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IMDS 113,7

How system quality and incentive affect knowledge sharing

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Department of Educational Technology, Tamkang University, New Taipei City, Taiwan, Republic of China, and

Li-An Ho Tsung-Hsien Kuo

Received 9 January 2013 Revised 23 February 2013 13 April 2013 Accepted 15 April 2013

Department of Information Management, Lunghwa University of Science and Technology, Gueishan, Taiwan, Republic of China Abstract Purpose – Virtual communities of practice (VCoP) are seen as effective means to facilitate knowledge building among professionals. The purpose of this paper is to investigate the relationships between system quality, attitude toward incentives and knowledge sharing in a VCoP. Additionally, individual and collective effect of system quality and attitude toward incentives on knowledge sharing are also examined. Design/methodology/approach – This is an empirical study that targets a major community of practice in human resource management (n ¼ 366), utilizing a survey questionnaire distributed on the internet as the data collection instrument to test the relationship among the three dimensions. Findings – The results indicated that: system quality and attitude toward incentives individually have demonstrated a significant effect on knowledge sharing behavior in a VCoP. Collectively, only factors within the attitude toward incentive dimension have demonstrated significant influences on the community participants’ knowledge sharing behavior. Practical implications – This study provides managers of VCoP with valuable information which aids in improving community members’ knowledge sharing. That is, a successful VCoP is an online environment which provides a variety of social exchange opportunities for the members to interact, as well as challenging topics or tasks enabling the members to practice or gain professional knowledge and skills. Social implications – Regardless of the fact that knowledge sharing processes are becoming increasingly complex and diverse, providing various kinds of incentive is still crucial in eliciting people’s engagement in knowledge sharing. Only reinforcing social exchanges and providing opportunities of self-growth will enhance knowledge sharing behavior. Originality/value – Knowledge sharing is a complex process. Literature indicated that some factors, such as motivation, attitudes, and individual preferences, are considered double-edged factors to knowledge sharing among individuals. The present study adds value by examining the individual and collective effects of these factors (i.e. the members’ perceived VCoP system quality and attitude toward incentives) on knowledge sharing.

Industrial Management & Data Systems Vol. 113 No. 7, 2013 pp. 1048-1063 q Emerald Group Publishing Limited 0263-5577 DOI 10.1108/IMDS-01-2013-0015

Keywords System quality, Attitude toward incentives, Knowledge sharing, Community of practice, Incentives (psychology), Knowledge management, Human resource management Paper type Research paper

The authors acknowledge the support from Taiwan National Science Council under Grant Nos. 99-2410-H-032-024 and 101-2410-H-032-009.

