Normative 0 1 0 0 1 0 1 1 0 0 1 1 0 6
RESULTS
The sets
Sets that came out are as a result of using the above
algorithms are as follows:
Set 1: {MSE; Incapability Index; Pp}
Set 2: {MSE; Unified Approach; Desirability Index; Cp}
Set 3: {MSE; Normative Approach; Cpm; Cp}
Set 4: {MSE; Normative Approach; Cpm; Pp}
Set 5: {MSE; Normative Approach; Cpmk; Cp}
Set 6: {MSE; Normative Approach; Cpmk; Pp}
Note: MSE became the seed approach for its
uniqueness of measuring bias of the process.
These are the sets that contain the indices that are
going to be used holistically. The control charts obtained
after using these sets are shown in Figures 6, 7, 8 and 9.
Set 1 and Set 2 were used for the for the results that
are shown in the Figures 6, 7, 8 and 9.
Process control charts
26 control charts were created and some of them are as
Retroactive
[assessment and monitoring]
follows; Figure 6 shows that the process is indeed out of
control as several points lie outside both the upper
control and lower control limits.
Figures 7, 8 and 9 also show that the production
process is out of control as points are clearly lying
outside either the specification limits or control limits or
both. This was revealed after the holistic application of
capability indices as compared to the scenario where
only one index was being used. These figures are
justifying the complains raised by customers that the
belts are not strong and are breaking easily. Corrective
measures have to be taken to address this problem,
which has been identified as a result of applying more
than one index to monitor the quality of a production
process.
Summary of results
Table 3 and 4 shows the summary of all the calculations
of the different indices on all the quality characteristics
considered in the study. The results of the formulars
previously of this holistic approach has to be
implemented. The strength and weakness of a process
Potential
Sum

11422 Afr. J. Bus. Manage.
Mpa
Elongation
440
420
400
380
360
340
320
300
1
5
PMB 68 Aged Elongation Control Chart
9
13
17
21
Samples
Figure 7. Control chart for PMB 68 aged at elongation.
27
26
25
24
23
22
21
20
19
18
1
4
7
25
PMB 50/67 Unaged Break Strength Control Chart
10
13
16
19
22
Samples
Figure 8. Control chart for PMB 50/67 unaged at break strength.
Mpa
Elongation
27.0
25.0
23.0
21.0
19.0
17.0
15.0
1
4
7
25
PMB 50/67 Aged Break Strength Control Chart
10
13
16
19
22
25
Samples
Figure 9. Control chart for PMB 50/67 aged at break strength.
28
28
29
31
31
33
34
34
37
37
37
40
40
43
43
S Mean
LCL
Means
UCL
LSL
USL
S Mean
LCL
Means
UCL
LSL
USL
S Mean
LCL
Means
UCL
LSL
USL

Table 3. Results of the process of producing the compound used for the covers of the belts.
Philimon et al. 11423
Cp Cpk Cpm Cpmk Pp Ppk MS ML MB MSE C”ia Cip C”pp P(U,V) Norm EDU
PMB 68 Unaged Elongation 1.00 1.58 0.51 0.27 0.42 0.57 465 1115 2074 3654 8 6 13 0.39 0.14 0.00
PMB 68 Unaged Break strength 2.36 5.19 0.25 -0.08 1.19 2.65 1 1 46 48 17 0 17 0.15 0.00 0.00
PMB 68 Aged Elongation 1.74 2.30 0.46 0.15 0.79 0.73 487 965 6799 8251 8 1 9 0.28 0.04 0.00
PMB 68 Aged Break strength 1.88 1.19 0.67 0.38 0.93 0.58 1 2 9 13 4 3 7 0.55 0.18 0.16
PMB 50/67 Unaged Elongation 1.9 1.2 0.83 0.57 0.6 0.4 551 2193 2270 5013 2 3 5 0.58 0.19 0.01
PMB 50/67 Unaged Break strength 1.5 1.4 0.67 0.391 0.5 0.4 1 4 5 9 5 4 9 0.46 0.16 0.13
PMB 50/67 Aged Elongation 1.6 1.2 0.91 0.677 0.7 0.5 436 982 1379 2797 2 2 4 0.68 0.26 0.01
PMB 50/67 Aged Break strength 1.7 1.7 0.75 0.494 0.6 0.6 5 5 8 13 4 3 7 0.55 0.18 0.16
Table 4. Results of the process of putting rubber to the plies.
Cp Cpk Cpm Cpmk Pp Ppk MS ML MB MSE C”ia Cip C”pp Vp(U,V) Norm EDU
PMB 68 Break strength 0.31 0.24 0.22 0.04 0.12 0.19 201 24 63 287 22 71 93 0.17 0.07 0.06
PMB 68 Elong @ ref load 0.25 0.33 0.18 -0.02 0.14 0.31 3362 459 2274 6131 32 51 83 0.14 0.06 0.01
PMB 68 Elong @ break 0.79 0.17 0.67 0.53 0.54 0.38 9 3 4 16 1 3 5 0.58 0.25 0.14
PMB 68 L-Tc/P 0.38 0.24 0.32 0.17 0.31 0.21 10 1 3 13 3 10 13 0.27 0.12 0.13
PMB 68 L-P/P 0.55 0.34 0.43 0.29 0.46 0.31 5 0 1 6 1 5 6 0.37 0.15 0.13
PMB 68 L-Bc/P 0.49 0.33 0.38 0.22 0.39 0.27 6 1 3 10 3 6 10 0.31 0.13 0.14
PMB 68 L-Tc/P 0.47 0.35 0.33 0.15 0.39 0.29 7 0 4 11 4 7 11 0.26 0.11 0.14
PMB 68 L-P/P 0.67 0.46 0.37 0.15 0.50 0.36 4 0 4 8 4 4 8 0.27 0.10 0.15
PMB 68 L-Bc/P 0.60 0.82 0.29 0.03 0.13 0.18 4 1 10 14 10 56 65 0.20 0.06 0.08
PMB 50/67 Break strength 0.58 0.43 0.33 0.10 0.33 0.29 241 47 198 486 7 8 16 0.24 0.09 0.03
PMB 50/67 Elong @ ref load 0.20 0.17 0.17 0.04 0.16 0.24 5124 163 566 1863 14 38 52 0.15 0.07 0.01
PMB 50/67 Elong @ break 0.95 0.28 0.70 0.51 0.67 0.46 4 1 3 8 1 2 3 0.55 0.24 0.17
PMB 50/67 L-Tc/P 0.36 0.26 0.30 0.15 0.29 0.23 11 1 4 16 4 2 16 0.25 0.11 0.11
PMB 50/67 L-P/P 0.56 0.25 0.37 0.20 0.36 0.23 7 1 2 10 2 8 10 0.30 0.12 0.13
PMB 50/67 L-Bc/P 0.39 0.33 0.32 0.18 0.32 0.27 9 1 3 13 3 10 13 0.27 0.12 0.12

11424 Afr. J. Bus. Manage.
Table 4. Cont’d.
PMB 50/67 L-Tc/P 0.62 0.30 0.39 0.18 0.44 0.23 5 0 4 9 4 5 9 0.29 0.11 0.16
PMB 50/67 L-P/P 0.86 0.68 0.38 0.15 0.46 0.37 4 1 5 9 5 5 9 0.27 0.08 0.09
PMB 50/67 L-Bc/P 0.59 0.92 0.27 0.00 0.46 0.65 4 1 11 16 11 5 16 0.18 0.06 0.10
are only exposed by a complete quality study of
that process, even when it is producing products
that meet the specifications. All the characteristics
of a paper were used to come up with the data on
these two tables.
CONCLUSION
If the quality of a product is to be guaranteed, the
production process and their corresponding set of
capability index have to be identified first. The
sets brought out more information about the
process than individual indices. The holistic
approach found out that the belting process was
incapable. Investigations have to be done to find
out the possible causes of variation and
necessary adjustments to the process have to be
done urgently.
REFERENCES
Bernardo JM, Irony TZ (1996). A general multivariate Bayesian
process capability index. J. Royal Stat. Society. 45: 487-
502.
Boyles RA (1991). Process capability with asymmetric
tolerances. Commun. Stat. Simul. Comput., 23(3): 615-643.
Chan PLY (1988). Optimal design for a linear log contrast
model. J. Stat. Plan. Infer., 20: 105-113.
Chang YC, Wu CW (2008). Assessing process capability
based on the lower confidence bound of Cpk for a
asymmetric tolerances, Eur. J. Oper. Res., 190: 205-227.
Chao MT (2005). Another look at the process capability index.
Qual. and Reliability Eng. Int., 22(2): 153.
Chen KS, Huang ML, Li RK (2001). Process capability analysis
for an entire product, Int. J. Prod. Res., 39(17): 4077-4087.
Choi BC, Owen DB (1990). A study of a new capability index.
Comm. Stats. Theo. Methods, 19: 1231-1245.
Greenwich M, Jahr-Schaffrath BL (1994). A process
incapability index. Int. J. Q. Reliab. Manage., 12(4): 58-71.
Herman JT (1989). Capability Index-enough for process
industries? ASQC Ann. Qual. Congress Trans, Toronto:
670-675.
Hsiang TC, Taguchi G (1985). A tutorial on quality control and
assurance- the Taguchi methods. ASA Annual meeting, Las
Vegas, Nevada.
Juran JM (1974). Quality control handbook, 3rd edition,
McGraw- Hill, New York.
Kane VE (1986). Process capability indices. J. Qual. Technol.,
18(1): 41-52
Kotz S, Johnson N (2002). Process capability indices- a review
1992-2000, J. Q. Technol., 34 (1): 2-19.
Montgomery DC, Somerville S E (1996). Process capability
indices and non-normal distribution. J. Q. Eng., 19(2): 305-
316.
Parlar W, Wesolowsky GO (1999). Specification Limits,
Capability Indices and Process Centering in Assembly
Manufacturing. J. Q. Technol., 31(3): 317-325.
Pearn WL, Kotz S, Johnson NL (1992). Distributional and
inferential properties of process capability indices. J. Q.
Technol., 24(4): 216-233.
Singpurwala ND (1998). The stochastic control of Process
capability Indices.
Vannman K (1995). A Unified Approach to Capability Indices.
Stat. Sci., 5:805-820.
Wang S, Wang D (2011). A multivariate process capability
index model system. J. Semiconduct., 32(1): 6001-6007.
Wu CC, Kuo HL, Chen KS (2004). Implementing process
capability indices for a complete product. Int. J. Adv.
Manage. Tech., 24: 891-898.
Yu KT, Sheu SH, Chen KS (2007). The evaluation of process
capability for a machining center. Int. J. Adv. Manage.
Tech., 33: 505.

African Journal of Business Management Vol. 5(28), pp. 11435-11441, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.542
ISSN 1993-8233©2011 Academic Journals
Full Length Research Paper
Training needs assessment practices in corporate
sector of Pakistan
Syed Kamran Sherazi, Irfan Ahmed*, Muhammad Zubair Iqbal, Muhammad Umar and Kashifur-Rehman
Iqra University, Islamabad, Pakistan.
Accepted 12 July, 2011
The purpose of this study is to investigate the manager training needs assessment techniques adopted
by Pakistan corporate sector, especially in Islamabad and Rawalpindi region, and its impact on training
outcomes. The finding related to TNA comprehensiveness was disparate because majority of the
organization are using only one level or personal level analysis for their managers training needs
assessment. So, it has negative impact on the outcomes of training. The study revealed that training in
Pakistan is mostly subjective based. Objectivity of the training is less emphasized during their training
session. These desperate results are basically due to lack of comprehensive managerial TNA approach
used by corporate sectors in Pakistan before conducting training program. The objective of our study is
to mention the drawback which corporate sector is facing regarding TNA and training outcomes and
also relate the TNA comprehensiveness to effective outcome of training.
Key words: TNA, managers training, katpatrick model, OTP model.
INTRODUCTION
In fact, training is a major tool for the development of
human resource. Companies are spending millions of
dollar in training for developed human asset. But, to what
extent are these training effective for developing the
employee? It’s a question for the companies to judge the
accuracy, reliability and effectiveness of the training
program. Cheng and Ho (2001) suggest that training is a
costly investment and this is even more important taking
into consideration that only 10% of total training expenses
could lead to positive transfer of training; it means
only 10% changes occur in employee efficiency and
effectiveness at workplace after training. Many theories of
training effectiveness only focus on factors relating to the
process of training, such as the design of the training and
implementation of training program. But less attention is
paid in the literature that focuses on level of managers
TNA, whether it is in the form of organization analysis,
personal analysis or task analysis. The best approach for
effective outcomes of training for organization, especially
in third world country for TNA approach is totally
*Corresponding author. E-mail: irfisam@gmail.com.
neglecting. Mostly, TNA method is conducted overseas.
The studies of Malaysian companies suggest that its
main reason is cultural and national differences among
west and south Asia regarding TNA practices (Poon and
Rozhan, 2000; Zakaria and Rozhan, 2000).
Another researcher, Arthur et al. (2003) reported that
only a few studies on training, especially regarding TNA
have been investigated and available in literature. Agnaia
(1996) reported that performance appraisals system is
one of the major TNA techniques which is adopt mostly
Libyan companies. Performance appraisal system is also
often considered the most common and widely used
method in manager TNA (Brown, 2002; Osborne, 1996).
Abu-Doleh (2004) investigate that much of Jordanian
Private and Public organization emphasis was placed on
the individual analysis and functional analysis rather than
to organizational analysis. Not surprisingly, no single
organizations in the two sectors are using all the three
approaches simultaneously for conducting their manager
TNA. Its mean any call for training that is undertaken
without a watchful analysis of whether it is needed, or not
required, is probable ineffective and a waste of money
and resources of the organization. Abu-Doleh and
Branine (1996) investigate that no single firm in Jordan

11436 Afr. J. Bus. Manage.
corporate sector is evolved to conduct their manager
TNA on the basis of three approach of OTP model. They
also found that all public organizations 100% and the
great majority of the private organizations 83.3% which
did regularly assess their managers TNA needs in the
absence of organizational analysis.
Another interesting fact which is considerable is that a
lot of research are being conducted to find out the
different aspect of TNA, like training evaluation and
Training analysis (Mathews, 2001), a technology-based
approach to training needs analysis (Grant et al., 1997),
TNA comprehensiveness and organizational effectiveness
(Saari et al., 1988), perception of manager training
and development needs and their view on future
prospective of TNA (Stewart and Waddell, 2003), and
obstacle for conducting Training needs assessment (Al-
Khayyat, 1998) but there is no particular study investigated
the impact of three level of TNA or fully systematic
manager TNA with relation to effective training. We believe
that after literature review, if organization especially
HRD personnel are adopted three level of TNA can be
more effective for a better result of training outcomes
instead of conducting only on level of analysis.
The literature reveals that majorities of companies rely
their manger TNA on the basis of performance gap or on
the basis of personal analysis. But there is a bigger flaw
or in-advocacy for conducting manager TNA on the
bases of performance appraisals, because performance
appraisal is totally dependent on the appraiser. This may
be that appraiser show bias or favor which results in a
waste of time, and money on training. Many organizations
lack proper job description system of a particular
job, in this case, the performance gap could be difficult to
measure, and again, results in conducting training on the
bases of task analysis is poor. In many cases, the
training is not a solution for employee; maybe the
employee’s motivation level is low due to lack of proper
compensation or other work place environmental effect or
organization does not provide the necessary input to the
employee to perform their job. Here, this special case
relying on only one TNA approach is not sufficient for
effective training. There is need for analysis at macro
level or analysis at organizational analysis to cater for the
organizational problem regarding training and development
of employees. Majority of previous researches
revealed that there is no single organization in private
sector which used all the three approaches of TNA or
comprehensive TNA techniques simultaneously, conducting
for assessing their manager training needs. Now,
the study objectives are to find out the type of manager
TNA approach organizations in Pakistan, especially the
organization situated in federal and Rawalpindi territory
that are widely conducting manager TNA in their organizations.
Secondly study also find out the relation between
comprehensive and detail analysis of Manger TNA and
its effect on outcomes of the training. This study
examines the effective outcomes of the training with the
relation of comprehensive level of TNA.
LITERATURE REVIEW
Training need assessment
The model for training needs analysis was first presented
by McGhee and Thayer in 1961. The OTP three level
idea of needs assessment is considered the foundation
framework for needs assessment in the academic
literature (Holton et al., 2000). Undertaking efficient
needs assessment before starting training program has
been recommended to organizations for almost 40 years.
This model of organization, task, and person analysis has
been cited and described in training and industrial/
organizational psychology texts. Further studies revealed
that needs assessment (or needs analysis) is the process
of determining the organization’s training needs and
seeks to answer the question of whether the organization’s
needs, objectives, and problems can be met or
addressed by training’ (Arthur et al., 2003). Training need
assessment is a critical part of the training system
because it provides data to determine who is to be
trained, what training programs are needed, and how the
results of training programs are to be evaluated (Walter
and Moshe, 2000). So, in extreme cases, inadequate or
incomplete training is even likely to promote performance
deficiencies (Goldstein, 1993). Therefore, any call for
training without a careful analysis of whether training are
needed, or not required, the solution for the problem is
possibly ineffective and a waste of money and resources
of organization (Abu-Doleh, 1996; Branine, 1996).
In sum, training needs assessment is considered as
one of the fundamental prerequisites of an effective
training program. Abu-doleh (2004) found that conducting
a full systematic training needs analysis and getting the
help of external training consultants in the analysis of
manger TNA were not common practice in the
investigated organizations. He also found that the skills
inventory is the most extensively used formal practice for
assessing manager TNA. Brian et al. (2001) investigate
that Finnish organizations are likely to pay more attention
toward customers and work groups, when observing
training needs. Wilson (1999) suggested the conventional
and simpler methods such as interviews, questionnaires,
observations, and focus groups to gather information for
HRD needs analysis. Gilley et al. (2002) suggested the
more analytical method such as is/should analysis,
critical analysis and root-cause analysis methods to
gather information. Needs analysis is viewed as a
process in which the HRD needs of both the employees
and the organizations are identified in order to address
the gap between employees’ abilities and performance
and the organization’s requirements. Hence, the four
levels of identification, namely, 1) organizations’ overall
performance; 2) the departments’ requirements;

3) individual employees’ skills, knowledge and attitudes;
and also 4) employees’ jobs and functionalities, were
examined (Haslinda, 2009).
Salas and Cannon-Bowers (2001) have proclaimed the
importance of training at strategic level. According to him,
training is a fundamental component of human resource
development (HRD). But only providing training to
employee is not sufficient, it is necessary to provide the
relevant training which actually fulfills the requirement of
the employee, so it is called effective training. Training
effectiveness, as defined by Baldwin and Ford (1988), is
effectiveness of training, which means to what extent the
employees of the organization acquired new skill and
also apply the new skill at their work place or transfer
their acquired knowledge into the job. If the relevant
training needs are identified and addressed, then the
training leads to the desired results. Eerde et al. (2008)
investigates that the reasons why training program does
not give the desired effect may be that the training does
not suit the needs of the organization. Despite all these
things related to training need assessment, organizations
still spend US$ 200 billion annually on training and development.
DeSimone et al. (2006) agreed that training
effectiveness is “relative,” but only to the extent that there
is no single measure of training success such as
productivity or job satisfaction. However, he suggests that
ill-conceived or poorly executed programs reflect more
than incompetence or unwillingness. A systematic
analysis of needs before making decisions about training
appears to in a result of defective’s outcome of training.
In other words, it is worth the effort to consider the
question why training is needed, what should be the
content of the training to fulfill this need and to whom it
should be provided (Eerde et al., 2008).
Training evaluation and effectiveness
The training evaluation literature reveal that training
program outcomes are a number of dimension and these
outcomes require multiple criteria to be evaluated
(Cascio, 1987; Landy, 1989). There are four main evaluation
models or frameworks available in literature through
which training outcome can be measured: (i) Kirkpatrick
(ii) the Bell system, (iii) Parker, and (iv) CIRO. However,
Kirkpatrick's assessment model is one of the most
commonly used by many organizations and researchers
as well (Hale, 2003). The model was presented by
Kirkpatrick in 1969. This model is considered the best
approach to measure the effectiveness of training. There
are four stages or four levels to be considered when
evaluating training effectiveness. The first two levels,
reaction and learning, is considered to determine only the
subjective nature of the training, whereas, the other two
levels, behavior and result, measured the objectivity of
the training. Reaction measures are obtained via verbal
comments or questionnaires. Typically, reaction measures
assess the impressions of participants and provide little
Rehman et al. 11437
meaningful information regarding training effectiveness.
Learning measures attempt to evaluate how much information
was learned by participants and usually involve
written knowledge or performance tests. The reaction
level measures whether people are happy with the
training inputs or not satisfied (Hale, 2003). The learning
level of Kirkpatrick model answers the following question,
"what do people remember from the training program; do
they acquire the knowledge and skill and what extent
they gain from training program?” (Hale, 2003). This
measurement is made through special standardized tests
at the end of the program, such as pre, post, paper and
pencil tests, skill practice, workshops and job simulation.
The behavior level addresses the issue of "do people use
what they know at work?" (Hale, 2003).
Finally, the last two level of Kirkpatrick model, results
level determines "what are the outcomes of applications
on the job over a period of time?" (Hale, 2003). This level
of evaluation focuses on the impact of behaviors change
on the organization’s performance. Because changing an
employee's behavior and attitudes is not the final
objective of T and D, the end results should include
important elements such as, what extent productivity is
improved, can training program increase the quality of
services, the efficiency of the organization, etc.
Kirkpatrick (1959, 1960) suggested that behavioral and
results measures are useful indicators of actual knowledge
transference to job specific performance. Examples
of objectives measures of training effectiveness include:
increase in profitability of organization and productivity,
decreased customer complaints, costs, turnover and
absenteeism. However, both subjective and objective
measures are useful for a complete understanding of the
effectiveness of training program but in majority, the
subjective measures have been utilized by organizations
(Alliger and Janak, 1989; Saari et al., 1988).
RESEARCH METHODOLOGY
Sampling
In order to investigate our research query, we select twenty important
organization situated in Islamabad and Rawalpindi region.
Total sample of our questionnaires was 120. 60% respondent
organizations were selected form services sector and 40% from
manufacturing sector. The study emphasized more to services
sector because in service industry, most workers are considered as
knowledge economy worker. They easily agree to give an
immediate and better response for the questionnaire. The selected
population of the study was mostly at managerial level from private
sector and officer cadre level from public sector. From private
sector, the respondents were HR managers, executives HR, HR
personnel, training managers and assistant training managers.
Whereas from public or government organization, the respondents
we included in our study were directors, deputy directors, assistant
directors, G.M, training directors and assistant training directors.
Over a period of two months in late 2010, the study visited twenty
organizations before getting prior appointment and we were careful
not to distribute our questionnaires or throw it to the managers. We
requested per day, five to six officers or managers to give us their
precious one and half hour time for participating in this study. The

11438 Afr. J. Bus. Manage.
Table 1. Respondents by industry sector.
Industry Percentage
Telecom sector 16.6
Govt. Authority and commission 33.3
Construction sector 8.3
Textile sector 8.5
Ghee, oil and gas 16.6
Insurance sector 5
Cement sector 8.3
Beverage sector 3.3
Total 100
study collected data through self administrative questionnaire as
well as additionally face to face meeting was made for the purpose
of accuracy and reliability of data. During conversation with
managers, they were also excited to share their experiences related
to TNA techniques and practical practices especially in Pakistani
scenario and its consequences on training. The sample of the study
from different organizations located in Islamabad and Rawalpindi
territory is listed in Table 1.
Variables measurement
Background variables
The study requested, for background analysis, variables such as
the nature of the enterprise either privately held or public
organization, total number of years organization is operating in their
particular industry and number of full time or permanent employees
currently working in organization.
Level of TNA
The study measured the level of TNA undertaken in the organization
prior to decisions made on training based on the OTP model
presented by McGhee and Thayer in 1961. Eerde et al. (2008) used
this scale for measuring the TNA comprehensiveness. We adopted
this scale from Rossilah and Hishamuddin (2007) study in
Malaysian companies on 5-point likert response scale ranging from
‘1 = Never’ to ‘5 = Always’ to assess the more frequent techniques
of TNA adopting by Pakistani corporate sector on each of the three
levels.
Training effectiveness
Kirkpatrick's four-level evaluation model criteria are mostly used for
measuring effectiveness of training by researchers. The study
adopted this scale from Atiyyah (1993), studying training evaluation
in Libyan banking sector which was previously adopted from the
model idea of Kirkpatrick. This model is assessed with a five point
likert scale ranged from 1 = to a very small extent to 5 = to a very
great extent.
Procedure
Three major areas were covered in the questionnaire. The first
section of the instrument were dealings with organizational and
employees demographics. The data is put in MS excel sheet for
computing the mean of perception of mangers and officer regarding
TNA practices and also perception regarding training effectiveness
in questionnaire. The means of different techniques used in TNA,
prior to training programs started, are listed in Table 2, whereas, the
third section required managers and officers to rate the perceived
importance and frequency of usage for specific methods of subjective
and objective training effectiveness measurement on reaction,
learning, behavior, and results. The respondents rated their
perception on a five point Likert scale. The perception of managers
and officer regarding training effectiveness are mentioned in the
form of mean in Table 3.
FINDINGS AND DATA ANALYSIS
Regarding manager training needs assessment, Table 2
shows that in a third world country, which is struggling for
development, the TNA techniques are practiced on ad
hoc basis. Mostly, organizations are not much familiar
with different kinds of TNA practices and techniques. It is
interesting to note that assessing managers training
needs, majority of investigated organizations is not
enough to claim that they are really engaged in effective
TNA analysis. The same finding was observed by Abu-
Doleh (2004) in Jordan companies. Here in Pakistan, in
the corporate sector, the respondent gives almost the
same result .The techniques which are used in first block
of the table in organizational analysis level are not
commonly or specifically developed for TNA techniques.
Mostly, it is used for strategic management. However, the
use of these techniques to identify organizational training
needs were mentioned and or discussed by several
authors (Chiu et al., 1999; Craig, 1994).
Perceived training needs assessment practices
The respondents have a perception that the organization
is not paying much attention toward the organization level
analysis. 46% responded that the organization never
adopt this SWOT analysis techniques for conducting
manager training needs. 35% employees responded that
the organization also not involved for conducting the
manger TNA through PEST analysis techniques. This

Table 2. Techniques used in conducting manager training need assessment (mean).
Technique N (%) R (%) S (%) F (%) A (%)
Organization analysis
SWOT Analysis 46.7 34.2 15.8 3.3 0
Organizational scanning 42.5 37.5 14.2 5.8 0
PEST analysis 42 35.8 17.5 4.2 0
Operational analysis
Task / KSA analysis 0 4.2 8.3 60 27
Competency analysis 39.2 53.3 7.5 0 0
Job analysis 65.2 28.3 6.5 0 0
Skill inventory 22.5 55 22.5 0 0
Individual analysis
Performance appraisals 0 0 0 18.3 81.7
Critical incident 0 37.5 61.7 0 0
Diary analysis 47.5 52.5 0 0 0
Indicators: SWOT: strength, weakness, opportunities, thereat; PEST: political, economical, sociological,
technological; N: never. R: rare. S: seldom. F: frequent.
Table 3. Training effectiveness analysis on Kirkpatrick model (mean %).
Measurement level for training
effectiveness
To a very small
extent
To a small
extent
To a considerable
extent
Rehman et al. 11439
To a great
extent
To a very
great extent
Perception of trainee about material
and facilities of training
Material and facilities of training 0 0 10.5 51.7 45
Methods and content of training 0 0 2.5 63.3 34.2
Acquisition during the training program
Attitude and abilities 0 0 5.8 54.2 40
Skills and knowledge 0 0 4.2 60 35.8
Change occurs after training program
Performance increase 40.8 54.2 9.2 0 0
Impact on effectiveness and efficiency
of employees
40.8 45 14.2 0 0
Effect on organization performance
Cost saving and impact on profit 36.7 54.2 9.2 0 0
Quality improvement 25 47.5 27.5 0 0
Customer satisfaction 41.7 58.3 0 0 0
Subjectivity measure of training program: 1 and 2 levels; Objectivity measure of training program: 3 and 4 levels.
ratio mention that the organizations in Pakistan sometimes
involve in preparing training program for their
employee due to changes in political and economical
situation of Pakistan, but contrary to fact, this method is
not so common. Majority of respondents using this technique
actually own their HRD department, because large
organizations have enough resources in term of finance
and human assets which support them to conduct
comprehensives managers TNA techniques before
conducting training program. Thus, the major and only
essential source of information in the selection of individuals
to be trained was the judgment of performance
appraisal. Various sources were considered moderately
important, however, discrepancy was mostly observed for

