(Social Behavior and Personality (New Zealand) Via Acquire Media NewsEdge) With the growing use of teamwork for strategic decision making in organizations, an understanding of the teamwork dynamics in the strategic decision-making process is critical for both researchers and practitioners. By conceptualizing team cognition in terms of a transactive memory system (TMS) and collective mind, in this study we explored the relationships among TMS, collective mind, and collective efficacy and the impact of these variables on team performance. Longitudinal data collected from 98 undergraduates were analyzed. Neither the TMS-team performance relationship nor the collective mind-team performance relationship was significant. Collective efficacy was found to play a mediating role in such relationships. We concluded that team cognition with collective efficacy is beneficial for understanding teamwork dynamics in strategic decision making.
Keywords: collective efficacy, collective mind, team cognition, team performance, transactive memory system.
Teamwork has become an essential element in most organizations (Tasa, Taggar, & Seijts, 2007). Organizations are increasingly using teamwork for effective strategic decision making with the aim of acquiring a sustainable competitive advantage in a rapidly changing business environment.
Strategic decision making requires cooperation among team members (Dooley & Fryxell, 1999). In the strategic decision-making process, team members exchange and process information (Parayitam & Dooley, 2009) and determine appropriate actions and directions for the team (Olson, Bao, & Parayitam, 2007). To achieve effective teamwork, members need to develop team cognition to transact their respective roles and coUaborate on a common task in the strategic decision-making process.
The literature indicates that team cognition is a key driver of team performance (DeChurch & Mesmer-Magnus, 2010). Researchers of coUective efficacy have also reported that teamwork behavior facilitates the formation of a team's coUective efficacy (Tasa et al., 2007), which in turn enhances team performance (Gully, Incalcaterra, Joshi, & Beaubien, 2002). However, few have discussed the relationship between team cognition and collective efficacy, and its impact on team performance, especially in a strategic decision-making context. Therefore, the issue of team cognition in relation to coUective efficacy and team performance is worth exploring. In this regard, we have drawn on two types of team cognition - the transactive memory system (TMS) and collective mind - to explore the role of team cognition in a team's collective efficacy and performance in strategic decision making. In this study we describe a conceptual model to study die relationships among the TMS, the coUective mind, collective efficacy, and team performance (see Figure 1). This contributes to the literature in several ways. Firstly, drawing on the TMS, the coUective mind, and coUective efficacy, we explored the antecedents of team performance in a strategic decision-making context. By combining the TMS, the coUective mind, and coUective efficacy, we provided a richer model of team performance in the strategic decision-making process. Secondly, in this article we empirically investigated the relationships among the TMS, the coUective mind, and collective efficacy and explored the development of coUective efficacy.
Team Cognition and Team Performance
Team cognition is the process of understanding how the knowledge that is important to team effectiveness is mentally held and distributed within the team (DeChurch & Mesmer-Magnus, 2010). Team cognition provides a foundation for team members to jointly coordinate dieir actions. The TMS and die coUective mind are two types of team cognition that can help explain teamwork dynamics. The TMS is a team's coUective awareness of who knows what, which provides the team with a cognitive architecture for knowledge (DeChurch & Mesmer-Magnus, 2010). The TMS thus provides a basis for understanding teamwork dynamics in terms of die processing and die exchange of information among team members wmle team members implement their strategic decision making.
The collective mind is a social system in which individuals contribute, understand, and coordinate their actions for the good of the team (Weick & Roberts, 1993; Yoo & Kanawattanachai, 2001). Brockmann and Anthony (1998) pointed out that members of a strategic decision-making team with a coUective mind would (1) use shared vocabularies and consensus for strategic means and aims and (2) develop shared betiefs about the organization's environment, strategic position, and prospects. With an effective coUective mind, members of a team coUectively exhibit intelligent behaviors during group tasks (Crowston & Kammerer, 1998).
Recent researchers have acknowledged that team cognition is related to team motivational-affective states (such as coUective efficacy) and team performance (DeChurch & Mesmer-Magnus, 2010). In the foUowing sections we discuss die relationships among the TMS, the coUective mind, coUective efficacy, and team performance, and propose nine corresponding hypotheses.
