International Journal of Educational Management

Factors affecting knowledge sharing intention among academic staff

Ali Jolaee Khalil Md Nor Naser Khani Rosman Md Yusoff

Article information:

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

To cite this document:

Ali Jolaee Khalil Md Nor Naser Khani Rosman Md Yusoff , (2014),"Factors affecting knowledge sharing
intention among academic staff", International Journal of Educational Management, Vol. 28 Iss 4 pp. 413 431
Permanent link to this document:
http://dx.doi.org/10.1108/IJEM-03-2013-0041
Downloaded on: 27 January 2015, At: 03:59 (PT)
References: this document contains references to 99 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 379 times since 2014*

Users who downloaded this article also downloaded:

Zahra Tohidinia, Mohammad Mosakhani, (2010),"Knowledge sharing behaviour and its
predictors", Industrial Management & Data Systems, Vol. 110 Iss 4 pp. 611-631 http://
dx.doi.org/10.1108/02635571011039052
Chin Wei Chong, Yee Yen Yuen, Geok Chew Gan, (2014),"Knowledge sharing of academic staff: A
comparison between private and public universities in Malaysia", Library Review, Vol. 63 Iss 3 pp. 203-223
http://dx.doi.org/10.1108/LR-08-2013-0109
Roger Fullwood, Jennifer Rowley, Rachel Delbridge, (2013),"Knowledge sharing amongst
academics in UK universities", Journal of Knowledge Management, Vol. 17 Iss 1 pp. 123-136 http://
dx.doi.org/10.1108/13673271311300831

Access to this document was granted through an Emerald subscription provided by 540740 []

For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.com

Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.

The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0951-354X.htm

Factors affecting knowledge

sharing intention among
academic staff
Ali Jolaee and Khalil Md Nor
Department of Management, Universiti Teknologi Malaysia, Skudai, Malaysia

Naser Khani

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Department of Management, Najafabad Branch, Islamic Azad University,

Najafabad, Iran, and

Knowledge
sharing intention

413
Received 15 March 2013
Revised 12 June 2013
Accepted 20 June 2013

Rosman Md Yusoff
Department of Management, Universiti Teknologi Malaysia, Skudai, Malaysia
Abstract
Purpose The purpose of this paper is to investigate factors affecting knowledge sharing among
academic staff in universities. Utilizing the theory of reasoned action (TRA) as the underlying research
framework, the main objective of this study was threefold. First, was to examine the relationship
between attitude, subjective norm, and trust with knowledge sharing intention. Second, was to
examine the relationship among factors, i.e., self-efficacy, social networks and extrinsic rewards with
attitude toward knowledge sharing intention and the third objective was to find out the relationship
between organizational support and subjective norm.
Design/methodology/approach A total of 200 questionnaires were distributed among academic
staff at three social science faculties in one public university in Malaysia. Collectively, 117 usable
responses were returned. Partial Least Square analysis was utilized to analyze the data.
Findings The results indicated that of the two components of the TRA, only attitude was positively
and significantly related to knowledge sharing intention. The findings also show that social network
and self-efficacy significantly affect attitude and organizational support showed a strong influence on
subjective norms toward knowledge sharing intention.
Research limitations/implications Future research should consider type of knowledge that is
being shared. Besides, it would also be interesting to investigate potential differences of the knowledge
sharing intention between academic staff in the private and public universities.
Practical implications This study offers a more clear vision of the factors that affect knowledge
sharing intention among academic staff. Therefore, managers can implement practical plan to support
those factors.
Originality/value Factors affecting knowledge sharing among academic staff in universities were
reviewed to suggest a framework to explain this behavior in a specific context.
Keywords Knowledge sharing, Attitude, Subjective norm, University, Academic staff
Paper type Research paper

1. Introduction
According to Riege (2005), knowledge sharing is the corner stone of many
organizations. Organizations might be unable to function well as knowledge based
entities due to their knowledge sharing disabilities. Knowledge, especially tacit,
is difficult to be shared particularly whenever the individual refused to do so. Even
though knowledge sharing among individuals has been recognized as a positive
force for the survival of an organization, the factors that promote or discourage
knowledge sharing intention in the organizational context are still poorly understood
(Bock et al., 2005).

International Journal of Educational

Management
Vol. 28 No. 4, 2014
pp. 413-431
r Emerald Group Publishing Limited
0951-354X
DOI 10.1108/IJEM-03-2013-0041

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

414

Individuals do not always share their knowledge and they may not be willing to
share as much as the organization would like them to. From both a research and
a practical standpoint, it is important to understand what make employees to share their
knowledge and how an organization can facilitate this type of intention and behavior.
Knowledge sharing in organizations is of great interest to researcher and practitioner
alike because it improves organizational performance (Lesser and Storck, 2001), promotes
competitive advantage (Argote and Ingram, 2000), organizational learning (Argote, 1999),
and innovation (Powell et al., 1996).
Many studies have been conducted to examine factors that influence knowledge
sharing intention in various organizational settings (Davenport and Prusak, 1998;
Bresman et al., 1999; Kim, 2000; Bartol and Srivastava, 2002; Ipe, 2003; Kim and
Lee, 2006; Ryu et al., 2003; Chatzoglou and Vraimaki, 2009; George, 2004). Similar to
other organizations, academic institutions tend to rely more on knowledge sharing; but,
interestingly, very little empirical research have investigated the knowledge sharing
intention of academic staff in higher institutions (Bock and Kim, 2002; Ryu et al., 2003),
especially in Malaysia (Ahmad, 2003; Ariffin, 2004). Universities play the role to
provide insights and ideas (Martin and Marion, 2005). In the university, academic
staffs intention and attitude toward knowledge dissemination is important and is
the major component of knowledge management success (Rowley, 2000). However,
Kong (1999) identified that faculty members emphasize more on their individual
achievements rather than the attainment of common organizational objectives and
goals. This issue weakened the willingness of achieving the common goal through
knowledge sharing.
In general, the role of academic staff includes teaching, researching, consulting, and
publishing. Besides, through teachings, academic staffs also play their role as
knowledge disseminator to their students. They are knowledge producers and better
knowledge sharing practices will definitely help the development of quality education
and also improve the performance of organization. Tacit knowledge that the academic
staffs create is embedded in their minds and constitutes the storehouse of an
educational institutions intellectual capital. Generally, lack of knowledge sharing
among academic staff not only leads toward the underutilization of resources but also
shrinks the learning opportunities. Therefore, investigating knowledge sharing among
academic staff is important.
This study intends to examine factors that influence knowledge sharing intentions
among academic staff of social sciences faculties at one Malaysian university.
More specifically, based on the theory of reasoned action (TRA), and social capital
theory (SCT), we examine the role of influential factors that form the intention of
academic staff to share their knowledge.
2. Literature review
2.1 Previous research on knowledge sharing
Previous researches have shown many factors that influence knowledge sharing.
These factors include attitude (So and Bolloju, 2005; Bock et al., 2005), extrinsic
rewards (Bock et al., 2005; Kim and Lee, 2006), organizational climate with fairness and
trust, innovativeness and affiliation (Bock et al., 2005; Sun and Scot, 2005), subjective
norm (Bock et al., 2005), social networks (Kim and Lee, 2006), fear of loss of control and
ownership of knowledge (Sun and Scot, 2005), and anticipated reciprocal relationships
and co-operative behavior (Bock et al., 2005; Lu et al., 2006). The above mentioned
studies were conducted on various group of employees; for instance, among IT

