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International Journal of Information Management
j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / i j i n f o m g tSocial capital, behavioural control, and tacit knowledge
sharing—A multi-informant design
Shu-Chen Yang
a,∗, Cheng-Kiang Farn
baDepartment of Information Management, National University of Kaohsiung, No. 700 Kaohsiung University Road, Nan Tzu District, Kaohsiung, Taiwan bDepartment of Information Management, National Central University, Taiwan
a r t i c l e i n f o
Keywords:Behavioural control
Intention–behaviour relationship Tacit knowledge sharing Social capital
a b s t r a c t
As suggested by prior studies, tacit knowledge sharing is a natural process of social interaction. The perspectives of social capital and behavioural control are thus employed in this study to investigate an employee’s tacit knowledge sharing and behaviour within a workgroup. This study collects data through a multi-informant questionnaire design. Three interesting results were obtained in this study. First, results show that tacit knowledge sharing intention can be induced by affect-based trust. However, shared value is negatively related to tacit knowledge sharing intention. Second, internal control has a positive effect on tacit knowledge sharing intention, but the relationship between internal control and tacit knowledge sharing behaviour could not be confirmed. Third, external control positively moderates the relationship between tacit knowledge sharing intention and behaviour. It is interesting to note that tacit knowledge sharing intention does not necessarily lead to tacit knowledge sharing behaviour unless the moderating effect of external control is taken into account. These findings and their implications are also addressed.
© 2008 Elsevier Ltd. All rights reserved.
1. Introduction
The importance of knowledge within organisations has been highlighted by several researchers (e.g.Alavi & Leidner, 2001;Bock, Zmud, Kim, & Lee, 2005). It is important to note that knowledge itself cannot create significant value without utilisation (Fahey & Prusak, 1998). As argued byAlavi (2000), knowledge sharing among organisational members is the most important and chal-lenging means to increase the value of knowledge utilisation. Based on Polanyi (1967)conceptualisation, Nonaka (1994) sug-gested that knowledge can be classified as tacit and explicit. Tacit knowledge—reflecting an individual’s know-how and experiences from past actions—is increasingly considered as a valuable intan-gible resource that is difficult to imitate and acquire, and can be regarded as the most important source of competitive advantage for an individual, a group, or a firm (Berman, Down, & Hill, 2002). This is especially true in the context of innovative works, where much of the task-related knowledge is tacit in nature, and tacit knowledge sharing among members is crucial for creating higher collective performance (Käser & Miles, 2002). However, an individual may hoard rather than share his/her tacit knowledge because it is
valu-∗ Corresponding author. Tel.: +886 7 5919326; fax: +886 7 5919328.
E-mail address:henryyang@nuk.edu.tw(S.-C. Yang).
able and important, and the contribution of tacit knowledge cannot be easily measured and compensated accordingly (Osterloh & Frey, 2000). Thus, tacit knowledge acquisition and sharing is one of most important issues for knowledge management within organisations. For a workgroup, tacit knowledge sharing among members is also critical for task completion and group performance. Accordingly, this study intends to answer the following two research questions: (1) what are the factors that influence an individual’s tacit knowl-edge sharing intention and behaviour within a workgroup? (2) Will an individual’s tacit knowledge sharing intention necessarily lead to tacit knowledge sharing behaviour?
While explicit knowledge sharing can be facilitated by infor-mation technology, tacit knowledge sharing is subject to social interaction (Käser & Miles, 2002;Nonaka, 1994). In other words, tacit knowledge sharing among organisational members is socially driven. Moreover, knowledge sharing behaviour is inherently a type of collective action (Bock et al., 2005) and sometimes beyond an individual’s volitional control. Accordingly, perspectives of social capital and behavioural control are employed in this study in order to investigate an employee’s tacit knowledge sharing behaviour within a workgroup. In this study, data are collected through multi-informant questionnaires in which each respondent’s social capital and tacit knowledge sharing behaviour are reported by his/her colleagues. In this manner the study avoids the perceptual bias from self-reporting and common-method bias from single infor-mants.
0268-4012/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijinfomgt.2008.09.002
2. Conceptual background
2.1. Tacit knowledge sharing
Nonaka (1994)addresses two types of knowledge within organ-isations: explicit and tacit. Explicit knowledge is regarded as knowledge that can be formally and systematically stored, artic-ulated, and disseminated in certain codified forms such as manual or computer files (Becerra-Fernandez & Sabherwal, 2001). On the other hand, tacit knowledge is deeply rooted in action, experience, thought, and involvement in a particular context (Alavi & Leidner, 2001), and thus is difficult to be transformed into explicit form in order to be easily transferred and shared (Berman et al., 2002). Polanyi (1967, p. 4)describes the nature of tacit knowledge with the following phrase: ‘we know more than we can tell’. That is to say, tacit knowledge is deeply embedded in the mind to the extent that the knowers are not fully aware of that knowledge they pos-sess (Koskinen, Pihlanto, & Vanharanta, 2003). Nevertheless the tacit knowledge determines the behaviour of the knower. Common examples of tacit knowledge include the ability to ride a bicycle, the knowledge of an expert baseball player, and skill debugging a computer program.