Collective Efficacy and Team Performance
Collective efficacy refers to group members' shared belief in their ability to achieve a desired result through joint actions (Bandura, 1997). In general, team members with higher degrees of coUective efficacy are likely to devote more effort to rjerforming a task jointly (Bandura, 1997), which in turn leads to better team outcomes. When team members perceive a higher degree of coUective efficacy, they are capable of performing coUaborative activities well, thus enabling better performance for the team. Given mat coUective efficacy is a predictor of team performance and that in previous studies it has been revealed that collective efficacy is critical to group performance in various work group settings (Tasa et al., 2007), we propose the following hypothesis:
Hypothesis 1 : Team coUective efficacy will positively influence team performance.
The TMS and Team Performance
Wegner, Erber, and Raymond (1991) proposed the concept of the TMS to describe the way that individuals treat their coworkers as external memory aids mat complement their knowledge. In general, a weU-developedTMS is indicative of good teamwork because a team with a better TMS is tikely to develop team specialization, thereby allowing team members jointly to perform a given task effectively. For example, Berry (2006) suggests diat any organizational decision-making team should include members from interdependent disciplines or functional areas to make such teams capable of processing information and creating synergistic effects from the coUaboration of diverse talents.
Yoo and Kanawattanachai (2001) confirmed the positive effect of a TMS on team performance. Michinov and Michinov (2009) investigated the effect of a TMS on the performance of coUaborative teams and confirmed that team performance improves when teams have a high TMS indicative of coUaborative behaviors. On the basis of previous findings, we propose the foUowing hypothesis to address the relationship between the TMS and team performance:
Hypothesis 2: A TMS wiU positively influence team performance.
The Collective Mind and Team Performance
The term "coUective mind" refers to "a pattern of heedful interrelations of actions in a social system" (Weick & Roberts, 1993). According to Weick and Roberts, members of a team with a coUective mind demonstrate tiiree behavioral features: contribution, representation, and subordination. Contribution describes team members' actions, such as participating in social processes and making decisions mat contribute to the team's outcomes. Representation refers to the team development of a coUective mental model mat provides team members with a clear understanding of how to connect dieir own actions to the actions of others. Subordination describes how team members coordinate their actions to place team goals ahead of individual goals.
In essence, the coUective mind is a social cognitive system (Yoo & Kanawattanachai, 2001) that shares the "understandings of me group's tasks and of one another that faciUtate group performance" (Crowston & Kammerer, 1998). Thus, we propose me foUowing hypothesis to test the impact of the collective mind on team performance:
Hypothesis 3: The coUective mind wiU positively influence team performance.
The TMS and Collective Efficacy
A TMS is a cooperative cognitive system that enables team members to know which team members have expertise on a particular issue (Prichard & Ashleigh, 2007). Mannix, Griffith, and Neale (2002) suggest that the TMS influences the formation of a team's coUective efficacy. According to Kanawattanachai and Yoo (2007), when a team has an effective TMS, its members can locate expertise within the team and buUd a sense of trust in otiier members' abitities, which facititates the efficient processing of knowledge for a given task. Thus, members of a team with a TMS can gain confidence in dieir teamwork and correctly perceive their team's abitity to perform a task. In short, an effective TMS is likely to enhance a team's coUective efficacy. Thus, we propose the following hypothesis:
Hypothesis 4: A team's TMS will positively influence its coUective efficacy.
The TMS and the Collective Mind
Akgiin, Byrne, Keskin, and Lynn (2006) indicated that the TMS is related to die coUective mind. Researchers have shown diat an effective TMS can help team members become aware of others' expertise and knowledge domains (Yoo & Kanawattanachai, 2001). Thus, die members of a team with such a TMS can efficiently retrieve necessary information from otiiers and jointly make decisions for performing a given task. In doing so, a team with an effective TMS is capable of developing a global perspective that includes representations of how a given task should be divided, and who should perform each subtask. Accordingly, team members can carefully relate their activities to reaching task goals. This occurrence implies that a well-developed TMS is likely to facilitate die formation of a team's coUective mind. Thus, we propose the foUowing hypothesis:
Hypothesis 5: A team's TMS will positively influence its coUective mind.
