3.0 Chapter 3: Research Methodology and Data ... - DSpace@UM

3.0 Chapter 3: Research Methodology and Data Analysis
3.1. Introduction
Methodology refers to ‘branch of philosophy that analyses the principles and
procedures of an inquiry in a particular discipline’ (Neuman, 1997).
Research methodology is essential as it provides effective research with study results. It
can be defined as procedures, ways, methods and techniques that are applied to capture
and gather all relevant information for the research.
3.2. Qualitative Research
‘Qualitative research is the collection and analysis of extensive narrative data in order
to gain insights into a situation of interest not possible using other types of research’
(L.R.Gay, 1996). This research is used mainly for primary research to study the details
of research and emphasize on describing, understanding, and explaining the complex
research that are related to the KM in HLI. Besides, this research is also able to assist in
constructing and developing frameworks.
3.2.1. Document Review
Basically, the data was analyzed based on the documents such as journals, articles
and books as references for the literature review. The analyzed data was
transformed and compiled into report, in which the details of the report are attached
in Chapter 1, Chapter 2 and Chapter 5.
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3.2.2. Interview
In order to have a detailed understanding of KM in organization and HLI,
interview sessions were carried out to gather data and information. The results
of the interview are attached in Section 2.3.1, 2.4.2 and 2.4.3. The pilot study
was conducted to try-out the questions before the actual interview and the
interview questions were reviewed by Pn. Maizatul then a trial-interview prior
to the actual interview was carried out before the actual interviews.
3.3. Quantitative Research
‘The collection of numerical data to explain, predict and/or control phenomenal of
interest is called Quantitative Research’ (L.R.Gay and Peter, 1996). This research is
applied to gather the data from the samples through proper sampling method to reach
the right audience in large numbers efficiently through an inexpensive way. Besides, its
data can be evaluated using statistical means which helps to understand KM in
Malaysia HLI, and consequently this also assists in development of the proposed KMS.
3.3.1. Questionnaire Survey
The purpose of the questionnaire survey is to create an awareness of KM to the
academia in HLI as well as to understand the current status of KM in HLI. Besides,
responses about features to be implemented into the proposed KMS were gathered
through this survey too.
First of all, the samples for the questionnaire survey are identified through
sampling. According to L.R.Gay and Peter (1996), sampling is the process of
selecting a number of participants for a study in such a way that they represent the
larger group of samples from which they were selected.
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The Stratified Sampling is a method of sampling from a population in statistic. It is
used to select the samples based on the HLI. The steps taken for samplings are
described with the aid of diagram in Figure 3.3.1.
a. Step one: First of all, samples consists of academia from HLI in Malaysia
were selected.
b. Step two: Then, Selangor and KL, Malaysia was specified as the area for
samplings.
c. Step three: The online research was conducted to determine the estimated
number of HLI within Selangor and KL. It was found that there are about
three hundred and fifty five (355) HLI based on the research findings.
d. Step four: Eight (8) HLI in Selangor and KL, Malaysia were randomly
selected. Among the
HLI are Asia Pacific Institute of Information
Technology (APIIT), KL; Kolej Damansara Utama (KDU), Petaling Jaya (PJ);
Sunway College, PJ; Informatics Institute, PJ; Systematic College, KL;
University of Malaya (UM), KL, Multimedia University (MMU), Cyberjaya,
Universiti Putra Malaysia (UPM), Serdang. There are approximately twenty
(20) potential samples (the academia) from each of these HLI. Therefore, the
total is 8 HLI * 20 samples = one hundred and sixty (160) samples in this
survey.
e. Step five: The academia was divided into three different categories based on
their education level: Degree holder, Master holder and PhD holder, as it was
hypothesized that the academia’s understanding of knowledge sharing varies
based on their education level.
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HLI in Malaysia
Scope
Population: About 25 HLI in Selangor and
Kuala Lumpur, Malaysia
8 HLI, Random Basis
APIIT, KL
Academia
Sunway College
Academia
Systematic
College, KL
Academia
UPM
Academia
Degree
Group
PhD
Group
PhD
Group
PhD
Group
PhD
Master
Degree
Degree
Degree
Master
Master
Master
Degree
KDU, PJ
Academia
Group
Master
PhD
Degree
Informatics
Institute, PJ
Academia
Master
Group
PhD
Degree
Master
UM
Academia
Group
PhD
Degree
Master
MMU
Academia
Group
PhD
Figure 3.3.1
Diagram for Sampling
The questionnaire was designed according to the research objectives and it contains
seven (7) sections:
a. Questions concerning the person and HLI.
b. Questions concerning the general understanding of KM.
c. Questions concerning the existing end-user tools used in HLI.
d. Questions concerning the current status or condition of KM in HLI.