Tacit knowledge may be understood through the concepts of skill (Berman et al., 2002) or practical know-how (Koskinen et al., 2003), thus an individual usually will not share his/her knowl-edge when the knowlknowl-edge is regarded as valuable or important because of a fear of losing possible advantages (Bock et al., 2005). Furthermore, potential risk of losing advantage and lack of proper reward mechanism are the major reasons that an individual is usu-ally reluctant to share his/her tacit knowledge with others (Osterloh & Frey, 2000). Thus, tacit knowledge sharing can be only facilitated by intrinsic motivation, such as sociability and friendship (Osterloh & Frey, 2000).Choi and Lee (2003)also suggest that an individual can acquire tacit knowledge and personal experience only in a tacit-oriented manner that emphasises social interaction.Nonaka (1994) also considers that tacit knowledge is of a personal nature and can be shared through sharing metaphors or experiences during social interaction without substantial knowledge loss. Thus, social relationship may be the most important factor that facilitates tacit knowledge sharing among employees within an organisation.
2.2. Social capital
Social capital is conceptualised as a set of resources embedded in the social relationship among social actors and can be regarded as a valuable asset that secures benefits for social actors ranging from individuals to organisations (Adler & Kwon, 2002). For exam-ple, a higher degree of social capital is helpful in finding better jobs (Granovetter, 1995), receiving early promotions (Burt, 1997), mak-ing collective work easier (Bolino, Turnley, & Bloodgood, 2002), inter-unit resource exchange and collaboration within an organi-sation (Tsai & Ghoshal, 1998), creation of intellectual capital and dissemination of knowledge within an organisation (Nahapiet & Ghoshal, 1998), enhancing organisational flexibility (Leana & Van Buren, 1999), and facilitating IT outsourcing (Chou, Chen, & Pan, 2006).
In a broader sense, social capital is not a unidimensional con-cept (Putnam, 1995) and ‘encompasses many aspects of a social context, such as social ties, trusting relations, and value systems that facilitate actions of individuals located within that context’ (Tsai & Ghoshal, 1998, p. 465).Putnam (1995)argues that clar-ifying the dimensions of social capital is a top priority because social capital has many complicated attributes related to a social context. Accordingly,Nahapiet and Ghoshal (1998)propose that structural, relational, and cognitive aspects are the three
dimen-sions of social capital. This three-dimensional framework has been employed to investigate the relationship between social capital and intra-organisational phenomena, such as creation of intellectual capital (Nahapiet & Ghoshal, 1998), inter-unit resource exchange (Tsai & Ghoshal, 1998), and organisational citizenship behaviour (Bolino et al., 2002).
Structural social capital can be conceptualised as the overall pattern of relationships among social actors (Nahapiet & Ghoshal, 1998).Bolino et al. (2002)suggest that the structural social capi-tal can also be considered as the extent to which actors in a social network are connected. Relational social capital includes the assets created and leveraged through ongoing relationship that influence social actors’ behaviour (Nahapiet & Ghoshal, 1998). This dimen-sion bears some resemblance to Adler and Kwon’s (2002) concept of ‘goodwill’ and can be manifested by trust, norms, obligations, and identification (Nahapiet & Ghoshal, 1998). Besides,Nahapiet and Ghoshal (1998)regard cognitive social capital as the com-mon understanding acom-mong social actors through shared language and narratives. It is embodied in attributes such as shared vision or shared value that facilitates individual and collective actions and common understanding of proper actions and collective goals. Boland and Tenkasi (1995)suggest that higher cognitive social capital gives partners a common perspective that enables them to develop similar perception and interpretation toward events. Unlike the impersonal nature of structural social capital, both relational and cognitive dimensions describe the personal qual-ities of interpersonal relationship (Bolino et al., 2002) and can be categorised into relational embeddedness of social capital that represents the motivational characteristic of interpersonal social exchange. Thus, relational social capital and cognitive social capital are emphasised in this study.
Among the studies on relational social capital, trust quality has received much attention in organisational and management research (McEvily, Perrone, & Zaheer, 2003). Further, trust is an important dimension ofLeana and Van Buren’s (1999) conceptu-alisation of social capital.McAllister (1995)suggests that the two factors of trust are ‘affect-based trust’ and ‘cognition-based trust’. Trust is cognition-based in that ‘we cognitively choose whom we will trust in which respects and under which circumstances, and we base the choice on what we take to be “good reasons”, constitut-ing evidence of trustworthiness’ (Lewis & Weigert, 1985, p. 970). On the other hand, affect-based trust is based on the emotional ties linking individuals, such as friendship, love, or care (Lewis & Weigert, 1985;McAllister, 1995). This study employs affect-based trust to characterise the relational dimension because relational social capital represents the affective quality of interpersonal rela-tionship (Bolino et al., 2002). Furthermore, the concept of shared value is employed to characterise the cognitive dimension of social capital in this study.Morgan and Hunt (1994, p. 25)define shared value as ‘the extent to which partners have beliefs in common about what behaviours, goals, and polices are important or unimportant, appropriate or inappropriate, and right or wrong’.
