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Individual Factors Affecting Knowledge

The progress of the e-learning has been contemplated to be an essential stimulus for the advantage in online learning environment during the last decade. E learning environment is designed to alleviate individuals’ learning activities, along with the provision of capacity and resources required to assist make the activities successful. Most of the learners participate in e learning communities with the expectations that they can acquire and share valuable knowledge to fulfill their needs (Chen et al., 2009).

Consistent with the growing interest in virtual cooperation and collaborative learning, there has been an exponential growth of studies in elearning communities. For example, Daniel et al. (2003) addressed the importance of social capital in enhancing learning performance in online learning communities; Roca et al.

E learning community refers to a group of individuals engaged intentionally and collectively in the transaction or transformation of knowledge which is fully or partially delivered, enabled, or mediated by electronic means (Kowch and

Schwier, 1997). It provides a cooperative platform for learners to communicate, exchange knowledge, share good practices with each other, thereby enhancing individuals’ learning effectiveness (Hardaker and Smith, 2002). Elearning community can be used to facilitate learning both in distance learning and classroom!based environment. Many universities and schools have implemented elearning communities to stimulate collaborative learning, in which individuals gain new experience of learning which is beyond the traditional classroom and textbook (Teo et al 2003). However, like any other information system, the success of elearning depends largely on user satisfaction and other factors

that will eventually increase users’ intention to continue using it. Past research has found that most electronic communities are facing the problems with retaining members and motivating them for active participation (Sangwan, 2005).

2.1 Knowledge Sharing factors in elearning Communities

Numerous studies that explore elearning communities members’ motivations for contributing knowledge sharing behavior have revealed three broad motivator categories: personal factors (Chiu, 2006; Hsu et al., 2007; Lin et al., 2009; Wasko and Faraj, 2005), system factors (Lin et al., 2009; Ma and Agarwal, 2007; Phang et al., 2009) and environmental factors (Chiu, 2006; Hsu et al., 2007; Lin et al., 2009; Phang et al., 2009; Wasko and Faraj, 2005).

For example, in studies about electronic networks of practice, Wasko and Faraj (2005) found that people contribute knowledge when they perceive reputation enhancement, when they have experiences to share, and when they are structurally embedded in the network. In a study

integrating social cognitive theory with social capital theory, Chiu et al. (2006) found that social interaction ties, trust, norms of reciprocity, identification, shared vision, shared language, communityrelated outcome expectations, and personal outcome expectations may be important motivations for community members to contribute knowledge. A study by Ma and Agarwal (2007) reported that

perceived identity verification is strongly linked to member satisfaction and knowledge contribution.

Another study by Hsu et al. (2007), integrating personal and environmental perspectives, found that self-efficacy, outcome expectations, and multidimensional trusts (include economy-based trust, information-based trust, identification-based trust) tend to influence knowledge sharing behavior in online learning communities. Phang et al. (2009) adopted a socio-technical perspective of an online learning community and found that individuals perceived tracking fulfilment as more important for usability.

The same study also found social interactivity to be a more important factor regarding sociability when elearning community members contribute knowledge. Lin et al. (2009) investigated the relationship

between contextual factors, personal factors, sharing behavior, and community loyalty in virtual learning communities. They reported that trust significantly affects knowledge sharing self-efficacy, perceived relative advantage, and perceived compatibility, all of which in turn positively affect knowledge sharing behavior.

Knowledge sharing is the behavior of an individual diffuses their knowledge gained and information to colleagues within an e-learning system (Ryu, Ho, and Han, 2003).therfore Knowledge sharing requires a communication process whereby two or more parties are involved in the devolution of knowledge. Therefore, knowledge sharing is described as a process of communication between two or more participants for the provision and acquisition of knowledge (Usora, Sharratt, Tsui, and Shekhar, 2007). In addition some researchers have highlighted the various factors that affect a person's willingness to share knowledge, such as information technology and communication, the costs and benefits, motivation systems, extrinsic and intrinsic motivation, the social capital, social cognition, personal organizational climate, and the championship of management (Alavi and Leidner 1999, Bock and Kim, 2002, Bock et al, 2005;. Chiu et al. 2006, Hsu et al, 2007;. Kankanhalli et al, 2005;. Koh and Kim, 2004, Orlikowski 1996;. Purvis et al, 2001, Wasko and Faraj, 2005).