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

managers (So and Bolloju, 2005), and among managers of organizations (Bock et al.,
2005; Sun and Scot, 2005; Ma et al., 2008), and among employees of organizations
(Kim and Lee, 2006; Lee et al., 2010; Binz-Scharf, 2003; Chuck and Eric, 2008).
According to the previous researches, very little research has been conducted in
academic institutions (Li et al., 2008; Shah et al., 2009; Sivaporn, 2009; Lu et al., 2006).
Among these studies, few of them were conducted among students (Shah et al., 2009;
Sivaporn, 2009) and others were among university staff (Lu et al., 2006; Li et al., 2008).
Interestingly, there is no study that examines factors among academic staff especially
in social sciences faculties.
2.2 Theory selection
Two theories of behavior, TRA and theory of planned behavior (TPB) have been
used by many researchers to explain knowledge sharing behavior. Both TRA and TPB
are considered applicable in knowledge sharing research (Bock et al., 2005; So and
Bolloju, 2005).
2.2.1 TRA. The TRA was introduced by Fishbein and Ajzen (1975). TRA assumes that
individuals to be rationale and suggests that their behavior is being influenced by three
elements namely attitude toward the behavior, subjective norms, and behavioral intention.
According to TRA, individuals attitude and subjective norms affect their intention which
consequently affects the actual behavior. According to Miller (2005), attitude accounts for
the sum of a persons beliefs about a behavior, with specific weights given to each aspect of
that behavior, the subjective norm consists of the opinions of people in a persons
environment, and behavior intention is a function of both attitudes and the subjective
norm. According to the theory, these three factors are the predictors for actual behaviors.
In many studies, attitude and subjective norms, independently and collectively, have
shown positive relations with actual knowledge sharing (Bock et al., 2005; Kim and Lee,
1995; Koys and Decotiis, 1991; Kurland, 1995; Mathieson, 1991; Thompson et al., 1991).
2.2.2 TPB. According to the theory, human behavior is guided by three kinds of
considerations: beliefs about the normative expectations of others and motivation to
comply with these expectations (normative beliefs), beliefs about the likely results of
the behavior and the evaluations of these outcomes (behavioral beliefs), and beliefs
about the presence of factors that may facilitate or impede performance of the behavior
and the perceived power of these factors (control beliefs). Ajzen (2002) believe that in
their respective aggregates, behavioral beliefs produce a favorable or unfavorable
attitude toward the behavior; normative beliefs result in perceived social pressure
or subjective norm; and control beliefs give rise to perceived behavioral control.
In combination, attitude toward the behavior, subjective norm, and perception of
behavioral control lead to the formation of a behavioral intention. According to Ajzen
(2002) the more favorable the attitude and subjective norm, and the greater the
perceived control, the stronger should be the persons intention to perform the behavior
in question. Finally, given a sufficient degree of actual control over the behavior, people
are expected to carry out their intentions when the opportunity arises.
2.2.3 SCT. Social capital was introduced by Bourdieu (1984), Coleman (1988), and
Putnam (1993). They defined social capital as the aggregate of the actual or potential
resources which are linked to possession of a durable network of more or less
institutionalized relationships of mutual acquaintance or recognition. Generally, social
capital can be seen in terms of five dimensions: trust; reciprocity-expectation; networks
associations; social norms; and personal and collective efficacy (Bullen and Onyx, 2000;
Bourdieu, 1984; Coleman, 1988; Paxton, 2002). According to the dimensions of SCT, trust is

Knowledge
sharing intention

415

IJEM
28,4

416

a social mechanism that exists in the structure of social relations. Many researchers believe
that, social capital depends on trust. The communitys mutual commitment, relationships,
and cooperation that describe social capital could not exist without a rational of trust.
In addition, without some foundation of trust, social capital cannot improve. In light of the
above discussion it can be assumed that people build social network with others who they
have some reciprocal relations (Thibault and Kelley, 1952). In the context of knowledge
sharing, it can be assumed that people share their knowledge with those who trust them.
Therefore, trust as another important factor that influences knowledge sharing was
incorporated in this study (Hislop, 2005; Huber, 2001; Riege, 2005; Lucas, 2005).

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

2.3 Research model and hypotheses

A basic research model of this study was developed based on TRA in which attitude
and subjective norm were influential factors of the intention to share knowledge
(Fishbein and Ajzen, 1975). Also, trust was incorporated in the research model based
on the SCT. The developed research model is presented in Figure 1.
Generally, intention in performing an action is individuals specific purpose in doing
so, the end or goal that is aimed at, or intended to accomplish. According to the TRA
and TPB, a persons action is determined by the intention to perform, which is
a function of attitude and subjective norms, with the latter traced back to a persons
behavioral and normative beliefs. Self-efficacy, social network, and perceived extrinsic
rewards which are considered as determinant of attitude and organizational support as
antecedent of subjective norm were added to the research model.
2.3.1 Attitude. Ajzen and Fishbein (1980) believe that attitude has an influence on
behavioral intentions. This relationship has received substantial empirical support
(Pavlou and Fygenson, 2006; Bock et al., 2005; Kolekofski and Heminger, 2003; Kuo and
Young, 2008). The findings show that individuals feelings regarding knowledge
sharing reflect their readiness to be involved in the process of knowledge sharing.
Therefore, it seems that one of the important aspects of knowledge sharing intention is
attitude toward knowledge sharing. Therefore the first hypothesis is proposed:
H1. Attitude has a positive effect on the intention to share knowledge.

