antecedents of knowledge sharing behavior analyzing the...

International Journal of Recent Advances in Organizational Behaviour and Decision Sciences (IJRAOB)

An Online International Research Journal (ISSN: 2311-3197) 2015 Vol: 1 Issue 3

452

www.globalbizresearch.org

Antecedents of Knowledge Sharing Behavior – Analyzing the

Influence of Performance Expectancy and User’s Attitude

Hairol Adenan Kasim,

Faculty of Information Management,

MARA Technology University, Shah Alam, Malaysia

Email: [email protected]

__________________________________________________________________________

Abstract

Knowledge sharing is an important initiative in creating competitive advantage. As an

important tool in the successful implementation of Knowledge Management, sharing

knowledge is seen to be the most important practice and resource which organization

possesses. Previous researchers have defined knowledge sharing as the process through

which one group, department, or division is affected by the experience of another. Promoting

a knowledge sharing behaviour is a challenge for most knowledge-savvy organizations,

including the research organization. Developing a behaviour which values and practices

knowledge sharing is an effort involving attention to organizational and users perspective

and performance. Past literature mainly identifies organizational approach and users’

positive culture can influence the effective sharing of knowledge. The purpose of this study is

to evaluate the influence of performance expectancy and users attitude that could promote the

knowledge sharing behaviour. This research uses quantitative methodology for the collection

and analysis of data by conducting surveys from the researchers in these organizations. Self-

administered questionnaire were distributed to 510 researchers from selected Malaysian

Government-Linked Companies (GLCs). Hence, as organizational and users orientations are

determined by job performance and individual’s motivational traits, we can also confirm two

hypotheses regarding this relationship. The theoretical model developed in this paper is

empirically tested on a sample of researchers of five organizations, and significant

relationships among these constructs were found.

__________________________________________________________________________

Keywords: Knowledge Sharing; Behavior; Success Factors; Performance Expectancy; Users’

Attitude.

mailto:[email protected]



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1. Introduction

The recent emergence of globalization has been characterized by uncertainty and

continuous change in society, economy and technology. This rapid and dynamic change is

forcing major organizations that are accustomed to structure and routine have had to adapt

accordingly in order to compete and improvise rapid solutions correctly (Hildreth and

Kimble, 2004). Competitiveness is no longer centered on physical assets and financial capital,

but in effective routing of successful business growth and longevity where human capital

pushes organizations towards higher capacity learning and innovation (Phusavat et al., 2010).

This resource-based view of competitiveness has been re-emphasized by Drucker (2001), who

reveal that competitive organizations must have the ability to shift from tangible to value

based measures that would be based more on organizational internal resources. Recognizing

these competitive changes, many organizations are moving forward towards a structure based

on rising organizational internal resources, such as, network and virtual communities. Kimble

et al. (2000) and H. Adnan et al. (2014) recommend that increased competitiveness should

also bring an increased need for internal Knowledge Management initiatives and sharing

practices.

Influencing a knowledge sharing behavior is a challenge for most of knowledge-savvy

organizations, such as the research organization. Because behavior is difficult to pin down, it

is often underestimated in efforts to change how firms work. Developing a behavior which

values and practices knowledge sharing is an effort involving attention to organizational,

user’s attitude and social perspective of this behavior. Past efforts have often assumed that

implementing technology, such as media online will be enough to promote knowledge

sharing. While this has been consistently demonstrated as an ineffective practice, frequently

the majority of an organization’s knowledge resources are devoted to technology and not to

the organizational and individual factor, which stimulate knowledge sharing. Various gaps or

barriers were acknowledged by previous experts on knowledge sharing, that include

functional silos, individualism, in-effective means of knowledge capture, internal competition

and managerial gaps in the organization. Prior researchers have suggested several common

reasons given by individuals who are reluctant to share their knowledge, such as pride

syndrome because people have pride in not having to seek advice from others and in wanting

to discover new ways for themselves (Davenport and Prusak, 1998), not realizing how useful

particular knowledge is to others and lack of trust (Massey et al., 2002).

