Knowledge Management and Academic Performance: An Empirical Study of Iraqi HEIs

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International Journal of Academic Research in Business and Social Sciences
June 2012, Vol. 2, No. 6
ISSN: 2222-6990
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Knowledge Management Processes and Academic
Performance in Iraqi HEIs: An Empirical
Investigation
Ammar A. Ali Zwain
School of Technology Management & Logistics, College of Business (COB),
Universiti Utara Malaysia (UUM), Malaysia
E-mail: amalizw@yahoo.com
Lim Kong Teong
School of Technology Management & Logistics, College of Business (COB),
Universiti Utara Malaysia (UUM), Malaysia
E-mail: klim@uum.edu.my
Siti Norezam Othman
School of Technology Management & Logistics, College of Business (COB),
Universiti Utara Malaysia (UUM), Malaysia
E-mail: norezam@uum.edu.my
Abstract
This study examines the relationship between the processes of Knowledge Management (KM)
and educational organization outcome in respect to academic performance. The study is based
on a survey design and cross-sectional. The survey was conducted on 41 quality improvement-
adoption colleges in Iraqi higher-education institutions (HEIs). The study hypotheses were
tested through correlation and regression analyses. The results supported the main hypotheses
for the study, suggesting that Iraqi HEIs can benefit from KM processes. Pearson's correlation
pointed out that all processes of KM have significant correlations with academic performance
measures. Regression analysis showed significantly positive relationships. In addition, statistical
analysis also indicated that the KM processes should be implemented collectively rather than
separately. In conclusion, this study provided insight and further understanding of the effect of
KM processes on academic performance, and therefore, allows decision-makers to get in-depth
knowledge about the impact of KM processes in Iraqi HEIs context.
Keywords: KM, academic performance, Iraqi HEIs.
1. Introduction
Throughout the world, organizations are facing a universal challenge consequentially from rapid
changes in a new knowledge economy. Hence, organizations need to improve their activities in
order to gain sustainable competitive advantages. Many organizations accept KM as a

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management paradigm worldwide in order to cope with the changing expectations of the
organization (Safa, Shakir, & Boon, 2006; Yeh & Ta, 2005). Like other sectors, educational sector
is also affected by the rapid changes in the business environment. According to Amin (2006),
profound changes resulting from the emerging competitive business environment have made
HEIs and universities to think the same way like business organizations. Meanwhile, educational
markets are becoming global. Based on this fact, ability to compete and stay in business under
such a condition depends largely on how the changes and improvement are managed by
academic institutions.
In our modern world popularly referred to as the information age, knowledge is the key
resource in this era. The problem today is not how to find the information, but how to manage
it; the most important challenge for organizations is how to process knowledge and to make it
profitable in the recent knowledge-driven organization (Sallis & Jones, 2002). For this reason,
organizations are viewing KM as a critical success factor in today’s dynamic environment (Wong
& Aspinwall, 2005; Yeh & Ta, 2005; Zack, McKeen, & Singh, 2009). Therefore, understanding the
link between KM and organizational performance is important for successful integration of KM
into organizational strategy (Carlucci & Schiuma, 2006).
KM is relatively a new discipline, derived from other various disciplines, including management,
information system, business theory, organizational behavior and social psychology (Sallis &
Jones, 2002). Like other disciplines, a number of important theorists and academics are
influencing the direction and development of KM. In defining KM, there is a need to look at
what knowledge itself is. Anantatmula (2007) revealed that the perspective of knowledge by
organization in the current knowledge economy is that knowledge is viewed as the main
economic resource, and it is seen as a weapon that can be used in gaining competitive
advantage.
In HEIs context, Kidwell, Vander Linde and Johnson (2000) identified KM of great benefits in
higher-education environment in research process, curriculum development process, student
and alumni services, administrative services and business strategic planning. It can be found
that the use of KM in higher education will have many direct benefits for academic
achievements. However, KM has been applied to universities and colleges in the USA, UK, and
in Asian countries such as Malaysia (Chen & Burstein, 2006; Kebao & Junxun, 2008; Muhammad
et al., 2011; Sedziuviene & Vveinhardt, 2009; Yeh & Ta, 2005), and also in Iraqi HEIs. According
to Aljanabi (2007), KM in Iraqi HEIs is still a new concept, the higher-education sector responds
positively to KM practices in institution level and individual level.
In the past, Iraqi higher education system was ranked the best in the Middle East and Gulf
region not until after the economic sanction, when Iraqi HEIs suffered from a prolonged period
of relative isolation due to the sanctions imposed by UN (UNESCO, 2008). According to the
International Conference on Higher Education in Iraq (2007), Iraqi universities have suffered
more than necessary in terms of the curricula, resources, teaching methods, modern
technology and research. It was emphasized that there is an urgent need to bring the lost glory
to the Iraqi educational institutes.