11440 Afr. J. Bus. Manage.
training through performance evaluation. The data
analysis reveal that majority of the organizations (81%)
assess manager TNA through performance appraisal
system. Majority of the organization also focuses on to
conduct TNA at personal level. More then half of the
organization (60%) in large size organization also pay
attention toward the task or operational level analysis.
But very few (3.3%) organization focus on SWOT and,
4.4% focus on PEST analysis at organizational level TNA
technique. Its means no single organization in private as
well as in public sector used the organization analysis
techniques, frequently or always. And surprisingly, no
single organization involved or engage in three level TNA
or comprehensiveness TNA practices before conducting
training program which is actually recommended by
literature for better outcomes of training.
Perceived training effectiveness
The results of study for effective outcome of the training
are mentioned in Table 3. The first level of Kirkpatrick
model which is also called a behavioral measure of
training, the respondents rated very high at 51.7%( to a
great extent ) and 45%(to a very great extent) for training
material and facilities provided by organization. The
employees were also highly satisfied regarding method
and content of the training program given by their organization.
Whereas, the question regarding acquisition of
knowledge and skills through training program, the
respondents rated their satisfaction level at 54% (to a
great extent) and 45% (to a very great extent). But in the
last two level of Kirkpatrick model, which is also called a
result measure or objectivity measure of training, the
respondents were not much satisfied. 45% (to a very
small extent) respondents rated the change occur after
training program. They also rated below changes at 58%
(to a small extent) on their effectiveness and efficiency
and customer satisfaction at third level of training
evaluation model. Finally, at last level of Kirkpatrick, no
single respondent rated the organization performance
enhancement after training at “to great extent or a very
great extent”. This shows a bigger flow and a question
mark on the effectiveness of training program.
DISCUSSION
The results of this study provide some disparate findings
in terms of effective outcomes of training. The reason is
that the organizations in Pakistan only focus on conducting
the personal analysis TNA before starting training
program. As a result of not engaging in comprehensive
TNA approach, the result of the training program is poor
and considered a subjective nature. If we analyze the
ratio mentioned in Table 3, the first two level of Katpatrick
model were considered as a subjective whereas, the rest
of the two final levels are considered as objective level.
The result showed that lack of comprehensive TNA
approach gives less result and does not meet objectivity
criteria of training program. No single respondent have
perception that the organization achieved the training
objectivity in a real scene. Through the conversation with
managers, most organizations in Pakistan are not well
aware of the Kirkpatrick model which is used for the
evaluation of training. Most organizations achieve the
subjectivity of the training instead of forcing on the
objectivity of the training. Many managers respond that
training is a long term investment. Thorough analysis of
TNA or comprehensive TNA approach leads to better
result of training outcomes; because comprehensive TNA
starts from micro level to macro level or TNA scanning
start from organizational level and come to personal
level. If the problem is not at organization level, then it is
a possibility that the problem may be at operational or
task level and again, if the problem is not at task level,
the training manager comes up at personal level.
Through this comprehensive approach, organization can
appropriately assess the actual need of the training at
managerial level and obviously, it has a good impact on
the outcomes of the training. Simply, when the TNA
comprehensiveness increases, the last two levels of
Kirkpatrick model objectivity will also increase and a lack
in TNA comprehensiveness results in a low achievement
of training objectivity.
RECOMMENDATIONS
In this study, there are three variable TNA comprehensive,
organization size and effective outcome of training.
But in future, the research can explore more regarding
TNA comprehensiveness and its impact on training
effectiveness; like in OPT model, what kind or what level
greatly influence the training outcome. It is possible for
researchers to find newer technique for assessing the
TNA for managers. Some other environmental effects,
like social and economical situation which are varying
geographically, can be another possible variable which
researcher can add in the future. Like Pakistan where the
political instability and uncertainty has much influence on
the organization. So, according to the political situation,
there is need for changing TNA technique with the
circumstances of Pakistan. The study has already
mentioned in the background that organizations used
performance appraisal for assessing manager training
needs. But it has a drawback due to appraiser biased
behavior. So, it is important for Pakistan corporate sector
to go for comprehensive TNA rather than focus only on
personal analysis which is totally dependent on appraiser
evaluation.
In the future, researchers can explore a longitudinal
study for more appropriate analysis of training needs
assessment of managers. This may give a direction for

judging better technique of TNA, especially in Pakistan
circumstances. This study could not identify the best TNA
technique, study only explore the TNA current practices
and its impact on the training outcome. In future, the
study can explore the best technique of TNA which is
more suitable in Pakistani scenario.
REFERENCES
Abu-Doleh J (2004). Management Training and Development Needs
Assessment Practices in the Jordanian Private and Public Sectors:
Integrated or Isolated. J. Trans. Manage. Dev., 9(2): 107 - 121.
Agnaia AA (1996). Assessment of Management Training Needs and
Selection for Training: The Case of Libyan Companies. Int. J.
Manpower, 17 (3): 31– 51.
Al-Khayyat R (1998). Training and development needs assessment: a
practical model for partner institutes. J. Eur. Ind. Train., 22:18- 27.
Alliger GM, Janak EA (1989). Kirkpatrick's Levels of Training Criteria:
Thirty Years Later. Pers. Psychol., 42: 331 – 342.
Arthur W Jr., Bennett W Jr., Edens PS, Bell ST (2003). Effectiveness of
Training in Organizations: A Meta-analysis of Design and Evaluation
Features. J. Appl. Psychol., 88: 234–245.
Atiyyah H (1993). Management Development in Arab Countries: The
Challenges of the 1990s. J. Manage. Dev., 12(1): 3-13.
Baldwin TT, Ford JK (1988). Transfer of training: A review and
directions for future research. Pers. Psychol., 41: 63-105.
Brown J (2002). Training Needs Assessment: A Must For Developing
An Effective Training Program. Pub.. Pers. Manage., 31(4): 569 – 79.
Cascio WF (1987). Appl. Psychol. Pers. Manage. (3rd ed.) Englewood
Cliffs, New Jersey: Prentice Hall, Inc.
Cheng EWL, Ho DCK (2001). A review of transfer of training studies in
the past decade. Pers. Rev., 30: 102-112.
DeSimone RL, Werner JM, Harris DM (2006). Human resource
development (4th ed.). Orlando FL: Harcourt.
Eerde VW, Tang KC, Simon, Talbot G (2008). 'The mediating role of
training utility in the relationship between training needs assessment
and organizational effectiveness. Int. J. Hum. Resour. Manage.,
19(1): 63 –73.
Rehman et al. 11441
Gilley JW, Eggland SA, Gilley AM (2002). Principles of Human
Resource Development. (2nd ed) Cambridge, Perseus Publishing.
Goldstein IL (1993). Training in Organizations: Needs Assessment,
Development and Evaluation (3rd ed.), Monterey: Brooks/Cole.
Grant C, Scott T, Mctear M (1997). A Technology Based Approach to
Training Needs Analysis. Innov. Educ. Teachnol Int., 34(3): 188 -1
Hale R (2003). How training can add real value to the business. Ind.
Commer. Train., 35(2): 49-52.
Haslinda A (2009).Training Needs Assessment and Analysis: A Case of
Malaysian Manufacturing Firms. Eur. J. Sci. Res., 37(3): 351-360
Holton E, Bates R, Naquin S (2000). Large scale Performance driven
Training Needs Assessment: A Case Study. Pub. Pers. Manage.,
29(2): 249–268.
Mathews BP (2001). Quality Training: Needs and Evaluation-Finding
from a European Survey Total. Qual. Manage., 12 (4):483---490
McGhee W, Thayer P (1961). Training in Business and Industry. New
York, Wiley.
Poon JML, Rozhan (2000) Management Training and Development
Practices of Malaysian Organizations. Malay. Manage. Rev., 35(2):
77-84.
Rossilah J, Hishamuddin Md.Som (2007). Training Needs Analysis:
Practices of Top Companies in Malaysia. Int. Rev. Bus. Res., 3(3):
162-175.
Saari LM, Johnson TR, McLaughlin SD, M.Zimmerle D (1988). A Survey
of Management Training and Education Practices in U.S. Companies.
Pers. Psychol., 41: 731-743.
Salas E, Cannon-Bowers JA (2001). The Science of Training: A Decade
of Progress. Annu. Rev. Psychol., 52: 471-499.
Walter, Moshe M (2000). A Comparative Perspective on Executive
Development: Trends in Eleven European Countries. Pub.. Adm.,
78(1): 135-153.
Wilson JP (1999). Human resource development. Learning and training
for individuals and organizations. London, Kogan Page.

African Journal of Business Management Vol. 5(28), pp. 11352-11357, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.183
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
The impact of personality type on Chinese part-time
MBA students’ performance
Adahi Moulaye M’hamed Taher and Jin Chen*
College of Management and College of Public Administration, Zhejiang University, Zhejiang, Hangzhou, 310027,
People’s Republic of China.
Accepted 29 August, 2011
The present study examines the relationship between personality types of part-time Master of Business
Administration (MBA) students and their performance. A dataset was collected from the study sample,
using questionnaires. From the study results, it can be deduced that the degree of part-time MBA
students’ performance is significantly relevant to their personality types, based on the five-factor model
of agreeableness, conscientiousness, extraversion, neuroticism and openness to experience. The
major implication of the study is that it would provide business education stakeholders with a modest
model to predict MBA students’ performance from the perspective of personality type.
Key words: China, MBA, personality, performance.
INTRODUCTION
To deal with the new demands and challenges of
knowledge economy, organizations depend heavily on
their employees as a competitive resource. Hence training
and development become indispensable for building
competitive human resources (Rosow and Zager, 1988).
Management education, as one of the management
development strategies, provides trainees with a broad
range of management knowledge and skills in formal
classroom situations in degree-granting institutions (Keys
and Wolfe, 1988). Among the formal management
training programs, Master of Business Administration
(MBA) course is probably the most popular for managers
who want to upgrade their managerial skills (Cheng,
2000; Parvin et al., 2011).
Since long it has been evident that the effectiveness
and efficiency of management programs is dependent not
only on pedagogy, curriculum, and faculty expertise, but
the trainees’ attributes is also an important factor
determining the quality of the program (Mintzberg, 1973;
Herbert, 1980). Accordingly, to guarantee high quality
MBA programs, business schools worldwide use diverse
selection criteria to select high-quality students for their
MBA programs. Different evaluating methods have been
*Corresponding author. E-mail: chenjin@zju.edu.cn.
developed that could accurately predict the potential
performance of an applicant in the MBA programs.
Generally, the primary criteria of selection to ensure the
success of MBA program are as follows:
1. Undergraduate grade point average (GPA) (Fish and
Wilson, 2007; Clayton and Cate, 2004),
2. Graduate Management Admission Test (GMAT) score
(Fish and Wilson, 2009),
3. Number of years of work experience (Adams and
Hancock, 2000; Dreher and Ryan, 2000).
However, while selecting, some other individual attributes,
such as personality factors, which are also thought
to be good predictors of MBA students’ performance are
not taken into account, (Herbert, 1980).
Due to a fierce competition in today’s business environment,
MBA students have to learn not only the knowledge
in extensive fields such as accounting, marketing,
finance, economics, management and information systems,
but also the skills of teamwork, communication and
leadership are part of their immense training. Moreover,
they are supposed to acquire the skills for dealing with
international business, multiple cultures, merger and
acquisition and so on. Hence, it is a real challenge to
complete the MBA education in a more profound way
(Alam, 2009a; b). It becomes, even more difficult and

challenging task for a part-time MBA student who is
compelled to work full time, as he has a family to feed,
and attends school at night or on weekends to earn the
degree. For such a case, it becomes a prerequisite that
the skills must be learned in less time and with greater
efficiency (Randolph, 2008). Consequently, the part-time
MBA students’ performance may be seen as, particularly,
relevant to their individual type of personality harnessed
by a strong desire to obtain the MBA degree as a sine
qua non for joining managerial ranks from a technical job
function.
The earlier analysis provides a strong impetus for an
examination of the relationship between personality traits
of part-time MBA students and their performance in the
program. Therefore, we study the personality types of
part-time MBA students of a professional accreditation
business program in careers provided by Zhejiang
University and their influence on the students’ performance
which is represented by their reported grades
on the national MBA Entrance Exam (MBAEE).
LITERATURE REVIEW AND HYPOTHESES
FORMULATION
Literature review of personality traits
Although widely used in literature, the concept of
personality has not been definite, until recently. Among
various definitions of personality, the one presented by
Phares (1988) seems more relevant to this study, which
states as “personality is the pattern of characteristic
thoughts, feelings, and behaviors that distinguishes one
person from another and that persists over time and
situations” (cited in O’Neil, 2007). Nevertheless, the five
factors of personality have been accepted and identified
extensively in the academic community. Since their
findings, they have played an important role in
psychology and management studies (Zhang, 2003;
Taher et al., 2011). The five factors which are mostly
referred to as the “big five” include: agreeableness,
conscientiousness, extraversion, neuroticism and
openness to experience. The concepts of personality
traits, the interactions between themselves and their
impact on performance have become widely research
hotspots over the past few decades (Whittingham, 2006).
Theoretically, personality may be viewed as a system
(Mayer, 1998; McCrae and Costa, 1996). It is generally
considered that personality might have three components,
including the notion of individual traits; the notion
of relative consistency across time; and the notion of
relative consistency across situations (O’Neil, 2007).
Consequently, an individual’s experiences are the
ongoing interactions, which the individual has to come
across with his/her environment. The environment
determines some aspects of those experiences, that is,
location, time and events. Other aspects of an individual’s
Taher and Jin 11353
experiences are influenced by his/her personality or
disposition, such as seeking new experiences or
restricting novelty (Sloan, 2009). In the five-factor theory
(FFT), personality is seen as a system in which an
individual’s characteristic adaptations are developed
through dynamic processes that mediate their personality
traits and experiences (McCrae and Costa, 1999). One of
these dynamic processes is learning (Sloan, 2009).
Hence, an individual’s personality traits are good
predictors of future training and learning performance
(Wiggins et al., 1969), as personality traits show longitudinal
consistency across the life span (Tomas et al.,
2007). Numerous researches have supported the use of
the FFT in personality testing (McCrae, 2001; Mccrae and
Costa,1987, 1985), as a consequence, the development
of the five personality factors has provided researchers
with a reliable psychometric instrument to assess the
predictive validity of personality traits in many domains,
including educational settings (Tomas et al., 2007).
Personality traits as predictors of MBA students’
performance: Hypotheses
Various researchers have described people on the basis
of the five basic dimensions of personalities, as
mentioned in FFT (DeNeve and Cooper, 1998; Hogan et
al., 1994; O’Neil, 2007; Piedmont, 1998).
The first factor, agreeableness that refers to the quality
of interpersonal relationships, is defined as a measure of
an individual’s sympathy, cooperation and warmth. It
includes the facets of trust, straightforwardness, altruism,
compliance, modesty and tender mindedness. The
second factor which is extraversion focuses on both the
quality and the intensity of relationships. Extravert people
are considered to be energetic and tend to have the
company of others. Extraversion includes the facets of
warmth, gregariousness, assertiveness, activity, excitement
seeking and positive emotions. The third factor,
neuroticism which is inversely related to emotional
stability, refers to a lack of adjustment describing people
who are impulsive, irresponsible and not dependable. It
includes the facets of anxiety, hostility, depression, self
consciousness, impulsiveness, and vulnerability. The
fourth factor, conscientiousness which refers to task
behavior and impulse control, is attributed to individuals
who work hard, persevere and are organized. It includes
the facets of competence, order, dutifulness, achievement,
self discipline and deliberation. Finally, the fifth
factor is openness to experience, which indicates an
individual’s ability to be imaginative, broad minded and
curious. It includes the facets of fantasy, aesthetics,
feelings, actions, ideas and values.
Based on the above-mentioned theory, a significant link
between personality traits and performance of part-time
MBA students was hypothesized. It seems logical to
assume that all the five factors of personality traits are

11354 Afr. J. Bus. Manage.
Agreeableness
Extraversion
Neuroticism
Conscientiousness
Openness to experience
Type of
Personality
Figure 1. Conceptual model of the study.
correlated with the MBA students’ performance. Nevertheless,
it is vitally important to test these relationships
among MBA students. Consequently, the following
hypotheses were tested in this study:
H1: Agreeableness is significantly and positively
correlated with performance of part-time MBA students.
H2: Extraversion is significantly and positively correlated
with performance of part-time MBA students.
H3: Neuroticism is significantly and negatively correlated
with performance of part-time MBA students.
H4: Conscientiousness is significantly and positively
correlated with performance of part-time MBA students.
H5: Openness to experience is significantly and positively
correlated with performance of part-time MBA students.
Conceptual framework and proposed model
Considering the previous literature review and earliermentioned
analysis, the personality type is supposed to
have a great impact on the performance of part-time MBA
students. We considered the personality type from the
five dimensions (agreeableness, extraversion, neuroticism,
conscientiousness and openness to experience),
and the performance of students is represented by their
grades in the national MBA Entrance Exam (MBAEE).
Therefore, the main objective of the present study was to
seek the relationship between personality traits of parttime
MBA students and their performance. Hence, the
conceptual model in Figure 1 is suggested for depicting
the relationship between the research variables.
METHODOLOGY
Sampling and data collection
170 questionnaires, with each one encompassing two sections of
items, were administered to the first grade of part-time MBA
students of Zhejiang University during their class time session of 30
Performance of
part-time MBA
Students
min. After omission of questionnaires object of responses with
missing values, final number of valid questionnaires was 157 with
the response rate of 92.4%. The profile of the participants is
presented in Table1.
Measures
The primary data used in the study were directly obtained from the
earlier-mentioned questionnaire which had been splitted into two
sections. The first section consisted of 20 items; four items for each
of the five personality factor variables (agreeableness, extraversion,
neuroticism, conscientiousness and openness to experience),
developed based on a review of the short Five-Factor Personality
Inventory of the International Personality Item Pool (IPIP)
(Buchanan et al., 2005). The second section was about the
respondents’ personal information such as age, gender, work
experience and their performance indicator measured by their
reported grades in the national MBAEE. After translated into
Chinese, the questionnaire was then pre-tested for reliability and
validity. The data was obtained based on a five-point likert scale,
ranging from 1 as strongly disagree to 5 as strongly agree.
Reliabilities of the scales on alpha coefficients were 0.76 for
conscientiousness, 0.72 for openness to experience, 0.70 for
extraversion, 0.72 for agreeableness and 0.88 for neuroticism.
RESULTS AND DISCUSSION
In accordance with the purpose of the present study, the
impact of personality type on Chinese part-time MBA
students’ performance was examined through the degree
of correlation between the independent variables, namely
agreeableness (Agree), conscientiousness (Consc),
extraversion (Extra), neuroticism (Neurot) and openness
to experience (Openn) and the dependent variable, MBA
students’ performance as represented by the national
MBA Entrance Exam (MBAEE).
Pearson correlation was used to analyze the
relationship between variables of this study, as shown in
Table 2. Pearson correlations between target variables of
the study indicate a significant and positive correlation
between the three factors of Agree (0. 201**, p < 0.01),
Consc (0.169*, p < 0.05) and Openn (0.111, p < 0.05),

Table 1. Profile of the respondents.
Description Number (%)
Sample
Participants 170
Respondents 157
Gender
Male 105
Female 52
Age (Years)
27–30 75
31–35 67
36–40 11
>50 4
Marital status
Married 91
Un-married 66
Work experience (Years)
4–5 68
6–10 43
11–15 26
>15 20
Employment status
Full time worker 116
Part time worker 33
On leave 8
Table 2. Pearson correlations between variables.
Variable 1 2 3 4 5 6
MBAEE 1
Agreeableness 0. 201 ** 1
Extraversion - 0.023 0.740 ** 1
Neuroticism - 0.017 -0.033 -0.015 1
Conscientiousness 0.169 * 0.597 ** 0.624 ** -0.028 1
Openness to experience 0.111 0.695 ** 0.742 ** -0.003 0.570 ** 1
**p < 0.01; *p < 0.05; N = 157.
and MBA students’ performance. Meanwhile, the result
shows a negative correlation with the factors of Extra (-
0.023, p < 0.05) and Neurot (- 0.017, p < 0.05).
Then regression analysis was used to examine the
degree of relationship between independent and
dependent variables of the study and to measure the
impact of an independent variable on the dependent
Taher and Jin 11355
variable, as shown in Table 3.
The result of regression analysis reflects a good
prediction of MBA students’ performance by the
personality traits of Agree (t = 3.190), Consc (t = 1.925)
and Openn (t = 0.928). On the contrary, personality traits
of Extra (t = -3.913) and Neurot (t = -0.087) are proved to
predict a negative impact upon MBA students’

11356 Afr. J. Bus. Manage.
Table 3. Regression analyses.
Variable Beta t-value p-value
Constant 215.909 12.188 0.000
Agreeableness 2.581 3.190 0.002
Extraversion -3.633 -3.913 0.000
Neuroticism -0.098 -0.087 0.931
Conscientiousness 1.536 1.925 0.056
Openness to experience 0.854 0.928 0.355
N = 157, R2 = 0.135, adjusted R2= 0.107, F = 4.731, p < 0.000. Dependent variable= MBAEE.
performance.
The personality attributes of the present study sample
are likely dominated by the three factors of
agreeableness, conscientiousness and openness to
experience that correlate positively with MBA students’
performance. Therefore H1, H4 and H5 are supported. H3
is also supported, as the factor of neuroticism was found
negatively correlated with MBA students’ performance.
However, H2 is not supported because the factor of
extraversion indicated a negative correlation with MBA
students’ performance.
Conclusion
In summary, the present research supports, to some
extent, that the individual differences in MBA students’
performance can be more accurately predicted by the Big
Five personality factors along with individual attributes of
the trainees, such as qualifications and work experience.
Thus, an accurate prediction of MBA students’
performance that takes the personality traits of students
into consideration may have important implications for
MBA education in China.
However, this study has undergone some limitations
inevitably as follows:
1. Data of the sample were collected through self-ratings.
When participants rate themselves, there is always
possibility that they intentionally or unintentionally bias
their ratings.
2. Because the original survey materials were in English,
the translated questionnaires in Chinese may not have
conveyed the meaning intended very well. Although the
translations were done by native Chinese speakers who
were also very good at English, it is possible that the
construct meanings were not equivalent.
For furthering investigations about the impact of
personality traits upon MBA students’ performance, we
suggest to extend the research to the full-time MBA
program participants and to include more indicators of
students’ performance, such as the major course grades
during the terms of their academic year and the success
rate of the students to get an MBA degree.
REFERENCES
Adams AJ, Hancock T (2000). Work experience as a predictor of MBA
performance. Coll. Stud. J., 34 (2): 211-216.
Alam GM (2009a). The role of science and technology education at
network age population for sustainable development of Bangladesh
through human resource advancement. Sci. Res. Essays, 4(11):
1260-1270.
Alam GM (2009b). Can governance and regulatory control ensure
private higher education as business or public goods in Bangladesh?
Afr. J. Bus. Manage., 3(12): 890-906.
Cheng EWL (2000). Test of the MBA knowledge and skill transfer. Int. J.
Hum. Resour. Manage., 11(4): 837-852.
Clayton GE, Cate T (2004). Predicting MBA no-shows and graduation
success with discriminate analysis. Int. Adv. Eco. Res., 10(3): 235-
243.
DeNeve KM, Cooper H (1998). The happy personality: A meta-analysis
of 137 personality traits and subjective well-being. Psych. Bullet.,
124(2): 197-229.
Dreher GE, Ryan KC (2000). Prior work experience and academic
performance among first-year MBA students. Res. High. Educ., 41(4):
505-525.
Fish L, Wilson S (2007). Predicting performance of one-year MBA
students. Coll. Stud. J., 41(3): 507-514.
Fish L, Wilson S (2009). Predicting performance of MBA students:
Comparing the part-time MBA program and the one-year program.
Coll. Stud. J., 43(1): 145-160.
Herbert TT (1980). An exploratory investigation into the nature of the
part-time MBA Student. Hum. Rel., 33(5): 279-95.
Hogan R, Curphy GJ, Hogan J (1994). What we know about leadership:
Effectiveness and personality. Amer. Psychol., 49(6): 493-504.
Keys B, Wolfe J (1988). Management education and development:
Current issues and emerging trends. J. Manage., 14(2): 205-229.
Mayer JD (1998). A systems framework for the field of personality.
Psychol. Inq., 9(2): 118-144.
McCrae RR (2001). 5 years of progress: A reply to block. J. Resear.
Person. 35(1): 108-113.
McCrae RR, Costa PT Jr. (1999). A five-factor theory of personality. In
Pervin LA, John OP, (Eds.). Handbook of personality: Theory and
research (2nd ed., pp. 139-153). New York: Guilford Press.
McCrae RR, Costa PT Jr. (1996). Toward a new generation of
personality theories: Theoretical contexts for the five-factor model. In
JS Wiggins (Ed.), The five-factor model of personality: Theoretical
perspectives (pp. 51-87). New York: Guilford Press.
Mccrae RR, Costa PT (1987). Validation of the five-factor model of
personality across instruments and observers. J. Person. Soc.
Psychol., 52(1): 81-90.
Mccrae RR, Costa PT (1985). Updating Norman’s “adequate taxonomy”:
Intelligence and personality dimensions in natural. language and in
questionnaires. J. Person. Soc. Psychol., 49(3): 710-721.
Mintzberg H (1973). The nature of managerial work. New York: Harper
and Row.
O'Neil DP (2007). Predicting leader effectiveness: Personality traits and
character strengths. Ph.D. thesis, Duke University, Durham NC, USA.
Phares, EJ (1988). Introduction to personality (2 nd ed.). Glenview, IL:

Scott, Foresman and Co.
Parvin M, Salam GM, Alam GM (2011). The role of ICT in business
ethics courses: Evident from Bangladesh. Afr. J. Bus. Manage., 5(11):
4164-4175.
Piedmont RL (1998). The revised NEO personality inventory clinical and
research applications. New York: Plenum Press.
Randolph AW (2008). Educating part-time MBAs for the global business
environment. J. Coll. Teach. Learn., 5(8): 11-18.
Rosow JM, Zager R (1988). Training: The competitive edge. San
Francisco: Jossey-Bass.
Sloan K (2009). The role of personality in a manager’s learning
effectiveness. Eur. J. Soc. Sci., 12(1): 31-42.
Taher AMMH, Chen J, Yao W, (2011). Key predictors of creative MBA
students' performance: Personality type and learning approaches, J.
Technol.Manage.China, 6(1): 43-68.
Taher and Jin 11357
Tomas CP, Adrian F, Martin L (2007). Personality and approaches to
learning predict preference for different teaching methods. Learn.
Indiv. Differ., 17(3): 241-50.
Whittingham KL (2006). Impact of personality on academic performance
of MBA students: Qualitative versus quantitative courses. Decis.
Scien. Journ. Innov. Educ., 4(2): 175-190.
Wiggins N, Blackburn M, Hackman J (1969). The prediction of first-year
graduate success in Psychology: Peer ratings. J. Educ. Res.,63(2):
81-51.
Zhang L. (2003). Does the big five predict learning approaches? Pers.
Individ. Dif., 34(8): 1431-1446.

