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( 有很多量表)Social network, social trust and shared goals in organizational knowledge sharing


Information & Management 45 (2008) 458–465

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Information & Management
journal homepage: www.elsevier.com/locate/im

Social network, social trust and shared goals in organizational knowledge sharing
Wing S. Chow *, Lai Sheung Chan
Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong

A R T I C L E I N F O

A B S T R A C T

Article history: Received 17 January 2007 Received in revised form 13 February 2008 Accepted 5 June 2008 Available online 13 August 2008 Keywords: Knowledge sharing Social capital Theory of reasoned action Con?rmatory factor analysis

The aim of our study was to further develop an understanding of social capital in organizationalknowledge-sharing. We ?rst developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using con?rmatory factoring analysis. We then surveyed of 190 managers from Hong Kong ?rms, we con?rm that a social network and shared goals signi?cantly contributed to a person’s volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge. ? 2008 Elsevier B.V. All rights reserved.

1. Introduction Today, a ?rm’s employees must share their knowledge; indeed, such activities have become a competitive necessity. However sharing is hard to ensure, because knowledge is generated and initially stored within the employees. Early initiatives in knowledge management focused on providing electronic databases, network systems, and software to encourage the distribution of knowledge but these mechanisms have proved far from satisfactory. More recent efforts have focused on socio-cognitive approaches to motivate behavior that would help in promoting knowledge sharing, including factors such as incentive rewards, trust, relationships, etc. Knowledge sharing involves a set of behaviors that aid the exchange of acquired knowledge. A ?rm can be considered to be a social community creating, sharing and transferring explicit and tacit knowledge. The main objective of knowledge management is thus to turn individual knowledge into organizational knowledge [12,15]. But what makes organizational members willing to share their knowledge? Some studies have shown, by applying the theory of reasoned action (TRA), that success depends on a combination of volition and leadership. Extrinsic rewards, anticipated reciprocal relationships, a sense of self-worth, and organizational climate encourage sharing of knowledge; Wong et al. [25] suggested that building a long-term positive relationship with employees helped generate organizational knowledge. Ramasamy et al. [17] showed

statistically that relationship building played a signi?cant role in knowledge sharing between organizations. Many authors have also theorized that social capital contributes to knowledge sharing, while research has shown that such behavior is based on employees’ volition to share and perceived social pressure from the organization. Thus, we wanted to consider whether social capital played the same role in both decision functions. And, if so, which social capital factors had the greater in?uence. The objectives of our study were thus to (1) study how to quantify social capital, and (2) develop a theoretical framework to con?rm that social capital factors had a signi?cant impact on knowledge sharing. Our theoretical framework therefore examined the in?uence of the role played by social capital factors of organizational members that would increase or decrease their voluntary knowledge sharing, and in?uence it through behavioral change. 2. Theoretical background Our model was based on social capital factors and the TRA model. 2.1. Knowledge Knowledge can be considered either tacit or explicit. Here, we considered both to be equally important parts of organizational knowledge. 2.2. Social capital

* Corresponding author. Tel.: +852 3411 7582; fax: +852 3411 5585. E-mail addresses: vwschow@hkbu.edu.hk (W.S. Chow), sherry.ls.chan@gmail.com (L.S. Chan). 0378-7206/$ – see front matter ? 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2008.06.007

Social capital exists in the relationships between people [16]. It has been used to explain a variety of pro-social behaviors, like

W.S. Chow, L.S. Chan / Information & Management 45 (2008) 458–465 Table 1 Literature involving social capital factors Literature Chua [2] Structural dimension Social tie establishment; frequency of interaction Information channel; moral infrastructure Network ties; network con?gurations; appropriable organization Network ties, network con?gurations, network stability Bounded solidarity Social ties Network ties, network con?gurations, appropriable organization Social relations Weak ties; structural holes Centrality Social interaction Social interaction; relationship quality; customer network ties Network con?guration (labeled ‘‘social network’’) Relational dimension Trust; empathy; willingness to help; openness to sharing/ criticism; group identity Social norms; obligations and expectations; identity Mutual trust; norms; obligations and identi?cation Trust Cognitive dimension Shared language; shared narrative – Shared codes and language; shared narratives Shared goals; shared culture Nature of research Knowledge creation

