Why might AI-enabled interviews reduce candidates’ job application intention? The role of procedural justice and organizational attractiveness

Wenhao Luo, Yuelin Zhang and Maona Mu ()
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Wenhao Luo: North China University of Technology
Yuelin Zhang: North China University of Technology
Maona Mu: North China University of Technology

Palgrave Communications, 2025, vol. 12, issue 1, 1-12

Abstract: Abstract Despite substantial scientific developments, companies and professionals are unaware of or do not appreciate the negative repercussions linked to AI-enabled interviews. From the standpoint of job seekers, this raises questions regarding the reasons why certain candidates decline job chances that incorporate AI-enabled interviews. By integrating the capacity-personality framework, fairness heuristic theory, and signaling theory, we investigate the interactive effect between interview format (traditional video interviews vs. AI-enabled interviews) and industry type (high-tech industries vs. low-tech industries) on candidates’ intention to apply for a job. The results from an online scenario-based experiment suggested that interview format and industry type interactively influence candidates’ job application intention, with candidates being more inclined to attend AI-enabled interviews in high-tech industries. We also found that both perceived procedural justice and organizational attractiveness mediate the relationship between interview format and candidates’ intention to apply for jobs, but they do not mediate the interactive effects between interview format and industry type on candidates’ job application intention. Finally, we discuss the theoretical and practical implications of our findings, which contribute to the sustainable use of AI-enabled tools in the job application process.

Date: 2025
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DOI: 10.1057/s41599-025-05607-z

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