The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Abstract

This study examines the complex relationships among artificial intelligence (AI) adoption in organizations, employee work overload, and pro-environmental behavior at work (PEBW), while examining the moderating role of self-efficacy in AI learning. Drawing on several theories, we developed and tested a moderated mediation model utilizing a 3-wave time-lagged survey of 416 employees from diverse South Korean corporations. Our findings reveal that the link between AI adoption and PEBW is fully mediated by work overload, with AI adoption positively influencing work overload, which in turn negatively affects PEBW. Importantly, self-efficacy in AI learning moderates the AI adoption-work overload link, such that the positive influence is weaker for members with higher levels of self-efficacy. These results highlight the unintended consequences of AI adoption on environmental behaviors and underscore the significance of individual differences in shaping responses to technological change. The current research contributes to the literature by elucidating the mechanisms through which AI adoption influences PEBW and by identifying factors that can mitigate potential negative effects. The findings offer meaningful perspectives for organizations aiming to balance technological advancement with environmental sustainability goals, emphasizing the need for strategies that enhance members' self-efficacy in AI learning and manage workload effectively. This paper advances our knowledge of the complex interplay between technological adoption, work experiences, and pro-environmental behaviors in contemporary organizational settings.