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2. Problem Statement
Even though KM concept is well known, scholars, practitioners, and others in the field of
business management are still debating the concepts and definitions related to knowledge
management (Martin, 2005). In general, little empirical research has been conducted to
investigate the relationship between KM and performance (Kalling, 2003; Zack, et al., 2009). In
education context, Sallis and Jones (2002) emphasized, there is much need for KM in education
as there is in business. If excellent achievements are achieved in one area of the colleges or
universities, there should be a process for knowing how they were achieved. However, very few
empirical studies have been focused on KM processes and its effect on academic performance
specially, in the field of higher education (Muhammad et al., 2011).
Therefore, it became apparent to what was presenting that there is an acknowledged problem
concerning the subject of KM processes in the educational institutions in general. In addition,
KM program in terms of the form of implementation and the degree of importance are not
clear. The failure of identifying the feature of implementation (individual or collective) and
the degree of significance would lead to many deficiencies and ineffectiveness in reaching
competencies for universities, if such processes overlooked. However, the major question
that arises here and needs to be answered is: To what extent, do the processes of KM
affect academic performance in the Iraqi HEIs?
3. Research Importance and Objectives
The importance of the study derives from the ability of determining the key processes of KM
that affecting academic performance in the Iraqi universities. This understanding and empirical
analysis would help decision-makers to work on weak processes to cope with and strength
others for further improvements. Moreover, in line with the orientations of the Iraqi Ministry of
Higher Education and Scientific Research (MHESR-I) about the academic performance
improvement; this study tries to shed light on issues concerning the application of KM in Iraqi
HEIs to overcome the barriers blocking the enhancement of academic performance. However,
the study aims to:
- Enhance the understanding of KM processes and its importance in the higher-education
context.
- Identify empirically the feature of implementation of KM processes in Iraqi HEIs.
- Test empirically the influence of KM processes on academic performance of Iraqi HEIs.
4. Literature Review and Research Hypotheses
4.1 KM Processes
In this information era, virtually all organizations are becoming knowledge-driven in order to
achieve or maintain the competitive advantage. According to Choy (2006), KM has been
practiced in 80 percent of the most prominent companies in the world. The author concluded

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that the power of KM in an organization could not be overestimated considering the fact that
for organization to maintain her growth and development.
KM has been defined in different ways and from different aspects; interestingly, no sole
definition can explain the whole picture, as different authors viewed KM from a number of
perspectives, which dictates the way they define it. However, according to Salis and Jones
(2002), KM in education can be defined as such a tool that gives clues to managers and staffs of
educational organizations on the emerging world of KM to meet the challenge of the
knowledge era. KM helps educational organizations to realize the merits and beauty of
knowledge creation and sharing as means of enhancing teaching and learning process.
From literature, the concept of KM is generally described based on a number of key processes
of KM. Such processes have several interpretations; the term of processes is sometimes
referred to as activates or practices. Whichever a way it is addressed, it still refers to the same
thing which is the dimensions of KM and in this paper, the term “processes” is used, since it is a
way to emphasize that these processes are essential and should work together to improve the
performance of an organization. However, KM without certain key processes is expected to
yield little in the way of real benefits (Salis & Jones, 2002).
Various studies have addressed KM processes with a view to identify the key
aspects/dimensions of KM processes. These dimensions include acquisition, innovation,
protection, integration, and dissemination (Lee & Yang, 2000); acquisition, conversion,
application, and protection (Gold, Malhotra, & Segars, 2001); development, utilization, and
capitalization (Kalling, 2003); creation, accumulation, sharing, utilization, and internalization
(Lee, Lee & Kang, 2005); identification, collection, organizing, storage, sharing, and evaluation
(Kiessling, Richey, Meng, & Dabic, 2009). An examination of these diverse views enables the
researcher to group them into five processes: identification, acquisition, storage, sharing, and
application. These five processes have received the most consensus attention in KM literature
(Daud & Abdul Hamid, 2006; Gold et al., 2001; Kiessling et al., 2009; Lee & Yang, 2000; Liao &
Wu, 2009). A discussion of the five processes of KM and its relationship with academic
performance follows subsequent subsections.
4.2 Academic Performance (AP)
Higher education today is subject to the same pressures of the marketplace. Profound changes
in competition have made universities, and HEIs think like business to the extent that students
are now being treated as customers. In addition, the stockholders’ demands are getting more
and more complex, which must be attended to whether the educational organization must
maintain its competitive advantage (Amin, 2006). The HEIs then must ensure that the students
receive high-quality service. HEIs have responsibility to produce graduates that are able to
accommodate challenges emerging in society, such as graduates producing high-quality profile
and competence in their respective profession (Suryadi, 2007).

