Human–AI collaboration for green knowledge ecosystems in green supply chains – an empirical study on driving factors and psychological barriers

Purpose This study aims to investigate the driving factors that enhance the willingness to adopt Human–AI collaboration in green supply chains and examines the impact of psychological barriers on the transition from collaboration intentions to behavioral expectations. Design/methodology/approach A theoretical framework grounded in the Unified Theory of Acceptance and Use of Technology and the Uncanny Valley theory was empirically tested using survey data collected from Chinese manufacturing firms. A total of 467 valid responses were obtained across two independent samples collected at different time points. This dual-sample design captures perspectives from distinct groups and periods, providing broader insights into the proposed relationships. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to examine the theoretical framework. Findings The results show that performance expectancy, social influence and facilitating conditions significantly drive collaboration intentions, which, in turn, positively influence behavioral expectations. While emotion-driven digital trust conflicts showed no significant impact, societal fears remain a key barrier. Specifically, concerns over AI replacing human roles hinder the translation of intentions into behavioral expectations. Originality/value In addition to the identified driving factors, findings of this study emphasize the importance of addressing societal fears to ensure that Human–AI collaboration fosters a self-sustaining ecosystem, enabling continuous innovation in green knowledge generation and sustainable pollution control within green supply chains.