Self Efficacy

Social
Networks

Attitude

Perceived
Extrinsic
Rewards
Intention to Share
Knowledge
Organizational
Support

Figure 1.
Research model

Subjective
Norm

Trust

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

2.3.2 Subjective norm. Subjective norm is defined as a persons perception of whether

people important to the person think the behavior should be performed (Ajzen and
Fishbein, 1980; Pavlou and Fygenson, 2006). Subjective norm reflects participant
perceptions of whether the behavior is accepted, encouraged, and implemented by the
participants circle of influence. The literature suggests a positive relationship between
subjective norm and intended behavior (Karahanna and Straub, 1999; Thompson et al.,
1991; Venkatesh and Davis, 2000; Venkatesh and Morris, 2000; Taylor, 2006; Srite and
Karahanna, 2006; Bock et al., 2005). According to Evaristo and Karahanna (1998),
subjective norms, may through normative and informational influences, decrease
uncertainty with respect to whether use of a system is appropriate. It seems that there
is a positive relationship between subjective norm and intention to share knowledge.
Therefore the second hypothesis is proposed:
H2. Subjective norm has a positive effect on the intention to share knowledge.
2.3.3 Trust. The common definition of trust that most researchers are agreeing on is
the willingness to be vulnerable based on positive expectations about the actions of
others (Riegelsberger et al., 2003; Gambetta, 2000). Ghoshal and Bartlett (1994)
observed that trust is an important element in an organization. Trust is important for
strategic associations and for effective relationships. Moreover, it is argued that trust
can enhance the positive behavioral intention (Gambetta, 1988). Trust can supports the
formal and informal network associations (Miles and Snow, 1992), decreases damaging
conflicts and costs of transaction and increases the development of informal groups
(Meyerson et al., 1996). In the organizations, the lack of trust to share knowledge might
be due to the lack of reliability on the knowledge resources and uncertainty.
This develops the sense of unwillingness to share knowledge between the employees in
the organization. Hislop (2005) believed that trust can also be one of the contributing
factors that reflect the commitment of employees to share knowledge. It has been found
that employees normally share knowledge if they trust that knowledge they share
bring benefits for them and for the whole organization (Riege, 2005). Also, Sharrat and
Usoro (2003) state that when organizations keep mutual reciprocity, commitment,
reliability, and honesty as trustworthy values, the degree of motivation to participate
and intention to share knowledge will increase. Therefore, it can be concluded that
there is a relationship between trust and knowledge sharings intention (Kalantzis and
Cope, 2003). As a result, the third hypothesis is proposed:
H3. Trust has a positive effect on the intention to share knowledge.
2.3.4 Self-efficacy. Self-efficacy is defined as the belief in ones capabilities to organize
and execute the courses of action required to manage prospective situations (Bandura,
1997, p. 2). In other words, self-efficacy is a persons belief in his or her ability to succeed
in a particular situation. Bandura (1997) explained these beliefs as determinants of how
people think, behave, and feel. Ormrod (2006) defined self-efficacy as a persons belief
about his abilities to perform in a certain manner or obtain certain goals. Recently, the
concept of self-efficacy has been applied to knowledge management to validate the effect
of personal efficacy belief on knowledge sharing. Hsu and Chiu (2004) believe that the
desire to share knowledge is not adequate to perform knowledge sharing behavior,
and a knowledge producer must also have the perceived abilities to complete it. Moreover,
sharing useful expertise to the organization is an opportunity to enhance sense of

Knowledge
sharing intention

417

IJEM
28,4

418

self-efficacy. When knowledge self-efficacy increases, people gain confidence in terms of

what they can do (Constant et al., 1994). When people think that their expertise can
improve work efficiency and increase productivity, their attitude to share knowledge
will be changed and as the result, they will be more inclined to share knowledge with
others (Bock et al., 2005; Kankanhalli et al., 2005; Kulkarni et al., 2006; Shin et al., 2007;
Wasko and Faraj, 2005; Jarvenpaa and Staples, 2001). Therefore, the fourth hypothesis
is proposed:

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

H4. Self-efficacy has a positive effect on knowledge sharing attitudes.

2.3.5 Social network. Churchill and Halverson (2005) believed that, generally, social
network is built based on a structure of how people know each other. In this kind of
network, people directly and indirectly connect to each other through common
association. Moreover, social network is a contact network that creates relationship,
mutual recognition, and understanding, which is similar to institutionalized
relationship. People in organizations establish many direct contacts with others.
In this situation, the social network provides increased opportunities for interpersonal
contact. Generally, when people are members of bigger networks, the number of their
contacts with others is larger. This in turn, will affect their attitude about sharing ideas
and knowledge:
H5. Social network has a positive effect on the attitude toward knowledge sharing.
2.3.6 Extrinsic rewards. Researchers argue that the rewards motivate employees.
Employees like tasks and activities when they see the rewards on successful achievement
of the activity or task (Cameron and Pierce, 1997; Jarvenpaa and Staples, 2000; Constant
et al., 1994). Generally, employees act in a way that they perceive as being rewarded
(Cameron and Pierce, 1997). This is not just monetary reward but the results that will
make an individual feel that he or she achieving his or her intrinsic or extrinsic needs
(Mullins, 2002; Palardy, 1994). Typically, extrinsic rewards might be immediately
successful and easy to use, but are not effective over the long term (Bock and Kim, 2002).
However, extrinsic rewards are effective factors which will make positive attitude about
sharing knowledge with others. Therefore the hypothesis is proposed:
H6. Extrinsic rewards have a positive effect on the attitude toward knowledge
sharing.
2.3.7 Organizational support. One of the important concepts in management literature
is organizational support. Concept of organizational support explains the relationship
between employees attitude and behavior toward their organizations and jobs.
According to the study of Igbaria et al. (1996), organizational support is positively
related to subjective norm. They believed that if organization provides available
resources, relevant training, meaningful incentives, and remove barriers in the way of
knowledge sharing, the quality of knowledge sharing would be better. Moreover, the
power of organizational support may influence employees perception regarding
knowledge sharing (Cabrera et al., 2006), and as the result, the quality of knowledge
sharing will be improved. Therefore the next hypothesis is proposed:
H7. The organizational support has a positive effect with subjective norms.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

3. Research method
3.1 Data collection
We tested the research model with data collected through a cross-sectional
survey of academic staff of three social science faculties at one university in
Malaysia. The paper questionnaire was personally distributed to office of the
respondents along with a letter requesting their participation and cooperation.
Based on stratified random sampling technique, researcher distributed 200
questionnaires from which 117 questionnaires returned giving the survey
a response rate of 58.5 percent. In total, 55 percent of the respondent were below
the age of 45 years, females represented 47 percent and males 53 percent of the
respondents, and 54 percent of the respondents had more than ten years academic
experience.
3.2 Measurements
In this study, a paper-based questionnaire was the instrument. The questionnaire was
in English (Appendix) as it is broadly used as academic language in Malaysian
universities. Previously validated items were used to measure the intention to share
knowledge (Bock et al., 2005), attitude (So and Bolloju, 2005; Bock et al., 2005),
subjective norm (Bock et al., 2005; Lu et al., 2006), trust (Kim and Ju, 2008), self-efficacy
(Bock et al., 2005), social networks (Kim and Lee, 2006), extrinsic rewards (Bock et al.,
2005), and organizational support (Sveiby and Simons, 2002).
The pretest of the measures was conducted by a panel of four senior academic staff
from participating faculties. After participants completed the pre-testing, we modified
the survey according to their feedback and the procedure was repeated again
using five other participants. To further validate the instrument, a small pilot survey of
randomly selected participants was carried out. The pilot test results showed
Cronbach as of the variables were adequate (i.e. bigger than 0.7).
4. Results
The PLS approach of the structural equation modeling was employed to validate the
research model. The PLS places minimal restrictions on sample size and residual
distributions and utilizes a component based approach for estimation (Chin, 1998).
As is usual in reporting structural model, data analysis of this study is presented in
two stages. First, the validity of the measurement model and then the structural model
were tested and reported.
4.1 Validity and reliability of the measures
PLS examines convergent validity by checking whether a measurement loads highly
on its assigned construct (Gefen et al., 2000). Factor loading and t-value of the
measurement items are shown in Table I.
Table I indicates that all items were highly loaded on their construct indicating
the condition of convergent validity is met (for all t-values reported in Table I,
p-value o0.05). For discriminant validity, first, items must load more considerably on
their related construct compared to other constructs. As shown in Table II, this
condition was met and item loadings are stronger on their assigned construct.
Second condition for discriminant validity is met when the correlation of each
construct with other constructs is smaller than the square root of the average variance
extracted (AVE) of that construct (Gefen et al., 2000). This condition is also met as is
shown in Table II.