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HEIs are changing from a public service to a market-driven one (Kettunen, 2003), and HEIs now
face pressing concerns such as international competition (Kebao & Junxun, 2008). For that
reason, HEIs are faced with the need to improvement many of their existing management
practices and attitudes. One of the current issues of significance is the need for performance
management, particularly measurement of key performance indicators (Suryadi, 2007). It is
believed that knowing such performance indicators will enable the organizations to achieve an
acceptable level of AP. According to Kanji and Tambi (1999), the performance indicators in HEIs
can be measured based on objective’s achievement; this has to do with how well core process
(educational process) is operating. Therefore, since the study focus on HEIs context (public
universities), the AP measurement takes into account students related academic achievement
as key indicators of AP. However, AP indicators as they have been detected in relevant
literature are the following (Table 1): academic status (CPA), undergraduates’ wastage rate,
classes of degrees, graduation rates.
Table1: The Indicators of Academic Performance
AP Indicators
Author/s (Year)
Academic Status (CPA)
Higgins (1989); Ball & Wilkinson (1994); Miller (2007)
Undergraduates Wastage
Rate
Johnes & Taylor (1990); Johnes (1996); Palmer & Bray (2003);
Sall, (2003); Pinilla & Munoz (2005); Agha (2007); Lee &
Buckthorpe (2008)
Classes of Degrees
Higgins (1989); Johnes & Taylor (1990); Ball & Wilkinson (1994);
Miller (2007)
Graduation Rates
Higgins (1989); Johnes & Taylor (1990); Ball & Wilkinson (1994);
Pinilla & Munoz (2005); Miller (2007)
4.3 The Relationship between KM Processes and AP
KM has been investigated at business industrials; however, there have been very limited studies
done to investigate KM processes at a public organization of higher-education level. The
researchers found through the reviewed literature that there are some related studies. Based
on these studies, the following dissection provides justification that KM processes influence AP.
Knowledge Identification (KID)
Knowledge identification is an action of discerning the location and value of knowledge,
restraints to knowledge flow, and opportunities to leverage the value of knowledge. Either
looking at this perspective, knowledge can be identified by individual employees or
organization (Asoh, Belardo, & Crnkovic, 2007; Darroch, 2005; Liao & Wu, 2009). Therefore,
knowledge identification is well known as the initial stage of managing knowledge. This

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dimension also captures all that is related to determining core competencies, recognize
strategic capabilities, and assess the expertise level for each knowledge domain. In short,
determining the knowledge gaps between the existing and needed (Hall & Andriani, 2002; Zack,
1999). According to Sarawanawong et al. (2009), identify the knowledge gap is necessary to
support staff daily work successful. Thus, knowledge identification plays a key role in enhancing
academic performance. In this regard, the following hypothesis is suggested:
H1: knowledge identification has a positive relationship with academic performance.
Knowledge Acquisition (KAC)
Once needed knowledge is identified, it has to be acquired for utilize. Thus, acquisition process
is this oriented to obtain needed knowledge from both internal and external sources
(Bouthillier & Shearer, 2002; Mohammad, Hamdeh, & Sabri, 2010). This requires accessing to
knowledge in knowledge-based resources to capturing the new knowledge, and exploiting the
available knowledge.
According to Lee and Yang (2000), there are two activities through which organization acquires
knowledge, which are; searching and organization learning. Knowledge acquisition through
searching can be achieved via three means such as scanning, focused research, and
performance monitoring. Meanwhile, organization learning takes a fundamental part in
knowledge acquisition since there is a need for organization to enhance its performance
constantly. This further stresses how significant it is for organizations to determine the best
practices to be adopted in order to achieve excellent performance (McKeen et al., 2006; Asoh
et al., 2007; Liao & Wu, 2009). As a result, knowledge acquisition is linked to academic
performance, and a hypothesis is proposed:
H2: knowledge acquisition has a positive relationship with academic performance.
Knowledge Storage (KST)
It is generally believed that if knowledge is valuable, then storing such valuable assets should be
given an utmost concern. After obtaining the required knowledge, it is expected to be coded
and recorded to enable easy access to such knowledge (Kiessling et al., 2009). From competitive
advantage perspective, there is no way one can talk about knowledge storage without
mentioning special kind of database is called the Knowledge Base, which allows collection,
organization and retrieval of knowledge to be carried out in a computerized manner.
Knowledge base can be categorized into two major types: The Machine-readable and the
Manual knowledge base (Kiessling et al, 2009; Asoh et al, 2007; Liao & Wu, 2009). According to
MBNQA (2004), academic performance measurement in HEIs should focus on students’
achievement, which requires a comprehensive and integrated reliable-based system. This can
be achieved through sound database and effective process of knowledge storage, which should
provide reliable data. Hence, ever since knowledge storage affects academic performance, the
following hypothesis is formed:

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H3: Knowledge storage has a positive relationship with academic performance.
Knowledge Sharing (KSH)
Knowledge sharing involves the exchange of information and knowledge from one source
(person, group or organization) to another (Fugate, Theodore, & Mentzer, 2009; Lee et al.,
2005; Liao & Wu, 2009). According to Botthillier and Sheare (2002), the success of any KM
processes in any organization relies on the effectiveness of the knowledge sharing. The general
problem in KM is that most of the large organization is not conscious of valuable knowledge
they have (Kiessling et al, 2009). With effective KM processes, hidden knowledge can easily be
discovered, and such process mostly facilitated via sharing. According to Liao and Wu (2009),
knowledge sharing plays an intermediate role to support knowledge exchange in the
organization and aids the achievement and sustenance of their competitive advantage.
Therefore, in higher-education context, knowledge sharing as a vital pillar of KM is critical to
academic performance (Daud & Abdul Hamid, 2006). It is clearly that knowledge sharing is
greatly supported to improve academic performance. In this regard, the following hypothesis is
proposed:
H4: Knowledge sharing has a positive relationship with academic performance.
Knowledge Application (KAP)
Knowledge application concerns the process of using of knowledge that has been stored in
organization. Zack (1999) revealed that knowledge as a process cannot be separated from its
respective action-application. Meaning that knowledge without application process is
considered as information. Within KM context, the concept of application has another
interpretation, sometimes in literature where it is referred to as utilization. Many researchers
stated that knowledge application process denoted actual utilization of the knowledge (Asoh et
al., 2007; Gold et al., 2001; Lee et al., 2005; Liao & Wu, 2009; Zack, 1999). Moreover, Nonaka
and Takeuchi (1995) argued that the process of applying knowledge happens when new
knowledge is acquired and put to use. Lee and Lee (2007) described knowledge application as
the effective retrieval mechanisms that enable access to knowledge. The authors further
revealed that the knowledge application is the actual process of knowledge retrieval and
knowledge dissemination. This means knowledge application involves effective retrieval
mechanisms that enable organization’s members to access relevant knowledge. Undeniable,
academic performance will be improved since the knowledge application is supported among
educational partners. Consequently, the following hypothesis is formed:
H5: Knowledge application has a positive relationship with academic performance.

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5. Research Framework
The main objective of this study is to investigate the relationship between KM processes and
academic performance. Based on the above literature review, a research framework was
developed. Figure 1 demonstrates these relationships. In this framework, KM processes are
independent variables and academic performance is a dependent variable respectively.
Figure 1: Research Framework
6. Research Methodology
6.1 Research Design
The study is based on a survey design and time horizon was cross-sectional. Since the objective
of this study is to measure the actual level of each of the KM processes on academic
performance in Iraqi HEIs, academic leadership (dean or dean assistant) which was
knowledgeable about organizational practices considered appropriate subject. The survey was
carried out in 64 colleges, which offered the undergraduate programs. The colleges are selected
randomly from four public universities in Iraq.
The final number of participates for this study was 41 colleges. The sample size comprised
about 63 percent of the total population. The study hypotheses were tested using correlation
and regression analyses. The academic leadership as respondents were requested to focus on
questions related to degree or extent of practices KM processes and academic performance in
their organizations with items followed a 5-point scale ranging from 1 = strongly disagree to 5 =
strongly agree. In this study, the indicators for academic performance of HEIs context are:
H2
H4
H5
H3
H1
KM Processes
Academic
Performance
Knowledge Storage
Knowledge Acquisition
Knowledge Sharing
Knowledge Application
Knowledge Identification
Dependent Variable
Independent Variables