African Journal of Business Management Vol.5 (28), pp. 11386-11398, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.225
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Sustainable technology management indicators:
Objectives matrix approach
Maja Todorovic, Maja Levi Jaksic, and Sanja Marinkovic*
Department of Technology, Innovation and Development Management, Faculty of Organizational Sciences,
Belgrade, Serbia.
Accepted 24 August, 2011
In this paper, the concept of sustainable development is viewed in the perspective of the relations,
influences and interactions in the spheres of technology management and natural resource
development. The managerial methods, techniques and tools strongly rely on the measurement
capacities and on the support of sustainability performance indicators. This paper, hence, focuses on
the identification of the set of sustainability indicators at the macro level of national economy. They
have been identified according to their social, economic, industrial and ecological dimensions, the
priorities set by their contribution to technological development. The elaborated set of sustainability
indicators are at the base of the Objectives matrix model (OMM), additionally equipped with the borders
and goals determined by ecological rationality and technological policy of sustainable development.
The model is empirically tested with concrete data on air quality in Serbia, while the benchmark
indicator values had been drawn from Denmark. The results obtained by implementing the OMM in
Serbia represent a base for valuable conclusions. The cause-consequential interaction of technological
development and natural resources shows the importance of an integral, holistic approach introducing
a new concept of sustainable technology management.
Key words: Technology management, sustainable development, indicators, objectives matrix model.
INTRODUCTION
The concrete dimensions of sustainable technology
management and development represent the basis for
sustainable development of the economy and society.
Technology management deals with the crucial decisions
concerning: a) the output to be offered in terms of
products and/or services, b) inputs and resources
necessary to be engaged in manufacturing products and
delivering services, c) location of the facilities, and d)
developing processes and operations in support of
business goals. Crucial responsibility and starting point
for sustainable development lies within technology
*Corresponding author. E-mail: marinkovic.sanja@fon.rs. Tel:
+381 11 3950 879, +381 69 8893 176. Fax: +381 11 2461 221.
Abbreviations: OMM, Objectives matrix model; SD,
sustainable development; STMD, sustainable technology
management and development.
management, with emphasis on the importance of
actions and guidance provided in the external
environment - legal, economic, political, social and
technological, in achieving sustainability goals. Managing
technological dynamics lies at the core of sustainable
competitiveness of business operations.
The United Nations Division for Political Coordination
and Sustainable Development (DPCSD) in its approach
to sustainability, observes society as a dynamic system,
attributively characterized and determined by four
dimensions: sociological, economic, institutional and
ecological. The focus of such an approach is to reflect
economic, sociological and institutional dimensions of
sustainability at equal plane with ecological problems. It
enables the determination of relationships - possible
synergies and crucial conflicts between different aspects
of sustainable society.
Economies and societies in transition focus on a set of
specific aspects of sustainability critical to their efficient

and effective transformation. The complexity of the transitional
processes is defined by radical changes occurring
in the domain of privatization, intensive technological
change, restructuring, business strategy and competitiveness,
developing markets and infrastructure with the
overall concern for the well being of all the actors,
environmental issues, satisfaction of all stakeholders -
employees, customers, society, etc. Creating the
sustainable and feasible development strategy takes into
account the diversified needs and goals and strongly
relies on the effort to evaluate the internal strengths and
resources from the perspective of their competitive
capacity. Traditionally, valuable, rare, non-substitutable
and non-imitable resources represent key factors that
create and maintain an advantageous position with
respect to competitors. (Barney, 2004) The sustainable
development perspective adds the sustainability as the
fifth significant resource attribute.
The success of reforms in countries in transition greatly
depends on the quality of management, its structure,
definition of organizational roles and processes and tasks
assigned to social actors at different levels
(Spangenberg, 1998).
The core objectives of sustainable society, as defined
so far in the political and scientific discourse, include
greater social cohesion, more and better jobs (social
dimension), economic competitiveness and stability
(economic dimension), declining resource use and
economic development, safeguarding biodiversity and
ecosystem health (environmental dimension); and an
open, participatory approach based on equality and nondiscrimination,
justice and solidarity (institutional
dimension). These specifics are, to a large extent,
already a part of the sustainable society models. Building
and maintaining such a system requires that policies and
strategies are developed based upon these principles
and resulting in a mixed economic system justified by
“value mix”: based on a market economy with its inherent
drive towards efficient and productive resource allocation,
but correcting the distributional (social), environmental
and institutional blindness of the market by means of
public policies. How the state, the market and the civil
society interact, is crucial for every model, be it positive
or negative (Spangenberg, 2002).
SUSTAINABLE TECHNOLOGY MANAGEMENT AND
DEVELOPMENT PRINCIPLES
Sustainable competitiveness means the achievement of a
set of different goals – economic and non-economic,
meaning that it is a concept based on quantitative and
qualitative performance indicators, namely, the integration
of traditional performance goals measured by
traditional economic indicators (for example, profitability,
GDP) and a set of new non-economic performance
criteria that emphasize the satisfaction of needs of the
customers, employees and all other stakeholders. The
Todorovic et al. 11387
performance balanced scorecard approach is based on
the efforts to build sustainable competitiveness taking
into account multiple factors (Meyer, 2002). This new
approach points to a set of new performance indicators
and goals found in qualitative attributes such as culture,
fulfillment, safety, health, natural resources preserving
and development, mutual understanding, creativity,
enhancing mutual trust, etc.Based on complexity,
dependency and contingency theories, much effort is
made to identify and select priorities by relevance criteria
attributed to factors influencing the concrete technological
strategies (Sanchez, 1996). The ultimate results of these
efforts are: strengths better appreciated and further
developed, while the weaknesses reduced and eliminated.
At the same time, the orientation is at building
capacities to grab opportunities and diminish threats in
the environment. Sustainable management is a concept
of strategic management oriented at the achievement of
sustainable competitiveness. Sustainable competitiveness
is based on appreciation of strategic goals
emphasizing competitive co-evolution, networking and
partnering be (Narayanan V, 2001), long-term perspective,
synergies, satisfaction, and high quality of life
standards. The emphasis on sustainable technology
management is related to the role of technology and its
position at the core of all the business operations, and
with focus on primary operations delivering value in the
form of products and services to the customers, but also
in satisfying the goals of the society, economy, local
community, while simultaneously developing profitable
business results. Table 1 shows the results of the effort to
relate the proclaimed principles of sustainable
development of society and sustainable technology
management. (Rainey, 2006).
The underlying principles of sustainable development
basically integrate economic, social, industrial and environmental
issues in decision and policy making, at all levels of
the society. The identification of sustainability indicators
means that actual criteria are defined and imposed by
sustainable development in the four aforementioned
societal dimensions which are setting the limits to the
existing, traditional approach to technological management
and development. Aims defined by sustainable
development, even though setting the boundaries and
limiting existing technological development practices, at
the same time are adding new perspectives and insights
in directing it towards search for new technological
solutions and innovations based on new principles, for
example, imitating natural cycles of material exchange,
eco-substitution, dematerialization, recycling and over-all
rationalization.
Sustainability indicators
The concept of sustainable development, as presented in
the documents and results achieved at the sumit in Rio
De Janeiro in 1992, has been accepted as worldwide

11388 Afr. J. Bus. Manage.
Table 1. Transforming the principles of sustainable development (SD) into the sustainable technology management and development
(STMD) framework.
SD STMD
Coexistence
(the right to)
Recognize
interdependence
Respect
relationships
Accept
responsibility
Create long-term
value
Strategic enterprise thinking, “cradle to grave“ approach, balanced objectives; strategies leading to followers
approaching leaders; reducing technological gap; life-cycle thinking; value- chain approach; competency
approach.
Technological cooperation – vertical and horizontal relations; in-sourcing R&D; R&D consortia; technological
fusion; competitive co-evolution.
Value networks – business environment and natural world; Strategic technological alliances and networking-
synergetic effects
Social responsibility – Integrity, Honesty, Enterprise Management; Leading technological change with
environmentally sound options, ecologically conscious innovation - ECI, finding the right measure of
technological change in relation to political, economic, social, technological and ecological factors - PESTE.
Value creation Create operations based on technologies that offer products and services satisfying the needs
of all the stakeholders.
Eliminate wastes Continuous innovativeness and creativity; Life-cycle assessment–LCA; sustainable technological products and
processes.
Rely on balanced
solutions
Openness, transparency, balanced scorecard thinking; Strategic fit as balancing of strategic and operational
technological goals
Design limitations Risk mitigation; LCA; Risk assessment; Managing technological risks and threats at the same time accepting
the chances and challenges; technological forecasting
Continuous
improvement
Technological forecasting as the base for
short-term/long-term plans and technological strategies
development strategy. It reaffirms the human legacy on
healthy and productive life in accordance with nature and
the integral approach giving equal importance to
economic and social dimensions of development and
environmental protection, as the basis of the environmental
protection and all other policies at local, regional
and global levels. Agenda 21, which had been adopted at
that time as a strategic document, in the section related
to the realization of sustainability objectives, recommends
the development of specific indicators of sustainability,
which would make the concept of sustainable development
operational. Their main purpose is to guide, direct
and manage the process of decision-making (United
Nations Agenda 21, 1992).
This integration implies the involvement of virtually all
traditional sectors of economic and governmental
engagement and activities, such as: economic planning,
agriculture, health, energy, water, natural resources,
industry, education and the environment, involving the
principal ministries of the governments. The assumption of
integration is reflected in the dimensions of sustainable
development, which contain social, economic, environmental
and institutional indicators, and should be included
in mechanisms for institutional integration (Figure 1). The
developed set of indicators provides support to decisionmakers
for redirecting governmental actions towards
sustainability. The indicators need to be understandable
and also to integrate different dimensions of sustainability
and technological challenge. Above all, they need to be, to
the highest extent, derived from existing data, in order to
provide easy data manipulation, fast and effective
calculations.
The clearly determined set of indicators is the base for
long-term monitoring of progress towards sustainable
development at different levels - national, regional, etc.
The creation of the set of indicators is under the strong
influence of cultural characteristics of the region and the
level of its industrial and technological development. In
the process of applying the principals of sustainable
development, each indicator can be updated or even
replaced according to the changes of the infrastructure.
Interpretations of indicators are different, and it is
perhaps more useful to identify the uses and desirable
properties of indicators. The major functions of indicators,
as emphasized by Tunstall (1992) and Gallopin (1997),
are as follows:

Todorovic et al. 11389
Figure 1. Sector and inter-linkage indicators (Spangeneberg, 1998).
i. to assess conditions and changes;
ii. to compare across space and situations;
iii. to assess conditions and trends in relation to goals
and targets;
iv. to provide early warning information, and
v. to anticipate future conditions and trends.
The set of indicators, organized individually or in groups,
aims at becoming a vital guidance in the process of
collecting data. They are an important instrument for
decision-makers, as they summarize crucial information
from different sectors/sources. Also, they recommend
logical grouping of information, by promoting their logical
interpretation and integrations. In the process of
collecting information, they help in discovering the needs
for different type of data. In such manner, it is much
easier to facilitate the reporting process, by structuring
collected information in all aspects of sustainable
development.
In order to define the set of indicators for the assessment
of societal and technological development, relevant
sectors or elements of the societal system are identified.
They include the relevant elements that constitute society
as well as the subsystems on which human society
depends. Useful distinction of elements is presented thus
i. Individual development (civil liberties and human rights,
equality, individual autonomy and self-determination,
health, the right to work, social integration and participation,
gender and class-specific role, material standard
of living, qualification, specialization, adult education,
family and life planning horizon, leisure and recreation,
art, etc.);
ii. Society (population development, ethnic composition,
income distribution and class structure, social groups and
organizations, social security, medical care, old age
provisions);
iii. Government (government and administration, public
finances and taxes, political participation and democracy,
conflict resolution (national, international), human rights
policy, population and immigration policy, legal system,
crime control, international assistance policy, technology

11390 Afr. J. Bus. Manage.
Individual
development
Economy
Government
Environment and
natural resources
Society
Infrastructure
Figure 2. Elements and subsystems of the human society system (Bossel, 1999).
policy); Infrastructure (settlements and cities, transporttation
and distribution, supply system (energy, water,
food, goods, services), waste disposal, health services,
communication and media, facilities for education and
training, science, research and development);
iv. Economy (production and consumption, money, commerce
and trade, labor and employment, income, market,
interregional trade);
v. Resources and environment (natural environment,
atmosphere and hydrosphere, natural resources, ecosystem,
species, depletion of nonrenewable resources,
regeneration of renewable resources, waste absorption,
material recycling, pollution, degradation, carrying
capacity (Bossel, 1999).
In order that the total system - the human system
embedded in the natural system - is viable, each of these
essential elements must be viable as well, that is, the
viability of the total system depends on the proper
functioning of the subsystems.
Although other classifications are possible, the
presented identification of subsystems is not arbitrary.
These subsystems are all essential parts of the “anthropologic
sphere”. The major relationships between the six
elements are shown in Figure 2. Decomposing the
system enables better insight into the relationships
present in the system. Each constituent part can be
observed from the aspect of its importance and potential
contribution to the sustainability of the whole system
(Bossel, 1999).
The number of indicators rises with the number of the
identified subsystems that are included. To keep the
number of the indicators at a reasonable level and by
taking into consideration the basic organization of the
society, the mentioned six elements are organized into
three subsystems: natural subsystem, support subsystem
and human subsystem.
The establishment and development of each of the
mentioned subsystems is specifically reflected through
technological development and initial resource usage.
That is the reason why it is necessary, when the representative
indicators are identified, to develop a
conceptual understanding of the whole system. Complete
and total understanding of the whole system is in the
sphere of hypothetical and practically impossible; but one
can search for missing information that will give the
answers concerning essential processes and relationships
between the components of the subsystems, and
the subsystems themselves. In order that each
subsystem is adequately represented and that all the
aspects of its contribution to the sustainability of the
societal system are covered, there is a need for a specific
number of indicators to be developed.
Nevertheless, in the effort to identify specific, sector
indicators, the components and subsystems that are the
most important in that domain are to be determined in the
first place (Bossel, 1999). Determining indicators based
on the relations represented by the model (Figure 2)
means that they are adapted to provide crucial guidance
for decision-making in a variety of ways: they can
translate knowledge in the natural, technical and social
sciences domain into manageable units of information

that can facilitate the decision-making process; they can
help to measure and calibrate progress towards
sustainable development goals; they can provide an early
warning, sounding the alarm in time to prevent economic,
social and environmental damage and limit the existing
management of technological development; they are also
important tools to communicate ideas, thoughts and
values, as “we measure what we value, and value what
we measure” (Shah, 2004).
Identification of sustainability indicators
Following the recommendations of the UN Agenda 21, the
three subsystems are further developed and themes and
sub-themes are derived. Later, in the stage of further
distinction, the identification of indicators follows, as
shown in Table 2. As presented, these indicators can
provide actual guidance in forming the national list of
indicators of sustainable development and cover issues
generally common to all regions and countries of the
world (United Nations Department of Economic and
Social Affairs UNDES, 1999).
In the case of examining the management of some
natural resource, the determined classification provides
the base for the basic set of indicators to be identified,
that will cover all crucial aspects (themes and subthemes).
These crucial areas will contain information
about driving forces and mechanisms that lead to
increased exploitation of natural resources. It should be
emphasized at this point that the defining of sustainability
indicators for some sectors has been limited by the
amount of available data, where Statistical Yearbooks,
World Bank Reports, Chamber of Commerce documents
and World Health Organization surveys have been the
main data sources. The proposed set of indicators
directly point to the critical dimensions of the social
system that have to be addressed and taken into
consideration, while creating an instrument for the
support of technology management and development in
view of preserving natural resources.
Implementation of the objectives matrix model in
measuring sustainability of technological
development
In order to estimate the level of development, especially
model (OMM) is developed. Originally, the matrix
approach was developed with the aim of reviewing
productivity of technology and its effectiveness and efficiency
impacts on the prosperity of the company (Riggs,
1984). The characteristics of the original objectives matrix
have been adapted to the needs of sustainable
technology management objectives, and the new model
built in this respect (OMM), has been developed and
tested empirically in Serbia in the domain of air quality
management. The factors that form the OMM are actually
existing sustainability indicators that have already
Todorovic et al. 11391
been selected, and for this purpose, chosen according to
their highest contribution to the level of air quality. This
especially refers to the indicators chosen from the natural
subsystem. Altogether, there have been 24 indicators
selected. Since the atmosphere and air quality have been
at the focus of the natural subsystem, some themes
represented in Table 2 have not been taken into consideration
because they do not carry adequate information
concerning air quality management (for example,
housing, security, population, land, water, oceans, coasts
and seas).Every indicator has been given weight
coefficient in the matrix and the weights represent their
estimated importance. In the presented case, since there
are a large number of indicators, weighting coefficients
are in the range from 2 to 6 (Table 3). The amount of the
summed weighting coefficients has to be 100. Criteria for
assigning certain weights to indicators are determined by
the relevance of information that indicators provide,
concerning in this case, the air quality management. In
that sense, the highest importance has been given to the
indicators that directly describe the state of air quality
(like maximum emissions of SO2 and NOx) and their
connections with technological, economical, political and
scientific aspects.
Indicators with weight coefficient 6
The greatest importance (weight coefficient 6) have
indicators that directly describe the air quality, so, there is
no need for giving their detailed description. These are:
maximum emissions of SO2, maximum emissions of NOx,
emissions of kg CO2 on annual level, per GDP and
maximum soot concentration. Priority is given to these
parameters also because of their global importance and
direct linkage to atmospheric problems like global
warming and ozone layer depletion. The rest of the
indicators, that are evaluated with lower importance are
linking above mentioned indicators with institutional,
scientific, technological, economical and industrial
aspects of the society.
Indicators with weight coefficient 5
The 6 parameters in Table 3, according to the importance
in the framework of sustainable development of
technology and natural resources, the objectives matrix
level, have coefficient 5. From the group of the human
podsystem themes, this would be indicators that imply on
the endagered populition due to lower air quality (number
of cancers of respiratory organs per 100 000 population)
and the readiness of the community to invest in the
improvement of environment (rate of budget investments
in the environmental protection).
One of the ways to contribute to the decreasing of mentioned
environmental problems (climate change, great
number of deseas due to low air quality) is by reorienting
the existing development towards sustainable- seraching

11392 Afr. J. Bus. Manage.
Table 2. Societal subsystems and derived indicators from themes and sub-themes.
Theme Subtheme Sustainability indicator
Human subsystem
Equity Poverty Unemployment rate %
Education Education level
Literacy
Adult literacy rate
Health Nutritional status Mortality rate under 5 years old
Mortality/diseases
Mortality rate (whole observed population)/rate of getting ill
from specific diseases per 100 000 Population
Drinking water Population with Access to Safe drinking water
Healthcare delivery Health care expenditures as a % GDP-a
Sanitation Improved sanitation conditions on annual level (%)
Housing Living conditions Floor Area per Person
Security Crime Number of Recorded Crimes per 100,000 Population
Population Population change Population Growth Rate %
Institutional
framework
Institutional
capacity
Support subsystem
Economic
structure
Technological
aspects
National development strategy (role of
government and institutions
Number of researchers per 1 000 000 Population
National legal system/laws Number of companies with adopted 14001 standards
International cooperation Implementation of Ratified Global Agreements
Decision-making participation Number of communities that have adopted local Agenda 21
Information access Number of Internet Subscribers per 1000 Inhabitants
Pollution prevention, disaster preparedness and
response
Economic performance Inflation rate
Financial status Amount of monetary supplies
Trade
Rate of budget investments in the environmental protection -
%
Import as a % GDP
Export as a % GDP
Material production and consumption GDP (const. $ 2000)
Competitiveness/innovativeness Number of registered patents - annual level
Energy use Amount of produced energy from renewable resources in %
Intensity of energy usage (koe per GDP)
Waste Generation and Management Amount of waste that can be recycled (%)
Infrastructure and transport
Total road length in km
Railway capacity, number of passengers per km
Natural subsystem
Atmosphere Climate change Emissions of kg CO2 on annual level, per GDP
Ozone layer depletion
Maximum emissions of SO2
Maximum emissions of NOx
Air quality Maximum soot emissions

Table 2. Contd.
Land
Oceans, seas and
coasts
Fresh water
Biodiversity
Agriculture Use of fertilizers
Forests Forest area as a percent of land area
Urbanization Area of urban formal and informal settlements
Coastal zone Percent of total population living in coastal areas
Fisheries Annual catch by major species
Todorovic et al. 11393
Water quantity Annual withdrawal of ground and surface water as a percent of total
available water
Water quality Percentage of first class water
Ecosystem Number of national parks
Species Number of protected species from total
Table 3. Selected sustainability indicators and assigned importance level/weights.
Sustainability indicator Importance level/weighting coefficient
Maximum emissions of SO2
6
Maximum emissions of NOx
6
Emissions of kg CO2 on annual level, per GDP 6
Maximum soot concentration 6
Number of cancers of respiratory organs per 100 000 population 5
Rate of budget investments in the environmental protection 5
Number of researchers per 1 000 000 population 5
Number of implemented ratified global agreements in area of air quality management 5
Intensity of energy usage (koe per GDP) 5
Amount of produced energy from renewable resources u % 5
Number of companies with adopted 14001 standards 4
Import as a % GDP 4
Export as a % GDP 4
GDP (const. 2000$) 4
Inflation rate 4
Unemployment rate % 4
Amount of waste that can be recycled (%) 4
Number of registered patents on annual level 3
Total road length in km 3
Railway capacity, number of passengers per km 3
Health care expenditures as a % GDP 3
Number of communities that have adopted local Agenda 21 2
Number of internet subscribers per 1000 Inhabitants 2
Adult literacy rate 2
for new solutions. These solutions mostly come as a
result of the scientific and innovative work. The level of
that potentiol in Serbia can be found in values of the
indicator number of researchers per 1 000 000 population.
The main matrix (Table 4) in the OMM has thus
been created. Other direction through which we can
influence the solving of environmental problems is the
tendency of fulfilling international obligations and
regulations – in this case, the implementing ratified global
agreements in area of air quality management. In that
way, we indirectly influence on the domestic legal framework
to get in compliance with international regulatives
and normatives. Indicator: number of implemented ratified
global agreements gives that information and shows

11394 Afr. J. Bus. Manage.
Table 4. The objectives matrix for measurement of technological and natural resources development, based on example of air quality management.
Sustainability indicator
1 2
Scale of grades
3 4 5
Real value of
indicator
Estimated
grade
Weight
f.
Value
Unemployment rate % 22 18 14 10 6 28 1 4 4
Adult Literacy rate % 92 94 96 98 100 98 4 2 8
Number of cancers of respiratory organs per 100 000
population
60 50 40 30 20 39.4 3 5 15
Health care expenditures as a % GDP 6 7 8 9 10 8 3 3 9
Number of researchers per 1 000 000 population 1000 2000 3000 4000 5000 1330 1 5 5
Number of implemented ratified global agreements in area
of air quality management
4 8 12 16 20 9 2 5 10
Number of companies with adopted 14001 standards 50 150 250 350 450 27 1 4 4
Number of communities that have adopted local Agenda 21 30 60 90 120 150 25 1 2 2
Number of Internet Subscribers per 1000 Inhabitants 50 150 250 350 450 200 2 2 4
Rate of budget investments in the environmental protection
(%)
0.2 1 1.8 2.6 3.2 0.3 1 5 5
Inflation rate (%) 23 18 13 7 2 16 2 4 8
Import per GDP (%) 47 45 43 41 39 45 2 4 8
Export per GDP (%) 20 25 35 40 45 24 1 4 4
GDP (const. $ 2000) 1000 1600 2200 2800 3400 1272 1 4 4
Number of registered patents on annual level 500 1200 1900 2600 3300 507 1 3 3
Amount of produced energy from renewable resources in
%
5 10 15 20 25 6.9 1 5 5
Intensity of energy usage (koe per GDP) 0.9 0.8 0.7 0.6 0.5 0.8 2 5 10
Amount of waste that can be recycled (%) 30 40 50 60 70 40 2 4 8
Total road length in km 20 000 30 000 40 000 50 000 60 000 36 500 2 3 6
Railway capacity, number of passengers per km 500 000 2 500 000 3 500 000 4 500 000 5 500 000 1 023 000 1 3 3
Maximum emissions of SO2 180 160 140 120 100 243 1 6 6
Maximum emissions of NOx 150 130 110 90 70 130 2 6 12
Emissions of kg CO2 on annual level, per GDP 4.5 3.5 2.5 1.5 0.5 3.1 2 6 12
Maximum soot concentration 100 85 70 55 40 90 1 6 6
Resulted value 161
the capability of state capacities to follow the
international requirements.
In Serbia, energy sector is the one that, with its
emisions in great deal, contributes to poor air
quality and the part of support subsystem that
greatly impacts the natural subsystem. Monitoring
the intensity of energy usage (koe per GDP) is a
direct indicator of energy (un)efficiency and has
weight coefficient 5. If we look in more detail

Todorovic et al. 11395
Table 5. Real values of indicators with assigned grades.
Sustainability indicator Real value of the indicator
Assigned grade to the indicator in relation to the its
real value in the matrix
Unemployment rate 28 1
Number of researchers per 1 000 000 population 1330 1
Number of companies with adopted 14001 standards 27 1
Number of communities that have adopted local Agenda 21 25 1
% of budget investments in the environmental protection 0.3 1
Export as a % GDP 24 1
GDP (const. 2000$) 1272 1
Number of registered patents on annual level 507 1
Amount of produced energy from renewable resources in % 6.9 1
Railway capacity, number of passengers per km 1 023 000 1
Maximum emissions of SO2 243 1
Maximum soot concentration 90 1
Number of implemented ratified global agreements in area of air quality management 9 2
Number of internet subscribers per 1000 inhabitants 200 2
Inflation rate 16 2
Import as a % GDP 45 2
Intensity of energy usage (koe per GDP) 0.8 2
Amount of waste that can be recycled (%) 40 2
Total road length in km 36 500 2
Maximum emissions of NOx 130 2
Emissions of kg CO2 on annual level, per GDP 3.1 2
Number of cancers of respiratory organs per 100 000 population 39.4 3
Health care expenditures as a % GDP-a 8 3
Adult literacy rate 98 4
(Table 5) at the grades and concrete values of
indicators, few critical areas are exposed, that are
mostly to blame for the decreasing of the air
quality in Serbia. How one of the principles of
sustainable development lowers the usage of the
fossil fuels and how it takes into account
alternative resources of energy, is an indicator of
the amount of produced energy from renewable
resources (u%) shown by Serbia concerning this
matter and it holds a weight coefficient of 5.
Indicators with weight coefficinet 4
Next group of seven indicators carries weight
coefficient 4. These indicators give information
that are not so closely related to the air quality,
but correspond to health, environmental protection,
and economical state that influence that
natural subsystem.
Adopting standards and regulatives, but at
microlevel where a starting point can be a technological
unit or a company is an actual basis of
management system transformations at

11396 Afr. J. Bus. Manage.
macrolevel. Monitoring the number of companies with
adopted 14001 standards can show the rate of the
developed conditions for sustainable development. Since
greater number of companies adopts this management
model, the society is closer to sustainable development
goals.
Economical parameters, like import as %GDP, export
as %GDP and GDP (const. $ 2000), directly describe the
industrial position and states the economical status. In
addition to this indicator, we can also derive the indicator
inflation rate that gives a picture of the states’ macroeconomical
stability.
Tightly linked parameter from the human subsystem
with economical parameters is unemployment rate since
it indicates the level of poverty which describes the population
capabiliteis to contribute to environmental
protection.
Human subsystem in its activities generates great
amounts of waste, that are mostly stored and eliminated
in natural subsystem. In attempts to protect it, in application
are recycling processes and the usage of secondary
raw materials. The amount of waste that can be recycled
(%) is an important indicator of how much the society has
advanced in that field with technology improvements.
Indicators with weight coefficient 3
In this category has been identified 4 parameters. As an
additional indicator of technological aspects in the area of
support subsystem is number of registered patents on
annual level. Competitiveness and innovativeness have
important role in development of clean technologies,
especially the ones that will contribute to lowering of
emissions and imissions. Mentioned parameter indicates
general state potential in area of innovations.
In the group of support subsystem indicators, which
imply on the infrastructural development are total road
length in km and railway capacity, number of passengers
per km. Higher level of infrastructural development influences
on the more efficient and secure transport, gives
the opportunities for savings, and is one of the bridges
that human subsystem “communicates” with naturally.
In this group of indicators also belongs the one that
indicates health care expenditures as a percentage GDP-
a and gives general information on the populations capabilities
to provide itself adequate health care.
Indicators with weight coefficient 2
This category of indicators carry level of importance 2.
Indicators in this group are evaluated like that, bacause
the information they are giving is not directly relevant to
atmospheric change, but rather shows general populations
motivation, access to information and willingness
to participate in decision making. The first indicator in this
group is number of communities that have adopted local
Agenda 21 and relates to application of local ecological
action plans.
How good is access to information, in the era when
information technology dominates, number of internet
subscribers per 1000 inhabitants can reveal that. The last
indicator: adult literacy rate, gives clearer picture of
educational level and individual developemnt.
In the OMM, grades are given from 1 to 5 in relation to
the obtained empirical values of observed indicators for a
certain period. As the highest obtainable values, carrying
the grade 5, used further on for defining the scale of
grades, relates to the data represented for the indicators
in Republic of Denmark, since this is one of the European
countries that is mostly advanced in terms of sustainable
development and innovations, and this data is publicly
available. For data resources, the following internet databases
from: World Bank, Denmark Statistics, Denmark
environmental protection agency, Serbian Statistics
Institute and World Health Organization, have been used.
For some indicators, such as the highest values graded
5, the maximum allowed values regulated by domestic
laws were used, while for the lowest limits graded 1, the
worst values that could be allowed were introduced.
The main matrix (Table 4) in the OMM has thus been
created, where grade 5 refers to the level of an indicator
achieved by a highly developed European country, and
should represent an optimistic goal towards which Serbia
needs to strive for in the future. It is determined and
limited to a certain time period, which depends on the
nature of indicator.
Considering numerical values, grade 3 is allocated to
average real values of the indicator, which means that by
multiplying with 100m (sum of weights), the overall
average value calculated at a certain point of time is 300.
The actual overall calculated value of the matrix based on
the real, empirical data for individual indicators, should
therefore be compared in order that significant
conclusions be drawn. The comparisons can be made in
the following manner:
a) Comparing the real obtained overall value with the
average value of 300 shows that all the values above are
to be considered acceptable and in the range of positive
results, while all the values below the average should be
considered alarming and should call for high priority
actions;
b) Comparisons should be made by periodically testing
the model with real values at different points in time for
monitoring progress in the domain of sustainability;
c) Comparisons are to be made with benchmark
countries, as in the case presented, where Denmark has
set the benchmark for the 500 grade value which is
translated in real value for certain indicators that are
compared and the ones who are most below the aimed
value are considered as critical.
The overall value of the matrix, in the case of Serbia, has
been calculated and amounts to 161. It represents 53.7%

elow of the average value of 300. The results by using
the OMM show the overall evaluated status of Serbia in
the field of sustainable technological development from
the point of view of air quality management. If we look in
more detail (Table 5) at the grades and concrete values
of indicators, few critical areas are exposed, that are
mostly to blame for the decreasing of the air quality in
Serbia.
The grades are very low with average display from 1 to
2. From a total of 24 indicators:
i. 12 indicators hold grade 1;
ii. 9 indicators hold grade 2;
iii. 2 indicatros hold grade 3; and
iv. 1 indicator hold grade 4.
When we look at the current structure of the indicators
according to their grades, it follows that practical reforms
are needed in great number in social and technological
aspects. However, the most critical points that have been
distinguished are in relation to increased poverty, lack of
clear direction of the strategy of scientific and technological
development, poor institutional organization and
weak economic structure. In the frame of technological
aspects are highlighted problems of poor energy
efficiency, which is associated with bad policies that are
implemented in the companies, and insufficient
investments in the infrastructure improvements.
The over-limitations of allowed values of emissions are
found to be the critical points. The problems of bad and
low energy efficiency have been detected as the critical
factors of the low result in air quality management features
in Serbia. This result is connected to the problems
of establishing optimal investment policies and problems
in the sphere of scientific and technological development
strategies. The data used for developing the OMM are for
the period from 2001 to 2003.
It should be emphasized that the selection of derived
indicators in this paper has been limited partly with the
availability of required data. Presentation and analysis of
indicators for air quality management in this way had the
purpose to show how through the knowledge of the
relevant parameters we can adequately manage the
technology development and natural resources. Some
identified indicators open other sectoral policies as well -
not only the area of air quality management. As such,
they carry within the information concerning great number
of industrial processes. They can be a good introduction
to the establishment (of the system) of national sustainable
development indicators in line with the capacities
and needs of Serbia, while respecting the requirements
of international comparability (standardized concepts,
definitions and classifications).
By implementing the model and testing it empirically for
the case of Serbia, we actually quantified and graded the
sustainability of Serbia, for the mentioned period. The
overall value of 161 obtained in the calculations of the
matrix is far below the average value of 300 and shows
Todorovic et al. 11397
that Serbia has to initialize quick responsive measures
and make a high priority action plan supported by great
efforts in numerous social, economic and technological
domains, in order to obtain progress towards
sustainability.
Conclusion
The results obtained in developing OMM for sustainable
technological management and development. The
empirical analysis for the case of Serbia has shown a
potential significant contribution to theory and practice of
managing sustainability at different levels more widely.
This paper points to the potentials of the OMM and its
robustness in terms of processing data related to different
indicators referring to concrete situations and aims of the
analysis.
Practically, it has been shown that OMM is a valuable
tool applicable to different countries, at the level of
national economies and regions in their effort to manage
sustainability. By forming a representative list of
indicators, one can easily monitor processes and provide
necessary measurements in order to progress in relation
to sustainability. Organizing the indicators into OMM
means going a step ahead in the level of processing relevant
sustainability indicators for management purposes.
The analysis has been developed at two levels: first,
the creation of a representative list of indicators for air
quality management, and second, establishing the objectives
matrix model using comparable data of a developed
country against which grades are created and the
concrete position of Serbia. Originally developed for
analyzing the productivity of technology at company level,
the OMM has shown its potentials for implementation at
macro-management levels as well which point to its
flexibility verified in practice by sustainability results of air
quality management obtained in the analyzed case.
REFERENCES
Barney JB (2004). Firm resources and sustained competitive
advantage. J. Manage., 17 (1): 99-120.
Bossel H (1999). A report to the Balaton Group: Indicators for
Sustainable Development: Theory, Method, Applications.
Gallopin G (1997). Indicators and their use: information for decision
making, in Moldan B. and Billharz S. Sustainability Indicators. Report
on the project on Indicators of Sustainable Development. John Wiley
& Sons.
Meyer M (2002). Rethinking Performance Management, Cambridge
University Press.
Narayanan V (2001). Managing Technology and Innovation for
Competitive Advantage. Prentice Hall.
Rainey D (2006). Sustainable Business Development. Cambridge
University Press. Cambridge.
Riggs JL (1984). The Objectives Matrix for Productivity Measurement,
Oper. Manage. Rev., 2 (4): 3-14
Sanchez R et al. ed (1996). Dynamics of Competence Based
Competition. Pergamon Press.
Shah R (2004). CSD Indicators of sustainable development – recent
developments and activities. ASI workshop. Pargue, Czech
Republick.

