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essential a particular statement is; in measuring the particular latent trait. Statements of the survey instrument were added, dropped and modified based on the focus group suggestions. The final survey instrument with 24 items was divided into the following subsections: altitude towards wealth acquisition (4 items), locus of control (7 items), paranormal beliefs (4 items), and readiness to seek wealth from under water spirits (8 items), and demographic section. The items were on a five Likert scales (1=Strongly Disagree; 2=Disagree; 3=neither agree nor disagree; 4=agree; and 5=strongly agree). The demographic section collected data on gender, age, education level, religion, and marital status of the respondent. The survey instrument was administered to business owner or manager using a face-to-face interview. Summary statistics of the responses from all 60 respondents are presented in Table 1. In the sample, majority of respondents were single and young males (less than 20 year), with secondary level education or lower. There was a slightly high representation for individuals from Catholic and Anglican denominations. In Table 1 and for items, percent frequency includes “neither agree nor disagree”, “agree”, and “strongly agree” categorical responses. For example, most of the responses in the attitude towards wealth acquisition were in the neither agree or disagree and agree category. Except for one item in the locus of control domain (other people tends to dominate my decision making process), most of responses to other items were in the last three response categories. For paranormal beliefs domain, most responses from one item (spirits exist but are not all that powerful to grant anything) was in the last three categorical responses. Responses on other items were in the strongly disagree and disagree response categories. Similarly, two items in the readiness to seek wealth from under water spirits domains (“If the spirits are able to grant wealthy, I will use them” and “I believe that spirits can make miracles in one's life and business”) were in the strongly disagree and disagree response categories. Most of responses on other items were in the upper response categories. In general, if the percent frequency is less than 50%, the most of the responses were in the strongly disagree and disagree response categories and vice versa. Data analyses Variable clustering approach was used to test the validity and reliability of the survey responses using alpha command in the statistical package Stata. The command computes inter-item correlations or covariance for all specified items. This is in addition to computing Cronbach’s coefficient alpha statistic for the scale formed by the items. This procedure also iteratively divides groups of items into subscales until a stopping criterion is reached. This a more direct way of creating a survey instrument that measure same construct and direction or grouping questions that measure the same construct. In Stata, the alpha command can be used to assess how well the items form a single scale that measure the same construct (Stata, 2009). Equation (3) was used to estimate the latent trait that measures locus of control scale and a composite scale constructed from items representing altitude towards wealth acquisition, paranormal beliefs and readiness to seek wealth from under water spirits. The three domains created a latent trait measuring readiness to conjure and believe in mystical powers. Due to relatively small sample size and few items that measures some of the latent traits the “Strongly Disagree” and Disagree” responses were combined to form a “disagree” response category. Therefore, the Rasch Rating Scale Model has four response categories: disagree=1; neither agree or disagree=2; agree=3; and strongly disagree=4). For demographic variables, due to low frequency of “Widowed” response, the marital status variable was collapsed to a dichotomous variable (that is, single or widowed=1; and zero Kaliba et al. 7263 otherwise). Also, in order to conform to the standard Rasch model, four dichotomous variables representing education levels (that is, University, College, Secondary, and Other) and religion affiliation (Anglican, Catholic, Muslim, and Evangelical) were constructed. For education variable, individuals with primary education were combined with those who did not attend school to form a single dummy variable. Therefore, the level-1 covariates included a dummy variable for gender (male=1; zero otherwise), an ordered variable for age, three dummy variables for education level, three dummy variables for religion affiliation, and a dummy variable for marital status. The other dummy for education and Evangelical dummy variable were not include in the model to avoid perfect collinearality. As specified in Equation (3), two latent traits (locus of control scale and readiness to conjure and believe in mystical powers) were estimated using gllamm program also in Stata. The program gllamm runs in the statistical package Stata and estimates GLLAMMs (Generalized Linear Latent and Mixed Models) by maximum likelihood. An advantage of using gllamm directly is that the latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution (Zheng and Rabe-Hesketh, 2009). In addition, simple linear correlation analyses (Pearson r) were conducted to determine the relationship between the estimated two latent traits. Pearson correlation assumes that the variables are measured on at least interval and it determines the extent to which values of the two or more variables are proportional to each other. The advantage is that the value of correlation (that is, correlation coefficient) does not depend on the specific measurement units used; which is important for latent traits constructed using different items (Stata, 2009) RESULTS AND DISCUSSION Validity and reliability tests Results of validity and reliability tests for all items are presented in Table 2. The estimated Cronbach’s coefficient indicated that items included to measures altitude towards wealth acquisitions and readiness to seek wealth from mystical powers strongly measure these latent construct (α>0.6). However, the items used to measure locus of control and paranormal beliefs were weak in measuring these latent construct (α
7264 Afr. J. Bus. Manage. Table 1. Summary Statistics for all variables used in the study 1 Subscale Category/Items Frequency (%) Demographic characteristic Business types Gender Age Education Religion Marital status Attitude towards Wealth Acquisition (WEA) Locus of Control (LOCI) Service 50.00 General traders 50.00 Male 63.33 Female 36.67 Less than 20 years of age 58.33 Between 20 - 30 years of age 31.67 Above 30 years of age 10.00 University 18.33 Some College 25.00 Secondary 45.00 Primary 10.00 Illiterate 1.67 Anglican 28.33 Catholic 31.67 Moslem 20.00 Evangelicals 20.00 Single 51.67 Married 46.67 Widowed 1.67 Life is meaningless without wealth (LIFE) 73.33 My reason for working is primarily to acquire wealth (WORKING) 73.33 I will do anything to become wealthy (ANYTHING) 91.67 Anything that is likely to bring in wealth is welcome (BRING) 58.33 Whatever happens in my life and business is my own doing (WATEVER) 61.67 I believe that I control my own destiny (CONTROL) 68.33 I always rely on others to make business decisions (RELAY) 41.67 Other people tend to dominate my decision making process (OTHER) 48.33 Factors other than me determine the direction of my business (FACTOR) 53.33 My success in business is achieved through personal effort (SUCCESS) 75.00 Misfortunes I face in business are consequences of my mistakes (MISFORT) 96.67