Knowledge
sharing intention

419

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

420

Table I.
Factor structure of loading
(along with t-value) and
cross-loadings

Scale itemsa

INT

ATT

EXR

INT-1
INT-2
INT-3
ATT-1
ATT-2
EXR-1
EXR-2
EXR-3
SON-1
SON-2
SON-3
SLE-1
SLE-2
SLE-3
SUN-1
SUN-2
SUN-3
ORS-1
ORS-2
ORS-3
TRS-1
TRS-2
TRS-3

0.73
0.84
0.87
0.28
0.25
0.01
0.00
0.01
0.22
0.28
0.22
0.25
0.34
0.13
0.12
0.09
0.19
0.05
0.07
0.11
0.12
0.10
0.15

0.23
0.23
0.28
0.87
0.88
0.21
0.15
0.08
0.13
0.13
0.38
0.13
0.25
0.20
0.20
0.19
0.22
0.02
0.22
0.06
0.13
0.27
0.29

0.06
0.03
0.02
0.17
0.15
0.94
0.92
0.80
0.16
0.25
0.35
0.24
0.15
0.06
0.19
0.16
0.23
0.07
0.27
0.11
0.35
0.30
0.23

SUN

ORS

TRS

t-valueb

0.11
0.18
0.25
0.25
0.33
0.32
0.33
0.32
0.67
0.71
0.93
0.21
0.21
0.18
0.07
0.003
0.18
0.12
0.23
0.20
0.37
0.31
0.36

0.004
0.03
0.11
0.20
0.22
0.23
0.16
0.26
0.12
0.01
0.10
0.32
0.26
0.19
0.77
0.88
0.88
0.08
0.24
0.18
0.30
0.35
0.36

0.05
0.001
0.10
0.20
0.09
0.16
0.25
0.24
0.13
0.24
0.23
0.36
0.34
0.35
0.09
0.26
0.23
0.67
0.87
0.75
0.26
0.36
0.34

0.06
0.13
0.18
0.3
0.21
0.27
0.34
0.36
0.29
0.34
0.44
0.49
0.33
0.30
0.38
0.36
0.38
0.31
0.47
0.17
0.71
0.73
0.74

5.17
7.66
7.82
17.86
20.95
8.49
5.74
4.14
4.13
5.06
24.88
6.65
8.08
5.90
7.72
18.75
16.65
3.96
11.58
5.94
3.52
4.30
3.81

0.09
0.24
0.28
0.21
0.19
0.08
0.19
0.29
0.09
0.29
0.22
0.83
0.89
0.76
0.25
0.26
0.25
0.25
0.41
0.27
0.32
0.37
0.30

Notes: INT, intention; ATT, attitude; EXR, extrinsic rewards; SON, social network; SLE, self-efficacy;
SUN, subjective norm; ORS, organizational support,;TRS, trust. aFive items were dropped due to low
or cross-loading. bt-value bigger than 1.96 means that item loadings (italics items) are significant
within the 0.05 level

Construct

Table II.
Correlations matrix of
constructs, composite
reliability, and AVE

Factor
SON
SLE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8) Cronbachs a CR AVE

Intention (1)
0.76 a
Attitude (2)
0.30 0.72 a
Extrinsic Rewards (3)
0.01 0.18 0.88 a
Social Networks (4)
0.28 0.34 0.35 0.72 a
Self-efficacy (5)
0.30 0.23 0.17 0.25 0.78 a
Subjective norm (6)
0.16 0.24 0.23 0.11 0.3 0.71a
Organizational support (7) 0.08 0.16 0.22 0.25 0.42 0.25 0.76 a
Trust (8)
0.19 0.29 0.34 0.46 0.44 0.43 0.42 0.70 a

0.86
0.76
0.76
0.84
0.78
0.87
0.88
0.78

0.85
0.85
0.91
0.81
0.86
0.88
0.80
0.80

0.59
0.52
0.78
0.52
0.62
0.84
0.58
0.50

Notes: CR, composite reliability; AVE, average variance extracted. aDiagonal items are square root
of AVE

To assess the reliability of the measurement items, Cronbach a, composite

reliability and AVE values were checked to not to be less than threshold levels
of 0.70, 0.70, and 0.50, respectively (Fornell and Larcker, 1981). As shown in Table II,
the Cronbach as range from 0.76 to 0.88, the composite reliabilities range from
0.85 to 0.91 and the AVE range from 0.52 to 0.84 indicating an acceptable
reliability level.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

4.2 Common method bias

In order to make sure that there is no issue of common method bias, Harmans
one-factor test was performed on the data. The underlying assumption of this test is
that a single factor will emerge from EFA test that contains a substantial variance of
the data (Podsakoff et al., 2003). In more detail, to conclude insignificance of the method
bias, a significant amount of the variance must not be explained just by a single factor
and the results must reveal more than one factors (eigenvalue41). The results of EFA
analysis revealed 8 factors with an eigenvalue greater than one and no single factor
explained a great amount of the variance (i.e. variances ranged from 3.97 to 23.95
percent). This test provided initial support for the insignificant influence of method
bias/variance. Moreover, as shown in Table II, it does not contain any bivariate
correlation that is higher than 0.85 (the highest correlation is 0.44). Therefore, these tests
support the lack of significant systematic common method bias/variance in the data.

Knowledge
sharing intention

421

4.3 Results of hypothesis testing

Having evaluated the measurement model, we tested the structural model. Figure 2
depicts the path coefficients and their significance along with the R2-values for each
dependent construct.
As indicated in Figure 2 and Table III, the results of hypotheses testing show that
attitude has a significant impact on knowledge-sharing intention. Besides, although
SelfEfficacy

0.153*

0.278**

Attitude

Social
Network

0.265**
0.063
0.06

Subjective
Norm

Extrinsic
Rewards
0.251**

Organizational
Support

Intention to
share
knowledge

0.09
R 2= 0.11

Trust

Figure 2.
Hypothesis testing result

Notes: --->, Not significant. *p< 0.05; **p<0.01

Hypotheses
H1: Attitude-intention
H2: Subjective norm-intention
h3: Trust-intention
H4: Extrinsic rewards-attitude
H5: Social networks-attitude
H6: Self-efficacy-attitude
H7: Organizational support-subjective norm

Path coefficient

t-statistics

Result

0.265
0.603
0.094
0.063
0.278
0.153
0.251

3.156
0.638
0.994
0.845
3.368
2.106
4.152

Supported
Not supported
Not supported
Not supported
Supported
Supported
Supported

Table III.
Hypothesis testing result
( path coefficients
and t-values)

