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Individual Differences in Within-Person Variability in Personality Positively Predict Economic Gains and Satisfaction in Negotiations

Author

Listed:
  • Pinar Celik

    (Université Libre de Bruxelles)

  • Martin Storme

    (IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management)

  • Nils Myszkowski

    (Pace University)

Abstract

Prior research on the value of personality traits for predicting negotiation outcomes is rather inconclusive. Building on prior research and in light of recent personality and negotiation theories, we discuss why the traditional approach to personality traits has had limited success and propose an alternative approach to predicting negotiation outcomes from personality assessments. More specifically, we argue that negotiations are tasks in which performance is conditioned by the ability to adjust one’s mental states and behaviors according to situational demands. We therefore hypothesize that it is especially individual differences in within-person variability in personality – that is, the variability trait – that can be expected to predict negotiation outcomes, rather than individual differences in average traits. We show in two empirical studies involving dyads that the variability trait is indeed a better predictor of economic gains and satisfaction than average traits. Implications for theory, education, and practice are discussed.

Suggested Citation

  • Pinar Celik & Martin Storme & Nils Myszkowski, 2022. "Individual Differences in Within-Person Variability in Personality Positively Predict Economic Gains and Satisfaction in Negotiations," Group Decision and Negotiation, Springer, vol. 31(3), pages 683-702, June.
    • Handle: RePEc:spr:grdene:v:31:y:2022:i:3:d:10.1007_s10726-022-09778-x
    DOI: 10.1007/s10726-022-09778-x
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    References listed on IDEAS

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    1. Martin Storme & Pinar Celik & Nils Myszkowski, 2020. "A forgotten antecedent of career adaptability: A study on the predictive role of within-person variability in personality," Post-Print hal-03001643, HAL.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
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