AI technology adoption and intergenerational knowledge transfer among older employees.

Introduction: This study employs the Job-Demands-Resources model and Conservation of Resources theory to examine the impact of artificial intelligence (AI) technology adoption on intergenerational knowledge transfer among older employees. It focuses on the psychological motivation underlying this phenomenon and identifies individual factors that affect intergenerational knowledge transfer. The purpose is to gain a deep understanding of the internal mechanisms of employee cognition and behavior change in the context of technological transformation. Methods: We surveyed 635 older employees from various industries in China and analyzed the data using SPSS 27.0, Mplus 8.3, and fsQCA 4.1. The data were analyzed via a moderated sequential mediation model to examine the relationships among AI technology adoption, identity threat, relational crafting, digital self-efficacy and intergenerational knowledge transfer, supplemented by fuzzy-set qualitative comparative analysis (fsQCA). The study tested the mediating effects of identity threat and relational crafting between AI technology adoption and intergenerational knowledge transfer, as well as the moderating role of digital self-efficacy. In addition, fsQCA was used to test antecedents of intergenerational knowledge transfer among older employees. Results: The findings indicate that AI technology adoption positively influences intergenerational knowledge transfer. Identity threat and relational crafting play mediating roles between AI technology adoption and intergenerational knowledge transfer and also serve as sequential mediators. Digital self-efficacy negatively moderates the impact of AI technology adoption on identity threat, thereby moderating both the mediating role of identity threat and the sequential mediating effect of identity threat and relational crafting. Additionally, fsQCA identified three antecedent configurations that trigger intergenerational knowledge transfer among older employees. Discussion: Prior research on AI technology adoption has tended to emphasize singular positive or negative impacts on specific variables. This study constructs a model that incorporates both positive and negative effects, elucidating the multifaceted mechanisms through which AI technology adoption influences intergenerational knowledge transfer and enriches research on the consequences of AI technology adoption. While existing literature often highlights negative psychological and behavioral impacts of AI technology adoption on older employees, the present findings show that AI technology adoption can significantly enhance intergenerational knowledge transfer among older employees, thereby complementing current findings. Finally, by adopting a configurational thinking, this study identifies multiple pathways through which various factors affect intergenerational knowledge transfer, providing a useful complement to single-factor analyses of AI technology adoption's impact. Thereby, the study offers practical insights for organizations seeking to develop inclusive technological-culture strategies.