Knowledge sharing self-efficacy, perceived relative advantage and compatibility are seen as predictors of personal factors, as they are seen as major influences the organization of user behavior (Bandura, 1982, 1986, 1997; Igbaria and Iivari, 1995, Rogers, 2003; Sia, Teo, Tan, and Wei, 2004, Verhoef and Langerak, 2001).

Therefore, we may assume that the behavior of individuals for knowledge sharing is affected by contextual factors and private perceptions of knowledge sharing in participating in. 

The norm of reciprocity and trust are treated as two main contextual factors that influence personal perceptions and behavior of a member.

Knowledge sharing self-efficacy, perceived easy to use,perceived usefulness,injoyment to help are seen as predictors of personal factors, as they are seen as major influences the organization of user behavior (Bandura, 1982, 1986, 1997; Igbaria and Iivari, 1995, Rogers, 2003; Sia, Teo, Tan, and Wei, 2004, Verhoef and Langerak, 2001).

The Theory of Reasoned Action

The Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) and the Theory of Planned Behavior (TPB) (Ajzen, 1985, 2002a) appear as the most preferred intention–behavior models for studying information technology (IT)-related individual people behaviors. TRA assumes that ‘‘most human social behavior is under volitional control and, hence, can be prognosticate from intentions alone” (Ajzen, 2002a, p. 28). In TRA, Fishbein and Ajzen (1975) contend that an individual intention to perform an action has two basic antecedents: attitude and subjective norms. Attitude displays a summary evaluation of a psychological object captured in such attribute dimensions as good–bad, harmful–beneficial, pleasant–unpleasant, and likable–unlikable. Attitude toward a behavior is described as the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question. The subjective norms toward a behavior are defined as the perceived social pressure to perform or not perform the behavior in question. In a variety of studies, the tentative findings have demonstrated that attitudes and subjective norms toward the various behaviors create significant contributions to the prediction of intentions (Ajzen, 2001; Fishbein & Ajzen, 1975; Taylor & Todd, 1995).

Theory of Planned Behavior (TPB)

To address this deficiency, the Theory of Planned Behavior (TPB) improves TRA by adding the construct of perceived behavioral control (Ajzen, 1985, 1991). Perceived behavioral control (PBC) adverts to the perceived ease or difficulty of performing the behavior and the amount of control one has over the achievement of personal goals. It is introduced to deal with situations in which people may lack complete volitional control over the behavior in question (Ajzen, 1985, 1988, 1991).

As in other IT-based behaviors, PBC for knowledge sharing behaviors is highly correlated with perceived ease of use or difficulty related to a particular technology, which have been shown to be major factors predicting intention to use that technology (Compeau & Higgins, 1995; Davis, Bagozzi, & Warshaw, 1989).

The Technology Acceptance Model (TAM)

The TAM is a further adaptation of TRA specifically tailored for modeling user acceptance of information systems ( Davis , 1989). TRA proposes that social behavior is motivated by an individual's attitude towards carrying out that behavior. However, it does not specify what specific beliefs would be important in a particular situation. TAM posits that the actual usage of technology can be predicted by user's behavioral intention and his/her attitude towards use, which in turn are influenced by the technology's perceived ease of use and perceived usefulness. TAM adopts the well-established causal chain as follows:

Beliefs > attitude > intention> behavior

Based on certain beliefs, a person forms an attitude about certain objects, on the basis of which one forms an intention as to how one should behave with respect to that object. The intention to behave is the sole determinant of actual behavior. Davis adapted the TRA by developing two key beliefs that specially account for information system usage. The first of these beliefs is perceived usefulness, defined as the 'degree to which a person believes that using a particular system would enhance his/her job performance' (Davis, 1989). The second is perceived ease of use, defined as 'the degree to which a person believes that using a particular system would be free of effort' (Davis, 1989). Researchers, such as Lin and Wu (2002), further modified the TAM and extended its application to the Internet or WWW. However, studies related to the usage behavior within the Internet environment is still at an infancy stage. It is not clear how external variables would affect the usage behavior and intentions. It is hoped that further research would provide greater understanding of the factors that influence acceptance of new technology like multimedia technology in the banking environment.