1. Introduction Knowledge is a strategic advantage which helps organizations sustain as well as maintain their market competitiveness (Jantunen, 2005). Kakabadse et al. (2001) mentioned that knowledge has emerged as one of the key strategic assets of organizations. Wenger (1998) suggested for organizations to build up necessary policies and infrastructure so that knowledge can be effectively managed. Cross et al. (2003) further argued that although organizations may enhance networking for knowledge cooperation through a number of different ways, supporting community of practice appears to be one of the most effective approaches to achieve the effect. The term “community of practice” (CoP), which was first introduced in 1991 by Lave and Wenger, refers to a group of people having shared visions, questions or compassion through continuous activities (Lave and Wenger, 1991). Through CoP activities, people may acquire expert knowledge and obtain higher professional practices. As world wide web becomes more important every day, new social structures have been formed by increasing use of new technology (Rı´os et al., 2009). Virtual communities of practice (VCoP) has been utilized as a form of CoP which allows online information exchanges, and is at the same time different from other kinds of information system. As Rı´os et al. (2009) stated, VCoP is not just a group of people accessing a web site, but also a structure that allows participants to “establish social relationship via the use of internet tools, allowing the formation of a communal identity and a shared sense of the world” (p.481). The challenge in hand is how to encourage people to participate and become a part of this online social structure. Lam and Lambermont-Ford (2010) pointed out that the facilitation of knowledge sharing is a difficult task. They further suggested that despite the voluminous literature on organizational learning and knowledge management, the association between individual motivation and knowledge sharing remains largely unexplored and poorly understood. Literature indicates that technology plays an important role in online information exchange and knowledge management (Nishimoto and Matsuda, 2007). The effective use of technology ensures timely access and exchange of knowledge (Harrison and Daly, 2009). Additionally, O’Dell and Hubert (2011) suggested that the back bone of successful knowledge management is the cultivation of a knowledge sharing culture for which reinforcement of desired behaviors through rewards and recognition must in place. However, there has been a debate about the effectiveness of both reward and recognition mechanisms to motivate people to share knowledge for several years. Several authors (Finerty, 1997) argued that the reward or incentive rarely enhance long-term knowledge sharing. Due to the inconsistent voices of technology and reward on knowledge sharing, this study aims to examine the relationship between the VCoP participants’ perceived system quality of the virtual community, as well as their attitude toward incentives provided by the VCoP in relation to their online knowledge sharing behavior. Furthermore, the individual as well as collective (i.e. joint) effects of VCoP system quality and attitude toward incentives on knowledge sharing is also analyzed. 2. Literature review 2.1 System quality Past researches have verified information technology as an effective way to support the donation, storage, acquisition, distribution and utilization of knowledge among individuals (Gottschalk, 2006). Harrison and Daly (2009) suggested that effective use of

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information technology results in quick access and exchange of knowledge, and technology plays an essential part in knowledge sharing (Nishimoto and Matsuda, 2007). Due to recent advances in information communication technologies, the functions of information system have quickly expanded to serve different purposes. The purposes of information systems vary from entertainment, such as online games and social communities, to instrumental purposes, such as e-learning, e-commerce, and knowledge management systems (Petter et al., 2008). VCoP, which uses technology to assist in communication through internet, is considered a form of information system that provides social as well as information exchanges in a virtual environment (Lin and Lee, 2006). To date, many have adopted virtual communities to share data, collaborate in research and exchange messages (Wang et al., 2002). However, providing a web-based community network does not secure a successful virtual community (Preece, 2001). Lin et al. (2007) pointed out that the design and development of any kind of information system demand tremendous investments in manpower, time and budgets. However, all the investments will be wasted if no one wants to use it. Thus, there are studies (Davis et al., 1989) conducted in this area in trying to build up conceptual models (e.g. the technology acceptance model) that help explain why users are willing to use information systems. Their study concluded that perceived usefulness and perceived ease of use have become two major characteristics of successful information systems. Furthermore, Gupta and Kim (2008) examined members’ commitment to a virtual community from the perspectives of balanced beliefs (including functional usefulness, system usefulness, and system quality). They found that functional usefulness of a virtual system positively relates to commitment to a virtual community. These studies serve to highlight technological attributes’ contribution to successful system utilization, but at the risk of neglecting other motivational reinforcements (e.g. reward system) that might be even more critical. To measure information quality, DeLone and McLean (1992) identified six interdependent variables of successful information systems, namely system quality, information quality, system use, user satisfaction, individual impact and organizational impact. Followed by their earlier works, DeLone and McLean (2003) identified service quality as a new construct and proposed an update model which consists of system quality, information quality, service quality, system use, user satisfaction, and net benefits. These dimensions have been found to be a useful framework for organizing successful information system measurements (Petter et al., 2008). Particularly, three constructs from the DeLone and McLean model are considered antecedents of users’ intention to use information systems; they are system quality, information quality and service quality. These three dimensions have been widely used to measure system quality of online communities (Lin and Lee, 2006; Chen, 2007; Medina and Chaparro, 2007), and hence are used in this study to measure system quality of VCoP. 2.2 Attitudes toward incentives A considerable number of scholars used a dichotomous method which divides incentives into two parts: intrinsic and extrinsic (Deci, 1976; Kwok and Gao, 2004). Deci (1976) defined extrinsic incentives as additional resources (e.g. money, promotion, profits, career progression, etc.) for motivational purposes. On the other hand, intrinsic incentives are “valued for its own sake and appears to be self sustaining”

