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Exploring Underrepresented Employee Perceptions of AI Receptivity Through Leaders’ Stakeholder Engagement

Journal: Business Ethics and Leadership (BEL) (Vol.9, No. 2)

Publication Date:

Authors : ;

Page : 108-119

Keywords : artificial intelligence (AI); human-centered artificial intelligence (HCAI); receptivity; stakeholder engagement; underrepresented employees;

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Abstract

This inquiry explores underrepresented employees’ perceptions and receptivity toward artificial intelligence (AI) adoption, specifically focusing on how organizational leaders’ stakeholder engagement influences these dynamics. Guided by stakeholder theory, the study critically synthesizes recent literature. It evaluates existing technology adoption frameworks, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Theory of Planned Behavior (TPB). Findings highlight that inclusive stakeholder engagement practices emphasizing transparency, ethical accountability, psychological safety, and culturally competent communication significantly enhance employee receptivity, particularly among marginalized groups. The study identifies substantial limitations in existing frameworks, which inadequately address intersectional diversity, transparency concerns, and ethical dimensions impacting underrepresented employees. To mitigate these gaps, this research proposes the innovative organizational role of the Chief People Readiness Officer (CPRO), strategically designed to manage AI transitions, facilitate authentic engagement, and provide transformational leadership explicitly tailored to diverse employee groups. The study concludes by emphasizing the necessity of targeted, inclusive engagement practices and recommends empirical validation of the CPRO model, longitudinal research on engagement effectiveness, and the development of intersectional receptivity metrics. This research contributes significantly to scholarly understanding and organizational practices, ultimately supporting equitable, inclusive, and effective AI adoption within contemporary organizations.

Last modified: 2025-07-15 17:35:39