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Space, time, and the development of shared leadership networks in multiteam systems

Published online by Cambridge University Press:  26 February 2015

SOPHIA D. SULLIVAN
Affiliation:
Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA (e-mail: [email protected])
ALINA LUNGEANU
Affiliation:
Technology and Social Behavior, Northwestern University, Evanston, IL, USA (e-mail: [email protected])
LESLIE A. DECHURCH
Affiliation:
School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA (e-mail: [email protected])
NOSHIR S. CONTRACTOR
Affiliation:
Industrial Engineering and Management Sciences, Communication Studies, and Management and Organizations, Northwestern University, Evanston, IL, USA (e-mail: [email protected])

Abstract

Digital technologies have created the potential for new forms of organizing among geographically dispersed individuals by connecting their ideas across the time and space in complex multiteam systems (MTSs). Realizing this potential requires novel forms of shared leadership structures to shepherd divergent and convergent thinking necessary to nurture innovation. While there is limited research on how space influences leadership and how the time influences leadership, there is virtually no theorizing on how space and time interact together to influence the emergence of shared leadership structures that facilitates innovation. A key contribution of this study is to utilize an agent-based model (ABM) that draws upon the research on leadership, networks, and innovation to specify generative mechanisms (or micro-processes) through which shared leadership structures emerge over space and time. The parameters in this model were estimated from empirical data. Results of virtual experiments (VE) yielded testable hypotheses suggesting that, over time, leadership capacity and between-team ties are negatively influenced by space. Furthermore, the computational model suggests that space increases the concentration of divergent leadership but decreases the concentration of convergent leadership. The study concludes by discussing the implications for the design of effective leadership structures to nurture innovation in MTSs.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