The Collective Mind and Collective Efficacy
As mentioned previously, a team's TMS is likely to influence its coUective mind and coUective efficacy. One can reasonably assume that the coUective mind relates to die formation of a team's coUective efficacy and is likely to mediate die TMS-coUective efficacy relationship. With an effective coUective mind, a team can develop a global perspective that includes each member's understanding of how his or her actions can maximize overaU team performance. Accordingly, team members are likely to enhance the team's beUef about their abitity to coUaborate to accomptish a specific task. Thus, we propose the foUowing hypotheses:
Hypothesis 6: A team's coUective mind will positively influence its perceived coUective efficacy.
Hypothesis 7: A team's coUective mind will mediate the effect of me TMS on its coUective efficacy.
Collective Efficacy as a Mediator
As mentioned eartier, the TMS and die coUective mind relate to the formation of a team's coUective efficacy, which in turn affects its performance. That is, the TMS and the coUective mind may transmit their effects on team performance via die team's coUective efficacy, which leads to the formulation of the foUowing hypotheses:
Hypothesis 8: A team's coUective efficacy wiU mediate die relationship between die TMS and team performance.
Hypothesis 9: A team's coUective efficacy wül mediate the relationship between die coUective mind and team performance.
The sample was drawn from students enroUed in an undergraduate course at a university in northern Taiwan. At the beginning of the semester, aU students were invited to join a team-based experiment. Ninety-eight participants, 46 women (46.94%) and 52 men (53.06%), were randomly assigned to 32 teams, of which 30 teams had tiiree members and two teams had four members. Each team was asked to participate in a business simulation game.
As in the experiment conducted by Yoo and Kanawattanachai (2001), in this study we employed a web-based business simulation game in which each team was asked to perform independently a given strategic decision-making task. The web-based simulation game employed in this study was the Business Operations Simulation System (BOSS) 2008. BOSS 2008 is a popular strategic business game used as a teaching supplement in business schools in Taiwan.
During the first week of class, the instructor explained the experimental procedure and me learning task to die participants. In addition to the BOSS 2008 operation manual (see Tao & Hung, 2010), aU students were provided wim a two-week training program. They were given sufficient opportunity to practice using the business simulation software to ensure mey understood how to operate it.
The business game began after the two-week training program and lasted for 10 weeks. During this period, aU teams were asked to buUd a new company using the BOSS 2008 platform. Each member of the 30 3-member teams was assigned to 1 of the 3 top managerial positions: (1) general manager and finance manager, (2) marketing manager and planning manager, or (3) production manager and procurement manager. The two 4-member teams assigned two members to one of the above three positions and the otiier two members to die remaining two positions.
Each team member was required to make weekly decisions based on his/her business function and the business information provided by BOSS 2008. While the team's functional managers made weekly decisions, the general managers confirmed and submitted the decisions to the system. The system would generate reports for each team and an interteam business competition report weekly. These reports included industry information, business finance spreadsheets, functional operations results, and die interteam competition report, which ranked the teams' performances. The above reports were announced on die BOSS 2008 platform. Each manager could access aU six divisions' detaüed weekly reports but could only make decisions for his or her own division. After die completion of the 10-week business game experiment, BOSS 2008 was used to produce a final report on die operations and performance rankings of each business.
The first survey measuring die TMS, coUective efficacy, and die coUective mind was deüvered in Week 5 of the experiment. The second survey was presented in Week 10. Three items measuring the TMS were adopted from Yoo and Kanawattanachai (2001) and have been vatidated in new product development teams (Akgiin et al., 2006) and virtual teams (Yoo & Kanawattanachai, 2001). A sample TMS item was: "Team members know who has speciatized sküls and knowledge relevant to dieir work". Four questions from Yoo and Kanawattanachai were refined to measure me coUective mind. A sample item was: "Our team members carefully interrelated their actions in this project". Collective efficacy is a team's belief in its ability to perform a given task in the team workplace. In this study we adapted four scale items from Salanova, Llorens, Cifre, Martinez, and SchaufeU (2003) to measure collective efficacy in the strategic decision-making context. A sample item was: "I feel confident about the capabüity of my group to perform the BOSS simulation game very weU". Five items from Hoegl and Gemuenden (2001) were adapted to measure team members' perceptions of team performance. A sample item for team performance was: "The project result was of high quaüty". AU scale items for the variables above were measured using 5-point Likert scales ranging from 1 = strongly disagree to 5 = strongly agree.
