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Simulation and Virtual Experimentation: Grounding with Empirical Data

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Group Processes

Part of the book series: Computational Social Sciences ((CSS))

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Abstract

Some examinations of group processes may require intensive data collection to answer initial research questions. Findings, however, often lead to more research questions that could be answered if additional data were available. In cases where collecting additional data is cost prohibitive, researchers may benefit from formulating “what-if” questions that can be answered via simulation and virtual experimentation. This chapter presents a step-by-step guide to demonstrate (1) how simulation procedures can be developed and validated with existing empirical data and (2) how these procedures can be executed to conduct virtual experiments. To demonstrate these steps, we provide a tutorial based on potential what-if questions about two different aspects of the relationship between team cohesion and team effectiveness using continuous and discrete empirical data, respectively, along with Matlab code for the simulation, validation, and virtual experimentation. We then present two more complex examples from our own published papers.

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References

  • Anderson Jr., E. G., & Lewis, K. (2013). A dynamic model of individual and collective learning amid disruption. Organization Science, 25(2), 356–376.

    Article  Google Scholar 

  • Avramidis, A. N., & L’Ecuyer, P. (2006). Efficient Monte Carlo and quasi—Monte Carlo option pricing under the variance gamma model. Management Science, 52(12), 1930–1944.

    Google Scholar 

  • Bhuiyan, N., Gerwin, D., & Thomson, V. (2004). Simulation of the new product development process for performance improvement. Management Science, 50(12), 1690–1703.

    Article  Google Scholar 

  • Burton, R. M., & Obel, B. (2011). Computational modeling for what-is, what-might-be, and what-should-be studies-And triangulation. Organization Science, 22(5), 1195–1202.

    Article  Google Scholar 

  • Carley, K., & Prietula, M. (Eds.) (1994). Computational organization theory. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Chance, D. M., Hillebrand, E., & Hillard, J. E. (2008). Pricing an option on revenue from an innovation: An application to movie box office revenue. Management Science, 54(5), 1015–1028.

    Article  MATH  Google Scholar 

  • Charnes, J. M., & Shenoy, P. P. (2004). Multistage Monte Carlo method for solving influence diagrams using local computation. Management Science, 50(3), 405–418.

    Article  MATH  Google Scholar 

  • Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32, 480–499.

    Article  Google Scholar 

  • Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575–600.

    Google Scholar 

  • Georgieva, A., & Jordanov, I. (2009). Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms. European Journal of Operational Research, 196, 413–422.

    Article  MATH  Google Scholar 

  • Gersick, C. J. G. (1988). Time and transition in work teams: Toward a new model of group development. Academy of Management Journal, 31, 9–41.

    Article  Google Scholar 

  • Gersick, C. J. G. (1989). Marking time: Predictable transitions in task groups. Academy of Management Journal, 32, 274–309.

    Article  Google Scholar 

  • Gorsuch, R. L. (1983). Factor Analysis (2nd ed., ). Hillsdale, NJ: Lawrence Erlbaum.

    MATH  Google Scholar 

  • Grand, J. A., Braun, M. T., Kuljanin, G., Kozlowski, S. W. J., & Chao, G. T. (2016). The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101(10), 1353–1385.

    Google Scholar 

  • Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. The Academy of Management Review, 32, 1229–1245.

    Article  Google Scholar 

  • Hulin, C. L., & Ilgen, D. R. (2000). Introduction to computational modeling in organizations: The good that modeling does. In D. R. Ilgen, & C. L. Hulin (Eds.), Computational modeling of behavior in organizations: The third scientific discipline (pp. 3–18). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Katzenbach, J., & Smith, D. (1999). The wisdom of teams: Creating the high-performance organization. Cambridge, MA: Harvard Business School Press.

    Google Scholar 

  • Kennedy, D. M., & McComb, S. A. (2014). When teams shift among processes: Insights from simulation and optimization. Journal of Applied Psychology, 99(5), 784–815.

    Article  Google Scholar 

  • Kennedy, D. M., McComb, S. A., & Vozdolska, R. (2011). Using simulation to analyze complex behavioral models: An investigation of project complexity’s influence on team communication. Journal of Engineering and Technology Management., 28(3), 109–127.

    Article  Google Scholar 

  • Kozlowski, S. W., & Chao, G. T. (2012). The dynamics of emergence: Cognition and cohesion in work teams. Managerial and Decision Economics, 33(5–6), 335–354.