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References

Aime, F., Humphrey, S., DeRue, D., & Paul, J. (2013). The riddle of heterarchy: Power transitions in cross-functional teams. Academy of Management Journal, 57 (2), 327352.CrossRefGoogle Scholar
Amabile, T. M. (1996). Creativity and innovation in organizations. Harvard Business School Background Note, 396–239.Google Scholar
Bledow, R., Frese, M., Anderson, N., Erez, M., & Farr, J. (2009). A dialectic perspective on innovation: Conflicting demands, multiple pathways, and ambidexterity. Industrial and Organizational Psychology, 2 (3), 305337.CrossRefGoogle Scholar
Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110 (2), 349399.Google Scholar
Burt, R. S. (2005). Brokerage and closure: An introduction to social capital: An introduction to social capital. New York, NY: Oxford University Press.Google Scholar
Carson, J. B., Tesluk, P. E., & Marrone, J. A. (2007). Shared leadership in teams: An investigation of antecedent conditions and performance. Academy of Management Journal, 50 (5), 12171234.Google Scholar
Carter, D. R., DeChurch, L. A., Braun, M. T., & Contractor, N. S. (2015). Social network approaches to leadership: An integrative conceptual review. Journal of Applied Psychology.Google Scholar
Chan, K.-Y., & Drasgow, F. (2001). Toward a theory of individual differences and leadership: Understanding the motivation to lead. Journal of Applied Psychology, 86 (3), 481498.Google Scholar
Chen, G.-M., & Starosta, W. J. (2000). The development and validation of the Intercultural Sensitivity Scale. Paper presented at the Annual Meeting of the National Communication Association, Seattle, WA.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 (3), 239290.CrossRefGoogle Scholar
Connaughton, S. L., Williams, E. A., & Shuffler, M. L. (2012). Social identity issues in multiteam systems: Considerations for future research. In Zaccaro, S. J., Marks, M. A., & DeChurch, L. A. (Eds.), Multiteam systems: An organization form for dynamic and complex environments (pp. 109139). New York, NY: Routledge.Google Scholar
Contractor, N. S., DeChurch, L. A., Carson, J., Carter, D. R., & Keegan, B. (2012). The topology of collective leadership. The Leadership Quarterly, 23 (6), 9941011.CrossRefGoogle Scholar
Cummings, J. N., & Kiesler, S. (2007). Coordination costs and project outcomes in multi-university collaborations. Research Policy, 36 (10), 16201634.Google Scholar
DeChurch, L. A., & Zaccaro, S. (2013). Innovation in scientific multiteam systems: Confluence and countervailing forces. Paper Comissioned by the National Research Council.Google Scholar
DeRue, D. S., & Ashford, S. J. (2010). Who will lead and who will follow? A social process of leadership identity construction in organizations. Academy of Management Review, 35 (4), 627647.Google Scholar
Druskat, V. U., & Wheeler, J. V. (2003). Managing from the boundary: The effective leadership of self-managing work teams. Academy of Management Journal, 46 (4), 435457.Google Scholar
Fayol, H. (1949). General and industrial management. London: Pitman.Google Scholar
Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47 (1), 117132.Google Scholar
Fleming, L., Mingo, S., & Chen, D. (2007). Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52 (3), 443475.Google Scholar
Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1 (3), 215239.Google Scholar
Gersick, C. J. (1988). Time and transition in work teams: Toward a new model of group development. Academy of Management Journal, 31 (1), 941.Google Scholar
Gong, Y., Huang, J.-C., & Farh, J.-L. (2009). Employee learning orientation, transformational leadership, and employee creativity: The mediating role of employee creative self-efficacy. Academy of Management Journal, 52 (4), 765778.Google Scholar
Gray, B. (2008). Enhancing transdisciplinary research through collaborative leadership. American Journal of Preventive Medicine, 35 (2), S124S132.Google Scholar
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). Statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24 (1), 1548.Google Scholar
Hanneman, R. A. (1988). Computer-assisted theory building: Modeling dynamic social systems. Newbury Park, CA: Sage.Google Scholar
Hatch, M. J., & Schultz, M. (2002). The dynamics of organizational identity. Human Relations, 55 (8), 9891018.CrossRefGoogle Scholar
Hinds, P. J., & Mortensen, M. (2005). Understanding conflict in geographically distributed teams: The moderating effects of shared identity, shared context, and spontaneous communication. Organization Science, 16 (3), 290307.Google Scholar
Hulin, C. L., & Ilgen, D. R. (2000). Introduction to computational modeling in organizations: The good that modeling does. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 318). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review. Creativity Research Journal, 19 (1), 6990.CrossRefGoogle Scholar
Hyatt, A., Contractor, N. S., & Jones, P. (1997). Computational organizational network modeling: Strategies and an example. Computational & Mathematical Organization Theory, 2 (4), 285300.Google Scholar
Jackson, C. L., Colquitt, J. A., Wesson, M. J. & Zapata-Phelan, C. P. (2006). Psychological collectivism: A measurement validation and linkage to group member performance. Journal of Applied Psychology, 91 (4), 884.CrossRefGoogle ScholarPubMed
