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Social learning in design teams: The importance of direct and indirect communications

Published online by Cambridge University Press:  18 April 2013

Vishal Singh ,
Andy Dong  and
John S. Gero
Vishal Singh*
Affiliation:
School of Engineering, Aalto University, Espoo, Finland
Andy Dong
Affiliation:
University of Sydney, Sydney, Australia
John S. Gero
Affiliation:
Krasnow Institute of Advanced Study, George Mason University, Fairfax, Virginia, USA
*
Reprint requests to: Vishal Singh, School of Engineering, Aalto University, Espoo, Finland. Email: vishal.singh@aalto.fi
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Abstract

This paper discusses the effects of direct and indirect communications on social learning and task coordination in design teams. The findings reported in this paper are based on a computational model that simulates the formation of transactive memory (TM) through social learning from direct and indirect communications. Direct communications are explicit information exchanged between team members whereas indirect communication may be opportunistic and coincidental, resulting in learning and information gained through observations of the actions of others. However, team structure mediates opportunities for communication. Three types of team structures are studied, which are differentiated on the basis of their constraints on and opportunities for direct and indirect communications across the team. The differences across the team structures are investigated through a series of simulations in which team member retention, cognitive busyness of team members, and task complexity are additional moderating variables, and task coordination and formation of TM are the dependent variables. Fewer communications to coordinate the same tasks are taken as the measure of efficient task coordination. Findings suggest that reduction in communication and learning opportunities are more detrimental to the task coordination in flat teams as compared to functional teams. Indirect communications contribute more to the formation of TM than to task coordination. Flat teams facilitate the formation of TM, whereas functional teams are more appropriate for efficient task coordination, indicating that the role of TM in mediating task coordination varies with team structure.

Keywords

BusynessDirect CommunicationMember RetentionSocial LearningTask CoordinationTeam StructureTransactive Memory
Type
Special Issue Articles
Information
AI EDAM , Volume 27 , Issue 2: Studying and Supporting Design Communication , May 2013 , pp. 167 - 182
Copyright
Copyright © Cambridge University Press 2013