Flow theory

The main concept of flow is defined as the holistic sensation that people sense when they execute with total involvement (Csikszentmihalyi and LeFevre, 1989; Csikszentmihalyi, 1990). The state of flow happens when an individual is partaking in an activity for its own sake; the state is so satisfying that individuals want to replicate the activity continually (Csikszentmihalyi, 1988). Csikszentmihalyi summarized the most commonly exhibited factors of flow into nine characteristic dimensions, including clear values, immediate feedback, potential control, the merger of action and awareness, individual skills well suited to given Challenges, concentration, loss of self-consciousness, time distortion, and autotelic experience (Csikszentmihalyi, 1975, 1988, 1990, 1993).

Flow experience

It proves that a learner’s flow experience and attitude towards knowledge sharing behavior in e-learning negotiate the impact of the relevant factors of knowledge sharing on e-learning usage. Flow experience is strong-minded as a holistic experience of total involvement and intrinsic interests with which an individual engages in the e-learning environment.

Attitude towards knowledge sharing behavior is influenced by his/her evaluation on the usage of a web-based e-learning system. The model indicates that flow experience also impact attitude towards knowledge sharing. We also presented that flow experience and attitude towards knowledge sharing affect knowledge sharing behavior in e-learning usage.

Research model and hypotheses

The theoretical models employed to study user acceptance, adoption, and usage behaviour include the Theory of Reasoned Action - TRA (e.g., Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), the Technology Acceptance Model – TAM (e.g., Davis, 1989; Davis et al., 1989), the Theory of Planned Behaviour – TPB (e.g.,

Ajzen, 1991; Mathieson, 1991), the Model of PC Utilisation (Thompson, Higgins, & Howell, 1991), the Decomposed Theory of Planned Behaviour (Taylor & Todd, 1995), Innovation Diffusion Theory (e.g., Agarwal & Prasad, 1997; Rogers, 1995), Integrated Technology Adoption and Diffusion Model (Sherry, 1998), and recently the Moguls Model of Computing (Ndubisi et al., 2004).

The current effort focuses on the Decomposed Theory of Planned Behaviour (Taylor & Todd, 1995). The DTPB model has advantages over other models in that it identifies specific salient beliefs that may influence information technology usage. Specifically, the model was found to have better predictive power compared to the traditional theory of planned behaviour model and the technology acceptance model. Taylor and Todd comparing their model with TPB remark, “In

comparing the two versions of TPB, we believe that there is value added as a result of the decomposition, in terms of increased explanatory power and a better, more precise, understanding of the antecedents of behaviour.

The decomposed TPB model uses constructs from the innovation literature. It also explores subjective norms and perceived behavioural control more completely by decomposing them into more specific dimensions. It provides a comprehensive way to understand how an individual’s attitude, subjective norms and perceived behavioural control can influence his or her intention to knowledge sharing between elearning communities.

An other hand, The flow create has been introduced as a possible metric of user experience in the context of information systems and computermediated environments (see Ghani, 1991; Ghani et al., 1991; Trevino and Webster, 1992; Webster et al., 1993; Ghani and Deshpande, 1994; Hoffman and Novak, 1996; Chen et al., 1999; Agarwal and Karahanna, 2000; Novak et al., 2000; Koufaris, 2002; Woszczynski et al., 2002; Finneran and Zhang, 2003). Within this context, the experience of flow has been shown to lead to increased exploratory behavior, communication, positive affect, satisfaction and acceptance of information technology, computer use, learning, and training (Woszczynski et al., 2002; Finneran and Zhang, 2003).

Especially, some previous studies have reasoned that there is a link between

Flow and e-learning (Csikszentmihalyi and LeFevre, 1989; Ghani et al., 1991; Webster et al., 1993; Hoffman and Novak, 1996).

Many of the features of flow are ones that we readily subordinate with environments that encourage learning: a sense of control, clear goals, timely and suitable response, enjoyment, concentration, and a sense that the challenges being offered are just within reach of one’s own skills to achieve them. Researchers have renowned the importance of flow in online educational settings (Clarke & Haworth 1994; Shernoff, Csikszentmihalyi, Schneider, & Shernoff 2003) and are reinforced by Research viewing that flow happens more often during study and schoolwork than other daily activities (Massimini & Carli 1988). 1986). However,

few studies explore the role of trust within the context of

professional VCs (Ardichvili et al., 2003; Kanawattanachai and Yoo, 2002). For fear of criticism or

misleading others, community members tend to shy away

from contributing knowledge (Ardichvili et al., 2003).