459

Hoffman and Michailova [6] Huysman and De Wit [7] Inken and Tsang [9] Lang [11] Liu and Besser [13] Nahapiet and Ghoshal [14] Requena [18] Seibert and Liden [19] Wasko and Faraj [24] Tsai and Ghoshal [21] Yli-Renko et al. [26] Factors considered in our study

Knowledge management and sharing Knowledge sharing

Knowledge transfer

Generalized trust; reciprocity Generalized trust; norms or expectations Trust; norms; obligations and expectations; identi?cation Trust; commitment; communication; in?uence Contacts in other functions; contacts at higher levels Commitment; reciprocity Trust and trustworthiness –

Value introjection – Shared codes and language; shared narratives – – Self-rated expertise; tenure in the ?eld Shared vision –

Knowledge integration Knowledge sharing Knowledge exchange and creation Quality of life in the workplace Career success Knowledge contribution Resource exchange and value creation Knowledge acquisition and exploitation Knowledge sharing

Trust (labeled ‘‘social trust’’)

Shared goals

collective action and community involvement. Coleman [3] claimed that it helped in promoting actions between persons or corporations. Social capital comes with many attributes have been collected into three clusters: structural, relational, and cognitive. The structural dimension [5] involves social and network relations whose connections de?ne who can be reached and how; factors in this dimension measure the network pattern, density, connectivity, and hierarchy [20]. The relational dimension describes the level of trust between people developed during interactions: norms, obligations, trust, and identi?cation raise awareness of actors toward their collective goals. The cognitive dimension refers to resources increasing understanding between parties. Wasko and Faraj [24] claimed that knowledge sharing required shared understanding; for example, shared culture and goals were important factors. Table 1 shows important literature in these three dimensions the last row indicates the equivalent social capital factor of our study. It is shows that network con?guration, trust, and shared goals were used to measure the performance of the structural, the relational and the cognitive dimensions. We adopted these three social factors to represent the three dimensions of social capital with ‘‘network con?guration’’ renamed as ‘‘social network’’ and ‘‘trust’’ as ‘‘social trust’’. 2.3. Theory of reasoned action (TRA) TRA [4] states: (1) the more favorable the attitude of an individual toward a behavior, the stronger will be the intention of the individual to engage in the behavior; (2) the greater the subjective norm, the stronger the intention of the individual to perform the behavior; and (3) the stronger the intention of the individual to engage in a behavior, the more likely the individual will be to perform it. TRA has been successfully applied in many

research studies in social psychology, knowledge management, medical studies, and IT adoption [8]. 3. The research model and hypotheses Fig. 1 shows our research model, which integrated social capital factors with the TRA. In an organizational context, people establish many direct contacts with others if the organizational structure is ?at and decentralized. In our study, the social network provided increased opportunities for interpersonal contact. People had more positive feelings about sharing ideas and resources with those with whom they had developed a close relationship. This lead to our ?rst hypothesis: H1. The more extensive the social network among organizational members, the more favorable will be the attitude toward knowledge sharing. Organizational members who had a more extensive social network with their colleagues would perceive greater social pressure for sharing their knowledge, because a good relationship results in high expectations of colleagues, including favorable actions. Thus, people who build a social network may be expected to share their knowledge. This lead to our second hypothesis: H2. The more extensive the social network among organizational members, the more favorable will be the subjective norm with respect to knowledge sharing. Many studies have suggested that social trust or mutual trust among members is one of many factors critical to the success of knowledge sharing. Social trust in an organization improves interactions between colleagues; people want not only to learn from each other and share their knowledge. This lead to our third hypothesis:

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Fig. 1. Con?rmatory factor analysis model and hypotheses.

