This commentary critiques and extends Wolf and Stock-Homburg’s (2025) study on employee acceptance of robotic lower-level managers by addressing overlooked issues of leadership legitimacy, ethical risks, and socio-cultural variation. It argues that relying solely on the Technology Acceptance Model (TAM) and Expectation-Disconfirmation Theory (EDT) limits our understanding of AI-driven leadership. Drawing on leadership identity theory, social exchange theory, and research on algorithmic bias and governance, the commentary advocates for hybrid decision-support models that combine AI capabilities with human judgment. It emphasizes the need for context-sensitive approaches to AI leadership, shaped by cultural and industry-specific expectations.
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