11398 Afr. J. Bus. Manage.
Spangenberg J, Bonniot O (1998). Sustainability Indicators – A
Compass on the road towards sustainability. Paper No. 81.
Wuppertal Institute, Germany.
Spangenberg J, Mesicek R, Metzner A, Luks F (2002). Sustainability
Indicators for the Knowledge based society. Futura.
Tunstall D (1992). Developing environmental indicators: definitions
framework and issues. Background materials for the world resources
institute workshop on global environmental indicators. Washington
DC.
United Nations Department of Economic and Social Affairs UNDES
(1999). Theme Framework and Indicators of Sustainability.
PriceWaterhouseCoopers for Division for Sustainable Development.
United Nations (1992). Agenda 21: Programme of Action for
Sustainable Development, United Nations Conference on
Environment and Development. Rio de Janeiro, Brazil, Chapters, 3,
4, 24, 25, and 26.

African Journal of Business Management Vol. 5(28), pp.11521-11531, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.1476
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Strategies to improve the level of employee motivation
in the fast food outlets in Cape Town, South Africa
Nnenna E. Ukandu and Wilfred I. Ukpere
1 Department of Business Administration, Faculty of Business, Cape Peninsula University of Technology, Cape Town,
South Africa.
2 University of Johannesburg, Department of Industrial Psychology and People Management, Faculty of Management,
Johannesburg, Republic of South Africa.
Accepted 19 September, 2011
Motivation increases the level of performances of employees and also increases their commitment in
the workplace. This implies that motivating workers is very important. The fast food workers are unique
and have their individual needs, potentials, values and goals. Job satisfaction leads to job motivation.
Therefore, when workers are satisfied, they tend to be motivated to work. The study points out the
strategies that could be used to improve the level of motivation of the fast food workers and also
discusses the causes of low employee motivation within the organization. This will also assist the
management of the fast food industries in improving the performances of their employees. A case
study approach was used for the survey because only the fast food industries were involved.
Information was obtained from both the operational workers, administration, cashiers and the
managers. A total of 200 closed-ended questionnaires and open-ended semi-interview questions were
distributed and 123 employees responded which gave a response rate of 62.5%. Research proved that
the rate of personal growth of the employee’s in their workplace was not satisfactory with a rate of
56.1% respondents. Also, the flexible time plan was not satisfactory to the workers because they were
being given a flexible time sometimes especially when their workplace is very busy. In addition, the
employee’s were not given the privilege of making decision in their workplace especially in the areas
that concerns them. However, the researcher recommended that the fast food workers should be
empowered in different ways, to give them opportunities to share their views. Also, the skills should be
developed through coaching and feedback system. The study includes only the fast food workers that
have at least six (6) months experience. This is to obtain a reliable and valid result.
Keywords: Motivation, work environment, working conditions, employee performance, organizational
commitment, job satisfaction, recognition, promotion.
INTRODUCTION
People management is an important aspect of any
organization, and this includes the fast food industry. A
well managed fast food industry will normally consider
*Corresponding author. E-mail: wilfredukpere@gmail.com.
Abbreviations: QWL, Qualities of work life; FASA, franchise
association of South Africa; NSDSFSA, national strategy for the
development and support of franchising in South Africa; BNA,
bureau of national affairs; HR, human resource.
employees rather than financial capital as the core
foundation of the business, which also contributes to the
industry’s development (Morato, 2008). Therefore, in
order to ensure achievement of the industry’s goals, the
fast food industry should create an atmosphere of
commitment and cooperation for its employees through
policies that facilitate employee motivation and satisfaction.
Satisfaction of human resources is closely linked
to highly motivated employees. Motivated employees
normally perform better, which result in greater productivity
and lower labour turnover rates. Moreover, highly
motivated employees strive to produce at the highest

11522 Afr. J. Bus. Manage.
Self - actualization
Esteem
Social
Safety
Physiological
Figure 1. Maslow`s hierarchy of needs.
possible level and exert greater effort than employees
who are not motivated (Schultz et al., 2003). When motivating
factors such as challenging jobs, responsibility and
so on are inherent in a job, satisfaction is perceived as
possible and work-directed energy is aroused and this
leads to motivation (Bloisi et al., 2007). The importance of
employee motivation should be emphasised within the
fast food industry. Some ways to improve motivation in
the fast food industry include good remuneration, effective
training and skills development, a proper recognition
and reward system, and employee growth prospects
(Schultz et al., 2003).
Employees’ motivation is one aspect of a human
resource management development strategy. Champion-
Hughes (2001, cited in Morato, 2008) states that a
holistic approach should be used to improve certain
qualities of work life (QWL) such as fringe benefits, better
employment conditions, and career development to support
facilitation of motivation, which is directed towards
achieving the organization’s goal (Ukandu and Ukpere,
2011).
LITERATURE REVIEW
Employee motivation is a process, which accounts for an
individual’s intensity, direction and persistence of effort
towards attaining a goal (Robbins et al., 2007). Therefore,
intensity relates to how hard a person tries, while
direction is the channel through, which a job is performed.
Persistence is a measure of how long persons
can maintain their effort (Robbins et al., 2007). Through
employee motivation, an organization can achieve a
competitive advantage through higher productivity and
improved customer service (Stone, 2005). Consequently,
when employee motivation is downgraded, the
Figure 1.1 Maslow`s hierarchy of needs
organization will be at risk in relation to finances and
other strategic objectives. Hence, employee motivation
regulates the behaviour of employees and enables them
to achieve the desired goals of an organization.
Therefore, motivation is the psychological process that
provides behaviour, purpose and direction. It is also
defined as an internal force, which is based on an
individual’s conscious and unconscious needs that drive
him/her to achieve a goal (Robbins, 1993) indeed; it is an
internal drive to satisfy an unsatisfied need (Higgins,
1994, cited in Lindner, 1998).
Motivation theories
When discussing motivation, some theories cannot be
ignored, since they explain why people act the way that
they do and why others refrain from doing certain things
(Swanepoel et al., 2003). Regarding this research,
theories of motivation will be explored in relation to work
motivation in the fast food industry, namely Maslow`s
hierarchy (Figure 1) of needs and Herzberg’s theory.
Maslow`s theory
Maslow proposed a theory, which he called the needs
hierarchy. These needs are physiological, safety, social
fulfillment, satisfaction of the ego and self-actualization.
People always have needs, and as soon as one need is
satisfied, another need takes its place (Nel et al., 2004).
From Figure 1, Maslow stated that individuals move up
the steps of a hierarchy and in order to be motivated, one
should know, which level of hierarchy that person is
currently on and focus as on satisfying those needs first
(Robbins et al., 2007). Maslow distinguished between

higher and lower order needs, hence, phy-siological and
safety are grouped as lower order needs while social,
esteem and self-actualization are referred to as higher
order needs. According to Maslow, when the lower order
needs, namely physiological and safety, are substantially
satisfied, the next need becomes dominant (Robbins et
al., 2007). Therefore, in order to motivate someone, a
person should understand what level of the hierarchy that
particular person is currently on and focus on satisfying
those needs within the particular level. Accordingly,
higher order needs can be satisfied internally while lower
order needs are satisfied externally. Incentives such as
pay, union contracts and tenure are examples of lower
order needs. Hence, if basic needs or lower order needs
are not met, efforts to satisfy higher order needs will be
postponed, according to Maslow’s perspective (Prasad,
2003).
Herzberg`s theory
Herzberg investigated the question: “what do people want
from their jobs?” (Swanepoel et al., 2003). There are
different variables that can make people feel either good
or bad about their jobs. These factors were indicated by
Herzberg, which he called the two-factor theory of
motivation, namely intrinsic and extrinsic factors. Intrinsic
factors include advancement, recognition, responsibility
and achievement, while extrinsic factors comprises of
status, security, company policy, administration, remuneration,
supervision and interpersonal relations, which
are referred to by Herzberg as “hygiene” factors. These
latter factors do not motivate employees, although they
should be present in the workplace to placard employees.
Employees are motivated by internal values rather than
external values. Hence, motivation should be internally
generated through those intrinsic factors, which are
known as “motivators”. Among many organizations that
need to motivate their employees is the fast food industry
(Ukandu and Ukpere, 2011).
Fast food industries in South Africa
Fast food industries started as a franchise in South Africa
under the canopy of the Franchise Association of South
Africa (FASA). FASA was established in 1979 and
operates as a non-profit, self-funding organisation
(National Strategy for the Development and Support of
Franchising in South Africa) (NSDSFSA). Its main aim is
to promote and maintain ethical franchising in South
Africa. This organisation consists of two memberships,
namely franchisors and affiliates, which are service
providers to franchisors and franchisees (FASA, 2000).
The FASA is universally accepted as one of the most
successful business formats in South Africa. They ensure
that all members follow internationally accepted franchise
Ukandu and Ukpere 11523
business principles, and serve the needs of the public
and the franchise community (FASA, 2009). FASA has
about 209 members, which comprise 170 franchisors and
39 affiliates (Sibeko and Tambani, 2000). They also have
a self-regulatory measure, which every member of the
franchisor should meet. These include.
i. FASA members are subject to FASA`s code of ethics
and business practice; and
ii. FASA assists its members with a dispute resolution by
providing a mediation service.
Fast food and restaurant industries, under the canopy of
the FASA, include the following: Barcelos Flamed
Chicken, Chicken Licken, Kentucky Fried Chicken,
McDonald’s South Africa, King Pie and Wimpy
Restaurants, to mention but a few. An observer declared
that there is a need to enhance career development and
job characteristics of the fast food employees in order to
increase the level of motivation among employees
(Ukandu and Ukpere, 2011).
Motivation and the work environment
Motivation of employees in the fast food industry is affected
by the environment in which they work (Smithers and
Walker, 2000). Research conducted proved that workers’
environments do affect their level of motivation, for
instance, long hours of work, non recognition for work
done and colleagues’ aggressive management style. In
order to motivate employees to perform their best, there
is a need to provide a work environment that provides
achievement, recognition, meaningful work, advancement
and growth (Musselwhite, 2007). Some of these variables
that affected employees’ level of motivation in the
workplace are discussed subsequently.
Working conditions
Fast food workers need enough resources such as time,
money and equipment to be able to do their work effectively.
However, resources are scarce, which means that
decisions should be made to distribute them fairly. The
needs and goals of the industry should be prioritized. The
physical layout of the work environment such as
neatness, organization, convenience, attractiveness and
stimulus (noise, air, hazards and so on) should not
threaten the well-being of employees (Nel et al., 2004).
(Ester, 1986; cited in Royle and Towers, 2002) states that
public humiliation; unsafe working conditions and total
lack of respect and dignity are amongst reasons why fast
food employees are not happy. Most jobs at fast food
outlets have reflected abusive working relationships, poor
working conditions, reduced wages, long working hours
and less participation in the decision making of the

11524 Afr. J. Bus. Manage.
organisation, especially in areas that concern workers
(Ukpere, 2007). Almost all the aspects of fast food work
are highly standardized and rigorously monitored (Royle,
2004). Their methods of work produce identical products
and their standards and productivity are broken down into
the smallest steps. Work in the fast food industry is
computerised, which reduces work movements and
hence speed up production. Employees’ skills are depleted
and their work becomes intensive with machinery
making decisions for them. Lights and buzzers tell
workers when to fry or bake. Furthermore, workers learn
a routinized job in a day with no previous experience and
the minimum of training (Royle, 2004), which results in
monotony.
Furthermore, employees in the fast food industry tend
to quit after a short term (Thoms et al., 2004). This is
because they make poor earnings and work in less than
satisfactory conditions. He further stated that their hours
of work are long, shifts unpredictable and promotions are
as scarce as water in a desert. There are times when
workers want to join a union to negotiate with the
company in which they work, but are not allowed to do so
because the fast food industry works under a policy of
anti-union, which means that they do not allow their
employees to join unions. If any employee joins the union
he/she may be sacked and management will not attribute
the sacking to joining of a trade union (Harikripahai,
2007). Another factor is the workforce, which consists of
75% of youths under the age of 21 years (Steinberg and
Greenberger, 1986; Harikripahai, 2007). This encourages
the industry to have no legal basis for paying minimum
wages to its workers. A witness mentioned that the
workers were not given sick leave and if any staff
member is sick, his/her schedule will be rewritten.
Working hours
Managements at fast food outlets have created an
environment, which looks like a family, while on the other
hand, it is an environment filled with intimidation and fear
(Reischman, 2003). Employees are pushed to their
physical limits with long hours of work, and are not given
enough breaks. In addition, their wages barely meet the
minimum requirement. A reason for this is that most
employees have a low standard of education and some
of them are immigrants and high school students
(Reischman, 2003). The working hours speculated by the
federal fair labour standard act is at least forty hours a
week and employees who work overtime should be paid
additionally for this. Fast food employees work long hours
for low pay (Schlosser, 1998). Furthermore, fast food
assistants who work sixty hours a week earn a lower
hourly wage than some of their crew members (ibid). Fast
food managers receive promotions, while most assistant
managers never receive promotions. Employees in the
fast food industry prefer work hours that are compatible
with their activities outside of work, thus a huge popularity
of flexi-time and part-time work (Brown et al., 1992). Their
schedules should give them freedom to integrate their
jobs with the rest of their lives; however, shift work is
typically disliked because it usually interferes with their
off-the-job routine (Reiter, 1986).
Pay and benefits
A low minimum wage has long been a critical issue of the
fast food industry`s business plan (Schlosser, 1998). In
view of this, they also import workers from other countries
to work for low wages (Reischman, 2003). According to
Streeten (2001, cited in Ukpere, 2007), outsourcing is a
cause of the decline in the demand for wages of semiskilled
and unskilled labour. Their employees are treated
poorly especially in the area of pay, which results in the
fact that an average worker that wants to make a living by
supporting his or her family, cannot do so with the
average pay or minimum wage that is given to him or her
(Harikripahai, 2007). He further states that instead of the
fast food industry increasing their employees’ wages,
they will prefer to hire other people who are willing to
work for that amount or even less. This mistreatment
does not only include poor hourly wages, but the denial of
the right to join a union (Schlosser, 2003). Workers
assume that picking strawberries is far better than
cooking French fries and jobs, which are filled with
people who are young and unskilled. The extreme
standardization of work in the fast food industry justifies
low wages and benefits (Leidner, 1993, cited in Royle
and Towers, 2002). Inexperienced people who are
capable of doing the work easily accept low paid jobs
because they do not have a choice. This also causes low
motivation and high labour turnover, since workers’
needs cannot be met with low paid jobs in the fast food
industry (Leidner, 1993). Benefits in the fast food industry
are almost non-existent. Other organizations provide
benefits to their workers such as medical insurance and
paid vacations. But the fast food industry does not. Also,
the work and life balance of the employee is not feasible,
since they have a lowly, erratic and unpredictable work
schedule that must be planned around, and if they do not
comply with this unfair treatment, they will be asked to
quit and their position will be filled by another person
(Williams and Hazer, 1986, Harikripahai, 2007).
Recognition
An employee’s performance can be recognized through
promotion. Employee recognition consists of personal
attention, expressing interest, promotion, pay, approval
and appreciation for a job well done (Robbins, 2003).
Generally, in the workplace, recognition is considered as
the most powerful tool for employee motivation (Robbins,

2003). Fast food workers want to be recognized. They
want to know that their superiors appreciate their work in
the industry. Most of the fast food industries value ‘people
principles’ and ‘social responsibility’. People principles
refer to the relationship between management, the
workers and the treatment of workers (Harikripahai,
2007). According to an observer, the fast food industry
holds five basic ideas: Respect and Recognition, Values
and Leadership, Pay, Learning and Developmental
Growth, and Resources. He adds that four of these five
are questionable because management regards workers
as passive people who can be employed to work for a
certain period of time and duped with a false sense of
employment security and individuality.
Promotion
Promotion is the reassignment of an employee to a
higher-level job (Grobler et al., 2006). Promotion means
more responsibility. Employees want promotion to be
given fairly. Fairness means promotion for the most qualified
workers, although few employees who are competent
may not have good managerial skills, or training that will
enable them manage people. In the first paragraph of
working hours, it was stated that fast food managers
receive promotion while most assistant managers never
receive promotion. Strober (1990) points out that race
and gender affect employees’ promotion on the ground of
human capital theory. This contends that variation in
upward mobility is largely attributed to differences in the
quantity and quality of the educational opportunities. The
most obvious aspect is the discrimination that exists
between race and gender in promotion decisions. This
discrimination, he states manifests by slowing the promotion
rates of the minority or by creating barriers to upward
mobility.
Employee promotions in the fast food industry are considered
as vital both to the industry and their employees.
Promotions provide direct economic and psychological
reinforcement for employees and in determining, which
employee should be selected for the promotion exercise;
job performance is a key factor (Sheridan et al., 1997).
‘Promotion may be an employee’s reward for good
performance, that is, positive appraisal, which leads to
employee motivation’ (Louis, 2009). La Motta (1995)
defines job performance as the result of motivation and
ability. Ability includes training, education, equipment and
simplicity of task. Despite all these factors, an employee
may still not perform well on the job if there is no motivation.
Motivation not only influences performance, but
performance, if followed by rewards, can influence
motivation (La Motta, 1995). In some South African fast
food outlets, research has shown that management
makes false promises of job promotion that never occurs.
This could be as a result of the downsizing of the industry
or the inability of management to pay workers when
Ukandu and Ukpere 11525
promoted (Inglish, 2010). Smart and talented employees
tend to give up and look for employment elsewhere
(Inglish, 2011). Management of fast food outlets,
including the South African fast food outlets, sometimes
promote their workers without proper training and
experience to become supervisors. This demoralizes staff
because these supervisors are then incompetent and
cannot perform in the post given to them.
Employee performance
It was stated earlier that jobs in the fast food industry are
highly routinized and capital intensive, which results in
employees living their job. Reduced work output, increased
accidents, absenteeism, employee turnover and
poor employee performances are examples of detrimental
employee behaviour that have a significant effect
on profitability (Spector, 2003, cited in Lo and Lamm,
2005).
About sixty two percent of first-time employees in the
fast food industry usually engage in theft and misconduct
in their workplace such as giving away goods, falsely
claiming to be sick, stealing, damaging the organizations`
property, or work while intoxicated during their first nine
months of employment (Reischman, 2003). Turnover is
high for non-management fast food employees at three
hundred percent per year, and vacancies are reported to
be eighty one percent (Bureau of National Affairs (BNA),
1985, cited in Reischman, 2003). It is the amount of
training that is provided to fast food employees that
enhances their performance rather than the quality or
quantity of education achieved prior to employment
(Sheridan et al., 1997). As mentioned previously,
employee job performance is a result of motivation and
ability. Motivation influences performance, but performance,
if followed by rewards, can influence motivation.
Since the subject of motivation has become increasingly
important in the fast food industry recently, there is need
for total quality management and a flatter management
structure, which will initiate an employee reward
management system. Deeprose (1994) postulates that
effective reward management can help the management
of a fast food outlet to achieve their business objectives
by attracting and retaining competent workers.
Management and supervision
Abuse by managements of fast food outlets have come
into the public interest increasingly because of increased
awareness and workers are no longer tolerating it.
Abusive scenarios such as unclear performance goals,
strange human resource (HR) practices, deadly teams
and unannounced relocation, have become the order-of-
the-day (Inglish, 2010). Managements at fast food outlets
do not create an environment that will allow them to

11526 Afr. J. Bus. Manage.
exchange ideas and participate with the workforce. They
also do not implement co-determination and participation
of workers in decision making, especially regarding
issues concerning the fast food industry (Butod, 2009).
He further states that co-determination and participation
can increase workers commitment, job satisfaction and
motivation and reduce resistance to change. It will also
improve communication and consultation between
management and employees (Butod, 2009). Some fast
food management training material suggests that failure
to provide adequate communication, correct management
styles, adequate praise and recognition, adequate
staffing levels and correct handling of holiday
entitlements and pay details, are the major reasons why
workers are becoming resistant to management power
(Royle, 2000). Regimentation and standardization of the
fast food industry have given managers authority over
their employees, which means that management determines
the way every task should be done and imposes
rules on pace, output, quality and techniques of
production (Leidner, 1993). Management has not been
taking the feelings and emotions of their workers to heart.
The workers are not given responsibilities in their
workplaces to make them feel as part of the company
(Ukpere, 2007).
Furthermore, some fast food employees have demonstrated
dissatisfaction with their work assignments and
the degree to which they are utilized within their workplace.
In addition, managers change their shift patterns,
while working night shifts is a big concern for female
workers. This is assumed to be affecting their primary
family responsibilities. Furthermore, it can lead to stress,
anxiety and depression, which affect their psychology
(Michailids and E-ali Elwkai, 2003). This is one of the
major dysfunctional aspects of the fast food industry.
Research has further been conducted to prove the
reliability of this information.
Need for the study
This research was done to enable the fast food industry
management implement practices that will increase job
satisfaction in order to enhance employee motivation. It is
needful to help direct the fast food managers on how to
achieve a high employee performance and higher levels
of productivity. The study also guides the fast food
managers on how to stimulate their workers and bring
about change in their job performances.
Objectives of the study
The objective of this research is to find out the various
strategies on how employees can be motivated in their
workplace especially in the fast food industry. This study
was conducted using fast food outlets within Cape Town
metropolis. This study also investigated the causes of low
employee motivation such as poor working conditions,
poor managerial services and supervision, poor pay and
others.
RESEARCH METHODOLOGY: SAMPLE AND RESEARCH
PROCESS
The aim of this study is to derive a motivation strategy for the fast
food outlets within Cape Town. A case study approach was used
for this research because it was dealing with a specific organization
which is the fast food outlets in Cape Town. The research methods
used were the qualitative and quantitative methods (Triangulation).
This was to know the opinion of the fast food employees in the area
of motivation. A total of 200 questionnaires were distributed and
only 123 employees responded and were used for the analysis.
Furthermore, a pilot study was conducted with few staff members
and managers of the fast food industry from different outlets and
corrections made before distributing it to the others. This is to allow
reliability and validity. Also, open- ended, semi-structured interview
questions were administered face-to-face to the senior and store
managers of the different fast food outlets to know their opinion in
the area of employee motivation, while a closed-ended questionnaire
was distributed to the other staff members. As mentioned
previously, the total population sampled was 200. The selected
sample composed of all the staff members that have at least six (6)
months of work experience within the fast food industry. This is to
enable the researcher obtain reliable information. A report was
made on the level of motivation of the workers. A high ethical
standard was maintained as far as the information is concerned.
Data analysis
To ascertain the motivation level of the fast food workers, the
researcher posed some questions and statements. In analysing the
data obtained, content analysis was used. Information from other
sources was also incorporated into the analysis for validity and
reliability. The responses received from the questionnaires are
given as follows:
The growth rate of the fast food workers and how it has affected
their motivation level was assessed. Emanating from the interviews,
it was evident that some fast food workers work in poor working
conditions and environment. Many interviewees maintained that
they were not satisfied with the layout of their workplace. It was
clear that the employees’ personal growth was not a major concern
to their managers; however, employees are expected to carry out
their jobs effectively. Few interviewees noted that they do not have
enough personal growth in their workplace which is discouraging
them from working with the fast food outlet. As indicated in Table 1,
the rate of personal growth among staff members was not
impressive. A total of 56.1% (25.2% plus 30.9%) of respondents
noted that they were not satisfied with the rate of their personal
growth within the fast food industry. From the result, one can
conclude that the personal growth of the fast food workers is not
satisfying. The questionnaires revealed that the fast food
employees were not having flexible time in their workplace.
Employees in the fast food industry prefer work hours that are
compatible with their activities outside of work, thus a huge
popularity of flexi-time and part-time work. The rationale for the
question was to determine whether or not flexible time is convenient
for the fast food workers, and to find out if they are satisfied with
their flexible time plan (Table 2). Of the 123 respondents, 7.3% are
highly satisfied, which means that they are highly satisfied with the
flexible time plan at their workplace and 19.5% are satisfied, which
totals 26.8% (7.3% plus 19.5%). A total of 5.7% are neutral.
However, 31.7% are not very satisfied, while 33.3% are not

Table 1. The rate of my personal growth in my workplace is?
Ukandu and Ukpere 11527
Variable Frequency Percent Valid percent Cumulative percent
Valid Highly satisfying 10 8.1 8.3 8.3
Satisfying 24 19.5 20.0 28.3
Neutral 17 13.8 14.2 42.5
Not very satisfying 31 25.2 25.8 68.3
Not satisfying at all 38 30.9 31.7 100.0
Total 120 97.6 100.0
Missing System 3 2.4
Total 123 100.0
(n=123).
Table 2. Flexible time plan at my workplace is?
Variable Frequency Percent Valid percent Cumulative percent
Valid Highly satisfying 9 7.3 7.5 7.5
Satisfying 24 19.5 20.0 27.5
Neutral 7 5.7 5.8 33.3
Not very satisfying 39 31.7 32.5 65.8
Not satisfying at all 41 33.3 34.2 100.0
Total 120 97.6 100.0
Missing System 3 2.4
Total 123 100.0
(n=123).
Table 3. What is your level of satisfaction regarding decision making in your workplace?
Variable Frequency Percent Valid percent Cumulative percent
Valid Highly satisfying 9 7.3 7.5 7.5
Satisfying 21 17.1 17.5 25.0
Neutral 24 19.5 20.0 45.0
Not very satisfying 42 34.1 35.0 80.0
Not satisfying at all 24 19.5 20.0 100.0
Total 120 97.6 100.0
Missing System 3 2.4
Total 123 100.0
(n=123).
satisfied at all, totalling 65% (31.7% plus 33.3%). Also, 70.7%
(34.1% plus 36.6%) of respondents stated that they were not
satisfied with overtime duty at their workplace (Table 2). The data
that was analyzed confirmed that fast food employees are not
satisfied with the flexi-time plan at their workplace.
The rationale for this question is to ascertain whether the fast
food employees are really satisfied with decision making in their
organization and whether they are allowed to participate in the
decision making of the organization. Findings from the data
analysis disclosed that workers are not encouraged to participate in
the decision making of most outlets and are also not recognized for
good performances. During the interviews, several respondents
indicated that they were not satisfied with decision making in their
workplace. They added that they were not given freedom to provide
input with regard to the organization’s objectives and standards.
However, the data collected are given subsequently. Table 3 points
out that 7.3% of respondents are highly satisfied with decision
making in the organization, and 17.1% said that they are satisfied,
totalling 24.4% (7.3% plus 17.1%). A total of 19.5% of respondents
are neutral regarding the question, while 34.1% of respondents said
that they were not very satisfied and 19.5% were not satisfied at all,
totalling 53.6% (34.1% plus 19.5%). The rationale for this question
is for the researcher to know if the fast food employees are given
freedom to make input with regard to objectives and standards of
the industry.
The interviewees maintained that they were not given freedom to
provide input with regard to the organization’s objectives and
standards. Again, some respondents maintained that their job

11528 Afr. J. Bus. Manage.
Table 4. Do employees have freedom to make input with regard to objectives and standards?
Variable Frequency Percent Valid percent Cumulative percent
Valid Yes 35 28.5 29.2 29.2
No 85 69.1 70.8 100.0
Total 120 97.6 100.0
Missing System 3 2.4
Total 123 100.0
(n=123).
execution was not made challenging by their supervisors. The data
analysis, as illustrated in Table 4, indicates that 69.1% of
respondents have not been given the freedom to provide input with
regard to the organization’s objectives and standards. Table 4
showed that 61.8% of respondents stated that their job execution is
not made challenging by their supervisors. As previously
mentioned, some interview questions were posed to the store
managers, senior managers, and ex-staff to obtain information on
the motivation level of the fast food workers. Content analysis was
used in analyzing the interview questions. The following are some
interview questions and responses obtained from the store
managers and senior managers:
Do you think your employees are motivated enough in your
workplace?
Two managers noted that employees are motivated because of the
type of work that they produce and of course they do not have a
choice because they need the job. In addition, one interviewee
noted that workers are sometimes motivated if a goal is set for them
or a reward is provided to them. Other managers said that workers
are not motivated because there is no training given to them, while
promises of recognition are made, but not delivered, and hence not
enough recognition is practiced. Furthermore, most employees
have no passion for their job because they feel that there is a lot of
negativity and unfairness amongst them.
What is your view about the performance of your staff
members?
The managers revealed that staff performances were poor because
they did not receive any formal training. They also lack self
confidence and self respect. An interviewee stated that sometimes
staff performance is good and sometimes it is not. Another two
managers stated that staff performance is good with no reason or
further explanation provided.
Do you think that employee motivation can improve the staff
performance?
All interviewees reported that employee motivation can improve
staff performance because it will make them stress–free at work;
the workers will feel more empowered, and have more confidence
and self respect. They will also feel more proud about their job
provided that they receive proper training in order to perform above
expectation; employee motivation enables staff to improve their
performance by yielding good results and by lifting their self
esteem. Furthermore, employee motivation will help them to
achieve organizational goals in a better manner. The following are
some interview questions for the ex-staff and their responses. This
is to find out their reasons for their leaving the fast food industry
and to know if their reasons were related to their level of motivation.
Were you motivated at the fast food outlets?
Four ex-staff members said that they were not motivated because
there was nothing to motivate them, except the fact that it was a
production company, which focused on business. Moreso, the
salary was too little and the shifts were long (24 h), while they also
felt that they were treated poorly. In addition, the work was too
much and managers were rude to their workers and unfriendly. No
transportation was available to and from work for late shifts, and
they were abused, because they worked as cleaners instead of
what they applied for. Lastly, they felt that they were just a number
doing duties, and could hence not be motivated under such
conditions. However, three (3) ex-staff members stated that they
were motivated because one said that she was promoted even
though it was after six (6) years, while another said that she
enjoyed working with people, and learned new ideas. The last said
that he was always given an opportunity to prove himself and to
learn new skills.
What was your reason for leaving the fast food industry?
The ex-staff members had different reasons for leaving. The first
two said that the business closed down so they had to leave;
another said that the working hours were too long and travelling
was too costly, hence she could not afford it any longer and
resigned to look for a better opportunity. The next two said that they
were going back to varsity and another noted that they had a better
job opportunity elsewhere with more pay. Different reasons were
given, but this shows that they were not motivated enough to
enable them to stay at the fast food outlet.
In your opinion, do you think that being motivated at your
workplace would have made you to stay longer?
They all noted that if they had been motivated at their workplace
they would have stayed longer for the following reasons: if their
salary was increased and transportation was made available for
them to and from work. They also said that a friendly attitude with
less work would have made them stay longer as well. Moreover,
one said that she likes the fast food industry and so being motivated
would have made her to stay longer and another said that if
he could see potential that would challenge him, then he would
have stayed longer. Lastly, the other two said that there was no
need for motivation, since the outlet had closed down.