H3. The greater the social trust among organizational members, the more favorable will be the attitude toward knowledge sharing. The level of social trust in?uences expectations of a colleague’s intention and behavior. Organizational members are thus more likely to expect those who are trustworthy to share their knowledge. This lead to our fourth hypothesis: H4. The greater the social trust among organizational members, the more favorable will be the subjective norm with respect to knowledge sharing. The presence of shared goals promotes mutual understanding and exchange of ideas. Shared goals can thus be considered the force that holds people together and lets them share what they know. Within an organization, shared goals can be achieved through cooperation and knowledge sharing [23]. This lead to our ?fth hypothesis: H5. The greater the shared goals among organizational members, the more favorable will be the attitude toward knowledge sharing. With collective goals, organizational members tend to believe that other employee’s self-interest will not affect them adversely and they all contribute their knowledge to help achieve their mutual goals. This lead to our sixth hypothesis: H6. The greater the shared goals among organizational members, the more favorable will be the subjective norm with respect to knowledge sharing. Personal attitudes toward a behavior are a signi?cant predictor of intention to engage in that behavior and behavioral intention to share knowledge is determined by a person’s attitude toward knowledge sharing. This lead to our seventh hypothesis: H7. The more favorable the organizational members’ attitude toward knowledge sharing, the greater will be the intention to share knowledge. The subjective norm in?uences people’s attitudes toward sharing knowledge; that is, people who perceive greater social pressure to share knowledge have a more positive attitude toward it. This lead to our eighth hypothesis:

H8. The greater the organizational members’ subjective norm with respect to knowledge sharing, the more favorable will be the attitude toward knowledge sharing. The subjective norm is also the other important antecedent of behavioral intention. Many studies have reported that it has a strong and positive effect on the intention to perform a behavior. The impact of the subjective norm on the intention to share knowledge is also a signi?cant in?uencing factor in knowledge sharing. This lead to our ninth hypothesis:

Table 2 Demographic and organizational information of respondents* Measure (a) Demographic information of respondents Gender Items Frequency Percent

Male Female Secondary Undergraduate Postgraduate Missing <1 1–5 6–10 11–15 16–20 >20 Missing

94 96 49 67 67 7 1 67 50 37 14 17 4 14 84 84 8 30 100 45 8 3 4

49.5 50.5 25.8 35.3 35.3 3.7 0.5 35.3 26.3 19.5 7.4 8.9 2.1 7.4 44.2 44.2 4.2 15.8 52.6 23.7 4.2 1.6 2.1

Education

Work experience (in years)

Position

Top manager Middle manager Operational manager Missing 25 26–35 36–45 46–55 >5 Missing

Age

W.S. Chow, L.S. Chan / Information & Management 45 (2008) 458–465 Table 2 (Continued ) Measure Items Frequency Percent

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H9. The higher the organizational members’ subjective norm with respect to knowledge sharing, the greater will be the intention to share knowledge. 4. Research methodology and analysis To test the model, we adopted a survey method for data collection and examined the hypotheses using structural equation modeling (SEM) on the data. 4.1. Measurement and data collection We developed measurement items by adopting measures that had been validated in prior studies, modifying them to ?t our context of knowledge sharing. Appendix A outlines the de?nitions of the constructs, while Appendix B lists the questions. For the construct of social network, three measurement items were derived from one question each from three studies [2,18,22]. The measurement items for shared goals were based on one question from [10] and two from [21]. The measurement items for the attitude toward knowledge sharing, the subjective norm with respect to knowledge sharing, and the intention to share knowledge were all adopted from [1] by simplifying their measuring items about the respondents’ normative beliefs on knowledge sharing into three items. The measurement of intention to share knowledge was composed of two items about the explicit knowledge and three about tacit knowledge. Respondents were asked to evaluate the signi?cance of measurement items using a Likert scale of 1–5, where a value of 5 represented ‘‘strongly agree,’’ and 1 represented ‘‘strongly disagree.’’ The study sample consisted of Hong Kong managers randomly selected from the directory of D&B Key Decision Makers in Hong Kong 2004/05. Direct telephone conversations with representatives of these companies were ?rst made to introduce our objectives o and to ask the names of appropriate persons to contact for the survey. A total of 136 companies agreed to participate and a total of 582 questionnaires were sent to each selected participant with a stamped return envelope. Two weeks later, a follow-up phone call was made to non-respondents to encourage their participation i. A total of 192 replies were returned, though two were incomplete and so discarded. Thus, 190 questionnaires were used for the data analysis, a response rate of 33%. Table 2 shows the demographics of the respondents. Respondents had obviously attained a signi?cant degree of knowledge from their education and jobs. 4.2. Analysis methods We ?rst analyzed the convergent validity of constructs, the reliability of our measurement items, and determined the signi?cance of the model using SEM.

