Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/233200 
Year of Publication: 
2020
Citation: 
[Journal:] Business Research [ISSN:] 2198-2627 [Volume:] 13 [Issue:] 3 [Publisher:] Springer [Place:] Heidelberg [Year:] 2020 [Pages:] 795-848
Publisher: 
Springer, Heidelberg
Abstract: 
Algorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.
Subjects: 
Fairness
Discrimination
Perceived fairness
Ethics
Algorithmic decision-making in HRM
Literature review
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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