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African Journal of Business Managem
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Editors Prof. Wilfred Isioma Ukpere
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Electronic submission of manuscript
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Fees and Charges: Authors are requi
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Table of Contents: Volume 5 Number
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7166 Afr. J. Bus. Manage. the high
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7168 Afr. J. Bus. Manage. Cargo mov
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7170 Afr. J. Bus. Manage. has produ
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7172 Afr. J. Bus. Manage. resources
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7174 Afr. J. Bus. Manage. celebrate
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7176 Afr. J. Bus. Manage. effective
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7178 Afr. J. Bus. Manage. interacti
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7180 Afr. J. Bus. Manage. firstly,
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7182 Afr. J. Bus. Manage. TQM pract
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7184 Afr. J. Bus. Manage. with lowe
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7186 Afr. J. Bus. Manage. in a diff
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7190 Afr. J. Bus. Manage. Decision
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7192 Afr. J. Bus. Manage. restructu
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7194 Afr. J. Bus. Manage. Performan
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7196 Afr. J. Bus. Manage. firm perf
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African Journal Of Business Managem
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7200 Afr. J. Bus. Manage. to establ
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7202 Afr. J. Bus. Manage. marketing
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7204 Afr. J. Bus. Manage. initial a
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7206 Afr. J. Bus. Manage. from the
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7208 Afr. J. Bus. Manage. Phillips
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7210 Afr. J. Bus. Manage. of the sa
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7314 Afr. J. Bus. Manage. Table 5.
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7318 Afr. J. Bus. Manage. expectati
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7320 Afr. J. Bus. Manage. Compensat
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7324 Afr. J. Bus. Manage. Psychol.,
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7326 Afr. J. Bus. Manage. the resea
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7328 Afr. J. Bus. Manage. Z( T, whe
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7330 Afr. J. Bus. Manage. (10) and
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7332 Afr. J. Bus. Manage. during wh
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7334 Afr. J. Bus. Manage. optimal v
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7336 Afr. J. Bus. Manage. satisfact
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7340 Afr. J. Bus. Manage. female re
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7344 Afr. J. Bus. Manage. Populatio
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7348 Afr. J. Bus. Manage. Figure 1.
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7352 Afr. J. Bus. Manage. ln S = ln
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7354 Afr. J. Bus. Manage. Figure 1.
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7356 Afr. J. Bus. Manage. but some
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7358 Afr. J. Bus. Manage. investiga
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7360 Afr. J. Bus. Manage. when conf
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7362 Afr. J. Bus. Manage. PCs in a
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7364 Afr. J. Bus. Manage. supported
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7366 Afr. J. Bus. Manage. Psychol.,
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7368 Afr. J. Bus. Manage. social pr
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Table 2. Companies and countries co
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to CSR clearly and reliably. The st
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consumers to participate with the t
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A Liao 7379 Figure 1. Example of ev
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Table 3. Summary statistics of the
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Table 5. Utilities for subject 1 Li
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REFERENCES Anderson JC, Narus JA (1
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7386 Afr. J. Bus. Manage. aggregate
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7388 Afr. J. Bus. Manage. Analysis
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(Bass and Avolio, 1994). Leaders fo
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the employees. When employees are e
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Table 1. Means, standard deviation
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negativity in the organization. Tra
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of psychological ownership. Thus te
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Meyer JP, Becker TE, Vandenberghe C
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goods industry in the future. LITER
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F + 2 0. 110 * F + Table 3. Factor
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APPENDIX Data for evaluation on the
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anks. Camanho and Dyson (2005) enha
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Step A-4: Correlation analysis Star
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Table 1. Original raw data of input
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Table 5. Scenario table. Wang and W
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Delmas M, Toka Y (2005). Deregulati
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Technology managers need to attract
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λ is the parameter for risk aversi
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specified ranges of return, for eac
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Carlsson C, Fulle´r R, Majlender P
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Stock Exchange, (Rao, 2010) in Nati
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etween size and unsystematic risk i
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The research schema Figure 1 indica
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Table 3. Result of Freidman rank te
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Table 8. The result of Spearman cor
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significant differences between the
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leadership. The process of manageri
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indices of quality of life work. On
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Figure 2. The values of standardize
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Table 4. The values of coaching ind
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Table 1. Contrast between hedonic a
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in consumers’ shopping behavior.