From the perspective of professional VCs, the willingness

of individuals to share with others the knowledge they have

acquired or created are major concerns (Bock et al., 2005),

Intentions and Actual Behavior of Knowledge Sharing

Behavioral intention has long been found to be significantly associated with actual behavior. According to the theory

of planned behavior (Ajzen, 1991), behavioral intentions are motivational factors that capture how hard people are

willing to try to perform a behavior. TPB suggests that behavioral intention is the most influential predictor of

behavior; after all, a person does what she intends to do (Pavlou & Fygenson, 2006).

Prior literature has corroborated the relationship between the two variables. For example, in a meta-analysis of 87

studies, an average correlation of .53 was reported between intentions and behavior (Sheppard et al., 1988). Results

of Pavlou and Fygenson’s (2006) longitudinal study validated the predictive power of TPB in online behavior and

showed strong associations between get-information intention and get-information behavior, and between purchase

intention and purchase behavior. Also, the Theory of Implementation Intentions (Gollwitzer, 1999) holds that a goaldriven behavior automatically activates a set of goal-enabling (implementation) intentions that help realize the

behavior (Sheeran & Orbell, 1999). In this study, receiving good grades is learners’ common goal and sharing

knowledge online is a goal-driven behavior which can be realized if learners intend to perform such behavior. A

positive relationship between intentions to share knowledge online and its actual behavior is thus expected.

H1: Knowledge sharing intention positively influences online knowledge sharing behavior.

Subjective Norm

Subjective norm suggests that behavior is instigated by one’s desire to act as important referent others act or think

one should act (Pavlou & Fygenson, 2006). Applied to the focal behavior, SN reflects participant perceptions of

whether the behavior is accepted, encouraged, and implemented by the participant’s circle of influence. The

literature suggests a positive relationship between SN and intended behavior, and empirical work has shown that SN

influences behavioral intentions toward system use (Karahanna et al., 1999). SN has been shown to be an important

determinant of acceptance behaviors in numerous studies (Karahanna & Straub, 1999; Srite & Karahanna, 2006;

Taylor & Todd, 1995; Thompson et al., 1991; Venkatesh & Davis, 2000; Venkatesh & Morris, 2000; Venkatesh et

Perceived Behavioral Control Knowledge Creation SelfEfficacy Attitude Toward Knowledge Sharing Subjective Norm Web-Specific Self EfficacySocial Network Ties Knowledge Sharing Intention KnowledgeSharing Behavior H3 H2H5 H4 H6 H7 H1H8

Figure 1. Research model 138

al., 2003). Bock et al. (2005) conducted a survey with thirty organizations to test a knowledge sharing model. Results

suggested that subjective norm has significant influence on knowledge sharing intention. Raaij and Schepers (2006)

conducted a survey with 45 Chinese participants in an executive MBA program. They found that subjective norm has

indirect effects on the use of virtual learning communities. One’s social environment is a valuable source of

information to reduce uncertainty and determine whether behaviors are within rules and are acceptable. Therefore,

subjective norms may, through informational and normative influences, reduce uncertainty with respect to whether

use of a system is appropriate (Evaristo & Karahanna, 1998; Srite & Karahanna, 2006). A positive relationship

between SN and intentions to share knowledge online is thus expected.

H2: Subjective norm positively influences knowledge sharing intention.

Attitude

Attitude has long been shown to influence behavioral intentions (Ajzen & Fishbein, 1980). This relationship has

received substantial empirical support (Pavlou & Fygenson, 2006). For example, Bock et al. (2005) conducted a

survey with thirty organizations to test a knowledge sharing model. Results suggested that attitude toward knowledge

sharing positively and significantly influence behavioral intention. Brown and Venkatesh (2005) employed the TPB

framework to propose a model presenting factors influencing household technology adoption. They concluded that

attitude toward IT usage positively influences technology adoption intention. Galletta et al. (2006) investigated the

interacting effects of website delay, familiarity and breadth on users’ performance of information search. They found

a positive and significant relationship between attitude and behavioral intention. Kolekofski and Heminger (2003)

proposed a model that defines the influences on one’s intention to share information. Using the workers in a unit of a

large governmental organization as samples, their study confirmed that attitude influences intention to share

information. Finally, the integrated model proposed by Wixom and Todd (2005) empirically validated the strong

association between attitude toward IS use and intention to use IS.