The primary challenge in promoting knowledge strategy for several organizations in

Malaysia is not technology but more on changing the employees’ behavior and attitude (Gan,

2006; Yap et al., 2010; Lee and Fariza Hanum, 2008). This finding is supported by

Ramanathan Narayanan et al. (2003) that revealed most organizations in Malaysia tend to be



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highly bureaucratic and have a centralized decision-making structure with lower levels of

Knowledge Management (KM) practices in place. In spite of the availability of advanced KM

systems and large quantities of information in the knowledge driven institutions, it is the

behavioral factors of users in sharing knowledge that is paramount in determining the success

or failure of KM technologies (Dyer and McDonough, 2001; Malhotra and Galleta, 2005).

Therefore, there is still a challenge for the key people in the organization to determine

organizational factors in developing the applications and users’ behavior to share their

knowledge within the communities. On that note, the objective of this research is to evaluate

the influence of organizational performance expectancy and users attitude that could promote

the knowledge sharing behavior among the researchers in the Government-Linked Companies

(GLCs).

2. Knowledge Management and Sharing

Knowledge Management (KM) has been widely recognized and practiced in many

organizations around the world. This initiative comprises a range of strategies and practices to

identify, create, capture, distribute, share, collaborate and enable adoption of insights and

experiences, either by individuals or organizations. KM’s definition has been suggested by

many experts, such as Argote et al. (2000) and Huber (1991) who refer to KM as how

organizations create, retain, and share knowledge. Landoli and Zollo (2007) also describe KM

as the process of creating, capturing, and using knowledge to enhance organizational

performance. From the above definitions, the experts have identified knowledge sharing as

one of the important pillars in KM. Grant (1996) describes knowledge sharing as an important

focus in KM field, where knowledge is seen as the most important resource which

organization possesses. Argote et al. (2000) also interpret knowledge sharing as the process

through which a group, department, or division is affected by the experience of another. They

further point out that the transfer of organizational knowledge can be observed through

changes in knowledge or performance of the recipient units. Recently, various organizations

have been critically undertaking strategies to ensure KM is successful by embedding

knowledge sharing practices in their routine work processes. In fact, the management and

stakeholders of the companies have realized the importance of sharing practices for their

staffs and embedded KM initiatives in their organizations (H. Adnan et al., 2014).

The strategic, learning, collaboration and innovation environments in which knowledge

sharing takes place can affect organization and knowledge sharing processes in many ways.

From previous researches, several items are identified as form of knowledge that is shared

among the professionals in the knowledge-based organization, such as the research centers.

As mentioned by Levitt and March (1988), knowledge may embedded in form of tasks or

routines. Routines are described as forms, rules, procedures, conventions, strategies and



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technologies around which the organizations are constructed and through which they operate.

While a routine may be easy to transfer, knowledge about ‘who is good?’ at using that routine

may take time to establish and develop (Levitt and March, 1988). Hence, as Sutton and

Hargadon (1996) posit, such knowledge allows communities to engage and collaborate in

joint brainstorming sessions that allows group members to explore new ideas or innovation

and discuss difficult issues, such as lesson-learnt and best practices. In addition, as described

by Teece (2000), since organizational knowledge is embedded in processes, procedures,

routines and structures, such knowledge cannot be moved into an organization without the

transfer of cluster of individuals with established patterns of working together.

2.1 Organizational Influence

Prior researches have pointed out that management and organizational context is crucial

for work group success and would influence knowledge sharing behavior (Aliakbar, Rosman

and NikHasnaa, 2012). In the context of knowledge communities, these scholars suggested

five variables that are important - management support, knowledge culture, topic relevance,

knowledge supply and knowledge type. Davis (1989) believes that strength of this element is

closely related to the organization’s environment and communities. This argument is

supported by Saenz, Aramburu and Rivera (2010) that proposed organizational culture,

policies, guidelines and strategy and also external alliances are all structural components that

can shape the organizational context and affect knowledge sharing and innovation.

Members of a community can be motivated to participate by using methods, such as

promotion, raises, bonuses, self-esteem and community interest. This factor is justified by

Hall and Graham (2004) who suggested - direct or indirect extrinsic factors, such as financial

rewards and promotion by the organization can influence their staff to actively participate in

the knowledge communities. Based on a study in a government agency in Malaysia, Ramlee

(2011) has recommended individual’s high expectation to be rewarded in terms of recognition

and promotion as one of vital motivation for promoting a knowledge sharing culture. This

author also suggested that implementation of this behavior is dependent on transforming the

individual’s attitude and behaviors to voluntarily share their knowledge. Other constraining

factors that are identified by Correia, Paulos and Mesquita (2010) are fear of losing the

position they occupied in their organization’s hierarchy and lack or opportunity to share the

knowledge, because nobody requested their assistance. These scholars have proposed

organizational factor as the main factors that influenced users’ involvement in knowledge

communities. Similarly, Hansotia (2003) has suggested that innovative and knowledge-based

organization does not penalize employees who risk trying out new knowledge or inventions.

