How trust and attachment styles jointly shape job candidates’ AI receptivity

Valerio Deriu, Rumen Pozharliev and Matteo De Angelis

Journal of Business Research, 2024, vol. 179, issue C

Abstract: Despite the growing application of artificial intelligence in hiring practices, job applicants still express general reservations about the use of this technology. What factors help to explain this low acceptance rate and are all job applicants equally affected? Across three controlled experiments, we demonstrate that job applicants perceive an AI evaluation agent (vs. a human) as less trustworthy, which reduced their job acceptance intentions (Study 1). This effect was stronger for job applicants who scored low on avoidant attachment style (Study 2). Using a serial mediation model, Study 3 tested one explanatory mechanism for how avoidant attachment style shapes trust, finding that perceptions of the AI agent’s ability to parse unique skills are an antecedent of job applicants' low trust toward the AI. These findings deepen our understanding of the factors that shape applicants’ job acceptance intentions regarding AI and provide actionable insights for integrating AI into hiring practices.

Keywords: Job interview; Artificial intelligence; Attachment style; Trust; Uniqueness (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:179:y:2024:i:c:s0148296324002212

DOI: 10.1016/j.jbusres.2024.114717

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