(Deci, 1976, p. 105), and they also provides immediate need satisfaction. Lin (2007) additionally pointed out that extrinsic motivational factors such as reciprocal benefits, and intrinsic motivational factors such as knowledge self-efficacy and enjoyment in helping others, were found to be significantly associated with the intention and attitudes in workers’ knowledge sharing. Clearly, incentives motivate people to perform. However, people have different needs, thus what motivates each person can vary. Knowing the motivational needs of people is the first step. Once understood, an incentive strategy can be developed to create a win-win result (Greenberg and Liebman, 1999). Another group of scholars separated incentives by their types. For example, Bock et al. (2005) identified three factors influencing individuals’ attitudes toward knowledge sharing: First one is expected reward, which refers to how one can have extrinsic incentives due to one’s knowledge sharing behaviors. Second, expected association refers to how one can improve mutual relationship through knowledge sharing. The third factor is expected contributions, which refer to the belief of improving organizational performance through knowledge sharing. Furthermore, Greenberg and Liebman (1990) suggested incentives fall into three categories: material, social and activity. Material incentives vary from the simplicity of straight salaries to the complexities of stock option programs or compensation packages. Social incentives are effective motivational reinforcers that operate on the interpersonal level by allowing people to identify themselves with the company, co-workers, customers or even competitors. Activity incentives provide opportunities to fulfill individual needs of achievement or growth by providing more new and challenging tasks. Bock et al.’s theory is clearly designed to be adopted in an organizational setting, and the constructs of the Greenberg and Liebman model are more suitable for a community context. Therefore, this study decided to adapt the latter model in measuring VCoP members’ attitudes toward incentives. 2.3 Knowledge sharing behavior Existing literature has different interpretations for knowledge sharing. Bartol and Srivastava (2002, p. 65) defined knowledge sharing as “individuals sharing organizationally relevant information, suggestions, and expertise with one another”, and the shared knowledge can be explicit as well as tacit. Ipe (2003) pointed out that knowledge sharing is a process of contributions and acquisitions. In this process, individuals voluntarily transferred their own knowledge into something others can understand, absorb and utilize. Christensen (2005) characterized knowledge sharing as an interdependent process involving an exchange in which individuals give something of value and in exchanging, receive something of value. Lin (2007) defined knowledge sharing as an important organizational process which enhances an organization’s ability to generate new ideas and develop new business opportunities via socializing and learning among knowledge workers. Aalst (2009) further distinguished knowledge sharing from knowledge construction and knowledge creation, and suggested that knowledge sharing as a transmission of information between people. Through knowledge sharing, individuals can quickly accumulate and expand their personal domain of knowledge (Quinn et al., 1996), as well as enhance problem solving ability and work performance (Kim and Lee, 2006), which consequently improve organizational competitiveness (Lin, 2007).