(b) Organizational information Type of industry

Academic/education Banking/?nance/insurance Computers/ tele-communications/ Networking Electrics/electronics Engineering/architecture Manufacturing Mass media/publishing Medicine/health Real estate Restaurant/hotel Retail/wholesale Textile/garment Transport/shipping/logistics Utilities Others Missing <100 100–249 250–499 500–999 1000–2499 !2500 Missing

10 14 17

5.3 7.4 8.9

11 7 17 2 3 4 4 8 7 39 3 41 3 56 16 7 15 26 58 12 14

5.8 3.7 8.9 1.1 1.6 2.1 2.1 4.2 3.7 20.5 1.6 21.6 1.6 29.5 8.4 3.7 7.9 13.7 30.5 6.3 7.4

Size (number of employees)

Operational period of the organization (in years)

<1

1–5 6–10 11–15 16–20 >20 Missing Market value of organizational assets (HK$ in million) <10

47 19 13 14 70 13 33

24.7 10.0 6.8 7.4 36.8 6.8 17.4

10–49.9 50–99.9 100–499.9 500–999.9 !1000 Missing Average organizational annual income (HK$ in millions) <10

18 17 20 11 37 54 39

9.5 8.9 10.5 5.8 19.5 28.4 20.5

10–49.9 50–99.9 100–499.9 500–999.9 !1000 Missing
*

33 10 23 8 28 49

17.4 5.3 12.1 4.2 14.7 25.8

Sample size = 190.

Table 3 Scaling of reliability test Constructs Social network Social trust Shared goals Attitude toward knowledge sharing Subjective norm about knowledge sharing Intention to share knowledge
a b

Measurement items SN1, SN2, SN3 ST1, ST2, ST3 SG1, SG2, SG3 AT1, AT2, AT3, AT4, AT5 SU1, SU2, SU3 IN1, IN2, IN3, IN4, IN5

Cronbach’s a 0.72 0.79 0.77 0.91 0.76 0.89

Loading range 0.72–0.89 0.80–0.85 0.79–0.85 0.82–0.88 0.72–0.90 0.80–0.88

Number of itemsa 3(3) 3(4) 3(3) 5(5) 3(3) 5(2,3) b

Final items (initial items). 2 and 3 questions were originally used for measuring respective explicit and tacit knowledge.

462 Table 4 Summary results of the model constructs Model construct Measurement item SN1 SN2 SN3 ST1 ST2 ST3 SG1 SG2 SG3 AT1 AT2 AT3 AT4 AT5 Subjective norm about knowledge sharing SU1

W.S. Chow, L.S. Chan / Information & Management 45 (2008) 458–465 Table 6 Overall model ?t indices Standardized estimates 0.70 0.85 0.53 0.82 0.87 0.62 0.68 0.73 0.78 0.81 0.83 0.82 0.86 0.76 0.78 t-Value 9.7046** 12.0521** 7.1544* 12.5622** 13.6015** 8.9351* 9.4747* 10.4565* 11.2016** –a 13.0357** 12.6686** 13.5395** 11.5562** –a Fit index Scores Recommended cut-off value from literature

Social network

Social trust

Absolute ?t measures x2/d.f. GFI RMR Incremental ?t measures NFI AGFI CFI Parsimonious ?t measures PGFI PNFI

1.877** 0.85** 0.044**

2**; 3*; 5* !0.9**0; !0.80* 0.05**; 0.08*

Shared goals

0.85* 0.81** 0.92**

!0.90** !0.90**; !0.80* !0.90** The higher, the better The higher, the better

Attitude toward knowledge sharing

0.66* 0.72*

Acceptability: ** acceptable, * marginal.

