Authors: Dan Ladley; Ian Wilkinson; Louise Young
Research using computational social science methods examines the effect of individual versus group evaluation and reward systems on work group behavior and performance under different task conditions. Agent based models, simulate work group interactions as different forms of iterated games. The results show that group based systems outperform individual based and mixed systems, producing more cooperative behavior and 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. The findings help firms engineer better performing work groups and have implications for the design of other business systems. In addition, the results suggest further lines of inquiry as well as guidelines for the design of real world experiments. Lastly, the research illustrates the potential role and value of computational social science methods.
Journal: Journal of Business Research ( – )
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Publish Year: 2015
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