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e. Questions concerning the enforcement of KM and the knowledge that can be
captured from HLI.
f. Questions concerning the benefits of KM in HLI.
g. Questions concerning the development of the new KMS.
These questions are mainly consists of close-ended structure and designed based on
attitude scales method, which includes questions with Likert scale, Rating scale,
Checklist and Free response. In this case, 'questions that incorporate the used of
Likert scale method require the samples to respond based on a series of scales from
‘disagree to agree’ or ‘one to ten’. On the other hand, questions with Rating scale
require the samples to rate the options for the most appropriate description’
(L.R.Gay and Peter, 1996).
The example of the questionnaire is attached in
Appendix A.
The pilot study was conducted to pre-test the questions before actual survey was
carried out. These questions were reviewed by Pn. Maizatul, researcher’s
supervisor, and then the pilot study was carried out on a small group of five (5)
academia for the survey. Next, the feedbacks from these groups of academia were
used to revise or enhance the questionnaire’s questions. This would serve as one of
the best ways to verify the validity of the question and determine the effectiveness
and appropriateness of the questions.
There are about one hundred and four (104) samples collected from eight (8) HLI in
Selangor and KL, Malaysia (one hundred and sixty (160) copies (with twenty (20)
copies for each HLI) questionnaires were distributed).
The data that was collected via this survey was coded and analyzed using Statistical
Package for Social Science (SPSS) based on Descriptive Statistic with Frequency
Test.
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3.3.1.1.Analysis: Academia’s Educational Level
50
40
Percentage (% )
30
20
10
UPM
University Malaya
Systematic
Sunway
MMU
KDU
Informatics
Education Level
Degree
Master
0
PhD
AP IIT
Higher Learning Institution
Figure 3.3.1.1
Educational Level
Table 3.3.1.1
Educational Level
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Degree 18 17.3 17.3 17.3
Master 67 64.4 64.4 81.7
PhD 19 18.3 18.3 100.0
Total 104 100.0 100.0
First of all, the academia were measured based on their educational level. As
shown in Figure 3.3.1.1 and Table 3.3.1.1among these eight (8) HLI, most of
the academia are the Master holders (64.4%), and then follow by PhD holders
(18.3%) and Degree holders (17.3%). Therefore, it can be concluded that most
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of the respondents are the Master holders in HLI, who will be the potential users
for KMS.
3.3.1.2.Analysis: Understanding of Knowledge Management among the
Academia
60
50
HLI
Percentage (%)
40
30
20
APIIT
Informatics
KDU
MMU
Sunway
10
Systematic
University Malaya
0
1
2
3
4
5
6
7
8
9
10
UPM
Level (range) of KM understanding
Figure 3.3.1.2
Level of Knowledge Management understanding
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Table 3.3.1.2
Level of Knowledge Management understanding
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 2 1.9 1.9 1.9
2 3 2.9 2.9 4.8
3 7 6.7 6.7 11.5
4 3 2.9 2.9 14.4
5 15 14.4 14.4 28.8
6 11 10.6 10.6 39.4
7 7 6.7 6.7 46.2
8 22 21.2 21.2 67.3
9 17 16.3 16.3 83.7
10 17 16.3 16.3 100.0
Total 104 100.0 100.0
The level of KM understanding is measured through level 1 to 10. The number
increases signifies the level of KM understanding increases.
With reference to Figure 3.3.1.2 and Table 3.3.1.2, there was a positive response
that majority of the respondents which is 53.8% (21.2% + 16.3% + 16.3%) of
them have good understanding of KM. However, 46.2% of the respondents may
not have detailed understanding of KM probably because they do not have
exposure to KM in HLI. Furthermore, KM is also not practiced in their daily job
routine, therefore these respondents may not able to realize the importance and
benefits of knowledge sharing in HLI.
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3.3.1.3.Analysis: Main purpose of Knowledge Management for the
Academia
80
70
HLI
60
APIIT
Percentage (% )
50
40
30
Informatics
KDU
MMU
Sunway
20
10
Systematic
University Malaya
0
Save Time
Information Access
UPM
K-Sharing
Avoid Mistakes
KM Purpose
Figure 3.3.1.3
Main purpose of Knowledge Management
Table 3.3.1.3
Main purpose of Knowledge Management
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Save Time 10 9.6 9.6 9.6
K-Sharing 54 51.9 51.9 61.5
Information
Access
24 23.1 23.1 84.6
Avoid
Mistakes
16 15.4 15.4 100.0
Total 104 100.0 100.0
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As shown in Figure 3.3.1.3 and Table 3.3.1.3 that more than 50%, which is total
of fifty-four (54) respondents agreed that the main objective of KM is about
sharing of knowledge. This is also stated in Section 2.4 that, the outcome for
implying KM in HLI is to exchange new ideas through knowledge sharing.