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academic status (CPA), undergraduates’ wastage rate, classes of degrees, graduation rates, and
overall academic achievements (Johnes, 1996; Lee & Buckthorpe, 2008; Miller, 2007). The
respondents are required to answer the questions regarding their organizations perceived
performance over the past three years in order to reduce the influence of temporary
fluctuations in those AP indicators.
6.2 Instrument Measures
To measure the two constructs of importance of this study, the researchers adopted the items
of instrument from relevant literature. The instrument was pre-tested and reviewed by four
academicians (heads of departments). The participants were involved to evaluate the
questionnaire in terms of readability, accuracy, and brevity of the instrument. However, Table 2
showed the sources of these items.
Table 2: The Number of Adopted Items and its Sources
Constructs
Code
No. of
Items
Sources
Independent Variables
Knowledge
Identification
KID
6
Asoh et al., 2007; Bothillier & Shearer, 2002;
Darroch, 2005; Liao & Wu, 2009; Zack, 1999
Knowledge Acquisition
KAC
6
Gold et al., 2001; Lee et al., 2005; McKeen et al.,
2006; Liao & Wu, 2009
Knowledge Storage
KST
5
Asoh et al., 2007; McKeen et al., 2006; Liao & Wu,
2009; Kiessling et al., 2009
Knowledge Sharing
KSH
5
Daud & Abdul Hamid, 2006; Fugate et al., 2009;
Sallis & Jones, 2002; Lee at el, 2005; Liao & Wu,
2009
Knowledge Application
KAP
7
Asoh et al., 2007; Gold et al., 2001; Lee et al.,
2005; Liao & Wu, 2009; Zack, 1999
Dependent Variable
Academic Performance
AP
5
Agha, 2007; Johnes, 1996; Miller, 2007; Palmer &
Bray, 2003; Pinilla & Munoz, 2005; Sall, 2003
In order to assess the goodness of the instrument measures, the instrument was
subjected to the construct validity and reliability tests. The construct validity was evaluated by
factor analysis with eigenvalues of at least 1.0, and factor loading of at least 0.40. Meanwhile,
the reliability was evaluated by the coefficient of Cronbach’s alpha with acceptable value of 0.7
and above (Hair, Black, Babin, & Anderson, 2010). Table 3 illustrates the results of validity and
reliability for the latent constructs.

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Table 3: Results of Validity and Reliability
Constructs
No. of
Items
Factor Loading
KMO
Eigen
Value
% of
Variance
Cronbach'
s Alpha
Independent Variables
Knowledge
Identificatio
n
6
.685, .757, .809, .742,
.807, .711
.825
4.615
65.929
.845
Knowledge
Acquisition
6
.781, .811, .738, .780,
.696, .634
.818
3.306
55.105
.834
Knowledge
Storage
5
.799, .816, .796, .747,
.746
.797
3.051
61.024
.839
Knowledge
Sharing
5
.743, .734, .680, .853,
.814
.817
4.325
68.868
.821
Knowledge
Application
7
.796, .810, .780, .742,
.851, .617, .673
.874
4.006
65.890
.873
Dependent Variable
Academic
Performanc
e
5
.715, .753, .817, .837,
.759
.835
3.380
67.606
.833
Based on the displayed in the Table 3, the results indicate that factor loadings for all constructs
were more than 0.4, and all constructs explain more than 50 percent of total variance.
According to Pallant (2007), KMO value should be greater than 0.60. KMO values are greater
than 0.60. Other than that, the Bartlett's test of sphericity was significant (α = 0.05). Moreover,
the results also show that all values of Cronbach’s alpha were greater than 0.70. In short, the
instrument measures used in this study was valid and reliable.
7. Data analysis and Results
According to Hair et al. )2010), before data analysis, we should check the assumptions
regarding normality, linearly, and outliers. Normality of the observed variables was evaluated
through the examination of skewness and kurtosis values. None of the observed variables are
significantly skewed or highly kurtosis (standardized residuals < ± 2.5). Meanwhile, all observed
variables shown to be linearly related (via scatter plots). Moreover, using Mahalanobis
distance, no obvious outlier was noticed (D2/df < 2.5). Thus, it can be suggested that these basic
assumptions are not violated.
As described at the earlier section, the sample sizes was 41 cases, which have achieved the
required assumptions. The sample size of 41 cases is practically sufficient to be analysed in this
study. According to Sekaran and Bougie (2010), sample sizes larger than 30 and smaller than
500 are fitting for all research.

