The impact of individual versus group rewards on work group performance and cooperation: a computational social science approach

Daniel Ladley, Ian Wilkinson, Louise Young

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Purpose: To examine the effect of individual versus group evaluation and reward systems on work group behavior and performance under different task conditions. Methodology: Uses computational social methods using Agent Based Models to simulate work group interactions as different forms of iterated games. Findings: Group based systems outperform individual based and mixed systems, producing more cooperative behavior, the best performing groups and individuals in most types of interaction games. A new role emerges, the self-sacrificer, who plays a critical role in enabling other group members and the group, to perform better at their own expense. Research Implications: Suggest opportunities for model development and guidelines for designing real world experiments. Practical Implications: Helps firms engineer better performing work groups as well as the design of other business systems. Social Implications: Identifies mechanisms by which cooperation can be developed in social systems. Originality/Value: Demonstrates the role and value of computational social science methods and agent based models to business research.

Original languageEnglish
JournalJournal of Business Research
Volume68
Issue number11
Pages (from-to)2412-2425
ISSN0148-2963
DOIs
Publication statusPublished - 2015

Keywords

  • Agent based models
  • Complex systems
  • Computational social science
  • Cooperation
  • Group versus individual reward systems
  • Incentive
  • Iterated
  • Work groups

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