An organization that is used to adopt innovation has a management that rewards, or at least

does not penalize the flexibility required for creativity and innovation (Hansotia, 2003).



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As described by O’Dell and Grayson (1998), organizational culture and management’s

support are vital elements in promoting knowledge sharing behavior. As suggested by Raja

Abdullah, Adnan and Kamaruzaman (2011), an effective KM practice needs not only

enthusiasm and exhilaration from the individuals but also commitment from the leader and

management in the organization. Furthermore, management capabilities that are crucial to

promote knowledge sharing behavior are leading the change effort, conveying the importance

of KM to employees and maintaining their morale in developing this behavior (Wong, 2005).

Organizations need to develop a culture to overcome environmental challenges and changes,

which encourage users toward knowledge sharing behavior (Kaffashpoor, 2013). Hence,

organizational culture is the foundation of KM, as it teaches individuals in an organization on

how to learn and to share their knowledge (Gray and Densten, 2009). Moreover, Gupta and

Govindarajan (2000) also describe knowledge sharing are influenced by several

organizational factors – organizational structure, reward systems, processes, people and

leadership. This argument is supported by Dalkir (2005) that proposed corporate culture as a

key component in ensuring critical knowledge and information flow within the organization.

2.2 Performance Expectancy

This variable refers to individuals’ perception and belief that KM platform can improve

their job performance and able to obtain significant rewards after using the platform

(Venkatesh and Davis 1996).This attribute can be categorized into the following sub-

dimensions:

2.2.1 Perceived Usefulness

Ardichvilli, Page and Wentling (2003) have revealed that people are motivated to become

active participants in KM platform when they view knowledge as meant for public good, a

moral obligation and as a community interest. There are few categories of recognition in

sharing knowledge, such as career advancement, which describes individuals’ perception that

knowledge sharing will positively affect their career and sense of community that defines the

sense of belonging in the related community (Yoo, 2002).

2.2.2 Extrinsic and Intrinsic Motivation

Teo, Lim, and Lai (1999) and Davis et al. (1992) have proposed extrinsic motivation as

users’ preference in performing an activity because it is perceived as the capability in

achieving valued outcomes, such as career progression, payment or job promotions.

Moreover, intrinsic motivation is defined as performing an activity for its inherent

satisfactions, rather than for some separable consequence, such as appreciation and

recognition from the organization (Ryan and Deci, 2000).



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2.2.3 Expectancy to the Achievement

Mclnearny and Mohr (2007) also suggested that in competitive environments, several benefits

such as recognition and status as a mentor or leader that could motivate the employees to

share knowledge within the society. These scholars also mentioned that specific benefits, such

as reward and recognition could be emphasized in the organization along with the

management who encourage that sharing behavior will increase organizational power to

compete with other players.

Therefore, this research hypothesizes:

H1: Performance Expectancy Positively Influences Knowledge Sharing Behavior

2.3 Users’ Attitude Factor

A user’s attitude towards behavior can accurately predict their intention for engaging in a

specific behavior (Ajzen, 1991). As described by Chow and Chan (2008), a person’s attitude

towards knowledge sharing determines his/her intention for sharing knowledge. When

individuals discover knowledge sharing is important and beneficial for their organization,

they will voluntarily engage in knowledge sharing activities. On contrary, if individuals lose

power or hierarchies by practicing a sharing behavior, they will restrain from sharing their

personal knowledge with their colleagues or rivals (Hsu and Lin, 2008). Furthermore, the

values that shape this sharing culture – trust, transparency and open mentality are considered

as learning opportunities within the organization (Friedman, Lipshitz and Overmeer, 2003;

Wiig, 2004). Furthermore, Kuo and Young (2008) propose that for knowledge sharing

practices, user’s attitude has been recognized to be a vital factor because this initiative could

influence one’s trade value. Hence, individuals would consider sharing their knowledge, if

they believe this practice will be important and valuable for their organization (Rusuli and

Tasmin, 2010; Chumg et al., 2015).