each construct identi?ed and used. All as ranged from 0.72 to 0.91; these are greater than 0.7 and thus the constructs were considered reliable. 4.2.3. Structural equation modeling The test of the model was carried out using SEM, a con?rmatory factor analysis that tests a model and its validity simultaneously. LISREL 8.3 was used to perform the SEM analysis. We used this software to provide maximum likelihood estimation for all path values simultaneously. To test for data normality, we performed the skewness statistical tests. The skewness statistics for tested constructs all had negative values ranging from ?0.334 to ?0.168. The critical z-value was obtained by dividing the corresponding statistics by the standard errors H(6/ n), where n represents the sample size. All critical z-values ranged from ?1.879 to ?0.945. Since these values did not exceed a critical value of ?1.96; we concluded that our data passed a data normality test. We followed the recommended two-stage analytical procedures of SEM: the measurement and structural model were checked to ensure that the results were acceptable and consistent with the underlying conceptual model, and the structural path model was then examined to determine relations among the constructs and their signi?cance. Table 4 summarizes the results of the measurement model; these show that all six model constructs, namely social network, social trust, shared goal, attitude toward knowledge sharing, subjective norm regarding knowledge sharing, and intention to share knowledge were all valid measures of their respective constructs based on their parameter estimates and statistical signi?cance. Table 5 shows the results of hypothesis testing of the structural relationships among the latent variables. Fig. 2 depicts the ?nal results of the measurement and structural models. To assess the

SU2 SU3 Intention to share knowledge IN1 IN2 IN3 IN4 IN5
* ** a

0.86 0.56 0.76 0.87 0.85 0.73 0.72

9.4728* 7.1249* –a 12.2849* 12.0583* 10.1029* 10.1029*

p 0.10. p 0.05 Values were not calculated, because loading was set to 1.0 to ?x construct variance.

4.2.1. Convergent validity This occurs when all items measuring a construct load on a single one of them. We assessed each factor by performing withinscale factor analysis. This showed that all measurement items converged onto their constructs with each factor loading having a value higher than 0.7. All of our factors demonstrated unidimensionality. Furthermore, the ?ve measurement items of intent to share knowledge, which we initially proposed as two separate clusters of tacit and explicit knowledge, were highly corrected and all converged into a single factor. This result implied that organizational members did not see a different between explicit and tacit knowledge when they shared knowledge. 4.2.2. Reliability test of constructs Cronbach’s alpha was used to assess the internal consistency of the proposed constructs. As a result, measurement item ST4 of social trust was dropped, leaving three measurement items for this construct. Table 3 summarizes the loading ranges and a value for
Table 5 Summary results of the structural model Path SN–AT SN–SU ST–AT ST–SU SG–AT SG–SU AT–IN SU–AT SU–IN
*

Description Social network ! attitude toward knowledge sharing Social network ! subjective norm about knowledge sharing Social trust ! attitude toward knowledge sharing Social trust ! subjective norm about knowledge sharing Shared goals ! attitude toward knowledge sharing Shared goals ! subjective norm about knowledge sharing Attitude toward knowledge sharing ! intention to share knowledge Subjective norm about knowledge sharing ! attitude toward knowledge sharing Subjective norm about knowledge sharing ! intention to share knowledge 0.1.

Hypothesis H1 H2 H3 H4 H5 H6 H7 H8 H9

Path coef?cient 0.24 0.27 0.06 ?0.10 0.37 0.31 0.44 0.25 0.26

t-Value 2.84* 2.65* 0.68 ?0.89 3.82* 2.72* 5.15* 3.14* 3.04*

Results Supported Supported Not supported Not supported Supported Supported Supported Supported Supported

p

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463

Fig. 2. Results of the con?rmatory analysis model.