Consistent with these studies, Bhattacherjee and Premkumar (2004) found that attitude is important to predict the

intention of continuing use of the computer-based training system usage and rapid application development software;

attitude is one of the key perceptions driving users’ IT use behavior. Agarwal and Prasad (1999) expanded on their

prior studies, which utilized learning theory and various individual difference variables, to address possible causes

for inconclusive results of prior studies. They found that attitude is an important antecedent of individuals’

behavioral intention. Following the TPB literature, a positive relationship between attitude and intentions to share

knowledge online is expected.

H3: Participants’ attitude toward online knowledge sharing positively influences knowledge sharing

intention.

Perceived Behavioral Control

In this study, perceived behavioral control refers to the learner’s perceived ease or difficulty of telling story and

experiences, writing documents, expressing opinion. Some researchers have suggested that PBC composes two

distinct dimensions: self-efficacy (SE) and controllability (e.g., Pavlou & Fygenson, 2006). Pavlou and Fygenson

(2006) defined self-efficacy as individual judgments of a person’s capabilities to perform a behavior, and

controllability as individual judgments about the availability of resources and opportunities to perform the behavior.

Given that each participant has equal opportunities to access the anytime, anywhere available resource in a VLC, this

study focuses on self-efficacy to discuss its role in the formation of learners’ decision to perform knowledge sharing.

Self-efficacy has been found to vary across activities and situational circumstance. For example, Joo et al. (2000)

found that Internet self-efficacy is able to predict students’ performance on search task in Web-based instruction.

Thompson et al. (2002) concluded that task-specific Internet self-efficacy has a significant effect on online search

performance. Marakas et al. (1998) indicated that task-specific computer self-efficacy is an individual’s perception

of efficacy in performing specific computer-related tasks within the domain of general computing. Other types of

self-efficacy such as social self-efficacy (Sherer et al., 1982), teaching self-efficacy (Gibson & Dembo, 1984), math

self-efficacy (Banta, 1989) have been applied in various domains to explore their predictive powers on task 139

performance, one’s decision on what behaviors to undertake, and the level of commitment and persistence in

attempting those behaviors. Flow experience

Flow experience is defined as ‘‘the holistic experience that people feel when they act with total involvement” (Csikszentmihalyi, 1977,

1997). When people are in the flow state, they become absorbed in their activities and unable to recognize changes in their surroundings.

Specially, they lose self-consciousness, concentrating only on their ongoing activity. This concept has been extensively applied in studies in

a broad range of contexts, such as sports, shopping, and gaming (Csikszentmihalyi, 1997). From a motivation perspective, people make an

effort to use an information technology due to both intrinsic and extrinsic reasons (Davis, Bagozzi, et al., 1992). Extrinsic motivation refers

to the desire to perform an activity because it is perceived to lead to distinct and valued outcomes. Intrinsic motivation refers to the desire

to engage in an activity for no other reason than the process of performing it (Deci & Ryan, 1985; Teo, Lim, et al., 1999). Compared with

perceived usefulness, which deals with users’ extrinsic motivation (Davis et al., 1992; Venkatesh, Morris, et al., 2003), flow experience can

be seen as an intrinsic motivation.

Flow is a complex concept, and researchers often measure it through multiple dimensions. Ghani, Supnick, et al. (1991) measured flow

using two constructs: enjoyment and concentration. Huang (2003) included four constructs to address flow, namely: control, attention

focus, curiosity, and intrinsic interest. In addition, Li and Browne (2006) suggested that flow experience has four dimensions: focused attention, control, curiosity and temporal dissociation. Finally, Koufaris (2002) developed three constructs to measure flow, including perceived enjoyment, perceived control, and concentration, and these are the dimensions adopted in this paper.

Research model

Trust can reside in a social relation between two individuals or in multiple social relations

among a group of people; and individual level trust can be expanded easily to social trust if

trust resides in social relations rather than a single individual or group (Park, 2006). The

social relation is constructed through social interactions which are empowered by

interpersonal trust, which makes socialisation the world where individuals share feelings,

emotions, experiences and mental model (Samara, 2007).