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

422

organizational support has a significant impact on subjective norm, subjective norm,

itself, does not show any significant effect on knowledge-sharing intention. Moreover,
hypotheses testing revealed that social networks and self-efficacy have a statistically
significant influence on attitude toward knowledge sharing. Extrinsic rewards, on the
other hand, do not have significant impact on attitude. Finally, the role of trust as an
influential factor on knowledge sharing was not supported.
5. Discussion
The main purpose of this study was to examine the factors that might influence
knowledge sharing intention among academic staff. The findings of the hypotheses
testing were as follows.
5.1 Attitude
As expected, the results of the study showed that attitude has a significant effect on the
intention to share knowledge. Attitude in this study was conceptualized as the degree
of a persons positive feelings about sharing knowledge. This finding was consistent
with other studies in the knowledge sharing intention field (Kuo and Young, 2008;
Kolekofski and Heminger, 2003; Bock et al., 2005; Pavlou and Fygenson, 2006).
The results indicate that a positive attitude about knowledge sharing by individuals
lead to their intention to share their knowledge. People who have a positive attitude
toward knowledge sharing are more likely to share it. Meaning that, whether a person
actually shares knowledge with others primarily depends on his or her personal,
favorable or unfavorable of the attitude in question (Ajzen, 1991). As discussed
later in this section, this study also reveals two factors significantly affect attitude
(i.e. self-efficacy and social networks). The practical implication of this finding is that
academic institutions should use and enhance these factors to create positive attitude
among their academic staff toward knowledge sharing.
5.2 Subjective norm
Subjective norm refers to the degree to which a person perceives people important to
him/her, expect knowledge sharing. Contrary to the hypothesized relationship, the
effect of subjective norm was found to be not significant on knowledge sharing
intention. The results seem to indicate that, in the context of this study, the subjective
norm was not important in forming an impression on knowledge sharing. Based on the
findings, it seems that social pressure is not an influencing factor in shaping academic
staffs intention to share knowledge; possibly, due to the nature of the profession in
which does not consider social pressure and expectations of colleagues and peer
groups to be important when it comes to knowledge sharing.
5.3 Trust
In the context of this study trust was defined as the degree of trusting colleagues and
their knowledge. Surprisingly, the result of this study appears to reject the hypothesis
that trust has a positive effect on the intention to share knowledge. This finding is in
contrast with previous studies which identified trust as an important factor affecting
knowledge sharing (Davenport and Prusak, 1998; Nelson and Cooprider, 1996; ODell
and Grayson, 1998). This inconsistency may be due to several factors as discussed
below. This might be related to the context of this study. In more detail, although trust
is proven to be an important factor in previous knowledge sharing studies; but,
it might not be among important factors of knowledge sharing among academic staff.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Nevertheless, we do not deny the need for further investigation regarding the role of
trust in knowledge sharing among academic staff.
5.4 Self-efficacy
In this study, self-efficacy refers to self-perceived ability to provide valuable
contributions for organization through knowledge sharing. The result of this study
shows that self-efficacy has positive effect on attitude toward knowledge sharing.
This result is consistent with previous studies such as Hsu and Chiu (2004), Bock et al.
(2005), Kankanhalli et al. (2005), Kulkarni et al. (2006), Shin et al. (2007), Wasko and
Faraj (2005), and Jarvenpaa and Staples (2001). Indeed, when people think that their
expertise can improve work efficiency and increase productivity, they will be more
interested to share their knowledge with others. This positive influence implies that,
individuals attitude is strongly influenced by ones confidence to perform it. In other
words, if academic staffs have greater self-efficacy to contribute valuable knowledge,
they have more favorable attitude toward knowledge sharing.
5.5 Social networks
Social networks refer to the extent of individuals contact with other people. This result
supports previous findings (Yang and Chen, 2007) which suggest that social
interaction help to enhance community members attitude toward knowledge sharing.
In this study, social networks appear to be positively and significantly associated with
attitude toward knowledge sharing. This implies that, once an individual builds up
relationships with other members that he or she feels comfortable to share his/her ideas
and thoughts with; his/her attitude to perform such behavior will be stronger.
Moreover, if academic staffs extend their social networks, this may create tendency to
have positive attitude toward knowledge sharing.
5.6 Extrinsic rewards
In the context of this study, extrinsic reward was defined as the individuals belief
that monetary incentives will be given for knowledge sharing activities. The result of
this study appears to reject the hypothesis that extrinsic reward has positive effect
on the attitude toward knowledge sharing. This is inconsistent with the findings of
other researchers such as Liebowitz (1999), OReilly and Pondy (1980), and Quinn et al.
(1996) as they found a significant relationship between extrinsic rewards and attitude
toward knowledge sharing. This inconsistency might be due to several factors as
discussed below. First, taking the context of this study into consideration, in academic
context and for academic staff, monetary and tangible rewards may not significantly
contribute to formation of attitude to share their knowledge. As Gustad (1960) noted,
academic staff may value intangible rewards, such as course reductions and additional
sabbatical leave dedicated to research. Moreover, the study was conducted in a public
university in Malaysia, where the respondents were predominantly Muslims.
Taking into account Islamic beliefs, knowledge sharing is encouraged by religion.
Hence, the respondents may think about sharing their knowledge without looking to
monetary rewards.
5.7 Organizational support
In the present study, organizational support refers to the level of support that organization
provides for knowledge sharing. Results of the study showed that organizational support
has a significant relationship with subjective norm. The result of this study is

Knowledge
sharing intention

423

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

424

consistent with the study of Igbaria et al. (1996). They indicated that organizational
support is positively related to subjective norm. Moreover, in another study by
Cabrera et al. (2006) indicated that support from organization could influence positively
staffs perception, attitude and their understanding of activities and their values.
The reason might be related to the importance of appropriate technology, and formal
and informal networks that an organization can provide for encouraging knowledge
sharing among its staff.
6. Contribution and implications
From a theoretical perspective, this study contributes to the literature in several ways.
First, our study combined TRA and SCT to investigate knowledge sharing intentions
in the academic context. This study provided a clear view on the relationship between
the dependents variables, independent variables and theory that contribute to the body
of knowledge. We classified antecedents of attitudes into self-efficacy, social networks,
and extrinsic rewards. Moreover, organizational support was considered as
antecedents of subjective norm. This offers a more clear vision of the factors that
affect knowledge sharing intention among academic staff.
Practically, a significant positive relationship between attitude and knowledge
sharing intention means that academic staffs share their knowledge when they have
positive attitude about knowledge sharing. Therefore, in an effort to make academic
staff share their knowledge, management should implement supportive knowledge
management culture, norms and practices that build positive attitude in the
organization. Meanwhile, self-efficacy and social networks were two factors that affect
positive attitude significantly. The influential role of self-efficacy implies that
a persons attitude is strongly influenced by ones confidence to perform it. If academic
staffs have greater self-efficacy, they have more favorable attitude toward knowledge
sharing. In other words, if academic staff perceived that they have the ability to
contribute valuable knowledge, they have a positive attitude toward knowledge
sharing. Besides, the importance of social network in the research model of the present
study implies that, once academic staff build up relationships with other members that
they feel comfortable to share their ideas and thoughts; their attitude to perform such
behavior will be stronger. Moreover, if academic staffs extend their social networks by
collaborating with other academic staff, this may create tendency to have positive
attitude toward knowledge sharing. Therefore, management should try to implement
the practical plan to support these two factors. The important role of social networks in
shaping attitude toward knowledge sharing implies that academic staffs who have
a more extensive social network with their colleagues would perceive greater social
pressure for sharing their knowledge, because a good relationship results in high
expectations of colleagues, including favorable knowledge sharing actions. Therefore,
management should provide such structure that would increase academic staff
interactions with each other. This can be achieved through a positive social interaction
network such as scheduled periodic formal and informal meetings, mentoring
interventions and social media. Particularly, the role of social media is important as
it allows people to create, share, and exchange information and ideas in virtual
communities and networks (Ahlqvist et al., 2008). As a group of internet-based
applications, social media allows academic staff to share user-generated contents
(Kaplan and Haenlein, 2010). Indeed, social media has introduced substantial changes
to communication between individuals, communities, and organizations; there is much
room to investigate its role in knowledge sharing among academic staff.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Moreover, the results indicated that self-efficacy will affect individuals attitude toward
knowledge sharing. Individual members will adopt a particular behavior when the
evaluation of their capability is adequate to accomplish the task. Therefore, management
should pay more attention on how to cultivate academic staffs ability. For instance,
management can offer an appropriate educational training for this purpose.
Lastly, the results indicate that organizational supports have positive and significant
impact on subjective norm. Therefore, management should provide and implement
supportive plans and culture for the employees. This can be achieved through processes
such as meetings, colloquiums, and intellectual discourse sessions. The management
should also provide appropriate technology for this purpose like academic portal, web
site, and e-mail settings. Forming an informal network such as community of practice is
the other way that management can do to improve subjective norm.
7. Limitation and future research
This study did not determine the type of knowledge that shared; thus, this is an area
for future research to consider. For instance, how knowledge type intervene the effects
on knowledge sharing. Besides, the data collection was restricted to academic staff in
one public university; consequently, in order to verify and generalize the research
results, the research should be expanded geographically within the same level of
education institutions. Lastly, it would also be interesting to investigate further the
potential differences of the knowledge sharing intention between academic staff in the
private and public universities and also considering the potential role of other factors
such as social media.
8. Conclusion
The study has fulfilled its three main objectives which were to examine the effect of
attitude, subjective norm and trust on knowledge sharing intention. A total of 200
questionnaires were distributed to academic staffs of one public university of which
117 questionnaire were returned. Partial least square analysis was used to tests the
hypotheses of the study. The results indicated that attitude was positively and
significantly related to knowledge sharing intention. However, subjective norms
and trust did not significantly affect knowledge sharing intention. Among other
factors, only social network and self-efficacy significantly affect attitude toward
knowledge sharing intention. Meanwhile, organizational support has shown strong
influence on subjective norms. Based on the findings, implications for practice and
theory, limitation, and future research were presented.
Note
1. Community of practice was defined as informal, self-organized, network of peers with
diverse skills and experience in an area of practice or profession.
References
Ahlqvist, T., Back, A., Halonen, M. and Heinonen, S. (2008), Social media road maps exploring
the futures triggered by social media, VTT Tiedotteita Valtion Teknillinen
Tutkimuskeskus, Vol. 13, p. 13.
Ahmad, H.S. (2003), Tacit knowledge dissemination among UUM lecturers, unpublished
master degree dissertation, Universiti Utara Malaysia, Kedah.
Ajzen, I. (1991), The theory of planned behaviour, Organizational Behaviour and Human
Decision Processes, Vol. 50 No. 2, pp. 179-211.