Table 7 Direct, indirect and total effects of signi?cant model constructs Construct AT Direct effect SN SG AT SU 0.24 0.36 – 0.25 Indirect effect 0.07 0.08 – – Total effect 0.31 0.44 – 0.25 SU Direct effect 0.28 0.31 – – Indirect effect – – – – Total effect 0.28 0.31 – – IN Direct effect – – 0.44 0.26 Indirect effect 0.21 0.27 – 0.11 Total effect 0.21 0.27 0.44 0.37

model ?t, we applied eight measures from three perspectives: absolute ?t measures (evaluated using x2/d.f.), goodness of ?t index (GFI), and root mean square error (RMR); incremental ?t were measured by the normal ?t index (NFI), the adjusted goodness of ?t index (AGFI), and the comparative ?t index (CFI); and parsimonious ?t measures were evaluated by the parsimonious goodness of ?t index (PGFI) and the parsimonious normal ?t index (PNFI). Table 6 shows the overall ?t indexes of our model. It shows that our model resulted in good results at the x2/d.f., GFI, RMR, AGFI, CFI, and marginal ?tness levels for the indexes of NFI, PGFI, and PNFI. We concluded that our ?ndings had reached an acceptable level and could be used to explain our hypotheses. Hypotheses H1 and H5 were supported, and showed that a higher level of social network and shared goals contributed to the willingness of organizational members to share knowledge. H7, H8, and H9 were also supported. Our results also con?rmed that social pressure imposed by coworkers and managers leads to knowledge sharing. H2 and H6, the relationship of social network and shared goals to the subjective norm on knowledge sharing, were also supported organizational members who felt pressure to share knowledge were those who had established a large social connection of employees with similar organizational visions or goals. H3 and H4 were not supported. Table 7 shows the direct, indirect, and total effects of all signi?cant model constructs. Apparently social network and shared goals had indirect effects on the intention to share knowledge through the mediators of attitudes toward knowledge sharing and the subjective norm on knowledge sharing.

5. Discussions and implications Our main objective was to understand the in?uence of social capital on organizational knowledge sharing. Our results revealed that: (1) organizational members did not distinguish tacit from explicit knowledge when they shared knowledge, (2) a social network and shared goals signi?cantly contributed to attitudes toward knowledge sharing, (3) a social network and shared goals signi?cantly contributed to the subjective norm on knowledge sharing; (4) social trust had no direct contribution to either attitudes toward knowledge sharing or its subjective norm though it in?uenced both attitude toward knowledge sharing and the intention to share knowledge; and (5) a social network and shared goals have indirect effects on the intention to share knowledge within the organization. Management must develop a clear mission and goal so that everyone in the organization can appreciate and contribute knowledge [27]. Recruiting employees who share common interests and goals is a critical task for human resources departments. Social ties between colleagues are important and a good relationship will enhance knowledge-sharing behavior. 6. Conclusion and limitations Our study was one of the ?rst to provide empirical evidence about the in?uence of a social network, social trust, and shared goals on employees’ intention to share knowledge. It offers insights to practitioners on the value of social capital and reasons why people are or are not willing to engage in knowledge sharing within an organization.

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We also found that social network and shared goals directly in?uenced the attitude and subjective norm about knowledge sharing and indirectly in?uenced the intention to share knowledge. Social trust did not play a direct role in sharing knowledge and organizational members do not differentiate between tacit and explicit knowledge when they share it. This study has a few inherent limitations. First, we hypothesized only three social capital factors in our model; other social capital factors (such as shared organizational cultures, society network ties, and organizational network stability) may

also affect outcomes. Second, our research sample consisted only of organizational managers. Third, the data collection was limited to knowledge-sharing behavior within organizations in Hong Kong. Acknowledgements The authors would like to thank the Chairman of Editorial Board, Prof E.H. Sibley, and three anonymous reviewers for their valuable comments and suggestions.

Appendix A. De?nitions of the constructs

Constructs Social network Social trust Shared goal Attitude toward knowledge sharing Subjective norm about knowledge sharing Intention to share knowledge
a b

De?nitions The degree of contact and accessibility of one with other people The degree of one’s willingness to vulnerable to the actions of other people The degree to which one has collective goals, missions and visions with other people The degree of one’s favorable or positive feeling about sharing one’s knowledge The degree of one’s perceived social pressure from important others to share or not to share one’s knowledge The degree of one’s belief that one will engage in knowledge-sharing behavior

References [14,25] [14] [25] [6,18] [6,18] [6,18]

Number of itemsa 3 3 3 5 3 (3) (4) (3) (5) (3)

5 (2,3)b

Final item numbers (initial item numbers). 2 and 3 questions were originally used for measuring explicit and tacit knowledge, respectively.