However, based on Figure 3.3.1.3, minority of the respondents have commented
that the main purpose of KM is to save time except respondents from MMU and
UPM. This is probably because in their (respondents from MMU and UPM)
opinion, time is not the main factor in retrieving the relevant knowledge, the
main concern could be whether is knowledge disseminated and shared among
them.
Since knowledge sharing is the key purpose of implication of KM in HLI, a
proposed KM framework need to be created as a guide to retrieve and gather the
relevant information, and this information are known as inputs to the proposed
KMS to be transformed into useful knowledge. With the assistance of the
proposed KMS, the knowledge can be shared, disseminated and distributed
among the academia.
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3.3.1.4.Analysis: Current status of Knowledge Management in Higher
Learning Institutions
120
100
HLI
APIIT
Informatics
Percentage (%)
80
60
40
KDU
MMU
Sunway
Systematic
20
0
University Malaya
UPM
None
With Research Dept
With KM Techniques
With KM Sharing
With KM Strategy
KM Status
Figure 3.3.1.4
Current Knowledge Management Status
Table 3.3.1.4
Current Knowledge Management Status
Valid
Percent
Cumulative
Percent
Frequency Percent
Valid With KM
Strategy
15 14.4 14.4 14.4
With KM
Sharing
52 50.0 50.0 64.4
With KM
Techniques
15 14.4 14.4 78.8
With
Research 12 11.5 11.5 90.4
Dept
None 10 9.6 9.6 100.0
Total 104 100.0 100.0
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Based on Figure 3.3.1.4 and Table 3.3.1.4, the result depicted that 50% of the
academia from these eight (8) HLI is practicing knowledge sharing. Then,
followed by 14.4% of the academia are currently using KM techniques to
acquire knowledge and applying KM strategies in these HLI. However, there
were ten (10) respondents (Table 3.3.1.4) who have feedback that no knowledge
is acquired and shared among the academia within the HLI. This could be due to
the factors or reasons stated in Section 2.6, that the academia is to contribute
incorrect or misleading knowledge or in fact, difference in individual’s beliefs,
cultures and ethnics can be also the barriers that affect knowledge sharing.
Throughout the survey, the data has shown that out of these eight (8) HLI, most
of the HLI do not have Research Department for knowledge capture. This can
be concluded that, the academia from these HLI may be sharing their
knowledge among themselves without proper KM procedures and place to store
the knowledge.
As a result, it was suggested that the KM process which is applied in Motorola,
Malaysia (Section 2.3.1, Figure 2.3.1(c)), features of KMS for MMU (Section
2.4.2, Figure 2.4.2) and UPM (Section 2.4.3) can be incorporated into the
proposed KMS so that, the knowledge can be managed and stored with proper
procedures and steps.
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3.3.1.5.Analysis: Potential Knowledge Management Leaders
120
HLI
100
APIIT
Percentage (% )
80
60
40
Informatics
KDU
MMU
Sunway
20
Systematic
University Malaya
0
Dean
Academic Staff
UPM
Admin Staff
KM Unit or Others
KM Leader
Figure 3.3.1.5
Management enforcement of Knowledge Management
Table 3.3.1.5
Management enforcement of Knowledge Management
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Dean 30 28.8 28.8 28.8
Admin
Staff
5 4.8 4.8 33.7
Academic
Staff
27 26.0 26.0 59.6
KM Unit
or Others
42 40.4 40.4 100.0
Total 104 100.0 100.0
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Most of these HLI have not created proper mechanism to allow knowledge
sharing like most of the business organizations did. As shown in Figure 3.3.1.5
and Table 3.3.1.5, forty-two (42), which is 40.4% of the respondents, do agree
that the CKO should be appointed as KM Leader as they are able to enforce the
use of KM and influence the academic staffs to apply KM concept in HLI.
Besides a KM unit which can be formed by non-academic or academic staffs,
can be managed by CKO, so that the support staff will help in developing the
KM system and the academia would involve in contributing the content and
knowledge to the system.
However, they were thirty (30) or 28.8 % of the respondents who have feedback
that, the Dean of the faculty should be assigned as the KM Leader.