Knowledge
sharing intention

425

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

426

Ajzen, I. (2002), Perceived behavioural control, self-efficacy, locus of control, and the theory of
planned behaviour, Journal of Applied Social Psychology, Vol. 32 No. 4, pp. 665-683.
Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behaviour,
Prentice Hall, Englewood Cliffs, NJ.
Argote, L. (1999), Organizational learning: creating, retaining, and transferring knowledge,
The Learning Organization, Vol. 11 No. 4, pp. 344-375.
Argote, L. and Ingram, P. (2000), Knowledge transfer, a basis for competitive advantage
in firms, Organizational Behaviour and Human Decision Processes, Vol. 82 No. 1,
pp. 150-169.
Ariffin, A.H. (2004), master thesis, Universiti Utara Malaysia, Kedah.
Bandura, A. (1997), Self-Efficacy, the Exercise of Control, Freeman Publication Co, New York, NY.
Bartol, K. and Srivastava, A. (2002), Encouraging knowledge sharing, the role of organisational
rewards, Journal of Leadership and Organisation Studies, Vol. 9 No. 1, pp. 64-76.
Binz-Scharf, M.C. (2003), Exploration and Exploitation, Toward a Theory of Knowledge Sharing in
Digital Government Projects, PhD dissertation, University of St Gallen, St Gallen, Canton.
Bock, G.W. and Kim, Y.G. (2002), Breaking the myths of rewards: an exploratory study of
attitudes about knowledge sharing, Information Resources Management Journal, Vol. 15
No. 2, pp. 14-21.
Bock, G.W., Zmud, R.W., Kim, Y.L. and Jae, N. (2005), Behavioural intention formation in
knowledge sharing, examining the roles of extrinsic motivators, social-psychological
forces, and organizational climate, MIS Quarterly, Vol. 29 No. 1, pp. 87-111.
Bourdieu, P. (1984), Distinction, a Social Critique of the Judgement of Taste, Harvard University
Press, Cambridge.
Bresman, H., Birkinshaw, J. and Nobel, R. (1999), Knowledge transfer in international
acquisitions, Journal of International Business Studies, Vol. 30 No. 3, pp. 439-462.
Bullen, P. and Onyx, J. (2000), Measuring social capital in five communities in NSW, Journal of
Applied Behaviour Science, Vol. 36 No. 1, pp. 23-42.
Cabrera, A., Collins, W.C. and Salgado, J.F. (2006), Determinants of individual engagement in
knowledge sharing, International Journal of Human Resource Management, Vol. 17 No. 2,
pp. 245-264.
Cameron, C.J. and Pierce, D.W. (1997), Rewards, interest and performance, an evaluation of
experimental findings, American Compensation Association Journal, Vol. 6 No. 4, pp. 21-27.
Chatzoglou, P.D. and Vraimaki, E. (2009), Knowledge-sharing behaviour of bank employees in
Greece, Business Management Journal, Vol. 15 No. 2, pp. 245-266.
Chin, W. (1998), Issues and opinion on structural equation modeling, MIS Quarterly, Vol. 22
No. 1, pp. 7-16.
Chuck, C.H. and Eric, W.T. (2008), An Empirical study of the effects of knowledge sharing and
learning behaviours on firm performance, Expert System with Applications, Vol. 34 No. 4,
pp. 2342-2349.
Churchill, E.F. and Halverson, C.A. (2005), Social networks and social networking, IEEE
Computer Society, Vol. 5 No. 2, pp. 14-19.
Coleman, J.S. (1988), Social capital in the creation of human capital, American Journal of
Sociology, Vol. 94 No. 1, pp. 95-120.
Constant, D., Keisler, S. and Sproull, L. (1994), Whats mine is ours, or is it? A study of attitudes
about information sharing, Information Systems Research, Vol. 5 No. 4, pp. 400-421.
Davenport, T.H. and Prusak, L. (1998), Successful Knowledge Management Projects, Harvard
Business School Press, Boston, MA.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Evaristo, J. and Karahanna, E. (1998), The impact of privatization on organizational information