Intentions and Actual Behavior of Knowledge Sharing

Behavioral intention has long been found to be significantly associated with actual behavior. According to the theory

of planned behavior (Ajzen, 1991), behavioral intentions are motivational factors that capture how hard people are

willing to try to perform a behavior. TPB suggests that behavioral intention is the most influential predictor of

behavior; after all, a person does what she intends to do (Pavlou & Fygenson, 2006).

Prior literature has corroborated the relationship between the two variables. For example, in a meta-analysis of 87

studies, an average correlation of .53 was reported between intentions and behavior (Sheppard et al., 1988). Results

of Pavlou and Fygenson’s (2006) longitudinal study validated the predictive power of TPB in online behavior and

showed strong associations between get-information intention and get-information behavior, and between purchase

intention and purchase behavior. Also, the Theory of Implementation Intentions (Gollwitzer, 1999) holds that a goaldriven behavior automatically activates a set of goal-enabling (implementation) intentions that help realize the

behavior (Sheeran & Orbell, 1999). In this study, receiving good grades is learners’ common goal and sharing

knowledge online is a goal-driven behavior which can be realized if learners intend to perform such behavior. A

positive relationship between intentions to share knowledge online and its actual behavior is thus expected.

H1: Knowledge sharing intention positively influences online knowledge sharing behavior. Rheingold (1993) pointed out that increased recognition is an important factor for participating communities. One effective method to enhance community members’ recognition is to increase their visibility within the community. In PVCs, the best way to establish a reputation is to share knowledge with other members. A certified expert, for example, might generate more confidence and interest from other members in his posted articles by sharing knowledge. Generally, the PVC would provide a reputation system to promote contributors’ quantity and quality of knowledge. For instance, many

PVCs provide contribution ranking systems and ways members can assess the quality of submitted articles. Prior research has shown that building reputation is a strong motivator for knowledge sharing (Davenport and Prusak, 1998) and could also develop a member’s positive attitude toward sharing (Hsu and Lin, 2008). Thus, if a member expects that he or she can establish a reputation by sharing

knowledge, he or she is more likely to develop a positive attitude toward knowledge sharing. Hence, we proposed:

H1. Reputation has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

According to Davenport and Prusak (1998), people’s time, energy and knowledge are limited. Therefore, except when profitable, people are usually unwilling to share these scarce resources with others. Reciprocity is a form of conditional gain; that is, people have a general expectation of some future return (Blau, 1964). People often believe that they could obtain mutual benefits (Hsu and Lin,

2008) or knowledge feedback in the future (Kankanhalli et al., 2005a) through knowledge sharing. In PVCs, except seek for important information, the latest theme and popular themes, it might also provide promising job opportunities. The anticipated reciprocity that users might benefit from while participating in PVCs comprises such job opportunities in favour of quick responses to all questions a

member asks. Many empirical studies have also confirmed that reciprocity benefits have continually helped drive PVC members to share knowledge (Bock et al., 2005; Chiu, 2006). Thus, if members believe they can obtain reciprocity benefits from other members by sharing knowledge, they will develop a more positive attitude toward knowledge sharing. This leads to the following hypothesis:

H2. Reciprocity has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

People are willing to share knowledge in virtual community, because they felt that it was a very interesting thing to help others in solving their problems and they were also very satisfied with themselves while helping others (Kollock, 1999). Through knowledge sharing, people can often obtain the perception of pleasure from helping others. Previous research has indicated that an

individual’s attitude toward knowledge sharing is affected by that individual’s enjoyment in helping others (He and Wei, 2009; Lin, 2007). Knowledge contributors who derive enjoyment from helping others, they will be more favorably oriented towards the knowledge sharing. Therefore, we

hypothesized the following:

Enjoyment in Helping Others

The benefit of enjoying helping others is originated from the concept of altruism (Kankanhalli et al., 2005). People gain satisfaction

and joy when they help others (Wasko and Faraj, 2000). Such fulfillment arises from their intrinsic enjoyment in helping others

(Constant et al., 1994). Thus, enjoying helping others is positively related to knowledge sharing (He and Wei, 2009; Hsu and Lin,

2008; Jarvenpaa and Staples, 2001; Shin et al., 2007; Wasko and Faraj, 2005).