Because of the smaU sample size, the partial least squares (PLS) approach exemplified by the SmartPLS 2.0.M3 software package was used to test die present research model. According to Chin, Marcolin, and Newsted (2003), die minimum sample size required for the PLS approach is equal to 10 times die largest number of structural paths directed at a particular construct. As depicted in Figure 1, the largest number of structural paths was tiiree padis directed at team performance; thus, the required sample size in our proposed model was 30 teams or more. Our study included 32 teams, which exceeded the aforementioned direshold value. Consequently, the PLS approach was suitable to test die proposed hypotheses.
The TMS, me coUective mind, collective efficacy, and team performance were analyzed at the team level. Therefore, individual scores for these variables were aggregated within each team to obtain team-level scores. The R^8 coefficient (James, Demaree, & Wolf, 1984), representing interrater reUability, was examined to determine whether team-level scores should be aggregated. The median Rwglj) scores across teams for the TMS, me coUective mind, coUective efficacy, and team performance were 0.940 (SD = 0.659), 0.960 (SD = 0.356), 0.950 (SD = 0.716), and 0.950 (SD = 0.855), respectively, which exceeded die threshold of 0.7 (George, 1990) and demonstrated highly acceptable levels of interrater reliability. Of me 32 teams, two teams' R", scores did not exceed the 0.7 direshold level of reUabUity. One team's Rwg score for collective efficacy was 0.62, while anotiier team's Rwg score for team performance was 0.56. To maximize our sample size, the aforementioned two teams were kept in the sample.
Testing the Common Method Effect
Following the recommendations of Podsakoff and Organ (1986), Harman's one-factor test was conducted to verify whether a common method effect existed after the variables had been measured. The basic logic of this technique is mat common method variance exists if either a single factor emerges, or the first unrotated factor extracted from me factor analysis containing all variable items, accounts for most of die covariance (Podsakoff & Organ, 1986). AU four variables in the proposed research framework, including the TMS, die collective mind, collective efficacy, and team performance, were entered in an exploratory factor analysis. The result of an unrotated principal components factor analysis revealed that six factors with eigenvalues greater than 1 accounted for 74.09% of the total variance. In addition, die first factor accounted for 24.089% of the total variance, which was less than half of 74.09% of the total variance. Consequently, the common metiiod effect was unlikely to confound the interpretation of the subsequent data analysis.
Testing the Measurement Model
Table 1 contains a list of the parameters of the structural model and depicts the results of the descriptive statistics. To assess the measurement model, we examined the foUowing values: factor loadings, Cronbach's alpha values, composite reliability, and average variance extracted (AVE).
The factor loading analysis for TMS, coUective mind, coUective efficacy, and team performance revealed mat all factor loadings on the corresponding constructs exceeded the direshold of 0.7. The values of Cronbach's alpha also exceeded the direshold of 0.7 for aU of die constructs (NunnaUy, 1978). The values of composite reliabUity aU exceeded die direshold of 0.6 (Fomell & Larcker, 1981). These results indicate that the present measurement model attains acceptable internal reliabiüty for each construct.
The AVE values measuring convergent validity are shown in Table 1, and all were above the direshold of 0.5 (ForneU & Larcker, 1981). Thus, the convergent validity of die measurement model is acceptable. The values of the square root of AVE in each construct can be seen in Table 1, and aU are larger than dieir corresponding interconstruct correlations. Such results indicate that the discriminant validity of die measurement model is also acceptable (ForneU & Larcker, 1981). In sum, data analysis confirms the validity of me measurements proposed in the present research model.
