Digital Leadership for Responsible AI Adoption: Linking Leadership Practices to Ethical Climate and Employee Performance
DOI:
https://doi.org/10.64882/ijrt.v14.iS1.1152Keywords:
Digital leadership, responsible AI, ethical climate, employee performance, trust, AI governanceAbstract
The rapid diffusion of artificial intelligence (AI) in organizations has intensified concerns about responsible adoption, particularly around fairness, transparency, privacy, and accountability. Yet, evidence remains fragmented on how leadership shapes the ethical conditions under which AI is introduced and used, and how these conditions translate into employee-level outcomes. This study aims to examine how digital leadership practices shapes ethical climate and employee performance in AI enabled workplaces using a secondary-data design, that synthesizes evidence from publicly available sources—such as corporate responsible-AI policies, AI governance disclosures, regulatory and standards-based guidance, and existing survey datasets. Drawing on a multi-level theoretical lens, the study conceptualizes digital leadership for responsible AI as a bundle of practices including ethical role-modelling, data stewardship, stakeholder transparency, human oversight, workforce upskilling, and accountability mechanisms. Ethical climate is treated as the intervening organizational context that signals acceptable conduct in AI-enabled decision-making and work redesign. The analysis proposes and evaluates a mediated pathway in which stronger responsible-AI-oriented digital leadership is associated with a more positive ethical climate, which in turn relates to improved employee performance through higher trust, reduced perceived surveillance and bias, and greater technology acceptance. The findings of the study show that digital leadership is effective only when it includes ethical governance. Ethical climate plays an important role and responsible AI adoption enhances performance only when trust, transparency and fairness are present. By combining fragmented secondary evidence into a systematic explanatory model, this research provides a framework for assessing responsible AI leadership and actionable guidance for leaders seeking to realize AI’s performance benefits without eroding ethical standards or employee well-being.
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