Among the academia, only five (5) respondents have agreed that the
administrative staffs should be assigned as KM Leaders as the administrative
staffs may be overloaded with their daily tasks and jobs.
On the other hand, it was suggested that, another way is to form a KM unit with
KM Research Department that in charge of the KM in the HLI. In this case, KM
unit should be formed by a group of academia whom would be the users of the
proposed KMS, and the CKO should be appointed to be responsible in
managing the KM unit, and the Dean of each faculty would be the KM Leader
to assist the appointed CKO.
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3.3.1.6.Analysis: Types of knowledge to be Acquired
60
50
HLI
APIIT
Informatics
Percentage (% )
40
30
20
KDU
MMU
Sunway
Systematic
10
0
University Malaya
UPM
Technical Knowledge
Other HLI Knowledge
Training Knowledge
Professional Experie
Personal Knowledge
Teaching Material
Knowledge Type
Figure 3.3.1.6
Type of acquired knowledge
Table 3.3.1.6
Type of acquired knowledge
Valid
Percent
Cumulative
Percent
Frequency Percent
Valid Teaching
Material
49 47.1 47.1 47.1
Personal
Knowledge
27 26.0 26.0 73.1
Professional
Experience
19 18.3 18.3 91.3
Training
Knowledge
6 5.8 5.8 97.1
Other HLI
Knowledge
1 1.0 1.0 98.1
Technical
Knowledge
2 1.9 1.9 100.0
Total 104 100.0 100.0
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Generally, individual’s knowledge can be categorized in different types besides
the type of acquired knowledge stated in Figure 3.3.1.6 and Table 3.3.1.6.
Based on the result in Figure 3.3.1.6 and Table 3.3.1.6, most of academia has
acquired their knowledge through teaching material such as lecture slides, notes
and exercises. This is because, the academia is required to prepare the teaching
materials before each class, and therefore most of the knowledge may be
acquired and captured through the previous teaching materials.
Next followed by 26% of the academia have feedback that knowledge can also
be acquired through personal knowledge such as tacit knowledge through daily
tasks and activities. An example of this knowledge sharing tool is BLOG in
which the academia would record and provide their views, experiences and
ideas in BLOG.
Apart from that, 18.3 % of the academia especially academia from UM acquired
most of their knowledge through professional experience such as teaching
experience, because teaching techniques and concepts may be part of the tacit
knowledge which can be acquired and gathered through years of teaching
experience.
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3.3.1.7.Analysis: Willingness of Knowledge Sharing among Academia
120
100
HLI
Percentage (%)
80
60
40
APIIT
Informatics
KDU
MMU
Sunway
20
Systematic
University Malaya
0
No
Yes
UPM
Willingnes of K-Sharing
Figure 3.3.1.7
Willingness of Knowledge Sharing
Table 3.3.1.7
Willingness of Knowledge Sharing
Frequency Percent Valid Percent
Cumulative
Percent
Valid No 10 9.6 9.6 9.6
Yes 94 90.4 90.4 100.0
Total 104 100.0 100.0
Based on the survey result in Figure 3.3.1.7 and Table 3.3.1.7, only 9.6% of the
respondents are refused to share their knowledge among themselves and out of
these respondents, majority of the respondents were from KDU College,
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ecause the concept of knowledge sharing was not enforced among the
academia at KDU College. This could be due to the unrecognized and no
incentives or rewards given to the academia who have contributed their
knowledge, and this issue was clearly discussed in Section 2.6.1.
Alternatively, there is a positive feedback from ninety-four (94), which consists
of 90.4% respondents (Table 3.3.1.7) who are willing to share knowledge
among them. Therefore, implication of KM in HLI is to encourage the academia
in knowledge sharing so that their knowledge can be recognized and re-used by
other academia.