Previous researches have recommended individual perceive attributes, such as knowledge

sharing self-efficacy, outcome expectation, perceive relative advantage and compatibility will

influence knowledge sharing behavior in the organization (Bock and Kim, 2002; Wasko and

Faraj, 2005). Likewise, Bandura (2001) in his social cognitive theory has defined outcome

expectation as a judgment of likely consequences that will be produced by performance.

Moreover, self-efficacy is described by this scholar as a judgment of one’s ability to organize

and execute in given types of performance. In other word, perceived self-efficacy is

interpreted as a form of self-evaluation that influences decisions about what behavior to

undertake, the amount of effort and persistence to put forth when faced with obstacles (Lin et

al, 2009). Thus, this behavior plays an important role in influencing individuals’ motivation

and behavior. Other constructs, such as relative advantage is described as an increase in

efficiency and effectiveness, economic benefits and social status enhancement (Rogers,

2003). Hence, when individuals perceived personal and organizational benefits of sharing



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knowledge, they are more likely to facilitate a knowledge-sharing culture in the organization

(Kaser and Miles, 2002). On contrary, Teh and Yong (2011) in their research stress that the

presence of individuals’ attitude toward knowledge sharing may not lead to intention to share

knowledge. Hence, organizational and management should also create a supportive

atmosphere in which knowledge can be shared via effective formal and informal

communication to promote this sharing behavior.

Another element that is essential to promote individual’s behavior towards knowledge

sharing is trust factor. As suggested by Davenport and Prusak (2001), trust element for

individuals who adopts new knowledge in the source of innovation is highly important for the

successful completion of adoption and knowledge sharing behavior. These scholars also

added trust among the employees of an organization are one of the outcomes of the

organizational sharing culture. Similarly, Inkpen and Tsang (2005) suggested trust plays a key

role in the willingness of an individual to share important and valuable knowledge. A lack of

trust may lead to competitive confusion and will limit the ability to develop skills in the

organization (Sherif, 2008). This scholar also suggested that the effectiveness of knowledge

sharing is facilitated by the strength of tie, trust and understanding between the recipient and

the source. Furthermore, in R&D organizations, intellectual property and patented product is

the most valuable asset and property. Therefore, trust can provides reassurance for knowledge

sharing activities for the research fraternity (Dunham and Burt, 2010). As argued by Ida

Madieha (2010), one of the main concerns in any organization is whenever their employees

shared confidential information with their colleagues or external parties. On that matter, it is

important for any organizations to remind their staff to avoid exchanging any information

with external parties that contain information of internal discussion, organizational policies or

anything related to their clients or customers (Ida Madieha, 2010).

Hence,

H2: Users’ Attitude Positively Influences Knowledge Sharing Behavior

3. Review of Previous Theory and Framework

In order to develop a conceptual model for this research, a theoretical model describing the

factors that influence knowledge sharing behavior from previous research was identified and

analyzed.

3.1 Theory of Reasoned Action (TRA)

Figure 1: Theory of Reasoned Action

Source: Fishbein and Azjen (1975)

Belief and

Evaluations

Normative

Beliefs and

Motivation to

Comply

Attitude toward

Behavior (A)

Subjective Norm

(SN)

Behavioral

Intention (BI)

Actual

Behavior



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This theory was developed by Fishbein and Azjen in 1975 that derived from previous

researches about theory of attitude, which led to the study of attitude and behavior. The

primary objective of the TRA is to predict the voluntary behavior and to understand the role

of intention as a mediator between action and attitudes (Fishbein and Azjen, 1975). TRA

recommends that attitudes towards beliefs are determined by perception of its outcome. As

illustrated in Figure 1, Miller (2005) defines each of the three important components in this

theory as follows:

a. Attitudes (A)

This component describes the sum of beliefs about a particular behavior weighted by

evaluations of these beliefs.

b. Subjective Norms (SN)

This component refers to the influence of people in one’s social environment on

his/her behavior intentions; the belief of people, weighted by the importance one attributes to

each of their opinions that will influence one’s behavioral intention.

c. Behavioral Intention (BI)

This component refers to a function of both attitudes toward a behavior and

subjective norms toward that behavior, which has been identified to predict the actual

behavior.