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Pearson's correlation analysis is conducted to measure the relationship between two variables
in the study. In examining the correlation among the KM constructs, Table 4 shows results of
Pearson’s correlation. The entire KM processes correlate significantly with each other (p ≤
0.01). Even though there are several (r) values in the level of medium and high correlation, high
correlation values are more frequently discerned among KM processes. These positive
associations tend to support the previous agreement that KM processes should be
implemented holistically, not individually. Many researchers (such as Choy, 2006; Shankar &
Gupta, 2005; Zivojinovic & Stanimirovic, 2009) have supported the concept of holistic approach
of KM processes.
Table 4: Pearson’s Correlation among KM Processes
No.
KM Processes
KID
KAC
KST
KSH
KAP
1
Knowledge Identification
(KID)
1.000
2
Knowledge Acquisition (KAC)
.637**
1.000
3
Knowledge Storage (KST)
.679**
.530**
1.000
4
Knowledge Sharing (KSH)
.570**
.736**
.464**
1.000
5
Knowledge Application (KAP)
.597**
.759**
.519**
.782**
1.000
(p**) Correlation is significant at the 0.01 level (1-tailed).
The relationships between KM processes and academic performance variables are exhibited in
Table 5. All processes of KM are positively and significantly related with academic performance
at α = .01 levels. Most of KM processes show strong correlation with academic performance.
Meaning that, all the KM processes are highly associated with academic performance. This
finding agrees with several studies that have been conducted to explain such relationships (e.g.,
Daud & Abdul Hamid, 2006; Muhammad et al., 2011).
Table 5: Pearson’s Correlation between KM processes and AP
KM Processes
KID
KAC
KST
KSH
KAP
Academic
Performance
.679**
.763**
.572**
.767**
.811**
(p**) Correlation is significant at the 0.01 level (1-tailed).
Table 6 demonstrates the multiple regression analysis between KM processes and academic
performance measures. In this model, AP acts as the dependent variable and KM with the five
processes: knowledge identification, knowledge acquisition, knowledge storage, knowledge
sharing, and knowledge application as the independent variables. From the results in Table 6,
the analysis shows that strong relationships existed as hypothesized; whereas the regression
model has moderately high values of adjusted R2 (0. 475), which means that 47.5 percent of the
variation in AP can be explained by knowledge identification, knowledge acquisition, knowledge

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storage, knowledge sharing, and knowledge application. Table 6 also shows that only two
variables had a significant and positive effect on AP. They are knowledge sharing (β=0.238,
p=0.013) and knowledge application (β=0.214, p=0.036). It can be concluded that knowledge
sharing has the greatest effect on AP followed by knowledge application. Furthermore, the
regression analysis result also revealed significant F value at level α = 0.01.
Table 6: Multiple Regression between KM Processes and AP
KM Processes
(Independent Variable)
Academic Performance (Dependent Variable)
Beta
Std. Error
Std. Beta
t
Sig.
(Constant)
1.062
.358
2.964
.003
Knowledge Identification
.000
.091
.000
.003
.498
Knowledge Acquisition
-.115
.112
-.098
-1.030
.105
Knowledge Storage
-.092
.079
-.093
-1.168
.245
Knowledge Sharing
.275
.110
.238
2.500
.013
Knowledge Application
.249
.118
.214
2.117
.036
R2 .496
Adjusted R2 .475
Significance of F .000
Nevertheless, based on the results in Table 6, multicollinearity was appeared. This is on line
with many researches position (Lim, Rushami, & Zainal, 2004; Miles & Shevlin, 2001). The
regression model has one or more standardized regression coefficients taking on negative
values when common sense and correlation analysis suggest a positive relationship exist
between the independent and dependent variables (see Table 5 and Table 6). Many of the
estimated coefficients are insignificant despite the F value is significant. The strong correlation
among KM processes (0.464 ≤ r ≤ 0.782) also proposing the presence of multicollinearity (see
Table 4). According to Pallant (2007), multiple regression doesn’t like multicollinearity; and this
definitely doesn’t contribute to a good regression model. This is because when the independent
variables are highly related, the estimated standard errors for the coefficients will be large, and
as a result the t-statistics will be small (Agus, 2000). The estimated coefficients with large
standard errors will be unstable and hence, weakened the analysis.
There are several techniques that researchers can utilize to reduce the effect of
multicollinearity. In this study, the Principal Component Analysis (PCA) was employed to handle
multicollinearity as suggested by Hair et al. (2010). The results of PCA indicated that the first
principal component of KM processes explained 63.50 percent of the total variance of the KM
processes. KM variables were analyzed collectively principal component scores of KM variables
were retrieved (Agus, 2000; Lim et al., 2004). A simple linear regression analysis was later
carried out between academic performance and the first saved of principal component scores
of KM processes as exhibits in Table 7.

