Collaborative dishonesty: A meta-analytic review.

Margarita Leib, Nils Köbis, Ivan Soraperra, Ori Weisel, Shaul Shalvi
Author Information
  1. Margarita Leib: Center for Research in Experimental Economics and Political Decision-Making, University of Amsterdam. ORCID
  2. Nils Köbis: Center for Humans and Machines, Max Planck Institute for Human Development. ORCID
  3. Ivan Soraperra: Center for Research in Experimental Economics and Political Decision-Making, University of Amsterdam. ORCID
  4. Ori Weisel: Coller School of Management, Tel Aviv University. ORCID
  5. Shaul Shalvi: Center for Research in Experimental Economics and Political Decision-Making, University of Amsterdam. ORCID

Abstract

Although dishonesty is often a social phenomenon, it is primarily studied in individual settings. However, people frequently collaborate and engage in mutual dishonest acts. We report the first meta-analysis on collaborative dishonesty, analyzing 87,771 decisions (21 behavioral tasks; k = 123; n = 10,923). We provide an overview of all tasks used to measure collaborative dishonesty, and inform theory by conducting moderation analyses. Results reveal that collaborative dishonesty is higher (a) when financial incentives are high, (b) in lab than field studies, (c) when third parties experience no negative consequences, (d) in the absence of experimental deception, and (e) when groups consist of more males and (f) younger individuals. Further, in repeated interactions, group members' behavior is correlated-participants lie more when their partners lie-and lying increases as the task progresses. These findings are in line with the justified ethicality theoretical perspective, suggesting prosocial concerns increase collaborative dishonesty, whereas honest-image concerns attenuate it. We discuss how findings inform theory, setting an agenda for future research on the collaborative roots of dishonesty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

MeSH Term

Deception
Humans
Male
Mass Gatherings
Morals

Word Cloud

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