3.3.1.8.Analysis: Suggested features for proposed Knowledge Management
System
120
100
HLI
Percentage (%)
80
60
40
APIIT
Informatics
KDU
MMU
Sunway
20
Systematic
University Malaya
0
No
Yes
UPM
Agree of New Feature1
Figure 3.3.1.8(a)
Feature 1 – Information from Academia
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Table 3.3.1.8(a)
Feature 1 – Information from Academia
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid No 12 11.5 11.5 11.5
Yes 92 88.5 88.5 100.0
Total 104 100.0 100.0
120
100
HIL
Percentage (% )
80
60
40
APIIT
Informatics
KDU
MMU
Sunway
20
Systematic
University Malaya
0
No
Yes
UPM
Agree of New Feature
Figure 3.3.1.8(b)
Feature 2 – End User Tools
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Table 3.3.1.8(b)
Feature 2 – End User Tools
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid No 32 30.8 30.8 30.8
Yes 72 69.2 69.2 100.0
Total 104 100.0 100.0
120
100
HIL
Percentage (%)
80
60
40
APIIT
Informatics
KDU
MMU
Sunway
20
Systematic
University Malaya
0
No
Yes
UPM
Agree of New Feature
Figure 3.3.1.8(c)
Feature 3 – ‘Who-Know-What’ information
Table 3.3.1.8(c)
Feature 3 –’Who-Know-What’ information
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid No 26 25.0 25.0 25.0
Yes 78 75.0 75.0 100.0
Total 104 100.0 100.0
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As shown in Figure 3.3.1.8(a) and Table 3.3.1.8(a); Figure 3.3.1.8(b) and Table
3.3.1.8(b); Figure 3.3.1.8(c) and Table 3.3.1.8(c) that majority of the
respondents have agreed these three (3) features to be implemented into the
KMS.
The following are the details of these three (3) features that are suggested in the
questionnaire:
1. Feature 1: Information from academia are stored and made available to
other colleagues. This encourages the academia to store reports, notes and
documents so that the amount of materials will be increased.
In this case, there are 88.5% of the respondents suggested this feature to be
implemented into the KMS.
2. Feature 2: End user tools such as word processing and presentation tool
are available to academia. The PCs are connected to the KMS with
standardized personal productivity tools such as word processing and
presentation tools, so that documents can be exchanged easily within a
HLI.
In this case, there are 69.2% of the respondents suggested this feature to be
implemented into the KMS.
3. Feature 3: Information about ‘Who Knows What’ will be made available
to all the academia. Its purpose is to record and disclose ‘who’ in the HLI
knows ‘what’ by building KMS. This idea is to make sure that all the
academia within the HLI are able to access, exchange and share their
knowledge via this KMS.
In this case, there are 75% of the respondents suggested this feature to be
implemented into the KMS.
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In conclusion, out of these three (3) features, most respondents would
prefer Feature 1 to be implemented into the proposed KMS as most of the
knowledge can be shared among the academics. On the other hand, least
respondents would prefer Feature 2 to be included into the KMS because
these end user tools can be obtained from other systems.
Based on the respondents’ feedback, the proposed KMS would be implemented
with the feature to motivate the academia in contributing the knowledge in order
to increase the knowledge capacity.
3.4. Analysis on Knowledge Management in Existing Higher Learning Institutions
With reference to the data that were analyzed through questionnaire and
interviews, the main problem is that most of the HLI do not implement KM due to
the lack of KM enforcement from top management and practices of KM among the
academia. The result has also shown that, majority of the academia from HLI that
have applied KM have better understanding of KM than those from HLI that have
not applied KM. This is because KM is part of the requirement for these HLI and
all the academia are required to follow the KM strategies and rules. Besides, these
KM strategies and rules can also be enforced by creating KM framework and KM
portal in HLI for example KM framework and KM portal in MMU (Section 2.4.2)
and UPM (Section 2.4.3).
Apart from that, knowledge sharing (Figure 3.3.1.3 and Figure 3.3.1.4) seems to be
one of the key elements of KM, which is being practiced by the academia during
their daily jobs even though KM is not implied in these HLI. However, there are
some significant differences between HLI with and without KM in knowledge
sharing. HLI that have applied KM may store the knowledge in the KM portal for
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knowledge sharing purposes, whereas HLI that have not applied KM are unable to
store and manage the knowledge in a proper manner as KM portal was not
implemented. As a result, the academia from these HLI was only able to share their
knowledge through verbal communication or hardcopy documents, and this may
definitely lead to loss of knowledge.
In view of this, research and analysis was conducted to understand the current KM
status in HLI, and to propose KM framework and implement KMS that integrates
E-Learning elements to avoid losing of knowledge. On top of that, the KMS is also
able to assist the academia to acquire, store, manage share, retrieve and distribute
knowledge systematically.
3.5. Summary of Chapter 3
The result was obtained through several types of research method such as document
review, interview and questionnaire survey. The reviews or opinions were gathered
through the data from document review and interview method was used to obtain
details about KM in Motorola, Malaysia, MMU and UPM. Lastly the data was collected
from most of the samples (academia) in HLI, within Selangor and KL, Malaysia before
the graphs and tables are generated. With these results that are gathered through
different researches, a proposed KM framework and KMS would be further explained
in Chapter 4.
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