This model defines the links between beliefs, attitudes, norms, intentions and behavior of

individuals and can be summary as follows:

Behavioral Intention (BI) = Attitude (A) + Subjective norms (SN)

This theory also suggests all other factors which influence the behavior in an indirect way

by influencing the attitude or subjective norms. Fishbein and Azjen (1975) refer these factors

as being the external variables. As for knowledge sharing, these external variables can be

described as the organizational structure, management support, user’s positive attitude and

other relevant factors that could influence knowledge sharing behavior.

4. The Proposed Conceptual Framework

Synthesizing from previous TRA model, the conceptual framework of this research has been

developed and shown as figure below:

Figure 2: The Proposed Conceptual Framework

Performance

Expectancy

Knowledge Sharing

Behavior

Users’ Attitude



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As illustrated in Figure 2, the proposed conceptual framework for this research has two

independent variables, i.e. performance expectancy and users’ attitude. Performance

expectancy attribute is defined by Davis (1989), Renaud and Biljon (2008) and Venkatesh et

al. (2003) as the degree to which an individual believes that using the system would improve

his/her job performance. Moreover, users attitude is described as as degree of one’s positive

feelings regarding sharing his/her knowledge (Bock and Kim, 2002). As example, a person

might have the beliefs that knowledge sharing is good for the organization, that culture makes

organization look strong, or the negative perception that this sharing culture will burden their

time and it is complicated to share their knowledge. For the dependent variable, i.e.

knowledge sharing is interpreted as social interaction that involve sharing or transfer of

knowledge, experience and expertise from an individual (or sharer) to his/her colleagues (as

the receiver) about their works through a formal or informal communication in a certain

organization (Lin, 2008; Kim and Lee, 2006; Haas and Hansen, 2007).

5. Research Methodology

This study will adapt the Quantitative method for the collection of data from the selected

respondents. Therefore, this research will adapt the Post Positivist paradigm that will study

the behavior and actions of human. As define by Creswell (2009), this research paradigm

holds a philosophy in which causes probably determine effects and outcomes. Thus, the

problems studied using this approach will reflect requirement to identify and asses the causes

that influence outcomes. Post Positivist paradigm also emphasizes meaning of new

knowledge to support social movements that aspire to change the environment and contribute

towards social justice (Ryan, 2006). This research will use Quantitative approach for the

collection and analysis of data by conducting surveys and questionnaires from related

participants in the Research and Development (R&D) organization. This method will focus on

related variables or factors with the purpose of formulating a theory or conceptual framework

at the conclusion of this research (Sekaran, 2006). A survey provides numeric report of

attitudes or behaviors through the exploration of a sample of population with the intention of

generalizing the hypotheses of the study (Creswell, 2009).

5.1 Population and Sampling

The participant of this study is the researchers in these R&D organizations which could

assist in generating meaningful information and explanation to fulfill the objectives of this

research. The list of respondents obtained from the respective research organizations is the

basic population and Stratified Random Sampling technique will be used. This sampling

design will provide the most efficient technique when differentiated information is needed

regarding various strata within the population. According to Sekaran (2006), this sampling

technique will involves a process of segregation, followed by random selection of subjects



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from each stratum. The population of researchers will be divided into mutually exclusive

groups that are relevant, appropriate and meaningful in the context of this study.

This research has distributed hardcopy of the survey to 150 respondents and uploaded the

formatted electronic version of the survey to 360 researchers. The hardcopy and link of this

website were distributed to a total of 510 researchers, of which 220 responded to the survey.

Table 1: Total Respondents

No R&D Organization Total

Population

Total

Respond

Percentage

(%)

1 Petronas 130 60 46.1

2 Tenaga Nasional Berhad 80 34 42.5

3 SIRIM 100 39 39

4 Nuclear Malaysia 150 68 45.3

5 Green Technology

Malaysia

50 19 38

TOTAL 510 220 43.1

Based on Table 1, the target respondents are drawn from total population of 510

researchers from five organizations and 220 have responded to the circulated survey. The

total respond or questionnaire returns for this research was on target since more than 40

percent (%) of the targeted respondent or more than 200 users had given the feedback on the

questionnaires that had been circulated. The response rate of more than 40% are also

consistent and equal to sample size decision model that is proposed by Krejcie and Morgan

(1970) and Sekaran (2006) as described in Appendix A.