2.3. Internal and external control
Although there are several empirical studies that support the Theory of Planned Behaviour (TPB) across a number of domains (e.g. Armitage & Conner, 2001;Conner & McMillan, 1999), the role and concept of perceived behavioural control (PBC) has not yet garnered a consensus. In the earlier versions of the TPB,Ajzen (1985)states that PBC, which reflects the availability of requisite opportunities and resources, will moderate the intention–behaviour relationship. That is to say, the relationship between intention and behaviour is stronger when PBC is high. Due to lack of evidence on the moderat-ing effects of PBC,Ajzen (1991)subsequently argues that PBC will
affect behaviour directly. However, the moderating effect of PBC has been supported by several empirical studies (e.g.Armitage & Conner, 2001;Conner & McMillan, 1999;Terry & O’Leary, 1995).
Conner and Armitage (1998)suggest that PBC can be dimension-alised into internal and external control. Internal control includes intrinsic control factors and is similar to the concept of self-efficacy, whereas external control reflects extrinsic control factors and is similar toTriandis’s (1977)concept of facilitating conditions orKuhl and Beckmann’s (1985)‘action control’. The distinctions between internal control and external control also embodyAjzen’s (1985) suggestion that non-motivational factors may relate to both inter-nal aspects (such as skill or ability) and exterinter-nal aspects (such as opportunities or resources). The external control factors can also be described in terms of available resources and opportunities that will facilitate the performance of focal behaviour (Fitch & Ravlin, 2005).Ajzen (2002)suggested that PBC can lead to distinct com-ponents of self-efficacy and controllability, which are similar to internal control and external control, respectively.
3. Research model and hypotheses development
Based on the previous conceptual background, this study pro-poses a research model in order to investigate tacit knowledge sharing intention and behaviour within a workgroup (as shown in Fig. 1).
Social capital can reflect strong interpersonal connections and extensive investment in interpersonal relationship. As an individ-ual has higher social capital in his/her social network, he/she will be likely to behave in ways that benefit other members in order to maintain an interpersonal friendship in their social network. There-fore, an individual will be willing to share his/her tacit knowledge with co-workers through the reciprocal relationship when he/she has a stronger social capital. This leads to the following hypothesis:
Hypothesis 1a. Affect-based trust positively affects tacit knowl-edge sharing intention.
Hypothesis 1b. Shared value positively affects tacit knowledge sharing intention.
As argued by TPB model, behavioural intention captures all the motivational factors that reflect the effort people plan to exert in order to perform the behaviour in question (Ajzen, 1991). Thus, when an individual expresses stronger intention to engage in a cer-tain type of behaviour, he/she is more likely to put the behaviour into practice. The following hypothesis is then proposed:
Hypothesis 2. Tacit knowledge sharing intention positively affects
tacit knowledge sharing behaviour.
The TPB model suggests that an individual’s perception of non-motivational factors (i.e. PBC) plays an important role in the intention–behaviour relationship when the focal behaviour is not under complete volitional control (Ajzen, 1991). However, the effects of PBC have not received consensus among different studies. On the one hand, as PBC is conceptualised as an internal non-motivational factor (such as self-efficacy), it is suggested to directly affect behavioural intention (e.g. Ajzen, 1991; Terry & O’Leary, 1995). PBC is conceptualised as an external non-motivational fac-tor; on the other hand, it is said to directly affect behaviour or to moderate the intention–behaviour relationship (e.g.Fitch & Ravlin, 2005;Terry & O’Leary, 1995). In order to clarify the role of PBC in an individual’s tacit knowledge sharing behaviour, this study adopts Conner and Armitage’s (1998)dimensionalisation of PBC as internal control and external control.
According to Conner and Armitage’s (1998) definition, inter-nal control refers to an individual’s confidence to perform the
behaviour in question. An individual with high internal con-trol may believe that he/she is able to easily handle the focal behaviour by him/herself, thus leading to a strong behavioural intention. However, external non-motivational factors (external control) exert their moderating effects on the intention–behaviour relationship when the focal behaviour is under partial volitional control (Armitage & Conner, 2001). On the other hand, external control describes all the environmental factors that may impede or facilitate focal behaviour performance and usually cannot be manipulated by an individual alone. Higher external control will facilitate the implementation of intentions into action. In gen-eral, an individual’s performance in knowledge sharing behaviour is sometimes beyond his/her own control (not under complete volitional control). For example, an individual who has strong moti-vation to share his/her tacit knowledge may not actually provide his/her know-how and experiences when other members do not request it. Besides, unimpeded and free communication among employees is also one of the most important facilitating factors for tacit knowledge sharing behaviour within an organisation. Accord-ingly, the relationship between an individual’s tacit knowledge sharing intention and behaviour will be stronger when he/she per-ceives that the external conditions are favourable for engaging in tacit knowledge sharing behaviour. Thus, the following hypotheses regarding the effects of internal and external control are proposed:
Hypothesis 3. Internal control positively affects tacit knowledge sharing intention.