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References

REFERENCES

Akgun, A.E., Lynn, G.S., & Yilmaz, C. (2006). Learning process in new product development teams and effects on product success: a socio-cognitive perspective. Industrial Marketing Management 35, 210–224.CrossRefGoogle Scholar
Allen, T.J. (1977). Managing the Flow of Technology, Cambridge, MA: MIT Press.Google Scholar
Argote, L. (2005). Reflections on two views of managing learning and knowledge in organizations. Journal of Management Inquiry 14(1), 43–48.CrossRefGoogle Scholar
Badke-Schaub, P., Neumann, A., Lauche, K., & Mohammed, S. (2007). Mental models in design teams: a valid approach to performance in design collaboration? CoDesign 3, 5–20.CrossRefGoogle Scholar
Bandura, A. (1977). Social Learning Theory. New York: General Learning Press.Google Scholar
Blinn, C.K. (1996). Developing high performance teams. Online 20(6), 56.Google Scholar
Bobrow, D.G., & Whalen, J. (2002). Community knowledge sharing in practice: the eureka story. Reflections 4, 47–59.CrossRefGoogle Scholar
Brown, D.G. (1996). Routiness revisited. In Mechanical Design: Theory and Methodology (Waldron, M., & Waldron, K., Eds.), pp. 195–208. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Brown, J.S., & Duguid, P. (1996). Stolen knowledge. In Situated Learning Perspectives (McLellen, H., Ed.), pp. 47–56. Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
Bucciarelli, L.L. (2003). Engineering Philosophy. Delft: Delft University Press.Google Scholar
Candy, L., & Edmonds, E. (2003). Collaborative expertise for creative technology design. Proc. Design Thinking Research Symp., University of Technology, Sydney.Google Scholar
Carley, K.M., & Gasser, L. (1999). Computational organization theory. In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (Weiss, G., Ed.). Cambridge, MA: MIT Press.Google Scholar
Carrizosa, K., & Sheppard, S. (2000). The importance of learning styles in group design work. Proc. 30th Annual Frontiers Education (Vol. 1). Washington, DC: IEEE Computer Society.Google Scholar
Cohen, S.G., & Bailey, D.E. (1997). What makes teams work: group effectiveness research from the shop floor to the executive suite. Journal of Management 23, 239–290.CrossRefGoogle Scholar
Conte, R., & Gilbert, N. (1995). Computer simulation for social theory. Artificial Societies: The Computer Simulation of Social Life. London: UCL Press.Google Scholar
Cooke, N.J., Salas, E., Cannon-Bowers, J.A., & Stout, R.J. (2000). Measuring team knowledge. Human Factors 42, 151–173.CrossRefGoogle ScholarPubMed
Cooke, N.J., Salas, E., Kiekel, P.A., & Bell, B. (2004). Advances in measuring team cognition. In Team Cognition: Understanding the Factors That Drive Process and Performance (Salas, E., & Fiore, S.M., Eds.). Washington, DC: American Psychological Association.Google Scholar
Cramton, C.D. (2001). The mutual knowledge problem and its consequences for dispersed collaboration. Organization Science 12(3), 346.CrossRefGoogle Scholar
Del Cerro, G., Lemee, J., Mar, E., Wei, C.S., Weiman, C., & Wortzell, A. (2001). Assessing communication modes in design project teams. Proc. 31st Annual Frontiers in Education Conf., pp. 287–294.Google Scholar
Desanctis, G., & Jackson, B. (1994). Coordination of information technology management: team-based structures and computer-based communication systems. Journal of Management Information Systems 10, 85–110.CrossRefGoogle Scholar
Eckert, C., Maier, A., & McMahon, C. (2005). Communication in design. In Design Process Improvement: A Review of Current Practice (Clarkson, J., & Eckert, C., Eds.), pp. 232–261. London: Springer.CrossRefGoogle Scholar
Entin, E.E., & Sarfaty, D. (1999). Adaptive team coordination. Human Factors 41, 312–325.CrossRefGoogle Scholar
Eppinger, S., & Salminen, V. (2001). Patterns of product development interactions. Proc. Int. Conf. Engineering Design, Glasgow.Google Scholar
Fulk, J., & Boyd, B. (1991). Emerging theories of communication in organizations. Journal of Management 17, 407–446.CrossRefGoogle Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gilbert, D.T., & Osborne, R.E. (1989). Thinking backward: some curable and incurable consequences of cognitive busyness. Journal of Personality and Social Psychology 57, 940–949.CrossRefGoogle Scholar
Gilbert, D.T., Pelham, B.W., & Krull, D.S. (1988). On cognitive busyness: when person perceivers meet persons perceived. Journal of Personality and Social Psychology 54, 733–740.CrossRefGoogle Scholar
Gilbert, N. (2004). Agent-based social simulation: dealing with complexity. Accessed April 10, 2008, at http://www.soc.surrey.ac.uk/staff/ngilbert/ngpub/paper165_NG.pdfGoogle Scholar
Gino, F., Argote, L., Miron-Spektor, E., & Todorova, G. (2010). First, get your feet wet: the effects of learning from direct and indirect experience on team creativity. Organizational Behavior and Human Decision Processes 111, 102–115.CrossRefGoogle Scholar
Grant, R.M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal 17, 109–122.CrossRefGoogle Scholar
Grecu, D.L., & Brown, D.C. (1998). Dimensions of machine learning in design [Special Issue]. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12.CrossRefGoogle Scholar
Hackman, J.R. (1987). The design of work teams. In Handbook of Organizational Behavior (Lorsch, J., Ed.). Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Hinds, P.J., Carley, K.M., Krackhardt, D., & Wholey, D. (2000). Choosing work group members: balancing similarity, competence, and familiarity. Organizational Behavior and Human Decision Processes 81, 226–251.CrossRefGoogle ScholarPubMed
Huckman, R.S., & Staats, B.R. (2011). Fluid tasks and fluid teams: the impact of diversity in experience and team familiarity on team performance. Manufacturing & Service Operations Management 13(3), 310–328.CrossRefGoogle Scholar
Jin, Y., Levitt, R.E., Christiansen, T., & Kunz, J.C. (1995). The virtual design team: modeling organizational behavior of concurrent design teams. Artificial Intelligence for Engineering, Design Analysis and Manufacturing 9(2), 145–158.CrossRefGoogle Scholar
Katz, R. (1982). The effects of group longevity on project communication and performance. Administrative Science Quarterly 27, 81–104.CrossRefGoogle Scholar
Katzenbach, S.D. Jr. (1993). The discipline of teams. Harvard Business Review 71, 111–120.Google ScholarPubMed