Jansen, J. J., Vera, D., & Crossan, M. (2009). Strategic leadership for exploration and exploitation: The moderating role of environmental dynamism. The Leadership Quarterly, 20 (1), 518.CrossRefGoogle Scholar
Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87 (4), 765780.Google Scholar
Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2013). Advancing multilevel research design capturing the dynamics of emergence. Organizational Research Methods, 16 (4), 581615.Google Scholar
Lagendijk, A., & Lorentzen, A. (2007). Proximity, knowledge and innovation in peripheral regions. On the intersection between geographical and organizational proximity. European Planning Studies, 15 (4), 457466.Google Scholar
Lee-Davies, L., Kakabadse, N. K., & Kakabadse, A. (2007). Shared leadership: Leading through polylogue. Business Strategy Series, 8 (4), 246253.Google Scholar
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2 (1), 7187.Google Scholar
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26 (3), 356376.Google Scholar
Marsden, P. V., & Friedkin, N. E. (1993). Network studies of social influence. Sociological Methods & Research, 22 (1), 127151.Google Scholar
Mathieu, J., Maynard, M. T., Rapp, T., & Gilson, L. (2008). Team effectiveness 1997–2007: A review of recent advancements and a glimpse into the future. Journal of Management, 34 (3), 410476.CrossRefGoogle Scholar
Mayo, M., Meindl, J. R., & Pastor, J.-C. (2003). Shared leadership in work teams: A social network approach. In Pearce, C. L. & Conger, J. A. (Eds.), Shared leadership: reframing the hows and whys of leadership (pp. 193214). Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415444.Google Scholar
Meister, C., & Werker, C. (2004). Physical and organizational proximity in territorial innovation systems: Introduction to the special issue. Journal of Economic Geography, 4 (1), 12.Google Scholar
Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. New York: Oxford University Press.CrossRefGoogle Scholar
Mortensen, M. (2013). The team unbound: The theoretical, methodological, and managerial implications of fluid and multiplex boundaries in teams. INSEAD Working Papers Collection; 2013, Issue 36, preceding p1.Google Scholar
Mumford, M. D., O'Connor, J., Clifton, T. C., Connelly, M. S., & Zaccaro, S. J. (1993). Background data constructs as predictors of leadership. Human Performance, 6 (2), 151195.Google Scholar
Oh, H., Labianca, G., & Chung, M.-H. (2006). A multilevel model of group social capital. Academy of Management Review, 31 (3), 569582.Google Scholar
Palazzolo, E. T., Serb, D. A., She, Y., Su, C., & Contractor, N. S. (2006). Coevolution of communication and knowledge networks in transactive memory systems: Using computational models for theoretical development. Communication Theory, 16 (2), 223250.Google Scholar
Pearce, C. L. (2004). The future of leadership: Combining vertical and shared leadership to transform knowledge work. The Academy of Management Executive, 18 (1), 4757.Google Scholar
Radchuk, V., Johst, K., Groeneveld, J., Grimm, V., & Schtickzelle, N. (2013). Behind the scenes of population viability modeling: Predicting butterfly metapopulation dynamics under climate change. Ecological Modelling, 259, 6273.Google Scholar
Rosing, K., Frese, M., & Bausch, A. (2011). Explaining the heterogeneity of the leadership-innovation relationship: Ambidextrous leadership. The Leadership Quarterly, 22 (5), 956974.Google Scholar
Somech, A. & Drach-Zahavy, A. (2013). Translating team creativity to innovation implementation: The role of team composition and climate for innovation. Journal of Management, 39 (3), 684708. doi: 10.1177/0149206310394187Google Scholar
Stonedahl, F., & Wilensky, U. (2010). BehaviorSearch (computer software). Center for Connected Learning and Computer Based Modeling, Northwestern University, Evanston, IL. Available at: http://www.behaviorsearch.org/.Google Scholar
Stonedahl, F., & Wilensky, U. (2011). Finding forms of flocking: Evolutionary search in ABM parameter-spaces. In Boss, T., Geller, A., and Jonker, C. (Eds.), Multi-Agent-Based Simulation XI (Vol. 6532, pp. 6175). Berlin Heidelberg: Springer.Google Scholar
Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using netLogo and'R'. Journal of Artificial Societies and Social Simulation, 17 (3), 11.Google Scholar
VandeWalle, D., Brown, S. P., Cron, W. L., & Slocum, J. W. Jr, (1999). The influence of goal orientation and self-regulation tactics on sales performance: A longitudinal field test. Journal of Applied Psychology, 84 (2), 249259.Google Scholar
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge and New York: Cambridge University Press.Google Scholar
Wilensky, U. (1999). NetLogo: Center for connected learning and computer-based modeling, Northwestern University. Retrieved from http://ccl.northwestern.edu/netlogo/Google Scholar
Zaccaro, S. J. & DeChurch, L. A. (2012). Leadership forms and functions in multiteam systems. In Zaccaro, S. J., Marks, M. A., & DeChurch, L. A. (Eds.), Multiteam systems: An organization form for dynamic and complex environments (pp. 253288). New York: Routledge.Google Scholar
Zaccaro, S. J., Marks, M. A. & DeChurch, L. A. (2012). Multiteam systems: An introduction. In Zaccaro, S. J., Marks, M. A., & DeChurch, L. A. (Eds.), Multiteam systems: An organization form for dynamic and complex environments (pp. 332). New York: Routledge.CrossRefGoogle Scholar