Hypothesis 4. Internal control positively affects tacit knowledge sharing behaviour.
Hypothesis 5. The effect of tacit knowledge sharing intention on tacit knowledge sharing behaviour is stronger for higher external control than for lower external control.
4. Research method
4.1. Procedure
This study targeted individuals who are engaged in jobs that are highly knowledge-intensive and involve certain degree of inter-personal interaction for task completion. These individuals include personnel in MIS departments, R& D departments, or various project teams. In such groups, lateral knowledge sharing among co-workers occurs more frequently than knowledge sharing between subordinates and managers. Besides, lateral knowledge sharing among co-workers is more susceptible to social relationship, whereas knowledge sharing between subordinates and managers is usually limited to a formal relationship. Thus, managers in such groups were not considered as our target respondents. Each depart-ment or team that satisfies above requiredepart-ments is referred to as a
qualified group hereinafter.
This study employed a multi-informant design to measure each respondent’s social capital and tacit knowledge sharing behaviour. The use of a single informant for all variables in one study is often criticised for the perceptual bias and common-method bias involved. In our study, an individual’s social capital and tacit knowl-edge sharing behaviour could be observed by other members in the same workgroup. An individual’s social capital refers to the degree of trust he/she earned and his/her shared values with the other members in the workgroup. Thus, it is reasonable to measure an individual’s social capital on the basis of his/her co-workers’ view-points. Besides, we also measured an individual’s tacit knowledge sharing behaviour based on the viewpoints of his/her co-workers in order to eliminate the common-method bias that results when both the intention and the behaviour were evaluated by oneself. The
Fig. 1. Research model. perceptual bias from self-reporting and the common-method bias
from single informants in one study can be overcome when these two constructs are evaluated by two peers. Multi-informant design has also been adopted by several studies to address the above prob-lems (e.g.Hogel, Weinkauf, & Gemuenden, 2004;Simons, Pelled, & Smith, 1999).
In order to reduce respondents’ tasks to a more manageable size, three members were invited from each qualified group. Each respondent was asked to rate actual tacit knowledge sharing behaviour and social capital for each of the other two members in his/her qualified group. For example, let us consider R1, R2, and R3 as the three respondents in a qualified group (as shown inTable 1). R1 was asked to evaluate the social capital and tacit knowledge sharing behaviour of R2 and R3, while his/her social capital and tacit knowledge sharing behaviour were evaluated by R2 and R3. All other remaining constructs were measured through self-ratings way by each respondent.
This study follows three steps in the data collection. First, because of the complexities of our research design, we selected qualified groups from several organisations with which the authors were in frequent contact in order to ensure a high participation rate in our sampling process. From each qualified group, three randomly selected members were asked to participate in this study. The random selection process continued for each qualified group until three members volunteered to participate. For each respondent, he/she was asked to report not only the other two members’ social capital and tacit knowledge sharing behaviour but also other self-reporting constructs. In order to ensure sim-plicity in our questionnaire design, questionnaires for these three respondents of a qualified group were tailored to meet research design. That is to say, in each respondent’s questionnaire, two other members’ names were explicitly mentioned in the measurement items for social capital and tacit knowledge sharing behaviour. For instance, in the above example of a qualified group with R1, R2, and R3, the names of R2 and R3 were mentioned in R1’s question-naire.
Second, the tailored questionnaires were then sent by e-mail. Each respondent was asked to fill out his/her tailored question-naire in a computer file and reply by e-mail after completion for the sake of confidentiality. We also sent follow-up e-mails to those who did not return their finished questionnaires within 3 days. Third, as each respondent’s social capital and tacit knowl-edge sharing behaviour were reported by the two other members, all members in a qualified group were required to complete and
return their questionnaires for further analyses. That is to say, if any one member failed fill out his/her questionnaire, the data pertaining to his/her fellow members in the same group were discarded. In order to encourage all three members in a qual-ified group to participate in our study, we sent NTD 200 (in New Taiwan Dollars) as an incentive to each respondent after all respondents in the same group completed their tailored question-naires.
4.2. Measurement
All constructs were measured using multiple-item scales, drawn from pre-validated measures in the previous related studies (see Appendix A). In order to ensure the accuracy of the translation from English to Chinese, this study involved the following three steps. First, all the items were translated into Chinese with mod-ification by the authors, one MIS professor, and four MIS doctoral students who had an average of 5 years of actual experience in sys-tem development. Second, one graduate student (hereafter referred to as SG) and one senior undergraduate student (SU) from the department of English in a University from Northern Taiwan were asked to translate the Chinese items into English. In order to check the semantic differences, the two back-translated English versions from SG and SU were compared with the original English ver-sion by SU and SG, respectively. The translated Chinese items that had significant semantic differences were then modified. Third, a pre-test with several respondents who had real-world work experience was conducted to ensure the translation was coher-ent.
