A Multilevel Framework Linking Generative AI, Neuroethics, and Human Resource Management in Hybrid Work Ecosystems: A Covariance-Based Structural Equation Modelling Approach

Abstract

The accelerated diffusion of Generative Artificial Intelligence (GenAI) has fundamentally transformed human resource management (HRM) practices within hybrid work ecosystems. While existing studies predominantly emphasise technological efficiency and performance outcomes, limited empirical attention has been paid to the neuroethical dimensions shaping employee cognition, trust, and behavioural alignment in AI-augmented workplaces—particularly within emerging economy contexts. Addressing this gap, the present study develops and empirically validates a multilevel theoretical framework integrating Generative AI capability, neuroethical governance, and strategic HRM outcomes in hybrid work environments. Drawing on the Resource-Based View, Social Exchange Theory, and Neuroethical Decision Theory, a theory-driven quantitative research design was adopted. Primary survey data were collected from 412 HR professionals and knowledge workers across Indian IT, consulting, and digital service organisations operating under hybrid work models. Covariance-based Structural Equation Modelling (CB-SEM) using AMOS 26 was employed to assess both the measurement and structural models. The findings reveal that Generative AI capability significantly enhances HRM effectiveness, mediated by neuroethical trust and moderated by hybrid work intensity. Neuroethical governance emerged as a critical mechanism through which AI-driven HR practices translate into sustainable employee engagement and organisational legitimacy. The study contributes to HRM and AI governance literature by integrating neuroethics into HR analytics discourse and offers actionable insights for managers and policymakers seeking ethically grounded AI adoption in hybrid work ecosystems.