Kiesler, S., & Cummings, J. (2002). What do we know about proximity and distance in work groups? In Distributed Work (Hinds, P., & Kiesler, S., Eds.), pp. 76–109. Cambridge, MA: MIT Press.Google Scholar
Kleinsmann, M., & Valkenburg, R. (2008). Barriers and enablers for creating shared understanding in co-design projects. Design Studies 29(4), 369–386.CrossRefGoogle Scholar
Kleinsmann, M., Valkenburg, R., & Buijs, J. (2007). Why do(n't) actors in collaborative design understand each other? An empirical study towards a better understanding of collaborative design. CoDesign 5(1), 1–15.Google Scholar
Knobe, J., & Malle, B.F. (2002). Self and other in the explanation of behavior: 30 years later. Psychological Belgica 42, 113–130.CrossRefGoogle Scholar
Laubacher, R., & Malone, T.W. (2002). Temporary Assignments and a Permanent Home: A Case Study in the Transition to Project-Based Organizational Practices. Cambridge, MA: MIT Sloan School of Management.Google Scholar
Leinonen, P., Jarvela, S., & Hakkinen, P. (2005). Conceptualizing the awareness of collaboration: a qualitative study of a global virtual team. Computer Supported Cooperative Work 14, 301–322.CrossRefGoogle Scholar
Lewis, K. (2003). Measuring transactive memory systems in the field: scale development and validation. Journal of Applied Psychology 88, 587–604.CrossRefGoogle ScholarPubMed
Love, P.E.D., Gunasekaran, A., & Li, H. (1998). Concurrent engineering: a strategy for procuring construction projects. International Journal of Project Management 16(6), 375–383.CrossRefGoogle Scholar
Macy, M., & Willer, R. (2002). From factors to actors: computational sociology and agent-based modeling. Annual Review of Sociology 28, 143–166.CrossRefGoogle Scholar
Maier, A.M., Eckert, C.M., & Clarkson, P.J. (2005). A meta-model for communication in engineering design. CoDesign 1(4), 243–254.CrossRefGoogle Scholar
Malle, B.F. (2005). Folk theory of mind: conceptual foundations of human social cognition. In The New Unconscious (Hassin, R., Uleman, J.S., & Bargh, J.A., Eds.). New York: Oxford University Press.Google Scholar
Malone, T.W. (1987). Modeling coorditation in organizations and markets. Management Science 33, 1317–1332.CrossRefGoogle Scholar
Marsick, V., & Watkins, K. (1997). Lessons from informal and incidental learning. In Management Learning: Integrating Perspectives in Theory and Practice (Burgoyne, J., & Reynolds, M., Eds.), pp. 295–311. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
Mcdonough, E.F., Kahn, K.B., & Barczak, G. (2001). An investigation of the use of global, virtual, and colocated new product development teams. Journal of Product Innovation Management 18, 110–120.CrossRefGoogle Scholar
Milne, A.J., & Leifer, L.J. (2000). Information handling and social interaction of multi-discplinary design teams in conceptual design: a classification scheme developed from observed activity patterns. Proc. DETC'00: 2000 ASME Design Engineering Technical Conf., Baltimore, MD.Google Scholar
Moore, D.R., & Dainty, A.R.J. (1999). Integrated project teams' performance in managing unexpected change events. Team Performance Management 5(7), 212–222.CrossRefGoogle Scholar
Moreland, R.L., Argote, L., & Krishnan, R. (1998). Training people to work in groups. In Theory and Research on Small Groups (Tindale, R.S., & Heanth, L., Eds.), pp. 37–60. New York: Plenum.Google Scholar
Perkins, S. (2005). Building and managing a successful design team. STEPMagazine. Accessed at http://www.stepinsidedesign.com/STEPMagazine/Article/28410/0/page/1Google Scholar
Rao, D.R., & Argote, L. (2006). Organizational learning and forgetting: the effects of turnover and structure. European Management Review 3, 77–85.CrossRefGoogle Scholar
Reagans, R., Argote, L., & Brooks, D. (2005). Individual experience and experience working together: predicting learning rates from knowing who knows what and knowing how to work together. Management Science 51, 869–881.CrossRefGoogle Scholar
Ren, Y., Carley, K.M., & Argote, L. (2006). The contingent effects of transactive memory: when is it more beneficial to know what others know? Management Science 52, 671–682.CrossRefGoogle Scholar
Rodan, S. (2008). Organizational learning: effects of (network) structure and (individual) strategy. Computational & Mathematical Organization Theory 14, 222–247.CrossRefGoogle Scholar
Simon, H. A. (1991). Bounded rationality and organizational learning. Organization Science 2, 125–134.CrossRefGoogle Scholar
Singh, V., Dong, A., & Gero, J.S. (in press). Developing a computational model to understand the contributions of social learning modes to task coordination in teams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing.Google Scholar
Sonnenwald, D.H. (1996). Communication roles that support collaboration during the design process. Design Studies 17(3), 277–301.CrossRefGoogle Scholar
Sosa, M.E., Eppinger, S.D., Pich, M., Mckendrick, D.G., & Stout, S.K. (2002). Factors that influence technical communication in distributed product development: an empirical study in the telecommunications industry. Engineering Management, IEEE Transactions on Engineering Management 49(1), 45–58.Google Scholar
Sosa, M.E., Eppinger, S.D., & Rowles, C.M. (2004). The misalignment of product architecture and organizational structure in complex product development. Management Science 50, 1674–1689.CrossRefGoogle Scholar
Staats, B.R. (2011). Unpacking team familiarity: the effect of geographic location and hierarchical role. Production and Operations Management. Advance online publication.Google Scholar
Tomasello, M. (1999). The Cultural Origins of Human Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Tuckman, B. (1965). Developmental sequence in small groups. Psychological Bulletin 63(6), 384–399.CrossRefGoogle ScholarPubMed
Wallace, D.M., & Hinsz, V.B. (2009). Group members as actors and observers in attributions of responsibility for group performance. Small Group Research 40, 52–71.CrossRefGoogle Scholar
Wegner, D. (1987). Transactive memory: a contemporary analysis of the group mind. In Theories of Group Behavior (Mullen, B., & Goethals, G.R., Eds.). Berlin: Springer–Verlag.Google Scholar
Wilson, H.J., Guinan, P.J., Parise, S., & Weinberg, B.D. (2011, July–August). What's your social media strategy? Idea watch, Harvard Business Review.Google Scholar
Wu, Z., & Duffy, A.H.B. (2004). Modeling collective learning in design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, 289–313.CrossRefGoogle Scholar