Abstract:

ABSTRACT

Artificial intelligence is transforming hiring at scale, yet its consequences remain poorly understood across the full range of actors who experience them. Theoretically anchored in Renkema's (2022) framework for AI consequences in HRM, this study examines: (1) How do AI consequences in hiring manifest differently across stakeholder groups? (2) Which AI consequences in hiring are transformational in nature, and how does consequence valence vary across stakeholder groups? (3) To what extent do AI consequences in hiring operate beyond organizational boundaries? Through 21 semi-structured interviews with senior HR leaders, talent acquisition professionals, frontline practitioners, and AI vendors, this study examines how AI-related consequences in hiring are distributed among stakeholder groups, which consequences are structurally transformational, and how far they extend beyond organizational boundaries. Analysis follows the Miles and Huberman methodology. The findings reveal that an organization, or ecosystem of organizations, signals some version of desirability that is not uniform. This means that some desirable consequences for one stakeholder can be undesirable or even ambiguous for others. Second, a transformational classification generally comes disproportionately from ambiguous consequences. Third, the study also extends Renkema's level of analysis from three to four layers by introducing a supra-organizational level, grounded in neo-institutional theory, that accounts for consequences produced and experienced beyond organizational boundaries, including societal, vendor, and university levels. Collectively, these extensions demonstrate that desirability is stakeholder-dependent, their depth is structurally transformational, and their full scope is institutionally embedded.

Please choose your submission type: Full Paper

Primary Track: Human Resource Management

Keywords: Future of work, technology & Innovation;Recruitment and selection

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Dr. Humberta Da Conceição Santos Silva