5.2 Multivariate Analysis

Multivariate analysis was the method for testing the research’s hypotheses that includes

Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). CFA and

SEM are recognized to provide rigorous analysis of model power in relation to construct and

content validity. CFA is a multivariate statistical procedure in research design stages that are

used to test how well the measured variables represent the number of constructs. According to

Raykov and Marcoulides (2008), the main objective in CFA lies in examining the pattern of

relations among the factors, as well as those between them and the observed variables.

6. Discussion of Findings

6.1 Descriptive Statistical Analysis for Variables

The descriptive analysis involved all constructs or variables in this research. This analysis

had determined the mean score and standard deviation value for all constructs. This analysis

had been split into two parts, to justify the performance expectancy and users’ factors that can

promote knowledge sharing behavior:

a. Performance Expectancy Factor

As described in Table 2, the majority of respondents believe knowledge platforms enable

them to retrieve knowledge needed for problem solving, decision making and learning. On the



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other hand, the use of knowledge platforms to promote innovativeness and creativity has the

lowest score in sharing knowledge through the virtual environment. The standard deviations

also show consistent variation or dispersion.

Table 2: Performance Expectancy Factor

Attributes

Mean

Std.

Deviation

a. Knowledge platform is very useful in my job. 4.06 .690

b. Knowledge platform enables me to accomplish tasks more

quickly.

4.07 .725

c. Knowledge platform will enable me to acquire knowledge from

the right people and time in my organization

4.05 .719

d. Knowledge platform will enable me to retrieve knowledge needed

for problem solving, decision making and learning

4.09 .711

e. Knowledge platform will improve the quality of my research 4.01 .703

f. Knowledge platform will promote innovativeness and creativity 3.99 .696

b. Users’ Attitude Factor

As shown in Table 3, majority of respondents believe the use of the knowledge platforms

will contribute to the new knowledge and benefit all (research) organizations. On the other

hand, they didn’t believe the use of knowledge platforms can promote their position and

reputation in their research organization. On that note, this item has the lowest score for

attitude towards knowledge sharing behavior. The standard deviations also show consistent

variation or dispersion.

Table 3: Users’ Attitude Factor

Attributes

Mean

Std.

Deviation

a. I voluntary share my knowledge using the Knowledge platform 3.71 .683

b. In my organization, there is a climate of trust in sharing knowledge

through the Knowledge platform

3.58 .659

c. Knowledge platform can improve my relationship, communication

and collaboration in the research community

3.95 .614

d. Knowledge platform can promote my position and reputation in my

organization

3.55 .793

e. Knowledge platform will provide more input and response for my

research work

3.97 .611

f. The use of the Knowledge platform will contribute to the new

knowledge and benefit all (research) organizations.

4.02 .640

6.2 Confirmatory Factor Analysis (CFA)

a. Performance Expectancy Factor

CFA for the independent variable – Performance Expectancy is performed to analyze how

well the measured variables represent the number of constructs. To determine the minimum

loading necessary to include an item in its respective constructs, Hair et al. (2010) suggested



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that variables with loading greater than 0.30 is considered significant, loading greater than

0.40 more important, and loading 0.50 or greater are very significant. For this study, the

general criteria were accepted items with loading of 0.50 or greater. As a result, all factors for

Performance Expectancy are having loading higher than the value of 0.5, ranging from 0.50 to

0.56. Hence, all items for this variable are retained for further analysis.

Table 4: CFA Performance Expectancy

Index Model

CMIN 75.755

Df 9

CMIN/df 8.417

P 0.000

GFI .887

AGFI .736

CFI .901

TLI .835

NFI .890

RMSEA .193

A CFA was conducted for this factor to determine whether the indicators measured the

factor and are assigned adequately. Maximum likelihood estimation was employed to

estimate the CFA model. The statistical application, namely Analysis of Moment Structures

(AMOS) version 19 is used throughout the study to conduct the analyses. Table 4 summarizes

the results of these tests. Empirical evidence in CFA is generally assessed using criteria, as

follows:

i Comparative Fit Index (CFI): This index compares a proposed model with the null model

assuming that there are no relationships between the measures. A CFI value greater than 0.90

indicates an acceptable fit to the data (Raykov and Marcoulides, 2000; Bentler, 1995). An

analysis of the Table 4 for this study reveals that the CFI values are high (0.901), which

suggests acceptable and good model fits.