Testing the Structural Model
The statistical significance of the structural paths depicted in Figure 2 was assessed using the SmartPLS 2.0.M3 bootstrap procedure, with 500 resamples. As shown in Figure 2, all except Hypodieses 2 and 3 were supported. Collective efficacy had a positive significant impact on team performance (ß = .563, p < .01), which supported Hypomesis 1 and was consistent with the result gained by GuUy et al. (2002). The path coefficient (ß = -. 1 83, p > .05) from the TMS to team performance was not significant; therefore, Hypothesis 2 was not supported. The patii coefficient (ß = .03 1 , p > .05) from the collective mind to team performance was not significant; thus, Hypothesis 3 was not supported. The confirmation of Hypothesis 1 and die refutation of Hypodieses 2 and 3 indicate that, compared with die TMS and die coUective mind, only coUective efficacy had a significant direct influence on team performance.
The TMS was found to be positively associated wim coUective efficacy (ß = .490, p < .01), tìiereby validating Hypodiesis 4. The results confirmed those gained by GuUy et al. (2002) and Mannix et al. (2002), indicating mat a team's TMS is related to its efficacy.
The TMS was found to influence a team's coUective mind positively (ß = .680, p < .01), thus confirming Hypodiesis 5. The coUective mind had a positive impact on coUective efficacy (ß = .318, p < .01), thereby supporting Hypothesis 6. The results suggest that a greater TMS enhances the collective mind, which in turn leads to greater coUective efficacy.
Testing the Mediating Effect
The Sobel test (Sobel, 1982) was used to evaluate the mediating effects of Hypodieses 7, 8, and 9. Four independent PLS models (Model 1, Model 2, Model 3, and Model 4) were developed foUowing the testing mediating procedure of Neufeld, Dong, and Higgins (2007) by using bootstrapping with 500 resamplings to yield t test values for me Sobel test. In Table 2, Model 1 included die patii from the TMS to the collective mind (t = 16.520), and Model 2 included the path from the coUective mind to coUective efficacy (t = 15.314). Taking Models 1 and 2 together, the z value of the Sobel test was significant (z = 11.231, z < .001). The results indicate that the coUective mind mediates die influence of the TMS on coUective efficacy. Hypothesis 7 was therefore supported.
In Table 2, Model 3 included die path from the TMS to coUective efficacy (t = 5.915) and die path from the coUective mind to collective efficacy (t = 4.170). Model 4 included die path from coUective efficacy to team performance (t = 9.022). The corresponding z values of die Sobel test were aU significant, confirming that coUective efficacy not only mediated the impact of the TMS on team performance (z = 4.947, p < .001) but also influenced die effect of the coUective mind on team performance (z = 3.785, p < .001). Thus, Hypotheses 8 and 9 were supported.
In this study we incorporated the TMS and die coUective mind to conceptualize how team cognition is shaped and contributes to coUective efficacy. The results from the data analysis confirm aU except Hypotheses 2 and 3. Contrary to the findings gained in previous studies, in our research we did not find support for the hypothesis that the TMS and the coUective mind are causaUy linked to team performance. Rather, our research results indicate that collective efficacy carries the impacts of the TMS to team performance. In addition, die coUective mind mediates die effect of die TMS on coUective efficacy.
The insignificant TMS-team performance relationship may be due to the confidence level among team members; if there is overconfidence among team members, it wiU hinder die TMS-team performance relations. The rationale is that if team members are overconfident in otiier members' expertise, they may overemphasize the capabihty of team members and fail to acknowledge die corresponding weaknesses, which would result in poor team performance. On the other hand, the team members' knowledge levels could provide anotiier plausible explanation for the insignificant TMS-team performance relationship. When an individual member's knowledge level is insufficient, he or she cannot provide other members with an adequate knowledge source for effective strategic decision making. This occurrence may hinder their coordinated efforts to enhance team performance.
By conceptualizing team cognition in terms of the TMS and the coUective mind, we have investigated the relationship among the TMS, the coUective mind, coUective efficacy, and team performance. Several important findings were revealed. Firstly, we found that the TMS affects the coUective mind, which in turn influences coUective efficacy. Therefore, integrating the TMS with the coUective mind can provide a clearer understanding of how coUective efficacy develops in team processes. Secondly, coUective efficacy not only influences team performance but also serves as a mediator. That is, the TMS and the coUective mind will help foster the team's collective efficacy, which in tum affects team performance.