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Different measures have been used by past research in measuring knowledge sharing. Wasko and Faraj (2005) defined knowledge sharing as knowledge contribution and examined it using two independent dimensions: the helpfulness of contribution and the volume of contribution. Similarly, from the source’s perspective, Usoro et al. (2007) measured online knowledge sharing behaviors using three dimensions, including frequency of engagement in knowledge sharing behaviors, usefulness of shared knowledge, as well as the orientation of knowledge sharing focus. In their study, Hsu et al. (2007) used four items to examine knowledge sharing behaviors by measuring the frequency, length of time, voluntariness, participation in discussing complex issues, and participation in a variety of topics with members of an online virtual community. Wu and Sukoco (2010) separated knowledge sharing behaviors into two dimensions: co-consumption and co-production, and used items measuring frequency, length of time, voluntariness, participations in discussing complex issues, and participations in a variety of topics, to test both dimensions. This study defines knowledge sharing as a means that allows transmission of information among people. Although the ultimate goal of knowledge sharing is knowledge construction and knowledge creation that bring values to organizations, the present study only focuses on examination of actual behavior of the sharing, not the helpfulness of contribution. Both Hsu et al. (2007) and Wu and Sukoco’s (2010) approaches are similar in their subjects (online community members) and their items (knowledge sharing behavior indicators), thus their measures are used in this study. 2.4 Relationship between dimensions A virtual community connects questions and answers among its participants (Earl, 2001) by interacting via information technology (Hew, 2009). When knowledge sharing takes place in a virtual community, members of the community must utilize the functions provided by the information system to ask questions, provide answers or assist with problem solving (Rı´os et al., 2009), thus, the quality of such online system becomes crucial. Lin and Lee (2006) studied 20 virtual communities and concluded that information quality, system quality as well as service quality have significant effect on members’ online behavior. Hew (2009) argued that a system’s functions must meet the needs and preferences of the users, otherwise it actually discourages people from using it. The easier the system is to operate, the more people will be likely to use it (Kim and Lee, 2006). Moreover, content provided in a system is critical, especially for VCoP. Alvarez et al. (2010) pointed out VCoP must provide useful content in order to draw people’s attention and participation. In addition, a vital part of knowledge sharing is identifying what kind of incentives can be utilized to increase individuals’ willingness to share knowledge (Bartol and Srivastava, 2002). A growing amount of studies argued that non-financial rewards are far more imperative than financial rewards in relation to knowledge sharing (Faraj and Wasko, 2002). Thus, recent studies seem to be inclined toward the preference of non-financial rewards, such as self-achievement, personal growth or enjoyment of sharing on knowledge sharing (Christensen, 2005). However, some still emphasize financial rewards as an important motivator to facilitate knowledge sharing (Nickerson and Zenger, 2004). Furthermore, Hsu et al. (2007) provided an overall framework which argues that individuals’ behavior for knowledge sharing is guided by personal characteristics and the environment (physical or virtual) they are in. Therefore, adopting

the same logic, the purpose of this study is to not only identify the internal factor (i.e. attitudes toward non-financial and financial incentives) and external factor (i.e. system quality of VCoP) of knowledge sharing independently, but also to examine the collective effect of both internal and external factors on knowledge sharing. 3. Method 3.1 Research structure and hypotheses Based on above review literature, the study tests the following hypotheses: H1.

VCoP members’ perceived system quality significantly affects their knowledge sharing behavior.

H2.

VCoP members’ attitude toward incentives significantly affects their knowledge sharing behavior.

H3.

VCoP members’ perceived system quality and attitude toward incentives have significant and collective effect on their knowledge sharing behavior.

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The research model is shown in Figure 1. The study first uses Pearson correlation analysis to examine the correlation between system quality and knowledge sharing behavior, as well as attitudes toward incentives and knowledge sharing behavior. Next, multiple stepwise regression analysis determines the independent and collective effect of system quality and attitude toward incentives on knowledge sharing behavior. Stepwise regression analysis also helps to establish the predictive power of system quality and attitude toward incentives, individually as well as collectively on knowledge sharing behavior. 3.2 Questionnaire design The questionnaire includes four parts: VCoP system quality, attitude toward incentives, knowledge sharing behavior and personal background, including gender, age, education background, length of VCoP membership, and average hours spend on VCoP per day. The questionnaire utilizes a five-point Likert scale. I. VCoP system quality. Based on DeLone and McLean (2003) and Petter et al.’s (2008) studies, the system quality dimension includes three major constructs, namely system quality, information quality and service quality. The following explains the operational definition of each factor:

Attitude toward incentives

VCoP System quality

H2

H1

H3 Knowledge sharing behavior

Figure 1. Research structure

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(1) System quality: refers to the extent to which an individual finds: . the system easy to learn and use; . the system being reliable; and . the interface being easy to understand. (2) Information quality: refers to the extent to which an individual finds the content in VCoP to be accurate, relevant, understandable, complete, timely and useful. (3) Service quality: refers to the extent to which an individual finds the support provided by the information technology support personnel of the VCoP to be responsive, accurate, reliable, and enthusiastic. II. Attitude toward incentives dimension. The present study adopts Greenberg and Liebman’s (1990) three constructs consisting of material, social and activity incentives to measure the members’ attitude on incentives provided by the VCoP. The following explains the operational definition of each factor: (1) Material incentive: refers to the extent to which an individual likes to have online points, accumulated scores, promotion to the next level, as well as account privileges provided by the VCoP. (2) Social incentive: refers to the extent to which an individual likes to get acquainted and share experience or knowledge with other members, express concerns for others, as well as form a sense of belonging in the VCoP. (3) Activity incentive: refers to the extent to which an individual likes to be recognized by his/her contribution, be the person responsible for a particular discussing topic/issue, and be the lecturer of a given topic/issue. III. Knowledge sharing behavior dimension. Based on the literature review, the present study examines the knowledge sharing behavior of the VCoP members by the five items developed by Hsu et al. (2007) as well as Wu and Sukoco (2010). The items measured the frequency, length of time, voluntariness, participations in discussing complex issues, and participations in a variety of topics of the members in the VCoP. 3.3 Research sample The data are from questionnaire responses from members in a major professional virtual community in the field of human resource management. This virtual community was established in 2010. Membership of this VCoP is restricted to current human resource professionals, including practitioners, scholars and graduate students in the field and by referral only. Up to December 2012, it has memberships exceed 1,700, and the number is climbing. The main themes in VCoP include recruiting, staffing, benefits and compensations, employee relation, labor administration, training and development as well as strategic planning, etc. Most community activities take place virtually. However, there is informal lunch meetings scheduled every month as well. An online survey was distributed through the bulletin board of the VCoP and a total of 366 valid returns were collected. According to Lodico et al. (2006), for the size of research population more than 1,000, appropriate sample size is 20 percent (in this study the minimum of the returns should be 340). Therefore, the collected 366 questionnaires were ready for statistical analysis. For estimating non-response bias, the study followed

Armstrong and Overton’s (1977) suggestion, dividing the collected surveys into two groups (the former 75 percent and the latter 25 percent) based on their chronological order. The results indicated that no difference existed between the two groups, thus non-response bias did not exist. Table I presents the demographic statistics. 3.4 Reliability and validity tests Reliability and validity tests are conducted for each of the constructs with multivariate measures. Cronbach’s a reliability estimates are used to measure the internal consistency of these multivariate scales (Nunnally, 1978). In this study, Cronbach’s a of each constructs is greater than 0.8836, which indicates a strong reliability for the survey instrument in the present study (Cuieford, 1965). In addition, measures with item-to-total correlations larger than 0.6 are considered to have high-criterion validity (Kerlinger, 1999). Since the item-to-total correlations are between 0.6029 and 0.7478, the criterion validity of each scale in this study is considered to be satisfactory. Meanwhile, to ensure that the instrument has reasonable construct, this study employs validity exploratory factor analyses. The exploratory factor analysis applies the following rules: . eigenvalue . 1; . applying Varimax rotation and extracting factor with loading . 0.6; . compared factor loading variance . 0.3; and . item-to-total correlation value . 0.6.

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Table II illustrates the description statistics for the three dimensions, and the results of exploratory factor analysis are presented in Table III. Construct

Classification

Gender Age

Education background

Length of VCoP membership

Average hours spend on VCoP per day

Number

%

Accumulated %

Male Female , 30 31 , 35 36 , 40 41 , 45 . 45 Technical college graduates University graduates Masters PhD , 0.5 year 0.5 , 1 year 1 , 2 years . 2 years , 0.5 h

105 261 12 64 113 84 93 28 162 167 9 70 106 128 62 169

28.7 71.3 3.3 17.5 30.9 23.0 25.4 7.7 44.3 45.6 2.5 19.1 29.0 35.0 16.9 46.2

28.7 100.0 3.3 20.8 51.6 74.6 100.0 7.7 51.9 97.5 100.0 19.1 48.1 83.1 100.0 46.2