Strategies that could improve the motivation level of the fast
food employees
Emanating from the interviews conducted with the staff members, it
was revealed that the employees do not have enough time for their
families and other personal responsibilities. Therefore, in an effort
to improve the work life of the employees, the management needs
to recognize this balance in order to help improve the employees
work life. The following are factors that could contribute to the
improvement of the employee’s work life.
Training and development
It is appalling to note that the fast food employees are not given
proper training. Investigations revealed that employees have not
been performing well because of improper training. From the data
analysis, it was revealed that 56.1% (25.2% plus 30.9%) respondents
were not satisfied with their personal growth with regard to
their job descriptions at the fast food outlets. Most respondents also
noted that their job execution is not made challenging by their
supervisors. Three interviewees maintained that training workers
will help them to improve their general performances and also
motivate them to work harder and better.
Reduced workload
It was deduced from the interviews with staff members that there is
work stress in the fast food industry, and too much pressure and
heavy workloads. It is clear that employees’ workloads should be
reduced to prevent low employee motivation and to improve the
work life of employees. Results from the interviews showed that
employees do not have flexible work time and working hours were
too long, hence they do not have enough time to spend with their
families. Moreover, the pay is little, compared to the work that they
do. It is important to know that workers are eligible for flexible time
and less hours of work, which would allow them to have time with
their families and their personal life.
Incentive programmes
The study has revealed that workers were not properly remunerated.
Moreover, bonuses and fringe benefits were not given to
them. Employees stated that they were not recognized for good
performances. Results from Table 4.15 revealed that 63.4% (30.9%
plus 32.5%) of respondents disagreed that they had a bonus plan in
their organization. Table 4.19 also noted that 69.1% (32.5% plus
36.6%) of respondents reported that there is no health plan for
employees. Increasing workers’ pay, introducing a benefit plan and
recognizing employees` performance, should be important aspects
of improving employees` standard of living and work life.
Retention strategy
It was derived from the interviews with store managers that there is
a high staff turnover and absenteeism at fast food outlets, which
participated in the survey. An interviewee mentioned that some
workers that were absent complained of low motivation. Another
interviewee maintained that some staff members who resigned did
so because they had not received what they had expected from the
company. A store manager also said that some workers who left the
organization complained of job dissatisfaction and a lack of
transportation to work. The researcher was not able to obtain data
from employees who had resigned. Findings from the data analysis
disclosed that the quality of supervision at fast food outlets were not
Ukandu and Ukpere 11529
satisfactory. Additionally, the workers’ salary is not competitive.
Moreover, they are not involved in the decision making of the outlet
and are also not recognized for good performances. Most
interviewees noted that their jobs are too stressful and that their
workloads are too big, hence they do not have personal fulfillment
in their jobs. Results from Table 4 disclosed that 86.2% of respondents
agreed that they were experienced stress related to their
work. It is necessary to note that a reduced workload and
recognition of employees’ performance and contribution would keep
them in the industry. More than that, promotion of staff members
and flexible time of work will attract talented candidates, which will
boost the profile of staff members.
RESULTS AND DISCUSSION
It can be said that the fast food employees are not
motivated to work from the analysis in the foregoing. In
the first instance, the rate of their personal growth in their
work-place is not satisfying. Table 1 shows 56.1%
respondents who were not highly satisfied or satisfied
with their rate of personal growth in the organization.
More so, it was stated from the respondents that their
flexible time plan is not encouraging. In spite of this, their
level of satisfaction with the decision making in their
workplace is low. Res-pondents also claimed that they do
not have freedom to make inputs with regards to the
objectives and standards of the organization.
The interviews with the store managers and the ex-staff
also revealed that the fast food workers are not motivated
at work. This has been clearly stated from the interviews.
Some interviewees noted that one of the reasons why the
workers are not motivated is that there is no training
given to them; rather there are promises of recognition
which is not always delivered. Also, few managers noted
that most of the employees have no passion in the job
because they feel that there is a lot of negativity and unfairness
amongst them. However, the interviewees view
the performance of the staff as poor because of lack of
formal training.
Again, they noted that some of the workers lack self
confidence and self respect. Ex-staff members revealed
that they were not motivated at work because the salary
was too little and the shifts were long (24 h) and they also
felt that they were treated poorly. In addition, the work
was too much and managers were rude to their workers
and unfriendly. No transportation was available to and
from work for late shifts, and they were being abused and
so they resigned to look for a better opportunity.
Moreover, the ex-staff members revealed that being
motivated in the fast food industry would have made
them to stay back. Lastly, they added that if there were
potentials and challenges in the industry, they would
have stayed back.
LIMITATIONS AND FUTURE RESEARCH
This study was conducted only with the fast food outlets

11530 Afr. J. Bus. Manage.
in Cape Town. The intention is to develop a motivation
strategy that could improve the level of motivation of the
workers. It has also highlighted the need for further
research on job satisfaction of the fast food employees to
measure the level of satisfaction of the workers.
RECOMMENDATIONS AND CONCLUSION
Despite the good qualities of food provided from the fast
food outlets, there are weaknesses with the way the
management deal with their employees, which have
adversely affected the state of motivation of the workers.
Employee motivation is essential to achieve the business
goals of the fast food outlets in Cape Town and South
Africa in general. If the workers are satisfied with their
working conditions, they tend to perform well and customers
are also happy. The researcher has recommended
strategies that could improve the level of motivation of the
workers within the fast food outlets in Cape Town as
follows:
Fast food employees should be empowered and
given some degree of autonomy in the execution of
their job.
It is evident that when employees are empowered, they
tend to work harder and faster. They are found to be loyal
to their employers and they also enjoy their jobs more. It
is imperative to empower employees by making them feel
that the company has a high regard for them, and that
they are the reason for the organisation’s success. Fast
food workers should be empowered in different ways,
namely they should be given opportunities to share their
views in terms of making suggestions. Moreover, their
suggestions should not be taken for granted, but should
be used to solve problems in the organisation. The
management should be able to communicate with their
workers in a clear and understandable manner.
Managers should be able to know the abilities of their
employees and assign them tasks that will allow them to
enjoy the freedom of doing their work. Fast food
employees’ skills should be developed through a
coaching and feedback system. A good degree of
autonomy should indeed be given to fast food workers. In
other words, they should be permitted to give their
opinions on how a particular job should be performed.
Substantial freedom, independence, and discretion to
schedule work and determine procedures that should be
taken to do the job will increase levels of motivation for
fast food employees. Greater autonomy will give workers
a sense of accomplishment in the workplace, as they
begin to take charge of directing their jobs. Contrary to
popular belief, workers also enjoy taking responsibilities,
especially when they are asked to stand in for their managers,
in cases of absenteeism. This has an accelerator
effect on motivating workers to hope that one day they
may as well become managers/leaders in the organisation,
and hence their levels of motivation will positively
be affected.
Workers should be involved in the organisation’s
decision making
It is important to involve both managers and employees
in joint decision making on a regular basis. Employees
should be empowered through decision making for the
organisation, especially in areas in which they excel.
There is a need for managers to mark out how much
decision making authority will be assigned to employees
to prevent competition. Workers should be empowered to
compile schedules for their vacation because they may
have a better idea of how it will work. Managers should
consult employees before any decision is reached. This
will help to prevent overlooking solutions that may appear
obvious to front-line employees but unfamiliar to higherlevel
managers. This will also help managers to make
decisions since they may not have enough information to
make a quality decision without the employee’s input.
Moreover, managers should involve their employees in
decision making in order to make an effective decision.
Despite this, employees should be allowed to participate
in planning their personal career paths as this will render
a sense of ownership, and thereby increase their levels of
motivation at work.
Workers should be recognised for their contribution
towards organisational success
Every employee wants to be recognized and appreciated
for good performance. Fast food managers should
always recognize and appreciate the efforts of their
workers. Monetary reward is important, but recognition
and appreciation is critical for industrial harmony.
Therefore, managers may recognize their workers by
announcing their good performance in staff meetings or
by mentioning the good performers within the workplace.
It is also best practice to send emails to all staff or publish
staff members’ contributions and achievements in the
company’s newsletter or notice board. This will motivate
employees to do more for the organisation. Employees
should be recognized by giving them time off. Many
workers like to spend more time with their friends and
families and will appreciate if they are given time off at
least for a day in recognition of their good performance.
They will come back to work feeling refreshed and grateful
for the recognition. Recognizing employees for good
performance and contributions in the organization should
enhance employee motivation. Fast food managers
should also recognize their employees by providing
monetary incentives. This will not only motivate the

eneficiary, but will also motivate other workers to do
their best. Additionally, they should recognize their
employees by issuing a personal note to the worker for
outstanding performance. Managers can as organize
team events for their employees such as a lunch, party or
outing so that the team can enjoy themselves together,
which builds team spirit, which ultimately increases levels
of motivation amongst workers.
REFERENCES
Bloisi W, Cook CW, HunSaker PL (2007). Management and
Organisational Behaviour. Second European Ed. New York: McGraw
– Hill Education.
Brown C, Reich M, David M (1992). Becoming a High-Performance
Work Organization: The Role of Security, Employee Involvement and
Training. UC Berkeley: Institute for Research on Labor and
Employment. (Online) Available http://escholarship.org/uc/item/
0pj25436 Bureau of National Affairs. 1985. South Africa.
Butod M (2009). Management of Change: McDonald`s. (Online)
Available
http://ivythesis.typepad.com/term_paper_topics/2009/10/managemen
t-of-change-mc-donalds.html.
Champion H (2001). Thinking made easy: Research paper of effect of
Job satisfaction and Motivation on Organisational Commitment.
(Online) Available
http://ivythesis.typepad.com/term_paper_topics/2008/09/researchpap-10.html.
Deeprose D (1994). How to recognize and reward employees. New
York: AMACOM.
Grobler P, Warnich S, Carrell MR, Elbert NF, Hatfield RD (2006).
Human Resource Management in South Africa. 3 rd ed. Thomson
Learning, London.
Harikripahai Organization (2007). The Fast Food Industry, McDonald`s
Market Research. (Online) Available
http://www.echeat.com/essay.php?t=27045.
http://www.echeat.com/profile.php?mode=viewprofile&u=harikripahai.
Higgins JM (1994). The Management Challenge. 2 nd Edition. New York:
Macmillian.
Inglish P (2010). Top 10 Reasons Employees Quit. Hub Pages Elite.
America. (Online) Available: http://hubpages.com/hub/quit.
La Motta T (1995). Recognition: The quality way: quality Resources.
New York
Leidner R (1993). Fast food, Fast Talk: Service Work and Routinisation
of Everyday Life. University of California Press, Berkeley: CA.
Lindner JR (1998). Understanding Employee Motivation. Piketon
Research and Extension Center, Piketon, Ohio. J. Ext., 36(3).
(Online) Available www.joe.org.
Lo K, Lamm F (2005). Occupational Stress in the hospitality industry:
An Employment Relations Perspective. New Zealand Journal of
Employment Relations, 30(1), 23-47. Auckland.
Louis C (2009). Promotion (Rank). The Free Encyclopaedia. USA:
Chicago.
Musselwhite C (2007). Motivation at work: Motivation = Empowerment.
Newsletter Inc. Today`s small Business News.
Morato R (2008). Organizational Change. Technorati Tags: Essay
Samples. Thinking made Easy: Research paper on effect of Job
Satisfaction and Motivation on Organisational Commitment. (Online)
Available http://ivythesis.typepad.com/term_paper_topics/2008/
09/research-pap-10.html.
Nel PS, Van Dyk PS, Haasbroek GD, Schultz HB, Sono T, Werner A,
(2004). Human Resources Management. 6 th ed. South Africa: Oxford
University Press.
Prasad LM (2003). Organisational Behaviour. New Delhi: Sultan Chand
and Sons.
Reischman S (2003). Uncommon thought Journal: Fast food and
Ukandu and Ukpere 11531
Society. New York. (Online)
Availablehttp://www.uncommonthought.com/mt32/cgi-bin/mtpopupemail.cgi?entry_id=4355.
Reiter E (1986). “Life in a fast – food factory”. From on the job
confronting the labour process in Canada Kingston and Moocreal.
Canada: McGill Queen`s University Press.
Robbins SP (1993). Organizational Behaviour. 6 th ed. New Jersey:
Prentice-Hall.
Robbins SP (2003). Organisational Behavior. 10 th ed. Canada: Prentice
Hall, Pearson Education International, USA.
Robbins SP, Odendaal A, Roodt G (2007). Organizational Behaviour:
Global and Southern African Perspectives. 7 th ed. Cape Town:
Pearson Education.
Royle T (2000). Working for McDonald`s in Europe: The unequal
struggle? London: Routledge Publications.
Royle T (2004). Employment Practices of Multinationals in the Spanish
and German Quick-Food Sectors: Low Road Convergence. Eur. J.
Ind. Relat., 10(1): 51-71.
Royle T, Towers B (2002). Labour relations in the global fast food
industry. London: Routledge Publishers.
Schlosser E (1998). Fast-Food Nation: The True Cost of American’s
Diet. National Magazine Award Winner. Rolling Stone Magazine,
USA, Issue 794.
Schlosser E (2003). Fast food Nation. 2 nd ed. Canada: Perennial/Harper
Collins.
Schultz H, Bagraim J, Potgieter T, Viedge C, Werner A (2003).
Organisational Behaviour: A contemporary South African
Perspective. First ed. Pretoria: Van Schaik Publishers.
Sheridan JE, Slocum JW Jr., Buda R (1997). Factors influencing the
probability of employee promotions: A comparative Analysis of
Human Capital, Organisation Screening and Gender/Race
Discrimination Theories. New York: Human Science Press, Inc..
Hempstead. J. Bus. Psychol., 11(3)
Simons T, Hinkin T (2001). The effect of employee turnover on hotel
profits; a test across multiple hotels. Cornell Hotel Restaur. Adm. Q.,
42(4): 65-69
Smithers GL, Walker DHT (2000). The effect of the workplace on
motivation and demotivation of construction professionals.
Construction Management and Economics. Australia: Melbourne.
18(7): 833-847.
Spector PE (2003). Industrial and Organizational Psychology: Research
and Practice. 3 rd ed. New York: Wiley.
Steinberg SLD, Greenberger E (1986). When teenager work: The
psychological and social costs of adolescent employment. New York:
Basic.
Stone RJ (2005). Human Resource Management. 5 th ed. Milton: John
Wiley and Sons.
Strober MH (1990). Human Capital Theory: Implications for Hr
Managers. New York City. Ind. Relat., 29(2): 214-239.
Swanepoel B, Erasmus B, Van wyk M, Schenk H (2003). South African
Human Resource Management: Theory & Practice. 3 rd ed.
Landsdowne: Juta.
The Franchise Association South Africa (FASA) (2000). Fast food and
Restaurants. (Online) Available www.fasa.co.za/content/
memberslist/ Restaurants.aspx
Thoms P, Wolper P, Scott KS, Jones D (2004). The Relationship
between immediate turnover and employee Theft in the restaurant
industry. Human Sciences Press Inc. Erie, PA. J. Bus. Psychol.,
15(4).
Ukandu NE, Ukpere WI (2011). The causes of low employee motivation
within Cape Town fast food industry. Unpublished paper. Cape Town:
CPUT.
Ukpere WI (2007). The functional relationship between globalization,
Internationalisation, human resources and industrial democracy.
Cape Town: Cape Peninsula University of Technology PhD Thesis.
Williams LJ, Hazer JT (1986). Antecedents and consequences of
satisfaction and commitment in turnover models: A reanalysis using
latent variable structure equation methods. J. Appl. Psychol., 71(2):
219-231.

African Journal of Business Management Vol. 5(28), pp. 11497-11504,16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.1131
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Reasons to create a new venture: A determinant of
entrepreneurial profiles
Virginia Barba -Sanchez* and Carlos Atienza-Sahuquillo
Castilla-La Mancha University, Business Administration Department, Edificio Infante Don Juan Manuel, Avda de
España, s/n. Campus Universitario, 02071-Albacete (Spain).
Accepted 20 June, 2011
Newly created enterprises increase the dynamism of economies and generate employment. Thus, they
are subject to significant recent research. Forming a new company represents a decision based on both
personal and subjective motives, as much as on the environment. But regardless of the origin, a
founder’s motivation represents a commitment to a project or business idea and thus dictates the
future success of the enterprise. Therefore, this article investigates entrepreneurs’ motivational profiles
and why they choose to create new industrial enterprises. To detail this profile, we present the results
of an empirical study of 101 entrepreneurs who have founded companies. The results offer significant
conclusions for both academics and practitioners. Firstly, making money or being one’s own boss does
not appear sufficient reasons to create a new venture. Secondly, the motivation content of
entrepreneurs influences their decision to start a business. From these conclusions, some relevant
guidelines are suggested. The main guidelines would be that assessments of business projects
whether by venture capital firms, financial entities, or other actors the characteristics of the
entrepreneur should be weighted more heavily in decisions to support the business project or not.
Key words: Entrepreneurship, entrepreneurial motives, entrepreneurial profiles, entrepreneurial decisionmaking.
INTRODUCTION
The active participation of newly created enterprises in
dynamic economies has attracted significant academic
interest (Acs and Mueller, 2008), however, these studies
rarely involve economic theory (Lazear, 2005). Most
empirical work instead centers on the theory of the enterprise,
including the process and issues of free enterprise
(Cooper and Dunkelberg, 1986; Veciana, 1999). This
article instead adopts the perspective of entrepreneurs to
investigate their motivational profile and the behavioral
reasons that might lead them to create a new industrial
enterprise. Whereas there are different motivation theories
which attempt to explain the employee’s behaviour in
general, few academics have applied these theories to
the study of the entrepreneur (Canabal and Donnell,
2009). The review of the literature proves that having an
*Corresponding author. E-mail: Virginia.Barba@uclm.es. Tel:
(+34) 902 204 100. Fax: (+34) 902 204 130.
entrepreneurial psychological profile makes a strong
difference (Barba-Sánchez and Martínez-Ruiz, 2009).
Although the referred studies can not be directly
compared to each other since they differ in the variables
considered, all of them suggest the following as entrepreneurs´
features (Davidsson and Honig, 2003; Delmar
and Davidsson, 2000; Douglas and Shepherd, 1997;
Kolvereid and Isaksen, 2007; Parker, 2004; Reynolds et
al., 2004; Shepherd and DeTienne, 2005; Stewart et al.,
1999). Independence desire, higher tendency to risk,
higher need for achievement, locus of internal control and
higher preference for innovation. Yet, the question
remains unanswered as to whether certain individual
characteristics, traits and abilities among nascent
entrepreneurs tend to have a key influence on the
decision to become self-employed.
In this empirical paper, we draw on a number of
previous theoretical studies to provide an analysis of
such primary determinants and their corresponding
effects, in an effort to map traits and characteristics of

11498 Afr. J. Bus. Manage.
greatest relevance to start-up decision-making in the
context of the entrepreneurial personality. The start-up
decision is thus seen as the product of motives and intentions
that vary according to individual entrepreneurial
traits and abilities. The paper complements recent works
by combining different individual factors which form
attitudes towards self-employment in an entrepreneurial
decision model. We conclude that different
entrepreneurial motives follow different intensities of
these factors. From the empirical research, seven main
entrepreneurial motives can be established. We integrate
these seven major drivers of entrepreneurship into the
decision model by assigning determinants to motives and
use this framework to explain the orientation and type of
entrepreneur established. The remainder of the paper is
structured as followed. The second part presents the
theoretical framework for understanding the
entrepreneurial behavior and the third section presents
the motives for creating a business. The fourth section
presents the sample and the research methodology and
the fifth section examines the empirical results. The final
section offers a summary and conclusions.
Entrepreneurial behaviour
Rational models long served to describe and predict human
behavior, but they suffered from lack of information
(Busenitz and Lau, 1995). The vast number of alternative
behaviors is too many to consider individually, such that
researchers cannot reasonably predict satisfaction
related to the consequences of particular behaviors.
Furthermore, people usually cannot resolve their related
complications quickly or easily, which implies that the real
decisions are not given an absolute objective rationality,
but segments of rationality. Within each segment,
behaviour seems to follow a rational order, but in the
space between the segments there exists a lot of inconsistency
that does not respond to an idealized decisionmaking
scheme (Lee et al., 2011). The limitations in the
information available and human rationality prompted
Simon (1976) to propose motivation as a means to
supplement explanations of human behavior. A person
accepts a priori set of assumptions that simplify
subsequent behavioral choices. These assumptions are
based on the motivation or inclination to act in a certain
way. Accordingly, Birch (2009) distinguishes intention or
the commitment to act into two components: structural or
plan and dynamic or motivational.
This model reflects the dynamic theory of action proposed
by Atkinson and Birch (1978), in which a person’s
life is a continuous stream of behaviors, characterized by
changes from one activity to another. The action
preferred in a multiple choice situation is that for which
the motivation is more positive. The intensity and persistence
of the response then is a function of the intensity
of the motivation to perform that action, compared with
the force of the motivation to perform other actions.
In turn, the motivational system of a particular person
should have a decisive influence on his or her behavior,
though it is not the only determinant. According to
Naffziger et al. (1994), the performance of individuals is
influenced by its intrinsic nature and at the same time, it
is a reflection of their internal stimuli, that is, their needs,
attitudes and values. A person’s intrinsic nature depends
on his or her perceptions and subjective view of the
world, potential or innate abilities, and personality.
Variables such as effort, ability, previous experience,
age, education, family history and environment thus
influence behavior, such as the choice to become an
entrepreneur. Ultimately, the decision to start a business,
according to Plehn-Dujowich (2010), consists of two
levels: rational and motivational. The rational level
focuses on objective reasons to adopt the task, including
the environmental conditions that reinforce or penalize
certain behaviors (Skinner, 1987). The motivational level
instead refers to subjective reasons that reflect the
decision maker's expectations.
Motives for creating a business
Any analysis of entrepreneurial behavior must consider
the reasons for this decision. They are necessary, if not
sufficient, element to explain the entrepreneurial process
(Álvarez et al., 2010). Although prior literature does not
discuss the influence of psychological dimensions on
decisions, several authors agree that three distinctive
needs or motives mark entrepreneurs (Brockhaus and
Horwitz, 1986; Herron and Robinson, 1993), for achievement,
for competition and for independence. We also
consider other factors, such as a weak need for affiliation,
the need for power, a tolerance for ambiguity,
preferences for innovation, a willingness to take risks and
proactiveness or persistence. Starting with McClelland
(1961), the need for achievement has been associated
with entrepreneurial behavior. This need prompts a
strong desire to do things well, or better than others,
including those with authority. People with a high need for
achievement likely make plans in advance. They also
enjoy taking personal responsibility and prefer quick,
specific feedback about their actions. Empirical studies
recognize the need for achievement in the form of the
entrepreneurial intentions of a given population, as well
as in retrospective studies of the attitudes and
characteristics of existing entrepreneurs. Regardless of
the approach, many studies thus highlight the importance
of a need for achievement as a characteristic of
entrepreneurs and an influence on business success.
Regarding the need for competition, White (1959) has
proposed the notion of competence or an ability to deal
effectively with the surrounding environment. It pertains
to a person’s desire to understand the physical and social
environment and thereby learn how to obtain desired

outcomes from it. Most literature also notes the
importance of an internal locus of control, which implies
that the person believes his or her actions, rather than
random elements, luck, or chance, lead to outcomes.
Therefore, the need for competition is consistent with a
high achievement orientation, because an internal locus
of control causes the entrepreneur to believe his or her
actions will influence the results. Existing research on this
psychological attribute offers two uses: (1) as a
differentiating factor for entrepreneurs or (2) as critical to
the success of a business. In the first stream, some
authors distinguish entrepreneurs from the rest of the
population, but rarely do they discriminate between
different types of entrepreneurs, who all instead seem
characterized by a need for competition. However,
entrepreneurs may reflect different typologies, depending
on the extent of their need for competition. Finally, the
need for independence is a psychological trait that many
empirical studies offer as characteristic of entrepreneurs
or a driver that enhances entrepreneurship.
EMPIRICAL STUDY
Following prior research, we focus on the reasons people express
for why they start a business and the influence of those reasons on
their entrepreneurial behavior.
Design and sample characteristics
A lack of secondary information about the entrepreneurial
motivations of entrepreneurs prompted us to conduct a fieldwork
survey among Spanish business people who had established
manufacturing companies The design of the postal survey reflected
our review of major international studies. Of the 117 questionnaires
sent out, we received 101 completed responses, which indicated a
confidence level of 95% and a sampling error of 7.8%. The data
suggest that Spanish entrepreneurs are mainly men (only 9% of the
respondents were women), whose average age is 40 years but who
started working in the business world at 29 years of age.
Furthermore, 54% have a family; before they started their own
business, they mainly worked for others and accumulated nearly 10
years of experience, usually in the same sector. Their education
level is average (secondary), though 13% did not finish their
compulsory education (that is, primary school).
Regarding the characteristics of the companies they created,
most respondents chose limited liability companies, though they
retained most decision power and reserved rights to more than 50%
of the capital. These companies also mainly represented micro
businesses, because their average number of workers was only
6.505.
Objectives and research methodology
This research aims to identify the main reasons entrepreneurs start
their own businesses. Therefore, we began by reviewing contributions
from various authors (Birley and Westhead, 1994; Crant,
1996; Lee et al., 2011; Scheinberg and Macmillan, 1988) and
selecting 23 quantitative variables (Table 1) that likely define an
entrepreneur’s motivation (all measured on five-point Likert scales).
To reduce the number of variables and facilitate our interpretation
of the results, we then conducted a principal components factor
Virginia and Carlos 11499
analysis. After we identified the main motivational factors for
entrepreneurs, we attempted to analyze the influence of these
factors on entrepreneurs’ entrepreneurial behaviors. Therefore, in
line with Dubini (1988), Stewart et al. (1999) and Westhead and
Wright (1997), we used the motivational factors we previously identified
to establish a typology of entrepreneurs, according to a cluster
analysis. Finally, we conducted an analysis of variance (ANOVA),
with the decision to create the company again as the dependent
variable and cluster membership as the independent variable.
RESULTS
Motivational factors
Using the information provided by the company founders
in our sample, we examined the latent dimensions that
are summarized in the information contained in the 23
items related to the reasons to create a company, using
factor analysis, and thus determined which motivational
factors were most influential. However, before doing so,
we tested the appropriateness of the correlation matrix
for the factor analysis, using several methods that
revealed the adequacy of the data, namely, the
determinant of the correlation matrix (0.0000517), the
KMO index (0.824), and the Bartlett test of sphericity ( 2 =
893.220; = 0.000) (Bartlett, 1950). From the factor
analysis, we obtained seven factors with eigenvalues
greater than or equal to the value from all 23 items.
These seven factors, extracted through principal components
methods, together explained 67.4% of the total
variance. In addition, the commonalities between the
variables and factors were high, with values greater than
0.51, which indicated that they explained a high
percentage of the variability. To interpret the factors more
easily, we also performed a Varimax rotation and
obtained a new matrix with a linear combination that
explained the same amount of variance, though the
factors focused more on saturated variables. Table 2
displays this rotated factor matrix. On the basis of its
factor scores, we also could establish an interpretation of
the factors resulting from the analysis.
The first factor (FACT1) was strongly saturated with the
specific variables overcome a challenge (MOT17) and
personal growth (MOT23), with values greater than 0.7.
Both variables directly related to the need for achievement,
that is, people’s desire to test their ability to meet
challenges and perform daily activities better
(McClelland, 1961). Furthermore, the variables fulfill a
dream (MOT21) and develop an idea (MOT18) indicated
high loadings (0.69352 and 0.66396, respectively) on this
factor, which indicated the need for success, in that both
pose potential challenges. Moreover and with high
saturation (0.67915), we found that the variable personal
self-realization (MOT15) linked clearly to the need for
self-improvement. Understanding this variable as a desire
to mature psychologically, developmentally and personally,
it can apparently coincide with some aspects such
as the need for achievement (Ahmed, 1985). Although a