ii Convergent validity: The Bentler-Bonett Normed Fit Index (NFI) obtained from CFA can

be used to assess convergent validity. This index measures the extent to which different

approaches to measuring a construct produces the same results (Bentler, 1995). According to

the rule of thumb, NFI values of 0.80 or greater indicate an adequate model fit (Bentler,

1995). Thus, the NFI value (0.89) shown in Table 4 indicates an adequate model fit for this

study.

iii Goodness-of-Fit Index (GFI): The Goodness of Fit Index determines what proportion of

the variance in the sample variance-covariance matrix the model accounts for. This should

exceed 0.80 for a good model (Doll et al., 1994). As a result, the GFI value (0.89) revealed in

Table 4 indicates a good model for this study.



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iv Adjusted Goodness-of-Fit Index (AGFI): Adjusted GFI is an alternate GFI index in which

the value of the index is adjusted for the number of parameters in the model. According to

Doll et al. (1994), AGFI values of 0.80 or greater indicate an adequate model fit. On contrary,

the AGFI for this study are below the threshold (0.74) and not indicates a good model for this

study.

Table 5: Performance Expectancy Indexes

Fit Indices Recommended Level** Output Value Summary

P value (X2) Not significant p>0.05 0.000 Not Accepted

X2/df



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A CFA is conducted for this constructs to determine whether the indicators measured the

construct and are assigned adequately. Maximum likelihood estimation is employed to

estimate the CFA model. Table 7 (below) summarizes the results of these tests, after the

elimination process.

Table 7: Indexes Users’ Attitude


X2 Not significant p>0.05 0.00 Not Accepted

X2/df



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A CFA was conducted for this constructs to determine whether the indicators measured the

construct and are assigned adequately. Maximum likelihood estimation is employed to

estimate the CFA model. Table 8 summarizes the results of these tests, after the elimination

process.

Table 9: Indexes Knowledge Sharing Behavior


X2 Not significant p>0.05 0.00 Not Accepted

X2/df



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Figure 3: Analysis for SEM Model

Model Maximum Likelihood Estimates

Maximum Likelihood assumes that the underlying variables are normally distributed.

When the x variables are measured as deviations from their means it is easy to show that the

sample covariance matrix for x to determine whether the specified model is identified.

Through CFA, a model is identified if all of the unknown parameters can be rewritten in

terms of the variances and covariance of the x variables (Schumacker and Lomax, 2004). For

this study, the maximum likelihood estimates result as described in Table 10 shows that the

standardized residuals are technically fit index, and provide information about how closely

the estimated matrix corresponds to the observed matrix and described how well the data fits

the model.

Table 10: Maximum Likehood Estimates Result

Attributes Estimate P

Knowledge Sharing Behavior



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RMSEA



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behavior, such as knowledge sharing behavior (Matzler and Mueller, 2011; Swift et al.,

2010).

Previous scholars also revealed motivation to share knowledge through reward and

incentive system, social contributions and recognition that are influenced by organizational

structure, and these factors could influence members' perception in knowledge sharing

behavior (Bartol and Srivastava, 2002; Bock et al., 2005; Rusli et al.,2008; He and Wei, 2009;

Kankanhalli et al., 2005). Similarly, as demonstrated in recent studies, individuals are

motivated to share their knowledge when there are appropriate tangible and intangible return

and outcomes, such as recognition, rewards or career advancement. Hence, if organizational

policy is not designed specifically to promote knowledge sharing, a general reward or

incentive system can actually deter and discourage the knowledge sharing behavior. Thus,

based on the findings, this study has proposed this variable as an important factor for

researchers to promote knowledge sharing behavior through the knowledge platform.

b. Users Attitude Factor

The result of this study has recommended a positive relationship between user’s attitude

and knowledge sharing behavior. This finding is in line with study by Bock et al. (2005),

Chow and Chan (2008), Hsu and Lin (2008), Jarvenpaa and Staples (2001), Constant et al.'s

(1994), Kilduff and Tsai (2003), Hahn and Subrami, 2000; Cheng et al. (2009), Syed-Ikhsan

and Rowland (2004), Sondergaard, Kerr and Clegg (2007), Hartini, Normala and Sobry