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Table 7: Simple Linear Regression between Principal Component Scores of KM Processes and
AP
Model
Beta
Std.
Error
Std. Beta
t
Sig.
R2
(Constant)
.940
.325
2.890
.004
Regression
IV = Principal component
scores of KM
DV = Academic Performance
.261
.028
.573
9.174
.000
.293*
* F-statistics are significant at the .05 level.
From the result as shown in Table 7, the R2 is 0.293, which means that 29.3 per cent of the
variation in academic performance can be explained by KM variables. The results of simple
regression analysis as well indicate that KM variables (collectively) have a significant
relationship with academic performance variable. Regression coefficient (β=0.573) of the
regression model is statistically positive and significant at α = 0.05. Thus, based on β value, the
researchers conclude that KM processes have a significant and positive effect on AP. In short,
data analysis results provide sufficient evidence to support all five alternative hypotheses.
8. Discussion And Study Implications
Notwithstanding the significant affinity that exists between KM and performance, empirical
research on the link between KM processes and AP has hardly been touched, especially in HEIs
context (Muhammad et al., 2011). In Iraq context, most HEIs have started to consider KM as a
critical part of their activities in order to improve their performance (Aljanabi, 2007).
Unfortunately, there are very limited studies that touch KM and its effects on the educational-
institutes performance. Moreover, most of these researches were conceptual and case studies.
Considering the study’s domain, this study attempts to narrow the gap in literature, particularly
in developing countries.
The primary purpose of this study was to investigate empirically the relationships among KM
processes, and to identify the effects of KM processes on academic performance within Iraqi
HEIs context. Through testing the research hypotheses, which were developed based on
relevant literature, the purpose was accomplished. The significant implications from the results
for researchers and practitioners, respectively, are discussed in the rest of this section.
Results of Pearson's correlation indicated that all the correlations among the KM constructs
were significantly positive with each other. The findings also consistent with those in literature
that have demonstrated that KM processes should be implemented holistically rather than
individually (Choy, 2006; Shankar & Gupta, 2005; Zivojinovic & Stanimirovic, 2009).
Meanwhile, correlation results indicated that the KM processes had a strong association with
academic performance (see Table 5). This study is consistent with the prior research conducted

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by Daud and Abdul Hamid (2006) and Muhammad et al. (2011). In this regard, it is found that
Iraqi HEIs can benefit from KM processes. The correlation results of this study also revealed that
knowledge application recorded highest correlation with AP (0.811), followed by knowledge
sharing (0.767), knowledge acquisition (0.763), and knowledge identification (0.679). Thus,
focusing on these processes will enhance AP within Iraqi HEIs context. More detail, for example,
knowledge sharing involves the exchange of information and knowledge from one source to
another (Daud & Abdul Hamid, 2006; Liao & Wu, 2009). Therefore, knowledge sharing plays a
major role in ensuring that the shared thinking and provide adequate internal communication
throughout the educational-organization, and that help aids the achievement and sustenance
of their performance.
Knowledge application had the greatest correlation with AP as compared to the other KM
processes. One possible reason is that one of the common forms of this process is to adopt the
best practice from other leading organizations by discovering relevant knowledge and apply it
(Lee et al., 2005). Such practices create opportunities for educational partners to apply new
knowledge, which in turn leads to enhance their performance.
Within KM processes, knowledge acquisition also recorded great association with AP. As
mentioned by many researchers, knowledge acquisition requires accessing knowledge-based
resources to capturing the unknown knowledge, and exploiting the available knowledge (Lee et
al., 2005; Ooi, 2009; Liao & Wu, 2009). Thus, this process provides the approach to create new
knowledge that aimed at achieving better performance. As for the relationship between
knowledge acquisition and AP, The findings also highlight the importance of knowledge
identification and knowledge storage, which is found to have a significantly positive and high
correlation with AP. Therefore, these processes are a significant factors and very important in
achieving better academic performance. On the other hand, knowledge storage had the lowest
correlation with AP as compared to the other KM processes. One plausible reason is that
probably not all colleges have an effective system to support the process of knowledge storage.
Therefore, academic leadership in Iraqi HEIs must be taken into consideration this issue.
Concerning the effect of KM processes on academic performance, the regression model has
moderately high values of R2, adjusted R2 and significant F-values. Results of multiple regression
indicated that knowledge sharing was positively related to academic performance and had the
greatest impact on academic performance as compared to the other four processes. One
plausible reason is that knowledge sharing as a vital pillar of KM is critical to academic
performance in this knowledge era. According to Botthillier and Sheare (2002), the success of
any KM processes in any organization relies on the effectiveness of the knowledge sharing.
However, the values of overall standard errors and many insignificant independent variables
primed the researchers to the presence of the multicollinearity problem. Multicollinearity could
lead to incorrect variable estimations and eventually unstable regression models formation.
Hence, there is a need to employ other statistical techniques to handle this problem. In this
study, PCA technique was employed to reduce the effect of multicollinearity as recommended
by Hair et al. (2010).