H3. Enjoyment in helping others has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

Contributing knowledge in the virtual community, it comprises both the contextualization and interpretation of knowledge. This is a cost for the knowledge sharing, because knowledge contributors must take time and effort (Ba et al., 2001; Markus, 2001). It takes time in knowledge coding, and after

sharing knowledge, knowledge contributors will take more time to clearly explain or assist the knowledge seekers to have a better understanding of the knowledge provided (Goodman and Darr, 1998). Previous researches have suggested that the codification effort is a significant inhibitor for knowledge contribution (Ba et al., 2001; Goodman and Darr, 1998; Kankanhalli et al., 2005a).

Members might feel that sharing knowledge with other PVC members might require a lot of time, plus even more time to respond to additional questions proposed by other PVC members. Hence, we hypothesized:

H4. Codification effort has a negative effect on users’ attitudes toward knowledge sharing in PVCs.

Knowledge self-efficacy is the confidence in one’s ability to provide knowledge that is valuable to others (Kankanhalli et al., 2005a). Prior researches have also revealed that an individual with strong 1495knowledge self-efficacy would have the power of self-motivation to promote knowledge sharing (Bock and Kim, 2002; Hsu et al., 2007). Self-efficacy has long been suggested as the key determinant

factor for behavioral control (Bandura, 1977; Hsieh et al., 2008). In PVCs, the threshold for participation was higher than in the general virtual community, because of the increased need for professional knowledge. PVC members with higher knowledge self-efficacy would tend to agree with their own knowledge and thus feel more confident in providing valuable knowledge; hence, they would have more positive attitudes about knowledge sharing as well as a perceived behavioral control of sharing. Hence, we hypothesized:

H5. Knowledge self-efficacy has a positive effect on users’ attitudes toward knowledge sharing in

PVCs.

H6. Knowledge self-efficacy has a positive effect on users’ perceived behavioral control of knowledge sharing in PVCs.

3.2 Technological Factors

Taylor and Todd (1995) proposed that there are three technology acceptance factors able to affect an individual’s attitudes: perceived usefulness, perceived ease of use and compatibility. The technology

acceptance model (TAM) is an adaptation of the TRA, which has mainly been used to explore the factors influencing user acceptance of information technology (Davis, 1989).TAM suggests that the attitude toward using is influenced directly by both perceived ease of use and perceived usefulness.

TAM also claims that attitude toward using would further affect user intentions. An important concern for community members is system reliability (Phang et al., 2009) and the availability of task support whenever needed (Shneiderman, 1998). Therefore, the more people perceive the usefulness of a

system, the more favorable their attitude toward knowledge sharing will be. We thus proposed the following hypotheses:

H7. Perceived usefulness has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

Perceived ease of use is another core construct of TAM, as it has a positive effect on the user’s attitude (Davis, 1989). If it take extra time to learn a system, or if that system is more difficult to learn, then there is a natural tendency for people to avoid using it (Malhotra and Galletta, 2004; Venkatesh, 1999). Prior research has also provided evidence that the ease of technology use is associated with

behavioral control in home PC adoption (Brown and Venkatesh, 2005), electronic commerce adoption (Pavlou and Fygenson, 2006) as well as information and communication technology usage (Hsieh et al., 2008). If members feel PVCs can be used easily, they will be more likely to view knowledge sharing favorably. Furthermore, when members view PVCs as easy to use, they often perceive knowledge sharing behavior to be under their own full control. Thus, we proposed the following hypotheses:

H8. Perceived ease of use has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

H9. Perceived ease of use has a positive effect on users’ perceived behavioral control of knowledge sharing in PVCs.

Compatibility is elicited from the innovation diffusion theory (IDT). This theory suggests that compatibility represents the level of information technology complying with the users’ existing values, past experience, and current needs (Moore and Benbasat, 1991; Rogers, 1995). Previous research has

shown evidence that compatibility is strongly correlated with attitude towards the adoption of information technology (Cho, 2006). When members in PVCs perceive knowledge sharing as

compatible with their individual values and needs, they are more likely to share knowledge (Lin et al.,

2009). If PVC membership and involvement are similar to users’ prior experience, feelings of

familiarity might lead users to view PVC membership as consistent with the habits and needs of their

contributions. Thus, members would be better able to establish positive attitudes toward knowledge

sharing. Hence, we hypothesized:

