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Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions

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  • Ilaria Giannoccaro
  • Giovanni F. Massari
  • Giuseppe Carbone

Abstract

How are teams able to cope with environmental threats? Why are some teams better than others in facing this challenge? This paper addresses these questions by investigating two drivers of team resilience: the team size and the density of social interactions among team members. We adopt a complex system approach and employ a model of team decision-making where collective dynamics of team members are governed by a continuous-time Markov process. The model simulates team performance in complex and turbulent environments. It is used to measure the resilient ability of team to quickly adapt to disturbance and secure a new more desirable condition. Scenarios characterized by increasing levels of complexity and turbulence are simulated, and the resilience performance is calculated and compared. Results show that the team size negatively affects the team resilience, whilst the density of social interactions plays a positive influence, especially at a high level of complexity. We also find that both the magnitude and the frequency of disturbance moderate the relationship between team size/density and the team resilience.

Suggested Citation

  • Ilaria Giannoccaro & Giovanni F. Massari & Giuseppe Carbone, 2018. "Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions," Complexity, Hindawi, vol. 2018, pages 1-11, July.
    • Handle: RePEc:hin:complx:1923216
    DOI: 10.1155/2018/1923216
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    References listed on IDEAS

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    1. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    2. Capaldo, Antonio & Giannoccaro, Ilaria, 2015. "How does trust affect performance in the supply chain? The moderating role of interdependence," International Journal of Production Economics, Elsevier, vol. 166(C), pages 36-49.
    3. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    4. Edward D. Weinberger, 1996. "NP Completeness of Kauffman's N-k Model, A Tuneable Rugged Fitness Landscape," Working Papers 96-02-003, Santa Fe Institute.
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    Cited by:

    1. Shelley D. Dionne & Hiroki Sayama & Francis J. Yammarino, 2019. "Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations," Complexity, Hindawi, vol. 2019, pages 1-16, March.
    2. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).
    3. Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe, 2019. "Are distrust relationships beneficial for group performance? The influence of the scope of distrust on the emergence of collective intelligence," International Journal of Production Economics, Elsevier, vol. 208(C), pages 343-355.
    4. Massari, Giovanni Francesco & Giannoccaro, Ilaria, 2021. "Investigating the effect of horizontal coopetition on supply chain resilience in complex and turbulent environments," International Journal of Production Economics, Elsevier, vol. 237(C).
    5. Sutcliffe, Chloe & Knox, Jerry & Hess, Tim, 2021. "Managing irrigation under pressure: How supply chain demands and environmental objectives drive imbalance in agricultural resilience to water shortages," Agricultural Water Management, Elsevier, vol. 243(C).
    6. Giannoccaro, Ilaria & Galesic, Mirta & Massari, Giovanni Francesco & Barkoczi, Daniel & Carbone, Giuseppe, 2020. "Search behavior of individuals working in teams: A behavioral study on complex landscapes," Journal of Business Research, Elsevier, vol. 118(C), pages 507-516.
    7. Xue Cheng & Qingpu Zhang, 2018. "How to Develop the Interdisciplinary Innovation Teams Sustainably?—A Simulation Model from a Perspective of Knowledge Fission and Fusion," Sustainability, MDPI, vol. 10(9), pages 1-21, September.
    8. Dario Blanco-Fernandez & Stephan Leitner & Alexandra Rausch, 2020. "Dynamic coalitions in complex task environments: To change or not to change a winning team?," Papers 2010.03371, arXiv.org.
    9. Franck Marle & Hadi Jaber & Catherine Pointurier, 2019. "Organizing Project Actors for Collective Decision-Making about Interdependent Risks," Complexity, Hindawi, vol. 2019, pages 1-18, March.
    10. Jorge Moya & María Goenechea, 2022. "An Approach to the Unified Conceptualization, Definition, and Characterization of Social Resilience," IJERPH, MDPI, vol. 19(9), pages 1-15, May.

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