11500 Afr. J. Bus. Manage.
Table 1. Motives for creating a business.
Code Motive Average a ( )
Mot8l To create my own job 4.15 (1.23)
Mot18l To develop an idea 4.02 (1.20)
Mot10l To do things my way 3.93 (1.22)
Mot23l Personal growth 3.80 (1.31)
Mot4l To exploit a business opportunity 3.66 (1.26)
Mot1l To have an interesting job 3.58 (1.23)
Mot15l Personal self-realization 3.54 (1.49)
Mot20l To be my own boss 3.54 (1.36)
Mot13l A desire to be independent 3.50 (1.43)
Mot19l To cover my personal needs 3.43 (1.25)
Mot7l To have economic security 3.36 (1.32)
Mot17l To overcome a challenge 3.36 (1.49)
Mot16l To have financial autonomy 3.35 (1.25)
Mot9l To gain more flexibility in my personal life 3.26 (1.44)
Mot2l Warm work relations 3.24 (1.26)
Mot21l To fulfill a dream 3.12 (1.46)
Mot3l To contribute to the welfare of the community 2.97 (1.24)
Mot22l To earn a lot of money 2.75 (1.28)
Mot11l Social status and prestige 2.36 (1.24)
Mot12l Family tradition 2.36 (1.54)
Mot5l To follow the example of someone admired 2.20 (1.41)
Mot14l To be accepted socially 2.06 (1.14)
Mot6l Work frustration 1.70 (1.17)
a Medium calculated as the sum of all scores for each item, divided by the number of
individuals in the sample, with the minimum value of 1 and maximum of 5.
person’s ultimate goal or total satisfaction can be
achieved without wanting to prove anything to anyone
(not even the self), the need for success demands
improvement, such that satisfaction may only induce a
greater need to test one-s own ability. According to these
findings, and to avoid assimilating concepts, we denote
this factor need for success and self-realization.
The second factor includes two variables related to a
primary human motivation to survive: cover my personal
needs (MOT19) and financial autonomy (MOT16), both
with loadings greater than 0.7. In addition, we find high
values for the variables economic security (MOT7,
0.68376) and earn a lot of money (MOT22, 0.64299), so
this factor also includes indicates a classical motivation of
money as synonymous with fiscal strength. Finally, the
variable be my own boss (MOT20), with a saturation of
0.67416, suggests an innate need for independence
among entrepreneurs (Veciana, 1989). We name this
overall factor economic needs and professional
autonomy.
The third factor entails the variables create my own job
(MOT8) and more flexibility in my personal life (MOT9),
both with very high saturation (0.81226 and 0.77069,
respectively). Therefore, this factor indicates a
prioritization of the person’s personal life over his or her
career. In this context, this factor also means selfemployment
as a career (Gabrielsson and Politis, 2011).
The variable earn a lot of money (MOT22) also appears,
but here it takes a negative sign and smaller value (-
0.41345); that is, there is little attraction of the economic
dimension of the entrepreneurship phenomenon.
Therefore, we refer to this factor as need for personal
autonomy.
The fourth factor has the highest saturation for warm
work relations (MOT2), which relates to the need for
affiliation, understood as a desire to establish, maintain,
or renew friendships with others. Moreover, contribute to
the welfare of the community (MOT3) achieves a high
value (0.66614), which may entail a need for institutional
power or a desire to influence others by serving others
and exercise power for the benefits of others or society.
Finally, do things my way (MOT10, 0.65985) is a third
variable associated with this factor, which implies that
independence of action grants the possession and
exercise of some power. We denote this factor need for
affiliation and institutional power.
The fifth factor instead focuses on continue a family
tradition (MOT12, 0.84424) and follow the example of
someone admired (MOT5, 0.61868), in many cases a
father figure. Less weight accrues to the variable desire

Table 2. Rotated factor matrix of the factors of motivation.
VBLES. Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 Fact 7
Mot1 0.57274
Mot2 0.72579
Mot3 0.66614
Mot4 0.80405
Mot5 0.61868
Mot6 0.65847
Mot7 0.68376
Mot8 0.81226
Mot9 0.77069
Mot10 0.65985
Mot11 0.59379
Mot12 0.84424
Mot13 0.43423
Mot14 0.66164
Mot15 0.67915
Mot16 0.74892
Mot17 0.86926
Mot18 0.66396
Mot19 0.77801
Mot20 0.67416
Mot21 0.69352
Mot22 0.64299 -0.41345
Mot23 0.71902
Set values are less than or equal to -0.41 and greater than or equal to 0.41.
to be independent (MOT13, 0.43423), which initially may
seem contradictory with the previous variables. However,
it should be understood as a desire for labor emancipation,
achieved by being oneself, doing what is correct,
and expressing what the person has lived and known
since childhood. In this regard, we call this factor need for
continuity.
The sixth factor involves the highest values for the
variables accepted socially (MOT14, 0.66164), job frustration
(MOT6, 0.65847), and social status and prestige
(MOT11, 0.59379). These notions relate to social needs,
beyond a desire to belong to a group and be accepted by
it, that involve the need to feel important, or ego need
(Atkinson and Birch, 1978). Furthermore Jenssen and
Kolvereid (1992) recognize frustration at work as one of
the main triggers for making the decision to start a
business. In our case, the influence relates to the desire
to gain respect and social admiration. Therefore, the
entrepreneur hopes to create a successful company that
will grow and provide an influence on the immediate
environment (Álvarez et al., 2010). We call it social needs
and personal power.
Finally, the seventh factor shows the highest saturation
for exploiting a business opportunity (MOT4, 0.80405)
and interesting job (MOT1, 0.57274). These variables
reflect the notion of competition, understood as an
Virginia and Carlos 11501
autonomous need for environmental stimulation on the
part of the individual, based on an aversion to routine
situations and in-depth knowledge, tests of capacity and
skills and an ability to cope with problems and new
situations (Ray 1986, Williams and McGuire, 2010).
Thus, we call this factor need for competition.
Identification of entrepreneurs
To establish a typology of entrepreneurs in the study
region in terms of their motives for starting a business,
we establish a cluster analysis. Using the motivational
factors identified in the previous section, we adopt a
hierarchical method to establish the optimal number of
clusters. The best solution, in which the clusters are
maximally different from one another (minimum distance
between two groups = 2.1555) but contain elements with
minimal differences (maximum distance from a
businessperson to the center of a specified cluster =
0.944), features five clusters. Therefore, we analyze the
characteristics of each cluster in motivational terms by
undertaking a K-means cluster analysis. The results for
each variable (motivational factors obtained through
factor analysis) appear in Table 3. The interpretation of
the various clusters reflects the values adopted for each

11502 Afr. J. Bus. Manage.
Table 3. Cluster analysis.
Factor Cluster 1 n=19 Cluster 2 n=12 Cluster 3 n=7 Cluster 4 n=36 Cluster 5 n=27 F Prob
1 -.3615 .5132 1.1510 .2598 -.6090 .000
2 .4240 -.1836 -.8131 .3033 -.3991 .002
3 .0891 -.8525 -.9817 .1543 .3708 .000
4 -.0297 .3030 -.1933 -.3450 .3836 .046
5 .1574 -.3981 1.2313 -.0900 -.1365 .006
6 .1584 1.3841 -.6978 -.2981 -.1592 .000
7 -.4629 .3881 .7171 -.4693 .5757 .000
Notes: Factor 1 = need for achievement and self-realization 2 = financial need and professional autonomy; 3 = need for
personal autonomy; 4 = need for affiliation and institutional power; 5 = need for continuity; 6 = social needs and personal
power; and 7 = need for competition.
factor, according to the centroids of the different clusters.
Therefore, the more positive the value, the more
important is the motivational factor for the businesses
that constitute that cluster; the more negative the value,
the less important it is. Thus, we can describe the
different groups.
Cluster 1, with 19 business people, is characterized by
economic needs and professional autonomy as the main
motivations, with the highest centroid ranking in the
second factor. Furthermore, need for achievement and
self-realization from the first factor and need for
competition from the seventh factor are negative.
Therefore, the members of this cluster have low selfconfidence,
do not enjoy risk-taking or challenges, and
are immature from a psychological point of view. They
likely do not intend to create a company, make the most
of a business opportunity, or have an interesting job that
allows them to develop as individuals; they just want a
job that allows them to survive. Therefore, we call this
group self-employed entrepreneurs.
Cluster 2, consisting of 12 business people, shows a
maximum value for the centroid of the sixth factor, that is,
social needs and personal power, which indicates a
desire for personal enhancement from a work, originating
from a feeling of frustration or dissatisfaction with society.
This cluster also scores high on the first factor, need for
achievement and self-realization, with a target of being
and doing things better to demonstrate the person’s
worth to a wider society. In addition, it reveals a negative
value for the third factor, need for personal autonomy.
Instead, these business people need others’ opinions to
reassert themselves as a person. We call this group
ambitious entrepreneur.
Cluster 3 is the smallest, with only 8 entrepreneurs. Its
most remarkable aspect is the familial tradition of
entrepreneurship for these members. The highest
centroid value is for the fifth factor, need for continuity. In
addition, need for achievement and self-realization and
need for competition exhibit high values; these people
are motivated by challenges and situations that test their
skills and abilities to control the environment. However,
we find significantly negative values associated with the
second (economic needs and professional autonomy),
third (need for personal autonomy), and sixth (personal
and social needs and personal power) factors. Money,
independence, and self-enhancement thus do not drive
these founders. Instead, we refer to them as self-realized
family businesses.
Cluster 4, in contrast, is the largest, with 35 entrepreneurs.
It is characterized by its eclectic position; these
entrepreneurs cite both intrinsic and extrinsic reasons for
their decision to start a business. This intermediate
position involves positive values for both the first and
second factors (need for achievement and selfrealization;
economic needs and professional autonomy).
Yet it reveals negative values for need for competition,
such that these entrepreneurs have little interest in taking
advantage of opportunities or controlling the environment.
Their low need for affiliation and institutional power
suggests they have little desire to establish personal
relationships at work, and minimal social needs and
personal power signal their lack of personal ambition. In
summary, the members of this group intend, through the
creation of a company, to prove themselves capable and
measure business success in terms of the amount of
money they earn. Thus, we call them challenge
entrepreneurs.
Finally, Cluster 5 includes 27 business people and
exhibits a maximum value at the centroid in the seventh
factor, need for competition. They want to know and
control their environment, take advantage of the
opportunities it offers, and reduce their routine situation.
In addition, this cluster exhibits high values on the third
factor, need for personal autonomy, and fourth factor,
need for affiliation and institutional power, indicating their
independent and altruistic natures. We also find
significant negative values for the first and second
factors, that is, need for achievement and self-realization
and economic needs and professional autonomy. These
entrepreneurs do not seek personal gain, whether in
monetary terms or as personal satisfaction, but rather
hope to contribute to the welfare of the community by
creating a company that provides jobs and wealth.
Therefore, we call this group altruistic and competent

Table 4. Analyses of variance for decision to create the
business again
Cluster Mean
Self-employed entrepreneurs 1.2632 .4524
Ambitious entrepreneurs 4.7500 .4523
Self-realized family entrepreneurs 4.7143 .7559
Challenge entrepreneurs 4.2286 .8075
Competent and altruistic entrepreneurs 4.7037 .5417
Statistic F = 101.0466; -value associated with F = 0.0000. There
were significant differences, according to the Scheffé method
between the following pairs of Clusters: 1 and 2, 1 and 3, 1 and 4,
1 and 5. The mean takes values from 1 to 5 points.
entrepreneur. To test the validity of our cluster analysis,
we also performed a discriminant analysis. It classified
82% of the entrepreneurs correctly, that is, in the same
way as our cluster analysis, including 47.4% of
entrepreneurs in Cluster 1, 100% in Cluster 2, 100% in
Cluster 3, 91.4% in Cluster 4 and 81.5% in Cluster 5. We
thus validate the cluster analysis results. Finally, we
analyzed the Spearman’s rank correlation coefficient,
which revealed a value of 0.7081 and an associated
significance level of 0.0000. This result indicates that the
clusters generated do not simply reflect statistical inputs.
Impact of motivational factors on entrepreneurial
behavior
As the final step in our analysis, we reviewed the
influence of these various motivational factors, from the
point of view of prior theories about the decision to start a
business. Accordingly, in an analysis of variance (one
factor), we considered the potential decision to create the
company again (dependent variable) and membership in
a cluster (independent variable) to determine if there are
any significant differences between the means for each
group. The results of this analysis in Table 4 reveal that
the grouping pertaining to the decision to create the
company differs significantly from the other group (that is,
significant differences according to Scheffé’s method).
Only the group of self-employed entrepreneurs would not
be willing to create their company again, whereas the
other entrepreneurs indicated high responsiveness to this
idea, with averages exceeding 4.Thus, certain reasons
have more influence on entrepreneurial behavior, such as
the need for achievement, self-realization, independence,
affiliation, competence and power, than do other reasons,
such as making money or being one’s own boss, which
traditionally have been regarded as widespread but
actually are not sufficient to ensure entrepreneurship.
The process of starting a business usually involves a
series of obstacles that go beyond strict self-employment
(Gatewood et al., 1995).
Conclusions
Virginia and Carlos 11503
This research obtains interesting findings and makes
important contributions both for the management of small
and medium-sized companies and for the decisionmaking
policies of public administrative bodies. As the
findings have evidenced, the motivation that encourages
entrepreneurs to start up new business, their commitment
with the idea of the new firm or the efforts they are willing
to perform in order to start up the new business, along
with their flair for the process, are key in the start up of
the new ventures.
In this regard, the results we have obtained reflect our
efforts to address two objectives. First, with the
methodology we used, we can identify the main reasons
entrepreneurs to start their own businesses. Second, we
analyze the influence of each reason on the entrepreneurial
behaviors of entrepreneurs.Regarding the first
objective, the motivational factors we have identified are
similar to those that emerge from traditional classifications,
such as those published by Alderfer (1969)
Herzberg (1966) Maslow (1943) or McClelland (1961).
The classification by the latter author reveals the greatest
degree of coincidence, which suggests certain logic:
McClelland’s theory is based on empirical studies of
entrepreneurs. Therefore, we suggest that there may be
motivational differences between entrepreneurs and the
rest of the population (Begley and Boyd, 1987; Carland
and Carland, 1991; Sexton and Bowman, 1985).
Thus, our identified motivational factors largely coincide
with those proposed in prior literature, though in our
study, two needs traditionally associated with
entrepreneurs appear less significant: independence and
power. In the first case, we distinguish among personal
autonomy, independence, professional autonomy; in the
second, we recommend a distinction between personal
and institutional power, as proposed by McClelland.
Moreover, we corroborate the influence of certain reasons,
such as the need for achievement, self-realization,
independence, affiliation, competence and power, on
entrepreneurial behavior. However, making money or
being one’s own boss does not appear sufficient
motivations. In this context, Lee et al. (2011) questions
the appropriateness of traditional approaches based on
purely monetary incentives, such as the widely adopted
programs that aim to stimulate economic development or
business in depressed areas. Those responsible for
these programs suggest that the environment should be
changed; specifically, they advocate expanding the
opportunities to make money, in the hope that this
increased opportunity will invoke a strong response by
potential entrepreneurs, who can benefit from the opportunities.
However, like most assumptions, it applies only if
certain conditions are met, including those that
McClelland (1961) highlights for individuals, such as a
minimum level of the need for achievement. Therefore,
the motivation content of entrepreneurs influences their

11504 Afr. J. Bus. Manage.
influences their decision to start a business. We cannot
deny that financial support through grants or loans is
necessary to support the process of establishing a
company; lack of initial capital is one of the main obstacles
noted by entrepreneurs. However, in most cases,
financial support is insufficient, if it is not accompanied by
adequate support for and training that encourages other
motivations, beyond self-employment. Within this context,
education plays a role of great importance in the
development of entrepreneurial spirit among individuals
(Burke et al., 2002). Recent efforts made by certain
universities and academic institutions, which include
courses on business start-up are not enough. What is
necessary, though, is the inclusion of this issue as an
important subject from the lowest levels of education.
ACKNOWLEDGEMENTS
The authors acknowledge the input of the anonymous
reviewers on previous versions of this article. This
research has been funded by Junta de Comunidades de
Castilla-La Mancha (Consejería de Educación, Ciencia y
Cultura-PPII10-0236-2047).
REFERENCES
Acs ZJ, Mueller P (2008). Employment effects of business dynamics:
Mice, Gazelles and Elephants. Small Bus. Econ. 30: 85-100.
Ahmed SU (1985). NAch, risk-taking propensity, locus of control and
entrepreneurship. Personality Individual Differences, 6: 781-782.
Alderfer CF (1969). An empirical test of a new theory of human needs.
Organ. Behav. Hum. Perform., 4: 142-175.
Álvarez A, Valencia P, Barraza S, Legato AM (2010). Factors
determining the entrepreneurial consolidation in Latin America. Afr. J.
Bus. Manage. 4(9): 1717-1722.
Atkinson JW, Birch D (1978). Introduction to motivation. New York:Van
Nostrand.
Barba-Sánchez V, Martínez-Ruiz MP (2009). A longitudinal study to
assess the most influential entrepreneurs’ features on new firms’
growth. J. Small Bus. Entrepreneurship, 22(3): 253-266.
Bartlett MS (1950). Tests of significance in factor analysis. Brit. J.
Psychology (Statistical Section) 3: 77-85.
Begley TM, Boyd DP (1987). Psychological characteristics associated
with performance in entrepreneurial firms and smaller businesses. J.
Bus. Venturing, 2: 79-93.
Birch D (2009). The dynamics of the stream of behaviour. J. Math.
Psychol., 53(2): 106-118.
Birley S, Westhead P (1994). A taxonomy of business start-up reasons
and their impact on firm growth and size. J. Bus. Venturing, 9: 7-31.
Brockhaus RH, Horwitz PS (1986). The psychology of the entrepreneur.
In: DL Sexton and RW Smilor, (Eds.): The art science
entrepreneurship. Cambridge: Ballinger. pp. 25-48.
Burke AE, FitzRoy FR, Nolan MA (2002). Self-employment wealth and
job creation: The roles of gender, non-pecuniary motivation and
entrepreneurial ability. Small Bus. Econ. 19(3): 255-270.
Busenitz LW, Lau Ch (1995). A cross-cultural cognitive model of new
venture creation. Entrepreneurship Theory Pract., 20(4): 25-40.
Canabal A, O’Donnell E (2009). It’s not only about what you want, but
also how much you want it: Developing a new theoretical perspective
on entrepreneurial motivation. Frontiers Entrepreneurship Res. 29(4):
130.
Carland JA, Carland JW (1991). An empirical investigation into the
distinctions between male and female entrepreneurs and managers.
Int. Small Bus. J. 9(3): 62-72.
Cooper AC, Dunkelberg WC (1986). Entrepreneurship and paths to
business ownership. Strategic Manage. J., 7: 53-68.
Crant JM (1996). The proactive personality scale as a predictor of
entrepreneurial intentions. J. Small Bus. Manage. July: 42-49.
Douglas EJ, Shepherd DA (1997). Why entrepreneurs create
businesses: a utility maximizing response. Frontiers Entrepreneurship
Res., 17(4): 185-186.
Dubini P (1988). Motivational and environmental influences on business
start-ups: some hints for public policies. Frontiers Entrepreneurship
Res. 8(1): 31-45.
Gabrielsson J, Politis D (2011). Career motives and entrepreneurial
decision-making: examining preferences for causal and effectual
logics in the early stage of new ventures. Small Bus. Econ. 36(3):
281-298.
Gatewood EJ, Shaver KG, Gartner WB (1995). A longitudinal study of
cognitive factors influencing start-up behaviors and success at
venture creation. J. Bus. Venturing, 10: 371-391.
Herzberg F (1966). Leadership and motivation. Cambridge (MA): MIT
Press.
Herron L, Robinson RB (1993). A structural model of the effects of
entrepreneurial characteristics on venture performance. J. Bus.
Venturing, 8: 281-294.
Jenssen S, Kolvereid L (1992). The entrepreneurs’ reasons leading to
start-up as determinants of survival and failure among Norwegian
new ventures. In: S Birley, IC MacMillan: International Perspectives on
Entrepreneurship Research 1991. Elsevier Sci. Publishers.
Lazear EP (2005). Entrepreneurship. J. Labor Econ. 23(4): 649-680.
Lee L, Wong PK, Foo MD, Leung A (2011). Entrepreneurial intentions:
The influence of organizational and individual factors. J. Bus.
Venturing, 26(1): 124-136.
Maslow AH (1943). A theory of human motivation. Psychol. Rev. July:
370-396.
McClelland DC (1961). The achieving society. Princeton: Van Nostrans
Reinhold.
Naffziger DW, Hornsby JS, Kuratko DF (1994). A proposed research
model of entrepreneurial motivation. Entrepreneurship Theory Pract.,
18(3): 29-42.
Plehn-Dujowich J (2010). A theory of serial entrepreneurship. Small
Bus. Econ., 35: 377-398.
Ray DM (1986). Perceptions of risk and new enterprise formation in
Singapore: An exploratory study. Frontiers Entrepreneurship Res.
6(1): 119-145.
Reynolds PD, Carter NM, Gartner WB, Greene PG (2004). The
Prevalence of Nascent Entrepreneurs in the United States: Evidence
from the Panel Study of Entrepreneurial Dynamics. Small Bus. Econ.,
23(4): 263-284.
Scheinberg S, Macmillan IC (1988). An 11 country study of motivations
to start a business. Frontiers Entrepreneurship Res., 8(15): 669-687.
Sexton DL, Bowman NB (1985). The entrepreneur: a capable executive
and more. J. Bus. Venturing, 1: 129-140.
Shepherd DA, Detienne, DR (2005). Prior Knowledge, Potential
Financial Reward, and Opportunity Identification. Entrepreneurship
Theory Pract., 29(1): 91-112.
Simon HA (1976). Administrative behavior. New York: MacMillan.
Skinner BF (1987). Whatever happened to psychology and the science
of behaviour. Amer. Psychologist., August: 780-786.
Stewart WH Jr, Watson WE, Carland JC, Carland JW (1999). A
proclivity for entrepreneurship: a comparison of entrepreneurs, small
business owners, and corporate managers. J. Bus. Venturing, 14(2):
189-214.
Veciana JM (1989). Características del empresario en España. Papeles
de Economía Española, 39: 19-36.
Veciana JM (1999). Creación de empresas como programa de
investigación científica. Revista Europea de Dirección y Economía de
la Empresa, 8(3): 11-35.
Westhead P, Wright M (1997). Novice, portfolio and serial founders: are
they different? Frontiers Entrepreneurship Res. 17(5): 218-231.
White R (1959). Motivation reconsidered: the concept of competence.
Psychological Rev. 66(5): 297-333.
Williams LK, McGuire SJ (2010). Economic creativity and innovation
implementation: The entrepreneurial drivers of growth? Evidence
from 63 countries. Small Bus. Econ. 34:391-412.

African Journal of Business Management Vol. 5(28), pp. 11358-11374, 16 November, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM11.197
ISSN 1993-8233©2011 Academic Journals
Full Length Research Paper
Using fuzzy cognitive map and structural equation
model for market-oriented hotel and performance
Cheng-Hua Wang 1 , Shiu-Chun Chen 1 * and Kuan-Yu Chen 2
1 Graduate School of Business and Operations Management, Chang Jung Christian University, Taiwan.
2 Department of Recreation Sport and Health Promotion, National Pingtung University of Science and Technology,
Taiwan.
Accepted 17 May, 2011
Firms are different from markets, resources and managerial expertise. Many studies strongly advocate
that firms adopt a market orientation to achieve competitive advantage and can transform firm assets
into superior performance. A paucity of research exists on how to develop market-oriented hotel and
performance and is lacking an integrated framework to establish them. For establishing a guideline
enabling systematic approaches to understand them, find or choose which combinations of changes in
factors that would lead to the most desirable outcomes in order to adapt to the objectives of the hotel
and to fill some research gaps. This study adopts concept of Total Quality Management (TQM’s) IPO
(Input-Processing-Output) and Resource-Based View to construct research model and use the Fuzzy
Cognitive map and Structural Equation Model. By these objective methods make the decision maker
has a clear picture of building competitive advantages in the hotel industry, identify and assess a lot of
hypothetical situations which might occur in reality and to detect a group of the most desirable
outcomes in limited hotel resource.
Key words: Competitive advantage, market orientation, resource-based view, structural equation model, fuzzy
cognitive map.
INTRODUCTION
The hotel market is very similar, often easily substitutable
service offerings. It is difficulties for hotel managers to
differentiate an individual hotel from its competitors (Reid
and Sandler, 1992). From Resource-Based View, firms
should strive for developing and maintaining resources
that help the firms develop capabilities for implementing
value-creating strategies (Chatterjee and Wernerfelt,
1991; Hunt and Morgan, 1995). It is important in
explaining the development of competitive advantages
(Wernefelt, 1984; Barney, 1991; Bharadwaj et al., 1993),
which are difficult to duplicate by competitors. Many
studies strongly advocate that adopting market
orientation can achieve or build competitive advantage.
Market orientation can transform firm assets into superior
performance (Hult and Ketchen, 2001; Hult et al., 2005;
Zhou et al., 2005). Therefore, building market-orientated
*Corresponding author. E-mail: spssamos@xuite.net. Tel: 886-
8-7338241.
environment will strengthen competitive advantages of
enterprise (Barney, 1991). But a paucity of research
exists on how to develop a firm’s market orientation
(Wrenn, 1997; Han et al., 1998). Literature is lacking an
integrated framework to help organizations establish a
market orientation (Martin and Martin, 2005), or to
become market-oriented (Greenley, 1995; Harmsen and
Jensen, 2004).
Market orientation depends on other constructs to
strengthen its relationship with performance (Menguc and
Ash, 2006). These constructs may arise as to where the
influencing factors that may determine market orientation
come from (Avlonitis and Gounaris, 1997). In the
competitive market environment, the quality is regarded
as the basic consuming condition. If firms have customer
information without translating into quality products may
affect selling. Total Quality Management (TQM) is seen
as a means to increase marketing’s preponderance and
implementation within the organization through enhancing
customer focus acquires within overall management
system (Santos-Vijande et al., 2009), can be thought of

of as interrelated sets of dyads between internal customers
and suppliers (Goetsch and Davis, 1997) and is as
organizations strive for a competitive advantage in
markets (Sureshchandar et al., 2001). Some scholars
have suggested that in order to develop a market
orientation, a firm shall focus its internal customers and
suppliers who in turn serve external customers (Hauser
et al., 1996; Gronroos, 1990). But a lack of coordination
or rivalries and distrust among departments is not
unusual. Causing partial enterprises that have conducted
TQM has not properly used it in marketing making top
managers not to understand marketing topics. One-third
of TQM-adopting enterprises still have prejudice (Witcher,
1995). Moreover, TQM demands a large amount of time
that may be one of the reasons why TQM is not easily
implemented and do not deliver much positive results
(Ramayah and Saad, 2006). A lack of information regarding
TQM or barriers to developing market orientation
exists in the hotel and hospitality industry (Gray et al.,
2000; Harris and Watkins, 1998; Lazari and
Kanellopoulos, 2007). These factors may also cause
hotels not to adopt an integrated framework to
understand linkages between external market information
and internal activities’ quality situations, even help
organizations develop a market-oriented hotel.
Some firms have some weakness such as limited
markets, resources and lack of managerial expertise.
Some managers are also lack ability of analyzing relation
of all factors at the same time and usually tend to asses
individually or two or three factors simultaneously at best
(Kang et al., 2004), can not easily quantify the strength
and direction of the interrelationships among factors
depend on subjective or nondeterministic to evaluate and
determine. A great extent TQM is not applied because
executives have not dealt with it or consider it unnecessary
in the hotel (Lazari and Kanellopoulos, 2007).
A lot of controversies exist on elements made by the
different researchers and professionals about TQM
(Gehani, 1993). These elements of TQM will always be
the guidelines to appraise the effectiveness of implementing
TQM and results. Nevertheless, these elements
have different importance weights in terms of their final
contribution to the results (Montes et al., 2003). Do they
separately affect market orientation? That is to say. Do
these elements of TQM help to develop market-oriented
hotel environment? On the other hand, Folyey and Fahy
(2009) point out that examining the elements of a multidisciplinary
model of market orientation combine how to
create competitive advantage is a key to understand the
relationship with performance. But prior empirical results
are equivocal about the relationship between market
orientation and performance (Gray and Hooley, 2002;
Langerak, 2003). For establishing a guideline enabling
systematic approaches to develop market-oriented hotel,
find or choice which combinations of changes in factors
that would lead to the most desirable outcomes in order
to adapt to the objectives of the hotel and to fill above
Wang et al. 11359
some gaps. This study adopts TQM’s IPO (Input-
Processing-Output) concept model of Longo and Cox
(1997) and Youssef et al (1996) and Resource-Based
View to construct research model.
Fuzzy cognitive map is a kind of using soft computing
technique and is similar to human reasoning and the
human decision-making process to identify the most
relevant design factors in order to enhance outcome
variables (Stylios et al., 2008). The proposed map can
successfully represent knowledge and human experience
and can help the decision maker has a clear picture of
affecting factors and their relation in the hotel to resolve a
given decision-making problem. In addition to, for more
objective method gained to quantify the causality coefficients
and build an adjacency matrix to perform a FCM
simulation. In this paper, we use SEM to understand the
causality between variables or among multiple variables.
LITERATURE REVIEW
The relation of all variables
According to the Resource based view (RBV), competitive
advantage stems from a firm’s unique resources
that are valuable, rare, and inimitable (Barney, 1991).
The RBV of the business can sustain a competitive
advantage in respect of its competitors by owning certain
resources (Barney, 1991; Grant, 1991; Wernerfelt, 1984).
The Competitive advantage positively affects organization
performance and represents an important, yet
missing, component in existing market orientation
research (Ketchen et al., 2007). Market orientation places
the highest priority on the profitable creation and maintenance
of superior customer value (Slater and Narver,
1998), is borrowing from the management and strategy
domains (Foley and Fahy, 2009) and much of the market
orientation literature has emerged from the RBV (Gray
and Hooley, 2002). Many studies strongly advocate that
firms adopt a market orientation to achieve competitive
advantage and market orientation can transform firm
assets into superior performance (Hult and Ketchen,
2001; Hult et al., 2005; Zhou et al., 2005). Therefore, the
RBV is proposed as providing a meaningful framework to
develop understanding of market orientation (Wernefelt,
1984; Barney, 1991; Fahy et al., 2000), implementation of
the marketing orientation is the highest stage of development
for organizations and critical component of the
successful business performance (Kobylanski and Szulc,
2011). Building market-orientated environment will
strengthen competitive advantages of enterprise (Barney,
1991).
Market orientation reflects the firm’s propensity to adopt
the marketing concept. It is typically measured by
assessing firms’ commitment on customer-oriented
market intelligence (Day, 1994; Jaworski and Kohli, 1993;
Slater and Narver, 1995). During the 1990s, the study of