    Article  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), 581–615.

    Article  Google Scholar 

  • Kozlowski, S. W. J., Chao, G. T., Grand, J. A., & Braun, M. T. (2016). Capturing the multilevel dynamics of emergence: Computational modeling, simulation, and virtual experimentation. Organizational Psychology Review, 6(1), 3–33.

    Article  Google Scholar 

  • Larson Jr., J. R. (2012). Computer simulation methods for groups. In A. B. Hollingshead, & M. S. Poole (Eds.), Research methods for studying groups and teams (pp. 329–357). New York, NY: Routledge.

    Google Scholar 

  • Law, A. M., & Kelton, W. D. (2000). Simulation modeling and analysis (3rd ed., ). New York, NY: McGraw-Hill.

    MATH  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, 356–376.

    Google Scholar 

  • McGrath, J. E. (1981). Dilemmatics: The study of research choices and dilemmas. The American Behavioral Scientist, 25(2), 179.

    Article  Google Scholar 

  • McComb, S. A., Green, S. G., & Compton, W. D. (2007). Team flexibility’s relationship to staffing and performance in complex projects: An empirical analysis. Journal of Engineering Technology Management, 24, 293–313.

    Article  Google Scholar 

  • Metropolis, N., & Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247), 335–341.

    Google Scholar 

  • Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6(3), 328–362.

    Google Scholar 

  • Okhuysen, G. A. (2001). Structuring change: Familiarity and formal interventions in problem-solving groups. Academy of Management Journal, 44, 794–808.

    Article  Google Scholar 

  • Okhuysen, G. A., & Eisenhardt, K. E. (2002). Integrating knowledge in groups: How formal interventions enable flexibility. Organization Science, 13, 370–386.

    Article  Google Scholar 

  • Okhuysen, G. A., & Waller, M. J. (2002). Focusing on midpoint transitions: An analysis of boundary conditions. Academy of Management Journal, 45, 1056–1065.

    Article  Google Scholar 

  • Patrashkova, R., & McComb, S. A. (2004). Exploring why more communication is not better: Insights from a computational model of cross-functional teams. Journal of Engineering and Technology Management, 21(1–2), 23–81.

    Google Scholar 

  • Patrashkova-Volzdoska, R. R., McComb, S. A., Green, S. G., & Compton, W. D. (2003). Examining a curvilinear relationship between communication frequency and team performance in cross-functional project teams. IEEE Transactions on Engineering Management, 50(3), 262–269.

    Article  Google Scholar 

  • Prietula, M., Carley, K., & Gasser, L. (Eds.) (1998). Simulating organizations: Computational models of institutions and groups. Cambridge, MA: MIT Press.

    Google 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(5), 671–682.

    Article  Google Scholar 

  • Roth, P. L., Switzer III, F. S., & Switzer, D. M. (1999). Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques. Organizational Research Methods, 2, 211–232.

    Article  Google Scholar 

  • Solow, D., Vairaktarakis, G., Piderit, S. K., & Tsai, M. (2002). Managerial insights into the effects of interactions on replacing members of a team. Management Science, 48(8), 1060–1073.

    Article  MATH  Google Scholar 

  • Valenzuela, J. M., & Mazumbar, M. (2003). Commitment of electric power generators under stochastic market prices. Operations Research, 51(6), 880–893.

    Article  Google Scholar 

  • Wang, M., Zhou, L., & Zhang, Z. (2016). Dynamic modeling. Annual Review of Organizational Psychology and Organizational Behavior, 3, 241–266.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business, University of Washington Bothell, Bothell, WA, USA

    Deanna Kennedy

  2. School of Nursing and School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Sara McComb

Authors
  1. Deanna Kennedy
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  2. Sara McComb
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Correspondence to Deanna Kennedy .

Editor information

Editors and Affiliations

  1. University of Kentucky, Lexington, Kentucky, USA

    Andrew Pilny

  2. University of Illinois, Urbana, Illinois, USA

    Marshall Scott Poole

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Kennedy, D., McComb, S. (2017). Simulation and Virtual Experimentation: Grounding with Empirical Data. In: Pilny, A., Poole, M. (eds) Group Processes. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-48941-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-48941-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48940-7

  • Online ISBN: 978-3-319-48941-4

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