Igniting HR effectiveness and explorative and exploitative learning: role of work-related generative AI use and market turbulence

Purpose – Generative artificial intelligence (GenAI) has been widely used in businesses; however, limited empirical research examines how work-related GenAI use (WRGAIU) influences human resource (HR) effectiveness and learning mechanisms. This study aims to fill this gap by investigating the direct influence of WRGAIU on HR effectiveness as well as on explorative and exploitative learning. Further, this study also examines whether two learning mechanisms mediate the relationship between WRGAIU and HR effectiveness. This study also examines the moderating role of market turbulence in those direct and indirect relationships, uncovering new insights on how GenAI can ignite HR outcomes under dynamic circumstances. Design/methodology/approach – Survey data from 314 HR employees working at British private companies were collected using the online platform Prolific Academic. We used Partial Least Squares-based Structural Equation Modelling to test the hypotheses. Findings – The results reveal that WRGAIU directly influences HR effectiveness, explorative learning and exploitative learning. However, the learning mechanisms were found to have no mediating role in the relationship between WRGAIU and HR effectiveness. Moreover, results show that when employees use work-related GenAI, market turbulence becomes insignificant for them. Practical implications – The study uncovers how WRGAIU can be instrumental in igniting HR effectiveness and learning mechanisms among HR employees, regardless of market turbulence. Originality/value – The results suggest that WRGAIU should be developed in organisations to achieve multiple outcomes, especially in developing HR effectiveness and explorative and exploitative learning of employees to keep organisations protected from external market factors, i.e., market turbulence.