0.5 , 1 h 1 , 2h 2 , 3h .3 h

157 30 6 4

42.9 8.2 1.6 1.1

89.1 97.3 98.9 100.0

Table I. Sample demographic statistics

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4. Results The Pearson analysis is able to identify a statistically significant correlation between VCoP system quality and knowledge sharing behavior as well as attitude toward incentives and knowledge sharing behavior (r ¼ 0.309 2 0.705, p , 0.001 two-tailed). The following tests the hypothesis H1-H3 using multiple regression analysis.

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4.1 Individual effect of VCoP system quality and attitude toward incentives First, the study explored the effects of VCoP system quality (the independent variables) on VCoP members’ knowledge sharing behavior (Y). Factors of VCoP system quality (i.e. denoting by X1, X2, and X3) are the independent variables in the linear regressions. The resulting linear regression and their corresponding adjusted R 2 with standardization b are shown in Table IV. The results show that only two factors of VCoP system quality, namely system quality (X1) and information quality (X2), are significant in the regression model. The two independent variables of VCoP system quality were able to explain 19.6 percent of the variance of the dependent variable, knowledge sharing. Among the two independent variables, “information quality” demonstrated the strongest predictive power (standardized coefficient b ¼ 0.312, p , 0.001) toward knowledge sharing. Service quality (X3) is found to have no significant impact on knowledge sharing. The resulting regression equation for knowledge sharing with respect to system quality is: Y ¼ 1.567 þ 0.175 X1 þ 0.324 X2.

Dimension Table II. Survey structure and description statistics for dimension

Table III. Factor analysis and internal consistency values for the questionnaire

Table IV. Regression analysis for “knowledge sharing” with respect to “VCoP system quality”

Number of items per dimension

Mean

SD

Order

Cronbach’s a

12 16 5

4.0972 3.8058 3.5743

0.5489 0.6239 0.5923

1 2 3

0.9215 0.9360 0.8836

VCoP system quality Attitude toward incentives Knowledge sharing behavior

Dimension

Factor

VCoP system quality

Information System Service Material Social Activity

Attitude toward incentives Knowledge sharing behavior

Criterion

R

R2

Information quality (X2) 0.418 0.175 System quality (X1) 0.447 0.200 Note: Significant at: *p , 0.001

% of variance

Cumulative %

Cronbach’s a

28.171 24.697 21.596 26.335 21.799 19.950 68.234

28.171 52.868 74.464 26.335 48.134 68.083 68.234

0.8872 0.8669 0.9210 0.9069 0.8788 0.8675 0.8836

Adjusted Unstandardized R2 coefficient b 0.173 0.196

0.324 0.175

Standardized coefficient b 0.312 0.191

t

F

5.537 77.182 * 3.385 45.430 *

In addition, this study examined the influences of attitude toward incentives (the independent variables) for the community members’ knowledge sharing behavior (Y). Factors of attitude toward incentives (i.e. denoting by X4, X5, and X6) are the independent variables in the linear regressions. The results (Table V) show that only two factors of attitude toward incentives are significant in the regression model. The two independent variables, namely social (X5) and activity incentive (X6), were able to explain 42.9 percent of the variance of knowledge sharing. Between the two independent variables, “activity incentive” demonstrated the strongest predictive power (standardized coefficient b ¼ 0.373, p , 0.001) toward knowledge sharing. Material incentive (X4) is found to be of non-significance on knowledge sharing. The resulting regression equation for knowledge sharing with respect to attitude toward incentives is: Y ¼ 0.661 þ 0.353 X5 þ 0.381 X6.

