The configurational effects of artificial intelligence-based hiring decisions on applicants' justice perception and organisational commitment

Jun He Yu · Zhengcong Ma · Lin Zhu

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AI and HR Technologies Ethics and Social Impacts of AI Employer Branding and e-HRM

Abstract

Purpose This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC). Design/methodology/approach Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC. Findings The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP. Originality/value This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 1979The measurement of organizational commitment 2011Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research 2001On the dimensionality of organizational justice: A construct validation of a measure.2000A model for types and levels of human interaction with automation 2000Machines and Mindlessness: Social Responses to Computers 2014Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err 2007A set-theoretic approach to organizational configurations

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