Linking HR policy support and AI governance to workforce adaptation: evidence from an emerging economy

Purpose This study examines how HRM policy support and AI governance clarity shape employee adaptation to generative AI (GenAI) in an emerging economy. Focussing on employees who already use GenAI at work, it explains how these formal supports relate to technostress, AI dependency and professional identity adaptation and how they connect to adaptive performance. Design/methodology/approach Survey data were collected from 407 public- and private-sector employees in Indonesia with active GenAI usage, an AI-exposed employee population benchmarked against national labour-market statistics. We used PLS-SEM to estimate associations among the constructs and NCA to assess whether any conditions are required for high adaptive performance; multi-group analysis was then conducted to compare the public and private sectors. Findings HRM policy support and AI governance clarity were positively related to technostress and AI dependency, and higher AI dependency was associated with higher adaptive performance. NCA indicates that high adaptive performance is unlikely unless AI dependency exceeds a minimum level. Sector results show stronger HPS effects on AI dependency in the public sector and stronger AI-dependency–performance links in the private sector. Practical implications Organisations can strengthen GenAI-enabled adaptation by pairing role-relevant training and leadership support with clear, usable rules on acceptable use and responsibility. These insights can inform workforce planning and digital transformation initiatives. Originality/value Integrating structuration theory and the job demands–resources model, the study shows how HR and governance structures shape GenAI-enabled adaptation in an AI-exposed workforce and highlights that some conditions matter as prerequisites for high adaptive performance, not only as average predictors.