11360 Afr. J. Bus. Manage.
market orientation has two major streams of research.
The first stream is involved the refinement of the market
orientation measures and refine the measurement scales
of market orientation. The operationalization of market
orientation is reflected in the activities and behaviors of
an organization or as an organizational culture. Thereby
market orientation definitions within the research
community differ (Deshpande et al., 1993; Kohli and
Jaworski, 1990; Narver and Slater, 1990), but their basic
concept remains gathering information from customers,
sharing this information internally, and responding appropriately
to the changing needs of the market. Several
scales for measuring market orientation are available.
Kohli et al. (1993) develop a valid measure that includes
intelligence generation, dissemination and responsiveness.
Gray et al. (1998) suggested a parsimonious model
of market orientation from Narver and Slater (1990),
Jaworski and Kohli (1993), Deng and Dart (1994), comprising
five dimensions: customer orientation, competitor
orientation, inter-functional coordination, responsiveness,
and profit emphasis. Anwar (2008) determined that
market orientation should include customer focus,
competitive focus, environmental scanning, strategy
implementation, and development of new service.
Different firms may adopt different strategies. Market
orientation culture does not automatically lead to superior
performance. It must first enable certain organization
wide behaviors or activities, which in turn foster firm
performance (Zhou et al., 2005). Therefore market
orientation shall include information generation and
dissemination, shared interpretation, and organization
responsiveness.
The second stream is involved studying the antecedents
and consequences of market orientation. In fact,
the antecedents of market orientation as proposed by
some researchers, have a been a number of studies
linking various factors such as such as top management
(that is, emphasis, risk aversion), interdepartmental dynamics
(that is, conflict, connectedness), organizational
systems (that is, formalization, centralization, departmentalization
and reward systems) (Jaworski and Kohli,
1993), quality orientation (Sittimalakora and Hart, 2004),
interdepartmental environment and rules for job
execution (Vieira, 2010), leadership style (Farrell, 2000),
training (Ruekert, 1992; Lovelock and Weinberg, 1984),
process management (Anderson et al, 1994) and
learning environment (O’Driscoll et al., 2001). They show
that market orientation is borrowing from the
management and strategy domains and clearly makes
sense not to take an isolationist perspective, but to
acknowledge the broader (Dobni and Luffman, 2003;
Stoelhorst and van Raaij, 2004). Market orientation may
lead to success without the inclusion of other
complemented capabilities when analyzing the effect of
its value (Day, 1994; Slater and Narver, 1995). That is to
say, market orientation depends on other constructs to
strengthen its relationship with performance (Menguc and
Ash, 2006). These constructs may arise as to where the
influencing factors that may determine market orientation
come from (Avlonitis and Gounaris, 1997).
Total Quality Management (TQM) is seen as a means
to increase marketing’s preponderance and implementation
within the organization via the enhanced focus that
customer orientations acquires within overall management
system (Santos-Vijande et al., 2009), can be
thought of as interrelated sets of dyads between internal
customers and suppliers (Goetsch and Davis, 1997) and
is as organizations strive for a competitive advantage in
markets (Sureshchandar et al., 2001). Some scholars
have suggested that in order to develop a market
orientation, a firm shall focus its internal customers and
suppliers who in turn serve external customers (Hauser
et al., 1996; Gronroos, 1990). However, from Saraph et
al.’s (1989), many studies have tried to develop an
appropriate set of critical quality management constructs
representing an integrated approach to TQM
implementation in a business unit (Ahire et al., 1996a, b;
Anderson et al., 1995; Flynn et al., 1995; Grandzol and
Gershon, 1998, Rao et al., 1999). TQM system accords
to the IPO (Input-Processing-Output) concept model to
display the relationships between the TQM system and
participants (Longo and Cox, 1997; Youssef et al., 1996).
Input is defined as that which enlarges the process and
involves both the internal and external environments.
Processing is aimed at the needs of the customer in both
the present and future when top management must
combine input with the organizational capability of coping
with desired goals. Output is defined as all participants
(that is, an organization’s members and departments,
suppliers and customers) to deliver designed services as
reliably and economically as possible to ensure
profitability and customer satisfaction. TQM involves
developing an enhanced interdepartmental dialogue
within the organization, has pervaded organization
management levels to a far greater extent than market
orientation, and focuses on the internal processes to
improve the implementation of the marketing concept
(Mohr-Jackson, 1991). Moreover, market orientation is
gathering, sharing, and responding appropriately to the
changing needs of the market to achieve organizational
goals and satisfy the needs and wants of customers.
Therefore, market orientation can be regarded as a
processing variable in the IPO model of TQM.
The elements of TQM will always be the guidelines to
appraise the effectiveness of implementing TQM and
lead to different implementing results. Nevertheless,
these elements have different importance weights in
terms of their final contribution to the results (Montes et
al., 2003). Grandzol and Gershon (1998) have addressed
that elements of TQM include customer focus, continuous
improvement, leadership, internal/external cooperation,
employee fulfillment, learning, and process management.
In the hotel industry, firms reach collaboration with
internal and external departments or units, introduce

process improvement and invest in continuous improvement
that can help them to achieve higher performance
(Dale and Plunket, 1990; Claver-Cortés et al., 2006).
Enhancing the morale of employee fulfillment will result in
the increase of a hotel’s efficiency (Lazari and
Kanellopoulos, 2007), along with the means for
implementing the mechanism effectively such as reward
and performance management system. Leadership and
guest focus are the principles most commonly incorporated
into TQM programs of hotels (Breiter and Bloomquist,
1998; Eliza et al., 2007). Learning involves companywide
training that acquires a strategic value for hotels
(Boudreau et al., 2001; Claver-Cortés et al., 2006;
Tihanyi et al., 2000) and enhances both the skill level of
staffs and their degree of commitment to provide
excellent service (Costa, 2004; Haynes and Fryer, 2000).
Therefore, this study proposes that seven elements of
TQM will help to develop a work environment directed to
the adopting of the market orientation.
The consequences of market orientation are organized
into four categories: organizational performance,
customer consequences, innovation consequences, and
employee consequences (Jaworski and Kohli, 1996).
Market orientation provides a firm with market-sensing
and customer-linking capabilities that lead to superior
organizational performance (Day, 1994; Hooley et al.,
2005), enhance customer-perceived quality of the organization’s
products and services by helping create and
maintain superior customer value (Brady and Cronin
2001), in turn customer satisfaction and loyalty (Slater
and Narver, 1994). However, prior studies assess hotel
performance through the lodging index (Wassenaar and
Stafford, 1991), revenue growth rates (Van Doren and
Gustke, 1982), both objective and perceptual (Haber and
Reichel, 2005), or financial and non-financial performance
(Banker et al., 2000, 2005). Objective is measured by
occupancy rate per room, gross operative profit and
gross operative profit per available room per day.
Perceptual contains competitive performance, and
stakeholder satisfaction. Any organization wants to
continue operations need finance performance support.
To maximize its long-run performance, the business
knows it must build and maintain a mutually beneficial
relationship with its buyer (Narver and Slater, 1990).
Therefore, hotel performance is measured by Moorman
and Rust (1999) and Narver and Slater (1990), which
including financial and customer-based performance in
this paper.
Several research findings indicate that there is no
significant direct relationship between market-orientated
management and financial performance such as market
share, return on equity, profitability, growth rate (Jaworski
and Kohli, 1993; Pelham, 1997; Becker and Homburg,
1999; Sittimalakorn and Hart, 2004; Agarwal et al., 2003).
But others find to have direct effect (Pelham and Wilson,
1996; Slater and Naver, 1994, 2000; Siguaw et al., 1994;
Jaworski and Kohli, 1993; Naver and Slater, 1990).
However, effective information acquisition and
Wang et al. 11361
dissemination produced a high level of market orientation
which is essential for creating and managing closer
customer relationships with a good understanding of what
customer value and firms consistently and quickly deliver
high quality products and services in responding to
changing market conditions (Ahire et al., 1996a). This
study pro-poses that a market-oriented hotel can afford
better pro-duct or service quality based on consumer
data in order to achieve greater customer satisfaction and
finance performance.
Fuzzy cognitive map (FCM)
Cognitive map (CM)
Cognitive map (CM) is commonly considered best
methods of solute problems where experts can afford
diverse opinions to gain a correct answer. The concept of
CM is first proposed and applied by Axelrod (1976), is a
representation of the causal relationships among the
elements of a given environment or object and problem.
A cognitive map is composed of nodes that may
represent variables, states, events, inputs and output
which are the key elements of the problem and are
essential to model a system, arrows that indicate different
causal relationships among factors and causality factors
on each arrow indicating a negative (or positive) strength
with which a node affects another. The graphical
representation of a CM is given as an example in Figure
1 (Kardaras and Karakostas, 1999). The variables X, W,
Y, Z and F are represented as nodes; and the causal
relationships as directed graphs between variables, thus
constructing a signed digraph. A path between two
variables X and Y in a CM is a sequence of all nodes that
are connected by an arrow from the first node X to the
last node Y. There are two kinds of path. One is XWY.
Another is XFZY. The total effect of variable X to variable
Y is the sum of the indirect effect of X to Y through the
paths XWY and XFZY. Both indirect effects are positive,
which means that the total effect is also positive (+).
More specific and information rich cognitive maps are
achieved by replacing those signs by positive or negative
numbers, showing not only the direction but also the
magnitude of the change. It can yield insights into indirect
effects among nodes. Such indirect effects can be
understood only after the entire map is displayed. But CM
describes experts’ perceptions about the subjective world
rather than objective reality. It is difficult to determine and
gauge the precise strength of the interrelationships
among factors by experts. To quantify causality
coefficients objectively is difficult. CM can be generalized
into fuzzy cognitive map (FCM) by fuzzy edge values or
causality values.
Fuzzy cognitive map (FCM)
FCM is fuzzy-graph structures for representing causal

to variable Y is the sum of the indirect effect of X to Y through the paths XWY and XFZY.
th indirect 11362 effects Afr. J. are Bus. positive, Manage. which means that the total effect is also positive (+).
-
_
_
-
-
X
F
Figure 1. Cognitive map.
+ W +
Figure 1. Cognitive map.
re specific reasoning and (Kosko, information 1986). FCM rich is consisted cognitive by nodes maps and are value, achieved as B (t+1)=S〔B(t).F〕. by replacing Where those B (t) signs is the by state
weighted arcs. Nodes of the graph stand for the concepts
itive or negative numbers, showing not only the direction vector (1×n) but of concept also the at some magnitude time step t. F of is the FCM
that are similar to CM. Weighted arcs represent the
connection matrix (n×n). An FCM is constructed based on
nge. It causal can yield relationships insights that exist into between indirect the effects concepts. among knowledge nodes. from Such a number indirect of experts effects in regard can be to the
Each concept is characterized by a number Bi that it
erstood results only from after transformation the entire of the map fuzzy is real displayed. value of the But same CM issue describes can be combined. experts’ Each perceptions expert’s FCM about is
additively superimposed, whereby Kosko (1992, 1997)
subjective system’s world variable. rather Between than concepts, objective the edge eij reality. form It is difficult to det
the causal concept Bi to concept Bj measures how much
Bi causes Bj. When the weight is positive which the
relationship between the two nodes is positive, zero when
there is not any correlation, and a negative number when
the relationship is negative. Therefore, if specific nodes
are stimulated, the resulting activities can resonate
through other nodes on the map along positively or
negatively weighted connections (Lee and Ahn, 2009).
FCM is described by the connection matrix and the
activation levels of its nodes can be represented as a
state vector (Kosko, 1992). The matrix is formed by a
number of causality coefficients on paths among factors.
It is called the adjacency matrix that is composed of row
and column factors, and corresponding causality
coefficients between them. Row factors are perceived to
cause factors and column factors are construed to effect
factors. The values of nodes B1, B2,…..Bn together
represent the state vector B which is called ‘What-if’ that
is performed by decision maker’s intention. The value of
each element of the input vector can be 1 or 0 according
to whether one element is enhanced or not. For example,
an FCM state vector B (01101) means that the five nodes
that form the FCM, the 2nd, 3rd and 5th nodes are
activated, the 1st and 4th nodes are inactive at that
particular time. Therefore, through what–if simulations,
decision makers can identify a set of relevant decision
variables and their acceptable values intended results. In
order to compute an FCM state vector B at time step
(t+1) the connection matrix F is multiplied by the state
vector B (t). Kosko (1992, 1994) found that a threshold
function was then applied so as to normalize the state
+
Y
Z
-
_
_
-
-
address the equation is used, as seen below, where Fi
represented the augmented FCM matrix for expert i, n is
equal to the number of experts, wi is equal to the
credibility weight of expert i.
n
wiF
F= i
t 1
FCM is a soft computing technique that follows an
approach similar to human reasoning and the human
decision-making process (Stylios et al., 2008). FCM
closely corresponds to humans perceive and is easily
altered to incorporate new phenomena (Rodriguez-
Repiso et al., 2007). Therefore, it is a dynamic modeling
tool (Irani et al., 2002) and is easily understandable,
which can be used to analysis, test the influence of parameters
and predict behavior of the system (Rodriguez-
Repiso et al., 2007), provide an inference mechanism
that enables the fuzzy causal relations among factors to
be identified and their impact to be constructed (Lee and
Ahn, 2009) and accommodates this knowledge-base
building property (Papageorgiou et al., 2009). Clearly
FCM is significantly more flexible, valuable, and efficient
than CM, and is a proven vehicle for representing such
causal knowledge (Lee and Kim, 1997; Noh et al., 2000).
It has been applied in Web-log data containing useful or
meaningful information (Lee et al., 2002), design of
agents (Miao et al., 2002), relationships management
(Kang et al., 2004), support urban design (Xirogiannis et
al., 2004), design of EDI controls (Lee and Lee, 2007),

Table 1. Construct measurement.
Wang et al. 11363
Construct Construct definition Construct sources
Customer Focus
Hotel’s customers perceive their needs being met by the way hotel’s products and
services.
internal/external
cooperation
Continuous
improvement
Leadership
Hotel engages in noncompetitive activities among employees and externally among
suppliers.
Hotels pursue incremental and innovative improvements of its processes, products
and services.
Senior executives establish and lead a long-term vision for the whole organization,
driven by changing customer requirements, as opposed to internal management
control.
Employee fulfillment Employees of the hotel feel the degree which hotel satisfies their needs.
Learning
Process
management
Market Orientation
Finance performance
Customer
performance
Hotel recognizes and supports the development of employees’ skills, abilities, and
knowledge
Hotel has the set of technical and behavioral practices emphasizing the
management of processes, or means of actions
A series of market information handle including information generation and
dissemination, shared interpretation, organization responsiveness.
To assess the hotel’s strategic market and financial outcomes, such as market
growth and ROI
To assess the firm’s customer-based performance, including its customer
satisfaction and customer retention
for medical decision support systems (Stylios et al.,
2008), for the design of controls in business-to-consumer
e-commerce web-based systems (Lee and Ahn, 2009),
cotton yield management in precision farming
(Papageorgiou et al., 2009).
METHODOLOGY
Questionnaire development and pilot test
The main method used in this study was a survey research. To do
so, a questionnaire was designed. All the focal constructs of the
model were measured using multiple items based on validated
scales derived from Grandzol and Gershon (1998), Kohli et al.
(1993), Huber (1991), Moorman and Rust (1999) and Narver and
Slater (1990). Table 1 summarizes the constructs, the definition and
sources of scales.
The questionnaire was first developed in English, but as the
survey was conducted in Chinese, we used hotel managers and
academicians to aid in the process of translation. The wording and
interpretation of items and the extent which respondents would feel
them posses the necessary knowledge to provide appropriate
responses scrutinized until a final draft of the questionnaire.
After the draft questionnaire was developed, used respondent
Grandzol and Gershon
(1998)
Kohli, Jaworski, and
Kumar (1993) Huber
(1991)
Moorman and Rust
(1999)
Narver and Slater
(1990)
anonymity, meaning anonymity of the measurement items and pilottested
by 60 hotels’ managing directors in order to correct possible
defects and doubts. The result of pilot-test is that all variables’
dimensions reliability is greater than Hair et al. (1998) suggested
standard value 0.7. Items that do not significantly contribute to the
reliability and have lower reliability are eliminated. The questionnaire
included 45items that are retained for the main study (shows
in Appendix A). Items were measured on the 7-point Likert-type
scale ranged from strongly disagree to strongly agree.
Sample and data collection
The Tourism Bureau, M.O.T.C. Republic of China are responsible
for the administration of domestic and international tourism policy
making, execution and development in R.O.C. Our sampling frame
derives from Tourism Bureau in Dec. 30 th , 2009 statistics and
displays 2,613 hotels. Owing to managers are widely believed to
know the degree of development of all managerial factors, thereby
them can provide the best information about hotels’ business. The
questionnaire survey was mailed to them. A personalized cover
letter and a pre-paid envelope accompanying each questionnaire
explained the purpose of the study and assured confidentiality of
the responses. On the other, hand to obtain a high level of
participation, the study also offered respondents an executive
summary of the findings on completion of the study. The effective

11364 Afr. J. Bus. Manage.
Table 2. Construct reliability and convergent validity coefficients.
Construct Number of Items SFL 1 (min-max) t-value 1 (min-max) α 1 CR 1 AVE 1
Total quality management 7
Customer focus (TQM1) 3 0.91-0.93 37.15~37.62 0.94 0.94 0.85
Internal/External cooperation (TQM2) 5 0.84-0.90 26.30~30.20 0.94 0.94 0.76
Continuous improvement (TQM3) 3 0.91-0.93 37.29~37.74 0.94 0.94 0.85
Leadership (TQM4) 4 0.91-0.92 38.19~38.98 0.96 0.96 0.84
Employee fulfillment (TQM5) 3 0.92~0.93 38.09~38.22 0.95 0.95 0.86
Learning (TQM6) 4 0.73-0.79 16.93~17.49 0.85 0.85 0.60
Process management (TQM7) 6 0.91-0.93 38.0~40.67 0.97 097 0.85
Market orientation (2 nd order CFA) 4
0.65-0.75 10.98-11.54 0.90 0.80 0.50
Information generation (MO1) 2 0.90-0.94 17.71-19.98 0.92 0.92 0.85
Information dissemination (MO2) 2 0.91-0.93 18.67-20.47 0.93 0.92 0.85
Shared interpretation (MO3) 2 0.89-0.94 17.70-21.52 0.92 0.91 0.84
Organization responsiveness (MO4) 3 0.87-0.97 17.63-21.68 0.94 0.95 0.85
Hotel performance
2
Customer performance (HP1) 4 0.90-0.91 39.32~40.72 0.96 0.95 0.82
Finance performance (HP2) 4 0.90~0.93 38.66~41.19 0.96 0.95 0.93
SFL, standardized factor loading; α, Cronbach’s alpha coefficient; CR, composite reliability; AVE, average variance extracted.
sample size for this analysis is 588. The overall response rate is
22.5% (588/2,613). The sample size of 588 is adequate for models
with four constructs by Hair et al. (2006) recommended guidelines.
As in any type of survey research, non-response bias shall be
test. In this paper, we adopt Armstrong and Overton (1997) concept
that suggests to test for non-response bias in mail surveys and to
assume non-respondents to be late respondents. The dataset was
divided into two according to the number of days from initial mailing
until receipt of the returned questionnaire. Early respondents were
compared with late respondents along questionnaire items of each
of the scales and used t-test procedure finds indicate no significant
differences between the early and late respondent group variances
which show non-response bias is not a problem and don’t influence
in this research. In addition, all measurement items are filled by a
single respondent easily have Common Method Variance (CMV)
problem (Podsakoff and Organ, 1986), which is one of the main
sources of measurement error. Measurement error threatens the
validity of the conclusions about the relationships between
measures (Nunnally, 1978; Spector, 1987; Bagozzi and Yi, 1991).
We use Harman’s single-factor to test CMV (Andersson and
Bateman, 1997; Aulakh and Gencturk, 2000). All factors were
extracted with the first factor accounting for 34.252% of the total
variance. It is lower than 0.50 (Peng et al., 2006). Clearly the
observed relationships among constructs are not largely accounted
for by the systematic variance associated with the measurement
technique.
Research approaches
Causality is one of explanation form of events. The prior researchers
used for the statements of decision makers, Neural network,
or ask experts to suggest overall causality coefficients for each
causal relationship (Eden et al., 1979; Caudill, 1990; Lee and Kim,
1997). Causal relationships can also use FCM (Huff, 1990). FCM
allows a set of identified causality coefficients to form an adjacency
matrix and yield a simulation. This simulation enables designers to
identify the most relevant design factors to enhance outcome
variables and help the decision maker has a clear picture of
affecting factors and their relation in the hotel performance. But this
map is difficult to gauge their strength. And each map has less
accuracy and reliably, the results cannot precisely describe (Kang
et al., 2004; Lee and Ahn, 2009). For more objective method
required to quantify the causality coefficients, build an adjacency
matrix and indicate the significance of causal links to perform a
FCM simulation. In this paper, we use SEM to understand the
causality between variables or among multiple variables.
RESULTS AND ANALYSES
Reliability and validity analyses
A two-step structural equation modeling was used to test
the hypothesized model. Maximum likelihood was used
for all parameter estimation with Amos 16. The first confirmatory
factor analysis (CFA) is conducted to evaluate
the measurement model for modeled constructs. CFA
enables performance of tests regarding the reliability,
convergent validity and discriminate validity of the measurement
model. To assess reliability and internal validity
of the measurement model is examined by calculating the
composite reliability (CR) and average variance extracted
(AVE). As seen in Table 2, the composite reliability coefficients
of all the constructs are acceptable, being larger
than 0.6 (Bagozzi and Yi, 1988; Hair et al., 1998). The
AVE of each measure is more than 50% of the variance
as suggested by Bagozzi and Yi (1988) and indicates that
the variance captured by the construct is greater than the
variance due to measurement error (Fornell and Laker,
1981). Therefore, the internal validity of the measurement

Wang et al. 11365
Table 3. Discriminate validity coefficients a
TQM1 TQM2 TQM3 TQM4 TQM5 TQM6 TQM7 MO1 MO2 MO3 MO4 HP1 HP2
TQM1 0.92
TQM2 0.421 0.87
TQM3 0.418 0.444 0.92
TQM4 0.422 0.353 0.371 0.92
TQM5 0.429 0.478 0.431 0.404 0.93
TQM6 0.396 0.356 0.383 0.399 0.373 0.77
TQM7 0.456 0.368 0.333 0.392 0.334 0.386 0.92
MO1 0.341 0.198 0.252 0.366 0.305 0.236 0.244 0.92
MO2 0.321 0.288 0.243 0.236 0.392 0.224 0.228 0.382 0.92
MO3 0.185 0.136 0.129 0.174 0.170 0.100 0.187 0.301 0.314 0.92
MO4 0.217 0.207 0.160 0.232 0.175 0.170 0.151 0.347 0.369 0.259 0.92
HP1 0.392 0.327 0.394 0.348 0.372 0.300 0.325 0.386 0.403 0.172 0.261 0.91
HP2 0.347 0.357 0.365 0.347 0.338 0.341 0.354 0.321 0.284 0.230 0.243 0.646 0.96
a Diagonal elements (bold) are the square root of average variance extracted (AVE) between the constructs and their measures. Off-diagonal
elements are correlations between constructs.
model is adequate.
Convergent validity is a measure of the degree which
two observed variables to measure the same construct
correlated and is expected when each measurement’s
estimated pattern coefficient on its underlying construct
factor is significant. Items have a factor loading over 0.45
(Jöreskog and Sörbom, 1996). In this paper, the
convergent validity result of each latent variable is
presented in Table 2. Standardized factor loading of each
sub-dimension is all above 0.45 and significant. Therefore,
convergent validity was achieved for all the study
constructs.
Discriminate validity was assessed according to Fornell
and Larcker’s (1981) suggested approach. By examining
AVE for each of the latent constructs and comparing this
to the squared correlations among the constructs, the
shared variance among any two constructs (that is, the
square of their inter-correlation) was always less than the
average variance explained by the construct, which
suggests that discriminate validity has been achieved. In
this paper the result of discriminate validity shows in
Table 3. Given the discriminate validity, we conclude that
all measures exhibit construct validity. Based on all of the
reliability and validity analysis, the scale for the constructs
appears to exhibit satisfactory measurement qualities and
is adequate.
FCM simulation
For establishing a guideline enabling systematic approaches
to develop market-oriented hotel, helps the decision
maker has a clear picture of affecting factors and their
relation in the hotel industry. It is necessary to devise a
systematic way to estimate the causal relationships
among customer focus, internal/external cooperation,
continuous improvement, leadership, employee
fulfillment, learning and process management, market
orientation, finance and customer performance. Although
experts can assign numbers to the causal relationship but
it is difficult to gauge their strength and has less accuracy
and reliability, the resulting combined map cannot
precisely describe the actual state of the hotel performance.
For more objective method gained to quantify the
causality coefficients to perform a FCM simulation. In this
paper, we use SEM to understand the causality between
variables or among multiple variables. This approach can
validate the significance of causal links.
SEM analysis was performed and SEM results depicted
in Figure 2 are
2 =2064.93, df=731, X 2 /df=2.82;
GFI=0.86, RMSEA=0.056, AGFI=0.82, NFI=0.910,
CFI=0.94, RFI=0.91, IFI=0.940, PNFI=0.86, PGFI=0.72.
The results show in Figure 2 that the structural model
exhibits a good fit with the data, with fit indices of fulfilling
the respective benchmarks (Bagozzi and Yi, 1988; Doll et
al., 1991; Hair et al., 1998) and the path coefficients for
the model and their significance levels. Figure 2 shows
that customer focus, internal/external cooperation, continuous
improvement, leadership, employee fulfillment,
training and process management positively affected to
market orientation. Market orientation positively affected
to finance and customer performance.
The fuzzy cognitive map (FCM) yields an adjacency
matrix where is organized in the enhancement of some
factors causes an effect on other factors. It includes
important information such as direct effects as well as
indirect effects from SEM model. Table 4 shows
adjacency matrix that is derived from the standardized
estimates effect as suggested in Figure 2 and Table 5.
Row factors are perceives as cause factors and column
factors are construed as effect factors.
Therefore, this study categorized eight factors such as
customer focus, internal/external cooperation, continuous

11366 Afr. J. Bus. Manage.
Customer focus
Continuous improvement
Leadership
Internal/External
cooperation
Employee fulfillment
Learning
Process management
0.24***
0.13*
0.19**
0.21***
0.23***
0.12*
0.13*
Market orientation
0.66***
Customer performance
0.72***
Finance performance
Figure 2. Causal effects among factors.
*p

Table 5. Estimates of direct and indirect effects.
Wang et al. 11367
Causal path Standardized coefficient t value
customer focus → market orientation Direct effect 0.24 3.86 ***
internal/external cooperation → market orientation Direct effect 0.13 2.15*
continuous improvement → market orientation Direct effect 0.19 3.28**
leadership →market orientation Direct effect 0.21 3.66***
employee fulfillment →market orientation Direct effect 0.23 3.88***
learning →market orientation Direct effect 0.12 2.11*
process management → market orientation Direct effect 0.13 2.33*
market orientation →Customer performance Direct effect 0.72 11.38***
market orientation →Finance performance Direct effect 0.66 10.90***
customer focus →Customer performance Indirect effect 0.17 -
internal/external cooperation →Customer performance Indirect effect 0.09 -
continuous improvement →Customer performance Indirect effect 0.13 -
leadership →Customer performance Indirect effect 0.15 -
employee fulfillment →Customer performance Indirect effect 0.17 -
learning →Customer performance Indirect effect 0.09 -
process management →Customer performance Indirect effect 0.09 -
customer focus →Finance performance Indirect effect 0.16 -
internal/external cooperation →Finance performance Indirect effect 0.08 -
continuous improvement →Finance performance Indirect effect 0.12 -
leadership →Finance performance Indirect effect 0.14 -
employee fulfillment →Finance performance Indirect effect 0.15 -
learning →Finance performance Indirect effect 0.08 -
process management →Finance performance Indirect effect 0.09 -
*p