needs, lessons from the Brazilian telecommunications holding company, Information
Technology and People, Vol. 11 No. 3, pp. 207-216.
Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention, and Behaviour, an Introduction to
Theory and Research, Addison-Wesley Publication Co, New York, NY.
Fornell, C. and Larcker, D. (1981), Structural equation models with unobservable variables and
measurement error, Journal of Marketing Research, Vol. 18 No. 2, pp. 39-50.
Gambetta, D. (1988), Trust Making and Breaking Co-Operative Relations, Blackwell Publication
Co, Basil.
Gambetta, D. (2000), Can we Trust, Trust?, in Gambetta, D. (Ed.), Trust: Making and Breaking
Cooperative Relations, electronic edition, Chapter 13, Department of Sociology, University
of Oxford, Oxford, London, pp. 213-237.
Gefen, D., Straub, D. and Boudreau, M. (2000), Structural equation modelling and regression:
guidelines for research practice, Communications of the Association for Information
Systems, Vol. 27 No. 1, pp. 1-78.
George, J. (2004), The theory of planned behaviour and internet purchasing, Internet Research,
Vol. 14 No. 3, pp. 198-212.
Ghoshal, S. and Bartlett, C.A. (1994), Linking organisational context and managerial action,
the dimension of quality management, Strategic Management Journal, Vol. 15, Summer,
pp. 91-112.
Gustad, J.W. (1960), The Career Decisions of College Teachers, Department of Health, Education
and Welfare, Washington, DC.
Hislop, D. (2005), Knowledge Management in Organizations, A Critical Introduction, Oxford
University Press, Oxford.
Hsu, M.H. and Chiu, C.M. (2004), Internet self-efficacy and electronic service acceptance,
Decision Support Systems Research and Behavioural Science, Vol. 38 No. 3, pp. 369-381.
Huber, G.P. (2001), Transfer of knowledge in knowledge management systems, unexplored
issues and suggested studies, European Journal of Information Systems, Vol. 10 No. 1,
pp. 72-79.
Igbaria, M., Parasuraman, S. and Baroudi, J.J. (1996), A motivation model of microcomputer
usage, Journal of Management Information Systems, Vol. 13 No. 1, pp. 145-157.
Ipe, M. (2003), Knowledge sharing in organizations, a conceptual framework, Human Resource
Development Review, Vol. 2 No. 4, pp. 337-359.
Jarvenpaa, S.L. and Staples, D.S. (2000), The use of collaborative electronic media for
information sharing, an exploratory study of determinants, Journal of Strategic
Information Systems, Vol. 9 No. 2, pp. 129-154.
Jarvenpaa, S.L. and Staples, D.S. (2001), Exploring perceptions of organizational ownership
of information and expertise, Journal of Management Information Systems, Vol. 18 No. 1,
pp. 151-183.
Kalantzis, M. and Cope, B. (2003), Linking trust values and perceived benefits, Proceeding of
the International Conference on Knowledge Management (ICKM), Putra World Trade
Centre, Kuala Lumpur, July 7-9.
Kankanhalli, A., Tan, B.C. and Wei, K. (2005), Contributing knowledge to electronic knowledge
repositories, an empirical investigation, MIS Quarterly, Vol. 29 No. 1, pp. 113-143.
Kaplan, A.M. and Haenlein, M. (2010), Users of the world, unite! The challenges and
opportunities of social media, Business Horizons, Vol. 53 No. 1, p. 61.
Karahanna, E. and Straub, D.W. (1999), The psychological origins of perceived usefulness and
ease of use, Information and Management, Vol. 34 No. 5, pp. 237-250.

Knowledge
sharing intention

427

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

428

Kim, S. (2000), The roles of knowledge professionals for knowledge management, Inspel,
Vol. 34 No. 1, pp. 1-8.
Kim, S. and Ju, B. (2008), An analysis of faculty perceptions, attitudes toward knowledge
sharing and collaboration in an academic institution, Library and Information Science
Research, Vol. 30 No. 4, pp. 282-290.
Kim, S. and Lee, H. (2006), The impact of organizational context and information technology
on employee knowledge-sharing capabilities, Public Administration Review, Vol. 66
No. 3, pp. 370-385.
Kim, Y.B. and Lee, B.H. (1995), R and D project team climate and team performance in Korea,
R and D Management, Vol. 25 No. 2, pp. 179-196.
Kolekofski, J.K.E. and Heminger, A.R. (2003), Beliefs and attitudes affecting intentions to share
information in an organizational setting, Information and Management, Vol. 40 No. 6,
pp. 521-532.
Kong, S.C. (1999), A conceptual model of knowledge-based time-tabling system, Knowledge-Based
Systems, Vol. 12 No. 3, pp. 81-93.
Koys, D.J. and Decotiis, T.A. (1991), Inductive measures of psychological climate, Human
Relation, Vol. 44 No. 3, pp. 265-285.
Kulkarni, U.R., Ravindran, S. and Freeze, R. (2006), A knowledge management success model,
theoretical development and empirical validation, Journal of Management Information
Systems, Vol. 23 No. 3, pp. 309-347.
Kuo, F. and Young, M. (2008), Study of the intention-action gap in knowledge sharing practices,
Journal of the American Society for Information Science and Technology, Vol. 59 No. 8,
pp. 1224-1237.
Kurland, N.B. (1995), Ethical intentions and the theories of reasoned action and planned
behaviour, Journal of Applied Social Psychology, Vol. 25 No. 4, pp. 297-313.
Lee, J., Kim, J. and Han, Y. (2010), A study on factors influencing knowledge-sharing activity
for the innovation activity of team, IEEE Explore Digital Library, Vol. 10 No. 1,
pp. 134-146.
Lesser, E. and Storck, J. (2001), Communities of practice and organizational performance, IBM
Systems Journal, Vol. 40 No. 4, pp. 831-841.
Li, Z., Li, M. and Li, J. (2008), An empirical research on the influencing factors of knowledge
sharing willingness among virtual community members, IEEE Explore Digital Library,
Vol. 10 No. 2, pp. 158-169.
Liebowitz, J. (1999), Facilitating innovation through knowledge sharing, Computer Information
System, Vol. 42 No. 5, pp. 1-6.
Lu, L., Leung, K. and Koch, P.T. (2006), Managerial knowledge sharing, the role of individual,
interpersonal, and organizational factors, Management and Organization Review, Vol. 2
No. 1, pp. 15-41.
Lucas, L.M. (2005), The impact of trust and reputation on the transfer of best practices,
Journal of Knowledge Management, Vol. 9 No. 1, pp. 87-101.
Ma, Z., Qi, L. and Wang, K. (2008), Knowledge sharing in Chinese construction project teams
and its affecting factors an empirical study, Chinese Management Studies, Vol. 2 No. 2,
pp. 97-108.
Martin, J.S. and Marion, R. (2005), Higher education leadership roles in knowledge processing,
The Learning Organization, Vol. 12 No. 2, pp. 140-151.
Mathieson, K. (1991), Predicting user intentions, comparing the technology acceptance model
with the theory of planned behaviour, Information Systems Research, Vol. 2 No. 3,
pp. 173-191.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Meyerson, D., Weick, K.E. and Kramer, R.M. (1996), Swift Trust and Temporary Groups, Sage
Publications, CA, pp. 166-195.
Miles, R.E. and Snow, C.C. (1992), Causes of failure in network organisations, California
Management Review, Vol. 34 No. 4, pp. 53-72.
Miller, K. (2005), Communications Theories, Perspectives, Processes, and Contexts, McGraw-Hill,
New York, NY.
Mullins, L. (2002), Management and Organisational Behaviour, 6th ed., FT Prentice Hall, London.
Nelson, K. and Cooprider, J. (1996), The contribution of shared knowledge to IS group
performance, MIS Quarterly, Vol. 20 No. 4, pp. 409-429.
ODell, C. and Grayson, C. (1998), If Only We Knew What We Know: The Transfer of Internal
Knowledge and Best Practice, Vol. 20, The Free Press, pp. 163-183.
OReilly, C. and Pondy, L. (1980), Organizational Communications, Grid, Columbus.
Ormrod, J.E. (2006), Educational Psychology: Developing Learners, 5th ed., Pearson/Merrill
Prentice Hall, Upper Saddle River, NJ.
Palardy, M.J. (1994), Principles of learning, Journal of Instructional Psychology, Vol. 21 No. 4,
pp. 308-312.
Pavlou, P.A. and Fygenson, M. (2006), Understanding and predicting electronic commerce adoption,
an extension of the theory of planned behaviour, MIS Quarterly, Vol. 30 No. 1, pp. 115-143.
Paxton, P. (2002), Social capital and democracy, an interdependent relationship, American
Sociological Review, Vol. 67 No. 2, pp. 254-277.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), Common method biases
in behavioral research: a critical review of the literature and recommended remedies,
Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Powell, W., Koput, K. and Smith-Doerr, L. (1996), Inter organizational collaboration and the locus
of innovation: networks of learning in biotechnology, Administrative Science Quarterly,
Vol. 41 No. 1, pp. 116-145.
Putnam, R. (1993), Making Democracy Work, Civic Traditions in Modern Italy, Princeton
University Press, Princeton.
Quinn, J.B., Anderson, P. and Finkelstein, S. (1996), Managing professional intellect, making the
most of the best, Harvard Business Review, Vol. 74 No. 2, pp. 71-81.
Riege, A. (2005), Three-dozen knowledge-sharing barriers managers must consider, Journal of
Knowledge Management, Vol. 9 No. 3, pp. 18-35.
Riegelsberger, J., Sasse, M.A. and McCarthy, J.D. (2003), The researchers dilemma, evaluating
trust in computer-mediated communication, International Journal Human-Computer
Studies, Vol. 58 No. 3, pp. 759-781.
Rowley, J. (2000), Knowledge organisation for a new millennium: principles and processes,
Journal of Knowledge Management, Vol. 4 No. 3, pp. 217-223.
Ryu, S., Ho, H.S. and Han, I. (2003), Knowledge sharing behaviour of physicians in hospitals,
Expert Systems with Applications, Vol. 25 No. 1, pp. 113-122.
Shah, S., Abdullah, Z., Ishak, N. and Zain, Z. (2009), Assessing knowledge sharing behaviour
among employees in SMEs, an empirical study, International Business Research, Vol. 2
No. 2, pp. 115-122.
Sharrat, M. and Usoro, A. (2003), Understanding knowledge-sharing in online communities of
practice, Electronic Journal on Knowledge Management, Vol. 1 No. 2, pp. 187-196.
Shin, S.K., Ishman, M. and Sanders, G.L. (2007), An empirical investigation of socio cultural
factors of information sharing in China, Information and Management, Vol. 44 No. 2,
pp. 165-174.

