When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company

• Published in: Journal of business ethics Vol. 190; no. 4; pp. 841 - 859
• Main Authors: Yan, Chenfeng, Chen, Quan, Zhou, Xinyue, Dai, Xin, Yang, Zhilin
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Aversive; Business and Management; Business Ethics; Companies; Consumers; Decision making; Education; Ethics; Human resources; Human resources management; Interest groups; Management; Original Paper; Philosophy; Quality of Life Research; Stakeholders; Respectful employee treatment; Consumer perceived ethicality; Algorithm-based HR decision-making
• ISSN: 0167-4544; 1573-0697
• DOI: 10.1007/s10551-023-05351-x

The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies’ adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers’ unfavorable ethicality inferences of the company (study 1); because implementing a calculative and data-driven approach (i.e. algorithm-based) to make employee-related decisions violates the deontological principles of respectful employee treatment (study 2). However, this effect was attenuated when consumers had high (vs. low) power distance beliefs (study 3); the algorithm served as assistance (vs. replacement) for human decisions (study 4); or the adoption was framed as employee-oriented (vs. company-oriented) motivated (study 5). Our findings suggested that consumers are aversive to algorithm-based HR decision-making because it is deontologically problematic regardless of its decision quality (i.e. accuracy). This paper contributes to the extant understanding of stakeholders’ responses to algorithm-based HR decision-making and consumers’ attitudes toward algorithm users.