In conclusion, with the aid of team cognition in terms of the TMS and the collective mind, the current study contributes to the Uterature by empirically clarifying the impact of team cognition on coUective efficacy and team performance in a strategic decision-making context. Team cognition should therefore be acknowledged as an effective mechanism to facüitate collective efficacy and team performance. Exploring team cognition in relation to collective efficacy and team performance is beneficial in capturing teamwork dynamics in strategic decision making.
This study has several important impUcations for business managers. Firstly, it is important to develop a TMS because it facilitates the team's development of a coUective mind and collective efficacy. The findings suggest that a TMS and a collective mind alone are not enough to achieve better team performance. It is the team's collective efficacy that faciUtates team performance. When recruiting team members, managers should consider each member's specialized role to avoid any redundancy and help initiate TMS development. Furthermore, the literature suggests that interpersonal trust affects TMS development (Akgiin, Byrne, Keskin, Lynn, & Imamoglu, 2005). Managers should endeavor to create a climate of interpersonal trust to leverage a team's TMS. Finally, Tasa et al. (2007) pointed out that teamwork behaviors are positively related to coUective efficacy. Given the importance of collective efficacy on improving a team's performance, team leaders should train team members to acquire teamwork skills that facilitate coUective efficacy development.
This study has a few limitations that should be addressed. Firstly, because the participants were university students, the findings cannot be generalized to other team settings. Future researchers should increase the sample size to analyze the data at the team level with the structural equation modeling (SEM) method. Secondly, the team size in this study was limited to three or four members, which does not reflect the variety of team sizes found in real organizational contexts. Finally, role assignment and team composition in the current study were randomly conducted, which also did not reflect real-life organizational situations. In future studies, team size and team member diversity should be used as control variables and their impacts on the TMS and team performance should be scrutinized.
Akgün, A. E., Byrne, J. C, Keskin, H., & Lynn, G. S. (2006). Transactive memory system in new product development teams. IEEE Transactions on Engineering Management, 53, 95- 1 1 1 . http:// doi.org/g46
Akgün, A. E., Byrne, J. C, Keskin, H., Lynn, G. S., & Imamoglu, S. Z. (2005). Knowledge networks in new product development projects: A transactive memory perspective. Information & Management, 42, 1105-1120. http://doi.org/g47
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
Berry, G. R. (2006). Can computer-mediated asynchronous communication improve team processes and decision making? Learning from the management literature. Journal of Business Communication, 43, 344-366. http://doi.org/g48
Brockmann, E. N., & Anthony, W. P. (1998). The influence of tacit knowledge and collective mind on strategic planning. Journal of Managerial Issues, 10, 204-222. Accessed at http://www.jstor. org/stable/40604193
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14, 189-217. http://doi.org/fmswns
Crowston, K., & Kammerer, E. E. (1998). Coordination and collective mind in software requirements development. IBM Systems Journal, 37, 227-245. http://doi.org/g49
DeChurch, L. A., & Mesmer-Magnus, J. R. (2010). The cognitive underpinnings of effective teamwork: A meta-analysis. Journal of Applied Psychology, 95, 32-53. http://doi.org/bbdc2t
Dooley, R. S., & Fryxell, G. E. (1999). Attaining decision quality and commitment from dissent: The moderating effects of loyalty and competence in strategic decision-making teams. The Academy of Management Journal, 42, 389-402. http://doi.org/bvtmbx
Fornell, C, & Larcker D. E (1981). Evaluating structural equation models with !inobservable variables and measurement error. Journal of Marketing Research, 18, 382-388. http://doi.org/ cwp
George, J. M. (1990). Personality, affect, and behavior in groups. Journal of Applied Psychology, 75, 107-116. http://doi.org/g5b
Gully, S. M., Incalcaterra, K. A., Joshi, A., & Beaubien, J. M. (2002). A meta-analysis of team-efficacy, potency, and performance: Interdependence and level of analysis as moderators of observed relationships. Journal of Applied Psychology, 87, 819-832. http://doi.org/bcfc2f
Hoegl, M., & Gemuenden, H. G. (2001). Teamwork quality and the success of innovative projects: A theoretical concept and empirical evidence. Organization Science, 12, 435-449. http://doi.org/ bcbj7f
James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85-98. http://doi.org/g5c
Kanawattanachai, P., & Yoo, Y. (2007). The impact of knowledge coordination on virtual team performance over time. MIS Quarterly, 31, 783-808. Accessed at http://aisel.aisnet.org/misq/ vol31/iss4/81
Mannix, E. A., Griffith, T, & Neale, M. A. (2002). The phenomenology of conflict in distributed work teams. In P. J. Hinds & S. Kiesler, (Eds.), Distributed work (pp. 213-233). Cambridge, MA: MTT Press.
