Dependent Choices in Employee Selection: Modeling Choice Compensation and Consistency

Antonia Krefeld-Schwalb, Benjamin Scheibehenne, Jörg Rieskamp, Nicolas Berkowitsch

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Past choices can influence subsequent choices in employee selection. Previous approaches rather described similar sequential effects with feedback learning or the misperception of randomness. However, in the selection of job candidates also the accumulation of the moral impact of previous choices might influence subsequent choices. We investigated that question by making two major contributions to the literature. First, we developed an experimental paradigm for measuring sequential choices in employee selection and second, we implemented a widely applicable computational model, the Dependent Sequential Sampling Model, for explaining sequential effects in choices. By using this methodological approach, we uncovered sequential effects in employee selection. Participants (N=600) were especially motivated to compensate for morally dubious choices, with some participants showing consistent choice behavior if their previous choices had been morally virtuous. These results support the assumption of asymmetric compensation of morally dubious choices, sometimes referred to as the moral cleansing hypothesis.

Original languageEnglish
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
Pages2457-2462
Number of pages6
ISBN (Electronic)9780991196760
Publication statusPublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: 26 Jul 201729 Jul 2017

Publication series

SeriesCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17

Bibliographical note

Publisher Copyright:
© CogSci 2017.

Research programs

  • RSM MKT

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