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4.2 Collective effect of VCoP system quality and attitude toward incentives With multiple stepwise regression analysis, this study explores the collective effects of VCoP system quality trust and attitude toward incentives for knowledge sharing behavior (Y) of the community members. Factors of VCoP system quality and attitude toward incentives (i.e. denoting by X1-X6) are the independent variables in the linear regressions. Table VI presents the resulting linear regression and their corresponding adjusted R 2 with standardization b. Among the six independent variables, the results reveal that only two factors are significant in the regression model, namely activity incentive (X6) and social incentive (X5) from the attitude toward incentives dimension. The two factors were able to explain 42.9 percent of the variance of knowledge sharing. Among these two factors, “activity incentive” demonstrated the strongest predictive power (standardized coefficient b ¼ 0.373, p , 0.001) toward knowledge sharing. And the resulting regression equation for knowledge sharing with respect to motivation is: Y ¼ 0.661 þ 0.353 X5 þ 0.381 X6. Based on the results from stepwise regression analysis, the study accepts H1 and H2 and rejects H3.

Criterion

R

R2

Activity incentive (X6) 0.612 0.374 Social incentive (X5) 0.658 0.432

Adjusted Unstandardized R2 coefficient b 0.373 0.429

0.381 0.353

Standardized coefficient b 0.373 0.339

t

F

6.688 217.773 * 6.091 138.238 *

Note: Significant at: *p , 0.001

Criterion

R

R2

Activity incentive (X6) 0.612 0.374 Social incentive (X5) 0.658 0.432 Note: *p , 0.001

Adjusted Unstandardized R2 coefficient b 0.373 0.429

0.381 0.353

Standardized coefficient b 0.373 0.339

t

F

6.688 217.773 * 6.091 138.238 *

Table V. Regression analysis for “knowledge sharing” with respect to “attitude toward incentives”

Table VI. Regression analysis for “knowledge sharing” with respect to “VCoP system quality” and “attitude toward incentives”

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5. Discussion and implication The present study adds to the literature on knowledge sharing by investigating the relationship between the quality of VCoP system, the community members’ attitude toward incentives and knowledge sharing in an online CoP. The results showed that although the two dimensions (i.e. system quality, attitude toward incentives) have individual effect on knowledge sharing, when all six factors from the two dimensions joined together, only two factors of attitude toward incentives showed significant effect on knowledge sharing behaviors. More specifically, while comparing the individual and the collective effects of the two dimensions, it was noted that the influence of the VCoP system quality on knowledge sharing disappeared as the two dimensions joined altogether. The significant effect of system quality (X1) and information quality (X2) disappeared while the two dimensions (i.e. VCoP system quality, attitude toward incentives) jointed together to influence knowledge sharing behavior. Second, regardless whether standing along or joined together, service quality (X3) of the VCoP service quality dimension and material incentive (X4) of the attitude toward incentives dimension has no impact on knowledge sharing. Such findings suggest to VCoP managers that online services and material incentives are the least useful factors for promoting online knowledge sharing. Thus, this study confirms that VCoP system quality and the members’ attitudes toward incentives have independently influenced knowledge sharing behavior in a context where human resource professionals voluntarily participated in a virtual community with aspirations to advance or contribute their professional knowledge and skills. The finding is in line with past studies which support the correlation between system quality (in a broad term) and knowledge sharing (Alvarez et al., 2010; Hew, 2009) as well as attitudes toward incentives and knowledge sharing (Taylor, 2006; Lin, 2007; Lam and Lambermont-Ford, 2010). However, since knowledge sharing is a complex process, some factors, such as motivation, attitudes, individual preferences, etc. are considered double-edged factors to knowledge sharing among individuals (Søndergaard et al., 2007). It is valuable that the present study examines the collective effects of these factors on knowledge sharing. To this end, the present study reveals that although VCoP system quality alone influences the community members’ knowledge sharing behaviors, it no longer plays a critical role in the process of knowledge sharing when attitude toward incentive comes into the picture. This finding contradicts existing understanding of the importance of VCoP system quality on knowledge sharing, and implies that as long as an online system provides adequate functions as well as accurate, complete and useful information, the managers of VCoP should not invest too much time and effort on the system itself. The study also confirms that social incentives and activity incentives are stronger factors than material incentives for knowledge sharing. The results of this study suggest that the material incentive has no effect at all on promoting knowledge sharing in VCoP, which is consistent with Finerty’s (1997) argument that reward seldom enhances long-term knowledge sharing. On the other hand, social and activity incentives were found to significantly affect knowledge sharing, independently and collectively, which is in line with the literature pointing out that individual contribution of knowledge in informal interactions is primarily based on the social exchanges (Bartol and Srivastava, 2002). Empirical evidence has suggested that intrinsic motivation, such as self-actualization, learning, and advancement of community are major motivators of