Knowledge
sharing intention

429

IJEM
28,4

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

430

Sivaporn, W. (2009), International Conference on Computers in Educational Technology, pp. 800-807.

So, J.C. and Bolloju, N. (2005), Explaining the intentions to share and reuse knowledge in the content
of IT service operations, Journal of Knowledge Management Science, Vol. 9 No. 6, pp. 30-41.
Srite, M. and Karahanna, E. (2006), The role of espoused national cultural values in technology
acceptance, MIS Quarterly, Vol. 30 No. 3, pp. 679-704.
Sun, Y.-T.P. and Scot, J.L. (2005), An investigation of barriers to knowledge transfer, Journal of
Management and Organization Review, Vol. 9 No. 2, pp. 75-90.
Sveiby, K.-E. and Simons, R. (2002), Collaborative climate and effectiveness of knowledge work
an empirical study, Journal of Knowledge Management, Vol. 6 No. 5, pp. 420-433.
Taylor, E.Z. (2006), The effect of incentives on knowledge sharing in computer-mediated
communication, an experimental investigation, Journal of Information Systems, Vol. 20
No. 1, pp. 103-116.
Thibault, J. and Kelley, H. (1952), The Social Psychology of Groups, John Wiley and Sons,
New York, NY.
Thompson, R.L., Higgins, C.A. and Howell, J.M. (1991), Personal computing, toward
a conceptual model of utilization, MIS Quarterly, Vol. 15 No. 1, pp. 125-143.
Venkatesh, V. and Davis, F.D. (2000), A theoretical extension of the technology acceptance
model, four longitudinal field studies, Management Science, Vol. 46 No. 2, pp. 186-204.
Venkatesh, V. and Morris, M.G. (2000), Why dont men ever stop to ask for directions? Gender,
social influence, and their role in technology acceptance and usage behaviour, MIS
Quarterly, Vol. 24 No. 1, pp. 115-139.
Wasko, M.M. and Faraj, S. (2005), It is what one does, why people participate and help others
in electronic communities of practice, Journal of Strategic Information Systems, Vol. 9
No. 2, pp. 155-173.
Yang, C. and Chen, L.C. (2007), Can organizational knowledge capabilities affect knowledge
sharing behaviour?, Journal of Information Science, Vol. 33 No. 1, pp. 95-109.
Further reading
Chiu, C.M., Hsu, H.M. and Wang, E.T. (2006), Understanding knowledge sharing in virtual
communities, Decision Support Systems, Vol. 42 No. 3, pp. 1872-1888.
Appendix. Measurement items
Attitude
1. I like the idea of sharing knowledge with other members in the organization.
2. Sharing my knowledge with other organizational members is good idea.
3. Sharing my knowledge with other organizational members is beneficial idea.
Subjective norm
4. People who influence my behavior (colleagues, friends, etc.) think that I should share my
knowledge.
5. People who are important to me (colleagues, friends, etc.) think that I should share my
knowledge.
6. People whose opinion I value (colleagues, friends, etc.) think that I should share my
knowledge.
Intention
7. I intend to share my knowledge.
8. I plan to share my knowledge with other members in the organization.

9. I will share my knowledge with other organizational members in the near future.
10. All things considered, I will share my knowledge in the near future.
Trust
11. I generally trust my colleagues.
12. It is easy for me to trust my colleagues.
13. I and my colleagues trust each other.
14. I can trust the people I work with to lend me a hand if I needed it.

Downloaded by SEGi International Bhd At 03:59 27 January 2015 (PT)

Social networks
15. I communicate frequently with most members of the organization.
16. I interact and communicate with other people or groups outside the organization.
17. I communicate with other members in the organization through informal meetings.
18. I actively participate in community of practice[1].
Extrinsic rewards
19. I will receive monetary rewards in return for my knowledge sharing.
20. I will receive additional points for promotion in return for my knowledge sharing.
21. I will receive salary raise in return for my knowledge sharing.
Self-efficacy
22. Sharing my knowledge would help other members in the organization to solve problems.
23. Sharing my knowledge would create new opportunities for the organization.
24. Sharing my knowledge would improve work process in the organization.
25. My knowledge sharing would help the organization achieve its performance.
Organizational support
26. My organization has appropriate technology in place (e.g. academic portal, web site, e-mail) to
support knowledge sharing.
27. My organizational has process in place (e.g. meeting, colloquium, intellectual discourse
session, etc.) for knowledge sharing.
28. My organization supports forming informal networks (e.g. community of practice) where
knowledge sharing can be shared.
Corresponding author
Dr Naser Khani can be contacted at: khani451@yahoo.com

To purchase reprints of this article please e-mail: reprints@emeraldinsight.com

Or visit our web site for further details: www.emeraldinsight.com/reprints

Knowledge
sharing intention

431