5. Analyses and results
Three hundred and six respondents from 67 organisations agreed to participate in this study. Most respondents are employed in the Computer and Electronics, Software and Information System, and Consulting Industry. These respondents were from 102 groups that ranged in size from 3 to 26 people, with an average team size of 6.30 (S.D. = 3.67). Sixty percent of the respondents were male and 76% were 26–34 years old. With regard to tenure, the respon-dents’ work experience ranged from 1 month to 290 months with an average of 29.68 months (S.D. = 29.92). Half of these respondents are employed in R&D department. The characteristics of these 67 organisations and 102 groups are shown inTable 2.
Table 1
An example of research design in a qualified group.
Note: R1, R2, and R3 are three respondents in a qualified group. The value in each shadow cell represents the
target(s) to be evaluated.
5.1. Convergent and discriminant validity
In this study, an individual’s social capital and tacit knowl-edge sharing behaviour were evaluated by two other members in his/her workgroup. The two responses for each of these two con-structs are then summed up into a single score of each respondent’s social capital and tacit knowledge sharing behaviour. According to the two-step procedure recommended byAnderson and Gerbing (1988), we estimated and re-specified the measurement model prior to examining the structural model. A CFA was used to assess the convergent and discriminant validity of the operationalisa-tion. All the research constructs were modelled as five correlated first-order factors and employ LISREL 8.70 using the Maximum Likelihood (ML) estimation to estimate the measurement model. The construct external control was not included in the CFA analy-sis because it is measured with formative indicators. As suggested byCohen, Cohen, Teresi, Marchi, and Velez (1990), the reliability criteria employed for characterising the constructs measured by reflective indicators cannot be applied to the construct measured by formative indicators.
Based on the basic assumptions of the CFA approach proposed by Kaplan (2000), the independence assumption was met due to our random sampling design and there are also no missing values in our dataset. Kline (2005)suggested that multivariate non-normality Table 2
Demographic characteristics of sample (n = 306).
Characteristics Percentage Industry Banking 4.9 Computer and Electronics 28.4 Consulting 21.6 Government 2.9 Logistics 5.9 Mobile Phone 6.9 Software and Information System 24.5 Others 4.9
Group type MIS Department 23.5
Project Team 26.5
R&D Department 50.0 Group size (average = 6.30,
S.D. = 3.67) Less than 5 51.0 6–9 35.3 Over 10 13.7 Gender Female 39.9 Male 60.1 Age Under 25 7.8 26–29 37.6 30–34 37.9 35–39 12.1 40–49 4.6 Over 50 0.0 Tenure (average = 29.68, S.D. = 29.92)
Less than 12 months 27.8
13–24 months 33.6
25–36 months 13.8
37–60 months 13.4
61–120 months 10.4 Over 121 months 1.0
can be usually detected by inspecting of univariate distributions. As shown inTable 3, the absolute values of skewness and kurto-sis are smaller than 3.0 and 8.0, respectively, which are all well above the threshold suggested byKline (2005). Thus, the multi-variate normality of our data was assured. Besides, the number of observations (18 observed variables lead to 18× 17 = 153 observa-tions) is greater than the number of free estimated parameters (18 observed variables and 5 unobserved variables lead to 46 free esti-mated parameters) in this study; thus, the CFA model in our study is over-identified for further analyses (Kline, 2005).
We first performed the CFA with 18 items (seeAppendix Bfor the covariance matrix) and the threshold employed for judging the sig-nificance of factor loadings was 0.60 suggested bySharma (1996). As the measurement model estimation shown inTable 3, factor loadings of all measurement items range from 0.712 to 0.967, which indicates acceptable convergent validity. The CFA resulted in a chi-square statistic of 339.396 with 125 degrees of freedom. Since the chi-square is less than three times the degrees of freedom, a good fit is implied (Carmines & McIver, 1981). Furthermore, the values on other goodness of fit indexes also show a relative good fit between the measurement model and data.
This study also assessed construct reliability by calculating com-posite reliability that assesses whether the specified indicators are sufficiently representative of their respective latent factors, as sug-gested bySegars (1997). These estimates of composite reliability of latent factors range from 0.91 to 0.96, which are all well above the threshold of 0.70 suggested byJöreskog and Sörbom (1989); thus, acceptable construct reliability is implied. However, compos-ite reliability does not reflect the degree of variance that is captured by the construct in relation to the amount of variance due to mea-surement error (Fornell & Larcker, 1981). Thus, average variance extracted (AVE) estimate was employed to obtain this information. AVE estimate of 0.50 or higher indicates acceptable validity for a construct’s measure (Fornell & Larcker, 1981). All AVE estimates in this study (range from 0.72 to 0.90) are well above the cut-off value, which suggests that all measurement scales have convergent valid-ity. In order to assess discriminant validity among the constructs, we calculated square root of AVE for each construct and compared them with inter-construct correlations for each pair of constructs. Result also shows that square roots of all AVE estimates for each construct are greater than inter-construct correlations; thus, dis-criminant validity is supported.