informal knowledge exchanges (Faraj and Wasko, 2002). This study substantiates Farag and Wasko’s argument which suggests the two reasons responsible for knowledge contributions in CoP are that people want to create relationship with others who have similar interests, and that people want to actualize their potential and learning. Bartol and Srivastava also pointed out that factors which build expertise and feelings of competence are most critical for influencing knowledge sharing behaviors within communities. The finding implies that in order to promote knowledge sharing behavior, VCoP managers must foster an online environment which provides a variety of social exchange opportunities for the members to interact, as well as challenging topics or tasks for the members to practice or advance professional knowledge and skills. We hope this paper is interpreted as a call for future empirical study on the collective effect of VCoP system quality along with other factors in knowledge sharing, which would be substantially enriched by additional empirical results. Even so, in the long run, the question of how to motivate people in the participation of online community members deserves greater attention. 6. Conclusion and limitation The study explores the condition of online system quality and the members’ attitude toward incentives provided by the community as well as their effect on knowledge sharing, thereby empirically testing a theoretical model which is analyzed using the Pearson correlation analysis and multiples stepwise regression method. Similar models which discuss the association between what triggers individual motivation and knowledge sharing has been largely unexplored by prior researchers even though there exists numerous studies on organizational learning and knowledge management (Lam and Lambermont-Ford, 2010). The findings indicate that the system quality of a VCoP as well as the members’ attitude toward incentives independently influences knowledge sharing behaviors, which are consistent with existing literature. This study also further examined the collective effect of both factors and found that the system quality of VCoP cease its effect on knowledge sharing as it competes against the members’ perception on incentives. Although the present study finds system quality does not play a significant role in promoting online knowledge sharing, it is premature to conclude that the factor is inconsequential without further empirical evidence. Moreover, only social incentive and activity incentive can impact online community members’ knowledge sharing behaviors. Clearly, achievement, advancement, enjoyment, and social interactions are major drives that would draw people to participate in VCoP as well as elicit their knowledge sharing behaviors. As James-Gordon and Bal (2003) pointed out, people should be given more responsibility over their self-development and job-related training in order to enhance their self-directedness. Jude-York (1991) argued that a proper culture for self-directed learning is necessary to trigger the members of the organization to become independent learners, which increases the possibility for them to participate in effective learning activities (Dolezalek, 2004), such as learning communities. Even though the empirical results of this study largely support the proposed research model, at least three limitations should be noted. First, the survey of the present study was distributed via the bulletin board of the VCoP and was available over a period of time. However, the research participants were recruited only by invitation that may lower down the reliability of the analysis results. Also, the sample

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Further reading 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. Chen, I.Y.L. and Chen, N.-S. (2009), “Kinshuk Examining the factors influencing participants’ knowledge sharing behavior in virtual learning communities”, Educational Technology & Society, Vol. 12 No. 1, pp. 134-148. About the authors Dr Li-An Ho is a Professor at the Department of Educational Technology, Tamkang University in Taiwan. She received her PhD from Indiana University, Bloomington. Dr Ho has years of experience in professional development and performance improvement. Research interests include knowledge sharing, community of practice, and human resource development. Li-An Ho is the corresponding author and can be contacted at: [email protected] Dr Tsung-Hsien Kuo is an Assistant Professor at the Department of Information Management, Lunghwa University of Science and Technology. He received his PhD from National Taipei University of Technology. His research interests include knowledge management and business administration.

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