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How attitude certainty tempers the effects of faultlines in demographically diverse teams

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  • André Grow

    (University of Groningen)

  • Andreas Flache

    (University of Groningen)

Abstract

Lau and Murnighan’s faultline theory suggests that strong demographic faultlines can undermine cohesion in work teams. A strong faultline splits a team into internally homogeneous but mutually dissimilar subgroups based on demographic characteristics. Social influence processes within these subgroups then lead to the polarization of team members’ attitudes along the divisions imposed by the faultline. However, faultline theory hitherto neglects effects of attitude certainty. Research shows that the certainty with which individuals hold their attitudes affects social influence processes. We extend theoretical faultline research by integrating attitude certainty. For this, we incorporate the interplay of the dynamics of attitude certainty and social influence into a formal model of demographic faultline effects developed by Flache and Mäs. Computational experiments suggest a moderation effect. Demographic faultlines only affect team cohesion if attitude certainty is low. We discuss implications for future research.

Suggested Citation

  • André Grow & Andreas Flache, 2011. "How attitude certainty tempers the effects of faultlines in demographically diverse teams," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 196-224, May.
    • Handle: RePEc:spr:comaot:v:17:y:2011:i:2:d:10.1007_s10588-011-9087-5
    DOI: 10.1007/s10588-011-9087-5
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    References listed on IDEAS

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    1. Sherry M.B. Thatcher & Karen A. Jehn & Elaine Zanutto, 2003. "Cracks in Diversity Research: The Effects of Diversity Faultlines on Conflict and Performance," Group Decision and Negotiation, Springer, vol. 12(3), pages 217-241, May.
    2. Eric Molleman, 2005. "Diversity in Demographic Characteristics, Abilities and Personality Traits: Do Faultlines Affect Team Functioning?," Group Decision and Negotiation, Springer, vol. 14(3), pages 173-193, May.
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    4. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    5. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.
    2. Andreas Flache, 2018. "About Renegades And Outgroup Haters: Modeling The Link Between Social Influence And Intergroup Attitudes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-32, September.
    3. Liang Chen & Guy G. Gable & Haibo Hu, 2013. "Communication and organizational social networks: a simulation model," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 460-479, December.
    4. Fabrizio Maturo & Stefania Migliori & Francesco Paolone, 2019. "Measuring and monitoring diversity in organizations through functional instruments with an application to ethnic workforce diversity of the U.S. Federal Agencies," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 357-388, December.
    5. Thomas Feliciani & Andreas Flache & Michael Mäs, 2021. "Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines," Computational and Mathematical Organization Theory, Springer, vol. 27(1), pages 61-92, March.

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