Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306174 
Year of Publication: 
2023
Citation: 
[Journal:] Human Resource Management Journal [ISSN:] 1748-8583 [Volume:] 34 [Issue:] 3 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 733-752
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Machine‐learning algorithms used in personnel selection are a promising avenue for several reasons. We shift the focus to applicants' attributions about the reasons why an organization uses algorithms. Combining the human resources attributions model, signaling theory, and existing literature on the perceptions of algorithmic decision‐makers, we theorize that using algorithms affects internal attributions of intent and, in turn, organizational attractiveness. In two experiments ( N  = 259 and N  = 342), including a concurrent double randomization design for causal mediation inferences, we test our hypotheses in the applicant screening stage. The results of our studies indicate that control‐focused attributions about personnel selection (cost reduction and applicant exploitation) are much stronger when algorithms are used, whereas commitment‐focused attributions (quality enhancement and applicant well‐being) are much stronger when human experts make selection decisions. We further find that algorithms have a large negative effect on organizational attractiveness that can be partly explained by these attributions. Implications for practitioners and academics are discussed.
Subjects: 
algorithms
human resource attributions
personnel selection
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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