5.2. Direct effect testing
In order to test the direct effect hypotheses in our research model, a structural equation modelling (SEM) analysis was per-formed. We modelled six first-order factors (all constructs in this study except for external control) with five hypothetical causal paths and employ LISREL 8.70 with the Maximum Likelihood esti-mation to estimate the structural model.Fig. 2 shows that the structural model estimation resulted in a chi-square statistic of 318.284 with 123 degrees of freedom. The chi-square is less than three times the degrees of freedom; thus, a good fit is implied (Carmines & McIver, 1981). Furthermore, the values on other
good-Table 3
Measurement model estimation and basic statistics.
Construct Item Mean S.D. Item loading t-Value Error variance Skewness Kurtosis
Affect-based trust ABT1 11.255 1.982 0.814 17.095 0.338 −0.063 −0.007
ABT 2 11.258 2.118 0.912 20.549 0.169 −0.072 −0.043 ABT 3 9.944 2.514 0.812 17.048 0.340 −0.042 −0.040 ABT 4 10.729 2.214 0.913 20.598 0.167 −0.047 −0.038 ABT 5 10.477 2.330 0.874 19.134 0.236 −0.042 −0.044 Shared value SHV1 9.680 2.100 0.861 18.543 0.259 −0.014 0.017 SHV2 10.507 2.044 0.901 19.982 0.188 −0.037 0.005 SHV3 10.771 2.053 0.896 19.795 0.197 −0.046 0.000 SHV4 9.624 2.097 0.712 14.011 0.493 −0.009 0.012 Tacit knowledge sharing intention TKSI1 6.229 0.958 0.956 22.334 0.086 −0.635 −0.472 TKSI2 6.294 0.930 0.924 21.056 0.145 −0.698 −0.389 TKSI3 6.170 0.957 0.905 20.308 0.181 −0.566 −0.551 Tacit knowledge sharing behaviour TKSB1 11.000 2.170 0.944 22.002 0.108 −0.060 −0.074 TKSB2 11.402 2.096 0.960 22.683 0.078 −0.081 −0.052 TKSB3 10.974 2.124 0.939 21.775 0.118 −0.058 −0.061
Internal control ITC1 5.314 1.228 0.912 20.356 0.168 −0.177 −0.255
ITC2 5.438 1.208 0.967 22.479 0.066 −0.208 −0.257
ITC3 5.614 1.185 0.814 17.083 0.337 −0.273 −0.424
2(d.f.) = 339.396 (125); RMSEA = 0.0750; CFI = 0.979; NFI = 0.966; NNFI = 0.974; GFI = 0.890.
Fig. 2. Structural model estimation.
ness of fit indexes also show a relative good fit between structural model and data.
As shown inFig. 2, two out of five hypothetical causal paths are not significant. First, the effect of affect-based trust on tacit knowledge sharing intention is the opposite of that of shared value. Second, internal control positively affects tacit knowledge sharing behaviour while the relationship between internal control and tacit knowledge sharing behaviour is not significant. Third, results do not strongly support the direct effect of tacit knowledge sharing inten-tion on tacit knowledge sharing behaviour. That is, an individual may not put his/her strong tacit knowledge sharing intention into action; this indicates the importance of situational factors.
5.3. Moderating effect testing
This study employed moderated regression analyses (MRA) to identify the moderating effects without information loss result-ing from the artificial transformation of a continuous variable into a qualitative one in the subgroup analyses (Szymanski, Troy, & Bharadwaj, 1995). Three models in MRA for moderating effect of external control are designed as follows:
Model 1 : TKSB = b0+ b1TKSI + b2ITC
Model 2 :TKSB = b0+ b1TKSI + b2ITC + b3ETC
Model 3 :TKSB = b0+ b1TKSI + b2ITC + b3ETC + b4ETC × TKSI
Table 4shows the results of MRA for the moderating effect of external control on the relationship between tacit knowledge shar-ing intention and behaviour. This study found significant change in
R2between Model 2 and Model 3, which indicates the significant
moderating effect of external control. The relationship between tacit knowledge sharing intention and tacit knowledge sharing behaviour is stronger under higher external control than under lower external control. Thus,Hypothesis 5is supported. This study also found that the change in R2from Model 1 to Model 2 is
signif-icant; external control is thus regarded as a quasi-moderator. Table 4
MRA for moderating effect of ETC on TKSI→ TKSB.