11368 Afr. J. Bus. Manage.
(Gehani, 1993). These elements of TQM will always be
the guidelines to appraise the effectiveness of implementing
TQM and results. Nevertheless, these elements
have different importance weights in terms of their final
contribution to the results (Montes et al., 2003). In this
paper, the result was showed that customer focus was
the biggest weights to affect market orientation, customer
performance and finance performance, follow as
employee fulfillment and internal/external cooperation.
3. Prior empirical results are equivocal about the relationship
between market orientation and performance (Gray
and Hooley, 2002; Langerak, 2003). In this paper, we
found that market orientation positively affected customer
performance and finance performance. They are consistent
with Pelham and Wilson (1996), Slater and Naver
(1994, 2000), Siguaw et al. (1994), Jaworski and Kohli
(1993), Naver and Slater (1990) and Agarwal et al.
(2003). They might be effective information acquisition,
dissemination and sharing information produced a high
level of market orientation which was quickly responding
to change market conditions and lead to enhance hotel
performance.
4. Managers are lack ability of analyzing relation of all
factors at the same time and usually tend to asses
individually or two or three factors simultaneously at best
(Kang et al., 2004). In addition, managers depend on
subjective or nondeterministic to evaluate and determine
the interrelationships among factors. We use FCM and
SEM. In this paper, we found that managers could find or
choice which combinations of changes in design factors
that would lead to the most desirable outcomes in terms
of market orientation and hotel performance. For
instance, when the number of factors that were enhanced
is five, the result in the highest hotel performance is input
#54. Hotel managers should focus on customer focus,
internal/external cooperation, continuous improvement,
leadership, employee fulfillment and market orientation.
Theoretical and managerial implications
1. Current portrayals of the RBV make clear that a
resource of competitive advantage is valuable to
customers or enables the creation of value for customers.
Market orientation can achieve competitive advantage
and may enhance hotel performance. But it may not be a
unique strategic resource and requires complementary
resources. According to the result, hoteliers shall be
aware that changes in consumer perception and
competitor activities are important for the hotel. Hoteliers
must continuously educate and train employees to detect
and to understand such changes. Furthermore, sharing
information of customers and competitors within the hotel
fulfills customer needs and expectations with new
solutions. In addition, hoteliers should more effectively
reinforce customer focus, continuous improvement,
leadership, internal/external cooperation, employee
fulfillment, learning, and process management to facilitate
the implementation of the market orientation values and
beliefs, which will enable it to successfully respond to the
external challenges.
2. In this paper, the proposed map helps decision makers
have a clear picture of building competitive advantages in
the hotel industry, identify and assess a lot of
hypothetical situations that might occur in reality and to
detect a group of the most desirable outcomes in limited
organization resource. It proves that FCM is a very
usefully technique for capturing specify understanding of
managers and their perceptions in the hotel industry, offer
a lot of opportunities for objectively identifying the relative
strength and direction of research variables and simulate
comprehensive models which integrate practice and
theoretical approaches.
RESEARCH LIMITATIONS AND RECOMMENDATIONS
1. For more objective method gained to quantify the
causality coefficients and build an adjacency matrix to
perform a FCM simulation. In this paper, we use
questionnaire survey, respondent anonymity, meaning
anonymity of the measurement items and Harman’s
single-factor to eliminate and to test CMV. Beside this
study adopt SEM to understand the causality between
variables or among multiple variables. It suggests that the
questionnaire could be divided into half items and filled
out marketing directors, general managers or by multiple
participants separately to eliminate measurement errors.
On the other hands, more simulation results will be compared
the other methods can be showed in the future.
2. Hotel business must consider environment factors’
moderating effects. In the future, we suggest that the
following researcher can use FCM probe it and add more
relevant factors in view of the hotel industry for enhancing
customer or finance performance.
REFERENCES
Agarwal S, Erramilli MK, Dev CS (2003). Market orientation and
performance in service firms: role of innovation. J. Serv. Mark., 17:
68-82.
Ahire SL, Golhar DY, Waller MMA (1996a). Development and validation
of TQM implementation constructs. Decis. Sci. J., 27(1): 23-56.
Ahire SL, Waller MMA, Golhar D (1996b). Quality management in TQM
versus non-TQM firms: an empirical investigation. Int. J. Qual. Reliab.
Manage., 13(8): 8-27.
Anderson JC, Rungtusanatham M, Schroeder RG (1994). A theory of
quality Management underlying the Deming management method.
Acad. Manage. Rev., 19(3): 472-509.
Anderson JC, Rungtusanatham M, Schroeder RG, Devaraj S (1995). A
path analytic model of a theory of quality management underlying the
Deming management method: preliminary empirical findings. Decis.
Sci. J., 26(5): 637-658.
Andersson LM, Bateman TS (1997). Cynicism in the workplace: Some
causes and effects. J. Organ. Behav., 18: 449-469.
Anwar SA (2008). A factor analytic investigation of the construct of
market orientation. Int. J. Manage., 25(1): 186-197.
Armstrong JS, Overton TS (1997). Estimating nonresponse bias in mail
surveys. J. Mark. Res., 14(3): 396-402.
Aulakh PS, Gencturk EF (2000). International principal–agent
relationships-control, governance and performance. Ind. Mark.

Manage., 29: 521-538.
Avlonitis GJ, Gounaris SP (1997). Marketing orientation and company
performance. Ind. Mark. Manage., 26(1): 385-402.
Axelrod R (1976). Structure of decision: The cognitive maps of political
elites. Princeton University Press.
Bagozzi RP, Yi Y (1988). On the evaluation for structural equation
models. J. Acad. Mark. Sci., 16(1): 74-94.
Bagozzi RP, Yi Y (1991). Multitrait–multimethod matrices in consumer
research. J. Consum. Res., 17: 426-439.
Banker RD, Potter G, Srinivasan D (2000). An empirical investigation of
an incentive plan that includes nonfinancial performance measures.
Account. Rev. 75(1): 65-92.
Banker RD, Potter G, Srinivasan D (2005). Association of nonfinancial
performance measures with the financial performance of a lodging
chain. Cornell Hotel Restaurant Adm. Q., 46(4): 394-412.
Barney J (1991). Firm resources and sustained competitive advantage.
J. Manage., 17(1): 99-120.
Becker J, Homburg Ch (1999). Market-oriented management: a
systems-based perspective. J. Mark. Focused Manage., 4(1): 17-41.
Bharadwaj SG, Varadarajan PR, Fahy J (1993). Sustainable
competitive advantage in service industries: a conceptual model and
research propositions. J. Mark., 57: 83-99.
Boudreau JW, Boswell WR, Judge TA (2001). Effects of personality on
executive career success in the United States and Europe. J. Vocat.
Behav., 58: 53-81.
Brady MK, Cronin JJ (2001). Effects on customer service perceptions
and outcome behaviors. J. Serv. Res., 3: 241-51.
Breiter D, Bloomquist P (1998). TQM in American hotels: an analysis of
application. Cornell Hotel Restaur. Adm. Q., 39(1): 26-33.
Caudill M (1990). Using neural nets: fuzzy cognitive maps. Artif. Int.
Expert, 6: 49-53.
Chatterjee S, Wernerfelt B (1991). The link between resources and type
of diversification: theory and evidence. Strateg. Manage. J., 12: 33-
48.
Claver-Cortés E, Tarí-Guillo ’ JJ, Pereira-Moliner J (2006). Does quality
impact on hotel performance? Int. J. Contemp. Hosp. Manage., 18(4):
350-358.
Costa J (2004). The Portuguese tourism sector: key challenges for
human resources management. Int. J. Contemp. Hosp. Manage.,
16(7): 402-407.
Dale BG, Plunkett JJ (1990). Managing Quality. Philip Allan: Hemel
Hempstead.
Day GS (1994). The capabilities of market-driven organizations. J.
Mark., 58 (4): 37-52.
Deng S, Dart J (1994). Measuring market orientation: A Multi-factor,
Multi-item Approach. J. Mark. Manage., 10(8): 725-742.
Deshpande R, Farley JU, Webster FWJr (1993). Corporate culture,
customer orientation and innovativeness in Japanese firms: A
quadrad analysis. J. Mark., 57: 23-37.
Dobni CB, Luffman G (2003). Research notes and commentaries:
determining the scope and impact of market orientation profiles on
strategy implementation and performance. Strat. Manage. J., 24(6):
577-585.
Doll WJ, Xia W, Torkzadeh G (1994). A confirmatory factor analysis of
the end-user computing satisfaction instrument. MIS Q., 18(4): 453-
462.
Eden C, Jones S, Sims D (1979). Thinking in Organizations. London:
Macmillian.
Eliza LL, Tse CY, Xie L (2007). Hotel general manager profile in China: a
case of Guangdong Province. Int. J. Contemp. Hosp. Manag., 19(3):
263-274.
Fahy J, Hooley G, Cox T, Beracs J, Fonfara K, Snoj B (2000). The
development and impact of marketing capabilities in Central Europe.
J. Int. Bus. Stud., 31(1): 63-81.
Farrell MA (2000). Developing a market-oriented learning organization.
Aust. J. Manage., 25(2): 210-222.
Flynn BB, Schroeder RG, Sakakibara S (1995). The impact of quality
management practices on performance and competitive advantage.
Decis. Sci. J., 26(5): 659-692.
Folyey A, Fahy J (2009). Seeing market orientation through a
capabilities lens. Eur. J. Mark., 43(1/2): 13-20.
Fornell C, Laker DF (1981). Evaluating structural equation models with
Wang et al. 11369
unobservable variables and measurement error. J. Mark. Res., 18(1):
39-50.
Gehani RR (1993). Quality value-chain: a meta-synthesis of frontiers of
quality movement. Acad. Manage. Exec., 7: 29-42.
Goetsch DL, Davis SB (1997). Introduction to Total Quality. New
Jeresy: Prentice-Hall.
Grandzol JR, Gershon M (1998). A survey instrument for standardizing
TQM modeling research. Int. J. Qual. Sci., 3(1): 80-105.
Grant RM (1991). The resource-based theory of competitive advantage:
Implications for strategy formulation. Calif. Manage. Rev., 33(1): 114-
135.
Gray BJ, Hooley GJ (2002). Market orientation and service firm
performance-a research agenda. Eur. J. Mark., 36(9/10): 980-988.
Gray BJ, Matear S, Boshoff C, Matheson PK (1998). Developing a
better model of market orientation. Eur. J. Mark., 32(9): 884-903.
Gray BJ, Matear SM, Matheson PK (2000). Improving the performance
of hospitality firms. Int. J. Contemp. Hosp. Manage., 12(3): 149-155.
Greenley GE (1995). Market orientation and company performance
empirical evidence from UK companies. Br. J. Manage., 6(1): 1-13.
Gronroos G (1990). Service management and marketing: Managing the
moments of truth in services Competition. Lexington, MA: Lexington
Books.
Haber S, Reichel A (2005). Identifying performance measures of small
ventures-the case of the tourism industry. J. Small. Bus. Manage.,
43(3): 257-286.
Hair JF, Anderson RE, Tatham RL, Black WC (1998). Multivariate Data
Analysis with Readings. New York: Macmillan Publishing.
Hair JF, Black WC, Babib BJ, Anderson RE, Tatham RL (2006).
Multivariate Data Analysis. 6th ed. New Jersey: Prentice-Hall.
Han JK, Kim N, Srivastava RK (1998). Market orientation and
organizational performance: is innovation a missing link? J. Mark.,
62(4): 30-45.
Harmsen H, Jensen B (2004). Identifying the determinants of value
creation in the market: a competence-based approach. J. Bus. Res.,
57(5): 533-47.
Harris LC, Watkins P (1998). The impediments to developing a market
orientation: an exploratory study of small UK hotels. Int. J. Contemp.
Hosp. Manage., 10(6): 221-226.
Hauser JR, Simester DI, Wernerfelt B (1996). Internal customers and
internal suppliers. J. Mark. Res., 33: 268-280.
Haynes P, Fryer G (2000). Human resources, service quality and
performance: a case study. Int. J. Contemp. Hosp. Manage., 12(4):
240-248.
Hooley GJ, Greenly GE, Cadogan JW, Fahy J (2005). The performance
impact of marketing resources. J. Bus. Res., 58(1): 18-27.
Huber GP (1991). Organizational learning: the contributing processes
and the literatures. Organ. Sci., 2: 88-115.
Huff AS (1990). Mapping strategic thought. New York: John Wiley and
Sons.
Hult GTM, Ketchen DJ Jr. (2001). Does market orientation matter? A
test of the relationship between positional advantage and
performance. Strateg. Manage., J. 22(9): 899-906.
Hult GTM, Ketchen DJ Jr, Slater SF (2005). Market orientation and
performance: an integration of disparate approaches. Strateg.
Manage. J., 26(12): 1173-1181.
Hunt SD, Morgan RM (1995). The comparative advantage theory of
competition. J. Mark., 59: 1-15.
Irani Z, Sharif A, Love PED, Kahraman C (2002). Applying concepts of
fuzzy cognitive mapping to model: the IT/IS investment evaluation
process. Int. J. Prod. Econ., 75: 199-211.
Jaworski BJ, Kohli AK (1993). Market orientation: antecedents and
consequences. J. Mark., 57: 53-70.
Jaworski BJ, Kohli AK (1996). Market orientation: review, refinement,
and roadmap. J. Mark. Focused Manage., 1(2): 119-135.
Jöreskog KG., Sörbom D (1996). LISREL 8: User’s reference guide.
Chicago: Scientific Software.
Kang I, Sangjae L, Choi J (2004). Using fuzzy cognitive map for the
relationship management in airline service. Expert Syst. Appl., 26:
545-555.
Kardaras D, Karakostas B (1999). The use of fuzzy cognitive maps to
simulate the information systems strategic planning process. Inform.
Software. Technol., 41: 197-210.

11370 Afr. J. Bus. Manage.
Ketchen DJ Jr., Hult TGM, Slater SF (2007). Toward greating
understanding of market orientation and the resource-based view.
Strateg. Manag. J. 28(9): 961-964.
Kobylanski A, Szulc R (2011). Development of marketing orientation In
small and medium-sized enterprises evidence from Eastern Europe.
Int. J. Manage. Mark. Res., 4(1): 49-59.
Kohli AK, Jaworski BJ (1990). Market orientation: the construct,
research propositions, and managerial implications. J. Mark.,
54(April): 1-18.
Kohli AK, Jaworski BJ, Kumar A (1993). MARKOR: a measure of
market orientation. J. Mark. Res., 30: 467-477.
Kosko B (1986). Fuzzy cognitive maps. Int. J. Man. Mach. Stud., 24: 65-
75.
Kosko B (1992). Neural networks and fuzzy systems: a dynamical
systems approach to machine intelligence. New Jersey: Prentice-
Hall.
Kosko B (1994). The New Science of Fuzzy Logic: Fuzzy thinking.
London: HarperCollins.
Kosko B (1997). Fuzzy Engineering. New Jersey: Prentice -Hall.
Langerak F (2003). An appraisal of research on the predictive power of
market orientation. Eur. Manage. J., 21(4): 447-64.
Lazari CG, Kanellopoulos DN (2007). Total quality management in hotel
restaurants: a case study in Greece. J. Eng. Appl. Sci. 2(3): 564-571.
Lee K, Kim H (1997). A fuzzy cognitive map-based bi-directional
inference mechanism: an application to stock investment analysis.
Int. J. Intel. Syst. Account. Financ. Manage., 6: 41-57.
Lee KC, Kim JS, Chung NH, Kwon SJ (2002). Fuzzy cognitive map
approach to webmining inference amplification. Expert Syst. Appl.,
22: 197-211.
Lee KC, Lee S (2007). Causal knowledge-based design of EDI controls:
an explorative study. Comput. Hum. Behav., 23(1): 628-663.
Lee S, Ahn H (2009). Fuzzy cognitive map based on structural equation
modeling for the design of controls in business-to-consumer ecommerce
web-based systems. Expert Syst. Appl., 36: 10447-10460.
Longo CRJ, Cox MAA (1997). Total quality management in financial
services: Beyond the fashion the reality has to take off. Total Qual.
Manage. Bus. Excel., 8(6): 323-333.
Lovelock CH, Weinberg CB (1984). Marketing for Public and Nonprofit
Managers. New York: John Wiley and Sons.
Martin BA, Martin JH (2005). Building a market-oriented organizational
environment: an implementation framework for small organizations.
Mid. Am. J. Bus., 20(2): 45-73.
Menguc B, Auh S (2006). Creating a firm-level dynamic capability
through capotalising on market orientation and innovativeness. J.
Acad. Mark. Sci., 34(1): 63-73.
Miao CY, Goh A, Miao Y, Yang ZH (2002). Agent that models, reasons
and makes decisions. Know. Based Syst., 15: 203-211.
Mohr-Jackson I (1991). Broadening the market orientation: an added
focus on internal customers. Hum. Resour. Manage., 30(4): 455-467.
Montes FJL, Jover AV, Ferna´ndez LMM (2003). Factors affecting the
relationship between total quality management and organizational
performance. Int. J. Qual. Reliab. Manage., 20(2): 189-209.
Moorman C, Rust RT (1999). The Role of Marketing. J. Mark., 63: 180-
197.
Narver JD, Slater SF (1990). The effect of a market orientation on
business profitability. J. Mark., 5: 20-35.
Noh JB, Lee KC, Kim JK, Lee JK, Kim SH (2000). A case-based
reasoning approach to cognitive map-driven tacit knowledge
management. Expert Syst. Appl., 19: 249-259.
Nunnally JC (1978). Psychometric theory (2nd ed.). New York:
McGraw-Hill.
O’Driscoll A, Carson D, Gilmore A (2001). The competence trap:
exploring issues in winning and sustaining core competence. Irish J.
Manage., 22(2): 73-90.
Papageorgiou EI, Markinos A, Gemptos T (2009). Application of fuzzy
cognitive maps for cotton yield management in precision farming.
Expert Syst. Appl., 36: 12399-12413.
Pelham AM (1997). Market orientation and performance: the
moderating effects of product and customer differentiation. J. Bus.
Ind. Mark., 12(5): 276-296.
Pelham AM, Wilson DT (1996). A longitudinal study of the impact of
market structure, firm structure, strategy and market orientation
culture on dimensions of small-firm performance. J. Acad. Mark. Sci.,
24(1): 27-43.
Peng TK, Kao YT, Lin CC (2006). Common method variation in
management study: Question essence, influence, test and remedy. J.
Manage., 23(1): 77-98.
Podsakoff PM, Organ DW (1986). Self-reports in organizational
research: problems and prospects. J. Manage., 12(4): 531-544.
Ramayah NST, Saad NM (2006). TQM practices, service quality, and
market orientation: some empirical evidence from a developing
country. Manage. Res. News, 29(11): 713-728.
Rao SS, Solis LS, Raghu-Nathan TS (1999). A framework for
international quality management research: development and
validation of a research instrument. Total. Qual. Manage., 10(7):
1047-75.
Reid RD, Sandler M (1992). The use of technology to improve service
quality. Cornell Hotel Restaur. Adm. Q., 33(3): 68-73.
Rodriguez-Repiso L, Setchi R, Salmeron JL (2007). Modelling IT
projects success ith fuzzy cognitive maps. Expert. Syst. Appl., 32:
543-559.
Ruekert RW (1992). Developing a market orientation: an organizational
strategy perspective. Int. J. Res. Mark., 9: 225-46.
Santos-Vijande ML, Álvarez GL (2009). TQM’s contribution to marketing
implementation and firm’s Competitiveness. Total Qual. Manage.,
20(2): 171-196.
Saraph JV, Benson PG, Schroeder RG (1989). An instrument for
measuring the critical factors of quality management. Decis. Sci. J.,
20(4): 457-478.
Siguaw JA, Gene B, Robert E, Widing II (1994). The influence of market
orientation of the firm on sales force behavior and attitudes. J. Mark.
Res., 31: 106-116.
Sittimalakora W, Hart S (2004). Market orientation versus quality
orientation: sources of superior business performance. J. Strateg.
Mark., 12(4): 243-253.
Slater SF, Narver JC (1994). Does competitive environment moderate
the market orientation-performance relationship? J. Mark., 58: 46-55.
Slater SF, Narver JC (1995). Market orientation and the learning
organization. J. Mark., 59: 63-74.
Slater SF, Narver JC (1998). Customer-led and market-orientation: let’s
not confuse the two. Strateg. Manag. J., 19(10): 1001-1006.
Slater SF, Narver JC (2000). The positive effect of a market orientation
on business profitability: A balanced replication. J. Mark., 62: 99-111.
Spector PE (1987). Method variance as an artifact in self-reported affect
and perceptions at work: Myth or significant problem. J. Appl.
Psychol., 72: 438-443.
Stoelhorst JW, van Raaij EM (2004). On explaining performance
differentials: marketing and the managerial theory of the firm. J. Bus.
Res., 57(5): 462-77.
Stylios CD, Georgopoulos VC, Malandraki GA, Chouliara S (2008).
Fuzzy cognitive map architectures for medical decision support
systems. Appl. Soft Comput., 8: 1243-1251.
Sureshchandar GS, Rajendran C, Anantharaman RN (2001). A
holisticmodels for total quality service. Int. J. Serv. Ind. Manage.,
12(4): 378-412.
Tihanyi L, Ellstrand AE, Daily C, Dalton DR (2000). Composition of the
top management team and firm international diversification. J.
Manage., 26(6): 1157-77.
Van Doren CS, Gustke LD (1982). Spatial analysis of the US lodging
industry. Ann. Tour. Res., 9(4): 543-63.
Vieira VA (2010). Antecedents and consequences of market orientation:
a Brazilian Meta-Analysis and an International Mega-Analysis. BAR.
7(1): 40-58.
Wassenaar K, Stafford ER (1991). The lodging index: an economic
indicator for the hotel/motel industry. J. Travel Res., 30(1): 18-21.
Wernefelt B (1984). A resource-based view of the firm. Strateg.
Manage. J., 5(2): 171-80.
Witcher B (1995). Marketing and total quality management. Eur. J.
Mark., 29(5): 23-24.
Wrenn B (1997). The market orientation construct: Measurement and
scaling issues. J. Mark. Theo. Pract., 5(3): 31-54.
Xirogiannis G, Stefanou J, Glykas M (2004). A fuzzy cognitive map
approach to support urban design. Expert Syst. Appl., 26: 257-268.
Yam CM, Tam Y K, Tang PY, Mok CK (2005). TQM: A change

management model or market orientation. Total Qual. Manage. Bus.
Excel., 16(4): 439-461.
Youssef MA, Boyd J, Williams E (1996). The impact of total quality
management on firms’ responsiveness: An empirical analysis. Total.
Qual. Manage., 7(1): 127-144.
Zhou KZ, Yim CK, Tse DK (2005). The effects of strategic orientations
in technology- and market-based breakthrough innovation. J. Mark.,
69(2): 42-60.
Wang et al. 11371
Zhou KZ, Li JJ, Zhou N, Su C (2008). Market orientation, job
satisfaction, product quality, and firm performance: evidence from
China. Strateg. Manage. J., 29: 985-1000.

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APPENDIX
Appendix A. Questionnaire items.
[1] Total quality management
Customer focus
1. Our activities are centered on satisfying our customers.
2. Satisfying our customers, and meeting their expectations, is the most important thing we do.
3. Senior executives behave in ways that lessen the importance of customers.
Internal/External cooperation
1. Managers emphasize activities that lead to a lack of cooperation between our hotel and our suppliers.
2. Managers, supervisors, and employees from different departments work independently to achieve their own
department’s goals.
3. In the hotel, teamwork is commonplace - the expected way of doing business.
4. Employees are hesitant to voice their opinions, make suggestions, or inquire about any of the actives of the hotel.
5. In the hotel, everyone participates in improving our products, services, and processes.
Continuous improvement
1. Employees usually don’t get an opportunity to suggest changes or modifications to existing processes.
2. The hotel encourages continual study and improvement of all its products, services and processes.
3. The hotel has received recent compliments and recognition for improving its products/services/processes.
Leadership
1. Senior executives share similar beliefs about the future direction of this organization.
2. Activities and investments that have long-term benefits receive little support from management.
3. Managers and supervisors rarely allow employees to take necessary action on their own.
4. Senior executives anticipate change and make plans to accommodate it.
Employee fulfillment
1. My work duties and responsibilities contribute little to satisfying my need to create quality products/services.
2. I like my job because I’m doing what I want to do.
3. Employees in the hotel are dedicated to their jobs.
Learning
1. Managers and supervisors ensure that all employees receive training that helps them understand how and why
the hotel does what it does.
2. Managers and supervisors participate in specialized training on how to conduct business, whether dealing with
employees or external customers.
3. Many employees in the hotel do not possess sufficient knowledge about the basics of our industry.
4. Few employees in the hotel understand the basic processes used to create our products/services.
Process management
1. Preventing defective products/services from occurring is a strong attitude in the hotel.
2. The processes used in the hotel do not include in-process measures of quality.
3. The processes for designing new products/service in the hotel ensure quality.
4. Explaining the variation in processes is rarely used as an analysis technique in the hotel.
5. Senior executives look at the total costs of products and service, including indirect an overhead costs.
6. Managers and supervisors understand how to motivate employees and encourage them to perform at their highest
levels.
[2] Market orientation
Information generation
1. We are fast to detect changes in our customers’ product preferences.
2. We are fast to detect fundamental shifts in our industry (e.g., competition, technology).).

Appendix A. Contd
Information dissemination
1. When something important happens to major customers, the whole hotel knows about it shortly.
2. When one unit finds out something important about competitors, it is fast to alert other units.
Shared interpretation
1. We develop a shared understanding in our hotel of the available market information.
2. We develop a shared understanding in our hotel of the implications of a marketing activity.
Customer performance
1. Customer is loyal.
2 Customer is satisfied.
3 Our products/service bring for customer lifetime value.
4 Customer is willing to retain.
Finance performance
1. Our market share is growth.
2. Our sales are growth.
3. Our selling cost is reducing.
4. Our ROI is growth.
Appendix B. Inference results using various input cases.
Stimuli vectors
TQM1 TQM2
Changes in design factors
TQM3 TQM4 TQM5 TQM6 TQM7 MO
Outputs
CP FP
Input#1 1 0 0 0 0 0 0 0.24 0.17 0.16
Input#2 0 1 0 0 0 0 0 0.13 0.09 0.08
Input#3 0 0 1 0 0 0 0 0.19 0.13 0.12
Input#4 0 0 0 1 0 0 0 0.21 0.15 0.14
Input#5 0 0 0 0 1 0 0 0.23 0.17 0.15
Input#6 0 0 0 0 0 1 0 0.12 0.09 0.08
Input#7 0 0 0 0 0 0 1 0.13 0.09 0.09
Input#8 1 1 0 0 0 0 0 0.37 0.26 0.24
Input#9 1 0 1 0 0 0 0 0.43 0.30 0.28
Input#10 1 0 0 1 0 0 0 0.45 0.32 0.30
Input#11 1 0 0 0 1 0 0 0.47 0.34 0.31
Input#12 1 0 0 0 0 1 0 0.36 0.26 0.24
Input#13 1 0 0 0 0 0 1 0.37 0.26 0.25
Input#14 0 1 1 0 0 0 0 0.32 0.22 0.20
Input#15 0 1 0 1 0 0 0 0.34 0.24 0.22
Input#16 0 1 0 0 1 0 0 0.36 0.26 0.23
Input#17 0 1 0 0 0 1 0 0.25 0.18 0.16
Input#18 0 1 0 0 0 0 1 0.26 0.18 0.17
Input#19 0 0 1 1 0 0 0 0.4 0.28 0.26
Input#20 0 0 1 0 1 0 0 0.42 0.30 0.27
Input#21 0 0 1 0 0 1 0 0.31 0.22 0.20
Input#22 0 0 1 0 0 0 1 0.32 0.22 0.21
Input#23 0 0 0 1 1 0 0 0.44 0.32 0.29
Input#24 0 0 0 1 0 1 0 0.33 0.24 0.22
Input#25 0 0 0 1 0 0 1 0.34 0.24 0.23
Input#26 0 0 0 0 1 1 0 0.35 0.26 0.23
Wang et al. 11373

11374 Afr. J. Bus. Manage.
Appendix B. Contd.
Input#27 0 0 0 0 1 0 1 0.36 0.26 0.24
Input#28 0 0 0 0 0 1 1 0.25 0.18 0.17
Input#29 1 1 1 0 0 0 0 0.56 0.39 0.36
Input#30 1 1 0 1 0 0 0 0.58 0.41 0.38
Input#31 1 1 0 0 1 0 0 0.6 0.41 0.39
Input#32 1 1 0 0 0 1 0 0.49 0.35 0.32
Input#33 1 1 0 0 0 0 1 0.5 0.35 0.33
Input#34 0 1 1 1 0 0 0 0.53 0.37 0.34
Input#35 0 1 1 0 1 0 0 0.55 0.39 0.35
Input#36 0 1 1 0 0 1 0 0.44 0.31 0.28
Input#47 1 1 1 0 0 0 1 0.69 0.48 0.45
Input#48 0 1 1 1 1 0 0 0.76 0.54 0.49
Input#49 0 1 1 1 0 1 0 0.65 0.46 0.42
Input#50 0 1 1 1 0 0 1 0.66 0.46 0.43
Input#51 0 0 1 1 1 1 0 0.75 0.54 0.49
Input#52 0 0 1 1 1 0 1 0.76 0.54 0.50
Input#53 0 0 0 1 1 1 1 0.69 0.50 0.46
Input#54 1 1 1 1 1 0 0 1 0.71 0.65
Input#55 1 1 1 1 0 1 0 0.89 0.63 0.58
Input#56 1 1 1 1 0 0 1 0.9 0.63 0.59
Input#57 0 1 1 1 1 1 0 0.88 0.63 0.57
Input#58 0 1 1 1 1 0 1 0.89 0.63 0.58
Input#59 0 0 1 1 1 1 1 0.88 0.63 0.58
Input#60 1 1 1 1 1 1 0 1.12 0.8 0.73
Input#61 1 1 1 1 1 0 1 1.13 0.89 0.74
Input#62 0 1 1 1 1 1 1 1.01 0.72 0.66
Input#63 1 1 1 1 1 1 1 1.25 0.89 0.82

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