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The results of the simple regression analysis implied that KM processes (collectively) have a
significant and positive effect on academic performance. The analytical results as well
consistent with those in the literature that stated that KM processes positively and significantly
contributes to academic performance (Daud & Abdul Hamid, 2006; Muhammad et al., 2011).
The implications of this study can be divided into three aspects: theoretical contributions,
robustness of research methodology, and practical contributions. From the theoretical
perspective, this study demonstrated the importance of KM processes in the education service
sector. This study supports the studies (Gold et al., 2001; Kiessling et al., 2009; Lee & Yang,
2000; Liao & Wu, 2009) in which KM is operationalized as a multidimensional construct. In
addition, it gives contribution to the literature in terms of the impact of KM processes on
academic performance and provides to a better understanding of the relationship between KM
and AP in the educational organizations. Thus, implementation of KM is crucial since the KM
processes are found to have a significant positive impact on academic performance. Briefly,
academic performance will enhance if there is a sound management foundation like KM
processes. Considering the study’s domain, these findings have some important implications for
theory. It is also imperative to note that this study attempts to enrich the literature review
and make a contribution in KM-related studies, especially in developing countries.
Undeniable, there is a growing number of literature reviews on KM in education. However,
there has been almost anecdotal and no-methodologically rigorous research. With regard to
the research methodology, in this study, the survey instrument has achieved the validity and
reliability criteria, thus leading to greater accuracy of results. The findings contribute by using
HEIs in Iraq, which proves to be valuable as an example of a methodology that might be used to
track the extent of KM effects on academic performance.
In terms of practical implications, the study highlights management issues involving the
influence of KM processes on academic performance. In the other words, this study draw
attention to the role of academic leadership in creating relevant organizational knowledge
through KM processes. However, if HEIs as knowledge-driven organizations need to leverage
knowledge creation capabilities, stress should be given to KM processes, which are: knowledge
identification; knowledge acquisition; knowledge storage; knowledge sharing; and knowledge
application. Hence, by implementing these processes collectively and effectively, academic
leadership can use the items establishing KM in this study to assess where their organization
stand with regard to the use of KM processes or as a guideline in implementing them.
Moreover, they can use the AP indicators as a check instrument to appraise the results of AP
achievements over time.
The researchers believe this study contains findings that are useful to practicing managers not
only in the educational-service sector but also in the non-educational organizations. This study
has shed some light for managers how planning to improve organizational performance,
whereby the top management will be able to gauge the effects of KM processes and the
organizational performance.

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9. Conclusion And Future Research
This study explored the relationship between KM processes and academic performance. Results
have shown that the KM processes had a significant effect on academic performance;
educational organizations, therefore, need to find solutions on how to improve these processes
in order to improve academic performance among educational partners (students and
educators).
Currently, many Iraqi HEIs have been implementing knowledge management initiatives, in
order to improve their performance and obtain a sustainable competitive advantage. In this
regard, the current study serves as a guide to decision makers, who seek to improve academic
performance and capturing the particular knowledge via KM program. KM program as a
knowledge-based approach will guide and facilitate the process of performance improvement,
thereby assisting the organization to achieve excellence performance and better meet the
changing requirements of their customers.
The findings indicate that HEIs should emphasize greater attention to the key processes of KM
namely: knowledge identification; knowledge acquisition; knowledge storage; knowledge
sharing; and knowledge application. To other researchers, future studies should attempt to
identify the effect of critical success factors (CSFs) of KM implementation that may produce
such differences. The theoretical model used in this study can also be tested by conducting
cross-country studies. In addition, this study would help the researchers to identify important
variables of KM processes for educational organizations in developing countries, especially in
the study of KM in Iraq.
This study covers only 41 colleges within four public universities in Iraq. More variations of
results could be obtained through a wider coverage of respondents. Otherwise, a comparison
between public universities and private universities could provide additional insights. For future
study in line of this research, the researchers believe that the analysis pertaining to the effect of
KM processes on other performance indicators (such as non-students related academic
achievement) along with students’ related academic achievement is essential. The relation
between KM and academic performance has been studied before (Muhammad, et al., 2011),
but empirical studies in this field are very limited. Finally, the researchers hoped that this study
would encourage attention towards further research in domain area for more empirical studies.
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