Models R2 R2 F for R2
1 TKSB = b0+ b1TKSI + b2ITC 0.016 – 2.444 2 TKSB = b0+ b1TKSI + b2ITC + b3ETC 0.471 0.455 259.459***
3 TKSB = b0+ b1TKSI + b2ITC + b3ETC + b4ETC× TKSI 0.478 0.007 4.208* * p < 0.05.
6. Discussion and implications
As suggested by the previous literature, tacit knowledge sharing is subject to social interaction (e.g.Käser & Miles, 2002;Nonaka, 1994;Osterloh & Frey, 2000); this study investigates tacit knowl-edge sharing among workgroup members from the perspective of social capital and behavioural control. Results can be employed to answer the two research questions described in Section 1. First, an employee’s tacit knowledge sharing intention is affected by affect-based trust, shared value, and internal control. Second, an employee’s tacit knowledge sharing intention does not nec-essarily lead to tacit knowledge sharing behaviour. However, the external control for knowledge sharing moderates the relation-ship between an employee’s tacit knowledge sharing intention and behaviour. Furthermore, it is interesting to note that social capi-tal may be a double-edged sword, which can both facilitate and hinder knowledge sharing within a workgroup. As suggested by our results, shared value is negatively related to tacit knowledge sharing intention. One possible interpretation is that Taiwanese’s cultural values discourage employees to share knowledge, as men-tioned inHsu’s (2006)study. Most employees in Taiwan are not accustomed to sharing experiences and know-how unless they have an intimate relationship with other colleagues (as suggested inHypothesis 1). When an employee holds common understand-ing with other members who usually hoard knowledge in the same workgroup, he/she may also be reluctant to share his/her tacit knowledge.
Future research may be conducted with the limitations of this study in mind. First, three members were selected as respondents for each qualified group in order to reduce respondents’ tasks to a more manageable size. However, this research design may not be suitable for a workgroup with a large number of mem-bers. Second, as this study employed a cross-sectional design, all the hypothetical causal relationships can only be inferred rather than proven. A study with longitudinal design can enhance our understanding about the dynamics of tacit knowledge shar-ing among employees. Third, our findshar-ings were derived based on the sample from MIS departments, R&D departments, and project teams. However, one needs to be cautious while apply-ing our findapply-ings to other kinds of groups. Finally, the cultural factors should be taken into account while interpreting our results because this study was conducted in Taiwan. People’s atti-tudes and tendency to share knowledge in corporations in the East may be quite different from those in corporations in the West.
There are several theoretical implications of our study. First, this study employed perspectives of social capital to investigate an employee’s tacit knowledge sharing intention and behaviour within his/her workgroup. Several literatures have claimed that tacit knowledge sharing among employees is social driven, but extant empirical studies about antecedents of employees’ tacit knowledge sharing are not abundantly available. This study pro-vides a compelling theoretical framework for conducting an empirical study for this line of research. Future studies can extend this study to better explicate tacit knowledge sharing within organisations. Second, this study investigated both employee’s tacit knowledge sharing intention and behaviour in an inte-grated framework. Most knowledge sharing studies did not take the actual behaviour into account by assuming that behavioural intention is highly related to behaviour, as suggested by TPB mod-els (e.g. Liao, Shao, Wang, & Chen, 1999). However, this study found that an employee’s tacit knowledge sharing intention is not significantly related to tacit knowledge sharing behaviour and the intention–behaviour relation is positively moderated by his/her external control about tacit knowledge sharing. This study
performed a rather insightful examination of employees’ tacit knowledge sharing. Finally, a multi-informant research design was employed in this study. It is reasonable that an employee’s social capital and tacit knowledge sharing behaviour are reported by his/her colleagues in the same workgroup. Such a design can avoid the perceptual bias from self-reporting and the common-method bias from single informants and is useful for conducting a solid empirical study.
The empirical results of this study also have several manage-rial implications. First, the results show that affect-based trust is an important prerequisite for effective interpersonal tacit knowl-edge sharing. Thus, managers need to foster the formation of an intensive social network among employees in order to pro-mote tacit knowledge sharing within a workgroup through their exhibition of extra-role behaviours. Besides, employees in Taiwan are usually reluctant to share knowledge due to an authoritarian mindset and a desire to protect prestige (Hsu, 2006). Managers should aim to cultivate a sharing environment to alleviate this cul-tural burden. Second, internal control is an important determinant of employees’ tacit knowledge sharing intention. An employee’s internal control about tacit knowledge sharing usually derives from his/her individual characteristics and organisational experi-ences. When an individual is usually encouraged to share his/her tacit knowledge and encounters fewer obstacles, he/she will be confident of his/her ability to share tacit knowledge with col-leagues.
Third, external control has a contingent effect on an employee’s tacit knowledge sharing. When an employee perceives that he/she has no opportunities for tacit knowledge sharing, he/she will not actually share his/her experiences or know-how with others even though he/she has strong willingness to do so. For managers, this result implies that offering free communication mechanisms that are favourable for tacit knowledge sharing among employ-ees is of paramount importance. When an employee can be well aware of the problems and difficulties encountered by other col-leagues and the communication channels among employees are unhindered, he/she will easily put his/her tacit knowledge shar-ing intention into action. For example, a group meetshar-ing can be held frequently to offer opportunities for tacit knowledge sharing among employees. Thus, it is very important for managers to reduce the obstacles and difficulties in tacit knowledge sharing among employees.