1496H10. Compatibility has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

3.3 Environmental Factors

In this study, trust is defined as one’s belief that the results of others’ actions will be appropriate from

one’s point of view (Misztal, 1996). People believe that the other people will have good intentions,

competence and reliability while sharing and repeatedly using the knowledge (Mishra, 1996). Trust is

a key factor in effective knowledge exchange (Adler, 2001). Suppose a knowledge seller trusts his

knowledge buyers. He would then be willing to contribute his own knowledge, and would not fear

that buyers would misuse his knowledge (Davenport et al., 1998). PVCs, however, do not provide a

certain norm to compel members to share knowledge like in an organization. Also, PVCs do not give

real incentives to members who contribute knowledge. Therefore, trust has become a critical factor in

the virtual community (Lin, 2008). Previous research has also pointed out that trust might create an

atmosphere of knowledge sharing (Nonaka, 1994). Suppose both parties of a knowledge exchange

could establish a good relationship of trust with each other in the process of sharing knowledge. Trust

would then facilitate the knowledge exchange between these community members. Therefore, trust is

an important factor in influencing members’ attitudes toward knowledge sharing. Thus we

hypothesized:

H11. Trust has a positive effect on users’ attitudes toward knowledge sharing in PVCs.

People are often easily affected by and often imitate their peers. Peer influence refers to the fact that

one’s peers have the ability to influence one’s thinking and/or actions. With the emergence of the

Internet, the transmission of information is much quicker and thus the phenomenon of peer influence

is even more obvious. Prior studies have also shown evidence that peer influence would have a

positive effect on users’ subjective norm (Bhattacherjee, 2000; Hsieh et al., 2008; Hung et al., 2003).

We thus propose the following hypotheses:

H12. Peer influence has a positive effect on users’ subjective norm of knowledge sharing in PVCs.

In TPB, resources availability facilitates decisions regarding technology usage (Taylor and Todd,

1995). For an individual, the cost of sharing knowledge includes the time or effort that is required

(King and Marks, 2008). Szulanski (1996) presumed that knowledge contribution behavior is often

affected by lack of important factors, such as resources and time. With sufficient time or opportunity,

users might even like to contribute more knowledge. Such a situation is, therefore, expected to have a

greater influence on perceived behavioral control of knowledge sharing. We thus hypothesized:

H13. Resource availability has a positive effect on users’ perceived behavioral control of knowledge

sharing in PVCs.

3.4 TPB Beliefs

TPB asserts that behavioral intentions are determined by three general beliefs: attitude, subjective

norm and perceived behavioral control. Attitude reflects an individual's feelings of favorableness or

unfavorableness towards performing a behavior. Subjective norm indicates whether such behavior is

performed under the social pressure or is subject to the influences of other people (e.g. colleague, peer,

family). Perceived behavioral control reflects the perceptions of internal and external constraints on

such behavior. Attitude toward knowledge sharing has been shown to influence behavioral intention

to share knowledge (Bock and Kim, 2002). According to Bock et al. (2005), the more likely the

subjective norm is to share knowledge, the greater one’s intention to share knowledge will be.

Perceived behavioral control is a prerequisite for sharing knowledge because when an individual

perceives the ease of sharing knowledge, he will feel that such an act is completely under his control.

A positive relationship between perceived behavioral control of knowledge sharing and intentions to

share knowledge is thus expected. Thus we hypothesized:

H14. Attitude toward knowledge sharing has a positive effect on knowledge sharing intentions.

H15. Subjective norm of knowledge sharing has a positive effect on knowledge sharing intentions.

2.1. Self-efficacy and knowledge sharing

Self-efficacy is a form of self-evaluation that influences

decisions about what behaviors to undertake, the amount

of effort and persistence to put forth when faced with

obstacles, and finally, the mastery of the behavior. In

general, the perceived self-efficacy plays an important role

in influencing individuals’ motivation and behavior (Ban- dura, 1982, 1986; Igbaria and Iivari, 1995). People who

have high self-efficacy will be more likely to perform

related behavior than those with low self-efficacy.

Self-efficacy has been employed by many IS researchers

and formed a variety of research streams. One of this

research streams is focused on examining the effect of

computer self-efficacy (CSE) on computer training performance (e.g., Compeau and Higgins, 1995a, 1999; Johnson

and Marakas, 2000) and on IT usage (e.g., Easley et al.,

2003; Venkatesh et al., 2003). Another research stream is

concentrated on the construct of Internet self-efficacy

(ISE). Studies in this line also address the significant

relationship between ISE and Internet use (e.g., Hsu and

Chiu, 2004; Lam and Lee, 2005).

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