(2006), Bakhari and Zawiyah (2008), Norazah and Juhana (2011), Schultz (2003) and Chumg

et al. (2015). This research suggested that individuals are motivated to share their knowledge

is strongly influenced by their belief that this action is relevant to others’ work and will add

value to others. Previous experts also acknowledged the motivation for individual to

contribute to knowledge communities who hardly know each other have been subject to

extensive research (Hinds and Pfeffer, 2003; Schultz, 2003; Hsu and Lin, 2008). For instance,

these scholars conceded to the fact that knowledge contributions are a means of expressing

one’s identity and thus helping others might increase individual’s self-esteem, reputation and

respect from others. Furthermore, other personal factors, like recognition as experts or leaders

in the relevant fields of study and group identity are important considerations to determine the

passion to share their knowledge within their communities (Hahn and Subrami, 2000; Syed-

Ikhsan and Rowland, 2004; Sondergaard, Kerr and Clegg, 2007).

One of the important attributes in user’s attitude is the element of trust in sharing

knowledge through KM platform. Hence, this attribute is relevant and significant with the

previous studies that revealed knowledge sharing rely heavily on trust element among the

members involved (Akbulut et al., 2009; Canestraro et al., 2009; Chau et al., 2001; Luna-

Reyes et al., 2007; Mayer, Pardo and Tayi, 2007; Norazah and Juhana (2011); and Pardo et



470


al., 2004). Trust factor is also suggested to influence researchers to share knowledge because

it involved confidentiality attributes that are essential in securing the pattern or copyrighted

products from the R&D organizations.

9. Implication and Limitations of the Study

This research has focus on identifying gaps that would assist in effectively guide

government sectors, such as GLCs to be more competitive and innovative. This research has

both academic and practical implications, such as identifying knowledge sharing holistic

initiatives as a vehicle for success in creating valuable organizational development practices.

As a result, the formulation of knowledge platform policies and practices would assist

towards promoting the knowledge sharing behavior in Government agencies in Malaysia.

The analysis approach used in this research suits the formative and exploratory subjects

addressed in the objective of this study. However, several limitations are worth mentioning in

this study. For instance, the use of 510 participants from five Research and Development

(R&D) organizations in Government-Linked Companies are only meant for sampling and

does not described the whole population of research organization in Malaysia. Furthermore,

the sizes of samples from the five GLCs agencies in one country (Malaysia) limited the

possibility of this research generalization claim and maybe these participants would perceive

the knowledge platform utilization differently from other researchers in different sectors or in

other countries. Although there are several limitations, but this research has successfully

executed and achieved the proposed objectives.

As recommendation for future research, it would be necessary to conduct study with

similar objectives within companies of different sectors, develop more respondents and

eventually use other methods for data collection and sampling. In addition, it is recommended

that this research is repeated in other contexts or in different countries and other kinds of

knowledge platform which could complement other recent knowledge sharing studies that are

related to the objective of this research.

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Appendix A

Table for Determining Sample Size from a Given Population

N S N S N S N S N S

10 10 100 80 280 162 800 260 2800 338

15 14 110 86 290 165 850 265 3000 341

20 19 120 92 300 169 900 269 3500 246

25 24 130 97 320 175 950 274 4000 351

30 28 140 103 340 181 1000 278 4500 351

35 32 150 108 360 186 1100 285 5000 357

40 36 160 113 380 181 1200 291 6000 361

45 40 180 118 400 196 1300 297 7000 364

50 44 190 123 420 201 1400 302 8000 367

55 48 200 127 440 205 1500 306 9000 368

60 52 210 132 460 210 1600 310 10000 373

65 56 220 136 480 214 1700 313 15000 375

70 59 230 140 500 217 1800 317 20000 377

75 63 240 144 550 225 1900 320 30000 379

80 66 250 148 600 234 2000 322 40000 380

85 70 260 152 650 242 2200 327 50000 381

90 73 270 155 700 248 2400 331 75000 382

95 76 270 159 750 256 2600 335 100000 384

Note: “N” is population size, “S” is sample size.

Source: Krejcie, Robert V., Morgan, Daryle W. (1970) Determining Sample Size for Research

Activities, Educational and Psychological Measurement.

antecedents of knowledge sharing behavior analyzing the...

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