7. Conclusion
This study used responses from 306 employees in 102 work-groups across 67 organisations in order to examine the roles that social capital and behavioural control play in tacit knowledge shar-ing and behaviour among organisational members. The results reveal that social capital may be a double-edged sword for tacit knowledge sharing intention and that external control plays a moderating role in intention–behaviour relationship. Despite its limitations, we believe that this study may be useful for future research on knowledge sharing.
Appendix A. Measurement items
Affect-Based Trust (adapted fromMcAllister, 1995)
1. We have a sharing relationship. We can both freely share our ideas, feelings, and hopes.
2. I can talk freely to this individual about difficulties I am having at work and know that he/she will want to listen.
3. We would both feel a sense of loss if one of us was transferred and we could no longer work together in the same group. 4. If I shared my problems with this person, I know he/she would
respond constructively and caringly.
5. I would have to say that we have both made considerable emo-tional investments in our personal relationship.
Shared Values (adapted fromBrashear, Boles, Bellenger, & Brooks, 2003)
1. I feel that my personal values are a good fit with those of he/she. 2. He/she has the same values as I have with regard to the
distri-bution of work within our group.
3. He/she has the same values as I have with regard to the purpose of our group.
4. In general, my values and the values held by he/she are very similar.
Tacit Knowledge Sharing Intention (adapted fromBock et al., 2005) 1. I intend to share my working experience or know-how with other
group members more frequently.
2. I am willing to share my ways to solve problems at the request of other group members.
3. I am willing to try to share my expertise from my education or training with other group members in a more effective way.
Tacit Knowledge Sharing Behavior (adapted fromBock et al., 2005) 1. He/she always shares his/her working experience or know-how
with other group members.
2. He/she always shares his/her ways to solve problems at the request of other group members.
3. He/she always tries to share his/her expertise from his/her edu-cation or training with other group members in a more effective way.
Internal Control (Armitage, Conner, Loach, & Willetts, 1999) 1. I believe I have the ability to share my working experience or
know-how with other group members.
2. I am confident to share my working experience or know-how with other group members.
3. If it were entirely up to me, I am confident that I would be able to share my working experience or know-how with other group members.
External Control (Armitage et al., 1999)
1. Whether I share my working experience or know-how with other group members is entirely up to me.
2. I feel there is no obstacle to share my working experience or know-how with other group members.
3. I feel I often have no opportunity to share my working experience or know-how with other group members.
Appendix B. Covariance matrix
ABT1 ABT2 ABT3 ABT4 ABT5 SHV1 SHV2 SHV3 SHV4 TKSI1 TKSI2 TKSI3 TKSB1 TKSB2 TKSB3 ITC1 ITC2 ITC3 ABT1 3.928 ABT2 3.207 4.487 ABT3 2.763 3.482 6.321 ABT4 2.917 3.732 4.120 4.900 ABT5 2.986 3.735 4.350 3.931 5.431 SHV1 2.078 2.262 2.933 2.536 2.916 4.409 SHV2 2.121 2.219 2.445 2.478 2.539 3.189 4.179 SHV3 2.218 2.321 2.547 2.493 2.429 3.069 3.405 4.216 SHV4 2.076 2.099 2.550 2.298 2.641 2.870 2.210 2.170 4.399 TKSI1 0.206 0.324 0.159 0.260 0.150 −0.089 0.089 0.115 0.072 0.918 TKSI2 0.214 0.273 0.181 0.271 0.167 −0.134 0.049 0.079 0.003 0.755 0.864 TKSI3 0.235 0.293 0.244 0.295 0.154 −0.131 0.067 0.193 0.034 0.757 0.688 0.915 TKSB1 1.939 1.994 1.892 2.099 1.830 1.716 2.095 2.242 1.671 0.167 0.191 0.158 4.708 TKSB2 1.928 1.956 1.718 2.127 1.867 1.592 1.957 2.266 1.629 0.202 0.243 0.224 4.032 4.392 TKSB3 1.894 1.911 1.889 2.121 1.737 1.841 2.155 2.335 1.835 0.043 0.110 0.093 3.984 3.886 4.511 ITC1 0.194 0.464 0.581 0.643 0.573 0.213 0.320 0.268 0.165 0.344 0.320 0.386 0.111 0.097 0.142 1.508 ITC2 0.152 0.393 0.558 0.550 0.486 0.189 0.259 0.229 0.140 0.402 0.386 0.442 0.120 0.080 0.099 1.315 1.460 ITC3 0.201 0.492 0.532 0.598 0.610 0.208 0.293 0.259 0.239 0.486 0.459 0.505 0.205 0.220 0.185 1.028 1.089 1.405
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Shu-Chen Yang is an assistant professor in the Department of Information
Man-agement at the National University of Kaohsiung in Taiwan, ROC. His teaching and research interests focus on knowledge management, consumer behavior and e-business. His major publications have been in the areas of management of IS and consumer behavior.
Cheng-Kiang Farn is a professor in the Department of Information Management at
the National Central University in Taiwan, ROC. His teaching and research inter-ests focus on electronic business, SCM and knowledge management. His major publications have been in the areas of electronic business and management of IS. Cheng-Kiang is also a consultant to many government agencies and enterprises.