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et al., 2006; Carpenter, 2008). For
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Table 2. contd Shopping satisfactio
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value in the construct of clothing
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independence, assertiveness, innova
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over those of competitors in order
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Table 2. Demographic profile and ce
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Table 4. Service satisfaction level
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There is a strong significant corre
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Dean AM (2004). Rethinking customer
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as a margin rate of 10% and rest to
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(7) Table 1. Descriptive statistics
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APPENDIX A Table 1. Failure rate te
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Table 1. Results of the multivarian
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Figure 3. Entrepreneurship as a fun
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Furkukawa K, Shafer WE, Lee GM (200
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happen to firms of any and all size
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participants attributed their failu
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Table 1. Descriptive statistics of
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Table 4 Contd. Inconsideration of l
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Table 5 Contd. Manufacturing 2.17 1
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The CLRP is an NP-hard problem, so
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No START Form a new cluster. Select
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Table 1. Computational results of G
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Table 1. Correlation of facets of C
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Figure 1. Theoretical approach adop
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quadrant (III), including product v
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consistently showed negative (posit
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signal. This hypothesis predicts a
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Variables are defined as in the ful
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Table 3. The results of model (2).
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around the world. Q. Rev. Econ. Fin
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tourism industry development. In th
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Table 2. Canonical correlation. 2 C
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Table 5. Bilateral analysis and ran
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strategies (Hart, 1995; Tello and Y
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Table 1. Reliability and construct
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Table 3. Latent variables test. She
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Haines R, Street M, Haines D (2008)
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owned insurance firm to enter the m
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affective commitment; organizationa
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Figure 1. Structural relationships
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Prior to analysis, variables were e
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Table 1. Effect decomposition for M
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Figure 5. Unstandardized estimates
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prospects. In other words, less wor
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goal of a SCM process is to create
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Sale Lean Supply Chain (LSC) Growth
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Upper Saddle River: Pearson Prentic
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the green awareness movement by ado
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usiness practices than smaller firm
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Table 3. Descriptive statistics and
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China. Green leadership could be em
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APPENDIX Appendix 1: Measures of ob
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Emotional intelligence Self monitor
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ship between a people’s perceived
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Table 1. Analysis of the reliabilit
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Table 4. Results from hypothesis te
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Ajzen I, Driver BL (1992). Applicat
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mono production economy, and rural
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Table 2. Pearson correlation betwee
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Table 1. Behavioral Phenomenon that
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Table 3. Test period data. Table 4.
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Prospect value Figure 3. Expected v
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Tversky A, Gilovich T (1989). The H
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Table 1. Components of a mentoring
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Table 3. Perceptions on the success
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Table 5. Characteristics of a mento
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terms of sustained development, are
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Table 7. Ideal characteristics of m
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qualitative analysis of fifteen hou
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section. In Section 3, we will disc
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Table 1. Participants’ demographi
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of Am. Acad. of Bus., Cambridge. 4(
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directors and propose the board ind
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composition is the outcome of a neg
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Table 1. Descriptive statistics. Ti
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Table 3. The determinants of board
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Booth JR, Cornett M, Tehranian H (2
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provides a wider response and swift
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Correlation “Customary Approach
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Table 2. Correlation matrix of EAC
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Table 9. Intra-EAC imports as share
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Martin JC (1999). The European Mone
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participate in the highest degree o
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innovation. A secondary innovation
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Figure 2. The switching ecology of
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Table 1. Revenue growth rate and CA
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variables (Beattie and Jones, 1999a
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Table 3. Explanations of dependent
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Courtis JK (1997). Corporate annual
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treated in the literature, its rela
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company. METHODOLOGY Table 1. Quest
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as an important factor in the manag
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satisfaction may also reduce the ov
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Table 1. Customer expectations on s
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Table 2. Contd. Machirori and Fatok
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and travelling may lead to satisfac
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and professionally developmental pr
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Table 1. Organizational elements mo
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Table 2. Studying the demographical
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education might involve plenty of c
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(Deng et al., 2010). To date, there
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Table 2. Results of hierarchical re
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WOM. The comparison can be seen in
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UPCOMING CONFERENCES International
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