Appendix B. Questionnaire items
Constructs Social network Items SN1. In general, I have a very good relationship with my organizational members SN2. In general, I am very close to my organizational members SN3. I always hold a lengthy discussion with my organizational members ST1. I know my organizational members will always try and help me out if I get into dif?culties ST2. I can always trust my organizational members to lend me a hand if I need it ST3. I can always rely on my organizational members to make my job easier SG1. My organizational members and I always agree on what is important at work SG2. My organizational members and I always share the same ambitions and vision at work SG3. My organizational members and I are always enthusiastic about pursing the collective goals and missions of the whole organization AT1. Sharing AT2. Sharing AT3. Sharing experience AT4. Sharing AT5. Sharing of my knowledge with organizational members is always good of my knowledge with organizational members is always bene?cial of my knowledge with organizational members is always an enjoyable of my knowledge with organizational members is always valuable to me of my knowledge with organizational members is always a wise move Alpha = 0.76, mean = 3.58, S.D. = 0.69 Statistics Alpha = 0.72, mean = 3.53, S.D. = 0.61

Social trust

Alpha = 0.79, mean = 3.58, S.D. = 0.63

Shared goals

Alpha = 0.77, mean = 3.38, S.D. = 0.66

Attitude toward knowledge sharing

Alpha = 0.91, mean = 3.79, S.D. = 0.70

Subjective norm about knowledge sharing

SU1. My chief executive of?cer (CEO) always thinks that I should share my knowledge with other members in the organization SU2. My boss always thinks that I should share my knowledge with other members in the organization SU3. My colleagues always think that I should share my knowledge with other members in the organization IN1. I will share my work reports and of?cial documents with my organizational members more frequently in the future#1 IN2. I will always share my manuals, methodologies and models with my organizational members in the future#1 IN3. I will always share my experience or know-how from work with my organizational members in the future#2 IN4. I will always share my know-where or know-whom at the request of my organizational members#2 IN5. I will always try to share my expertise obtained from education and training with my organizational members in a more effective way#2

Intention to shared knowledgea

Alpha = 0.89, mean = 3.55, S.D. = 0.67

a These ?ve items were initially proposed as two separable factors as explicit#1 and tacit#2 knowledge but were merged into a single factor by the factor analysis test.

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[20] N.M. Tichy, M.L. Tushman, C. Fombrun, Social network analysis for organizations, Academy of Management Review 4, 1979, pp. 507–519. [21] W. Tsai, S. Ghoshal, Social capital and value creation: the role of inter?rm networks, Academy of Management Journal 41 (4), 1998, pp. 464–476. [22] A. Vaux, D. Harrison, Support network characteristics associated with support satisfaction and perceived support, American Journal of Community Psychology 13 (3), 1985, pp. 245–268. [23] J. Wagner, Studies of individualism-collectivism: effects on cooperation in groups, Academy of Management Journal 1995, pp. 152–172. [24] M.M. Wasko, S. Faraj, Why should I share? Examining social capital and knowledge contribution in electronic networks of practice MIS Quarterly 29 (1), 2005, pp. 35–57. [25] C.D. Wong, W.T. Wong, C. Hui, K.S. Law, The signi?cant role of Chinese employees’ organizational commitment: implications for managing in Chinese societies, Journal of World Business 36 (3), 2001, pp. 326–340. [26] H. Yli-Renko, E. Autio, H.J. Sapienza, Social capital, knowledge acquisition, and knowledge exploitation in young technology-based ?rms, Strategic Management Journal 22 (6/7), 2001, pp. 587–613. [27] C.P. Yu, T.H. Chu, Exploring knowledge contribution from an OCB perspective, Information & Management 44, 2007, pp. 321–331. Wing S. Chow is an associate professor of MIS in the School of Business, Hong Kong Baptist University. Dr. Chow published more than 70 papers in the journals and conference proceedings. His latest edited book is entitled ‘‘Multimedia Information Systems in Practice’’ published by Springer, 1999.

Lai Sheung Chan obtained the Bachelor of Business Administration from Hong Kong Baptist University in 2003 and the Master of Philosophy from the same university in 2007. Her research interest includes Information System Management and Knowledge Management.


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