Michinov, N., & Michinov, E. (2009). Investigating the relationship between transactive memory and performance in collaborative learning. Learning and Instruction, 19, 43-54. http://doi.org/g5d
Neufeld, D. J., Dong, L., & Higgins, C. (2007). Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16, 494-510. http://doi.org/ g5f
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
Olson, B. J., Bao, Y, & Parayitam, S. (2007). Strategic decision making within Chinese firms: The effects of cognitive diversity and trust on decision outcomes. Journal of World Business, 42, 35-46. http://doi.org/g5g
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 531-544. http://doi.org/czv
Parayitam, S., & Dooley, R. S. (2009). The interplay between cognitive and affective conflict and cognition- and affect-based trust in influencing decision outcomes. Journal of Business Research, 62, 789-796. http://doi.org/g5h
Prichard, J. S., & Ashleigh, M. J. (2007). The effects of team-skills training on transactive memory and performance. Small Group Research, 38, 696-726. http://doi.org/g5j
Salanova, M., Llorens, S., Cifre, E., Martinez, I. M., & Schaufeli, W. B. (2003). Perceived collective efficacy, subjective well-being and task performance among electronic work groups: An experimental study. Small Group Research, 34, 43-73. http://doi.org/g5k
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290-312). San Francisco, CA: Jossey-Bass.
Tao, Y H., & Hung, K. C. (2010). Who performs better in business simulation game learning? A case study of a college general course. In N. Mastorakis, V. Mladenov, & Z. Bojkovic (Eds.), The 10th World Scientific and Engineering Society International Conference on Applied Informatics and Communications (pp. 471-476). Taipei: WSEAS Press. Accessed at http://www.wseas. us/e-library/conferences/2010/Taipei/AIBE-78.pdf
Tasa, K., Taggar, S., & Seijts, G. H. (2007). The development of collective efficacy in teams: A multilevel and longitudinal perspective. Journal of Applied Psychology, 92, 17-27. http://doi. org/g5m
Wegner, D. M., Erber, R., & Raymond, P (1991). Transactive memory in close relationships. Journal of Personality and Social Psychology, 61, 923-929. http://doi.org/g5n
Weick, K. E., & Roberts, K. H. (1993). Collective mind in organizations: Heedful interrelating on flight decks. Administrative Science Quarterly, 38, 357-381. http://doi.org/d43gfp
Yoo, Y, & Kanawattanachai, P. (2001). Developments of transactive memory systems and collective mind in virtual teams. The International Journal of Organizational Analysis, 9, 187-208. http:// doi.org/dggwmv
National Central University
National Central University and Ming Chi University of Technology
National Taiwan Normal University
Huey-Wen Chou, Department of Information Management, National Central University; Yu-Hsun Lin, Department of Information Management, National Central University, and Department of Business Management, Ming Chi University of Technology; Shyan-Bin Chou, Graduate Institute of Design, National Taiwan Normal University.
This study was supported financially by the National Science Council of Taiwan under grant NSC97-2511-S-008-004-MY2.
Correspondence concerning this article should be addressed to: Yu-Hsun Lin, Department of Business Management, Ming Chi University of Technology, No. 84, Gongzhuan Rd., Taishan, New Taipei City 24301, Taiwan, ROC. Email: firstname.lastname@example.org
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