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The increasing adoption of artificial intelligence (AI) in financial institutions has transformed workplace practices, making it essential to understand how responsible AI influences employee outcomes. Drawing on Social Exchange Theory, this study examines the impact of responsible AI signals—specifically autonomy, beneficence, and explainability—on employee behavior through the mediating roles of AI-driven engagement and AI-driven satisfaction, while accounting for the moderating effect of techno-overload. Data were collected from 310 employees working in banking and financial institutions in Pakistan using a structured questionnaire. The conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that responsible AI signals significantly enhance AI-driven engagement, AI-driven satisfaction, and employee behavior. AI-driven engagement positively influences AI-driven satisfaction, while AI-driven satisfaction significantly improves employee behavior. Although the direct relationship between AI-driven engagement and employee behavior was not supported, dual-mediation analysis demonstrates that engagement and satisfaction mediate the relationship between responsible AI signals and employee behavior. Furthermore, techno-overload weakens the positive relationship between responsible AI signals and AI-driven engagement; however, it does not significantly moderate the relationship with AI-driven satisfaction. The study contributes to the literature on responsible AI and organizational behavior by demonstrating how ethical and transparent AI practices foster positive employee outcomes. From a practical perspective, financial institutions should prioritize responsible AI implementation and effective workload management to maximize employee engagement, satisfaction, and innovative workplace behavior.
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Hameed et al. (2026) studied this question.
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