When Less Confidence Reaps More AI Benefits: A Compensatory Pattern Through Future Work Self-Salience

Abstract

Introduction: In the context of AI becoming deeply embedded in professional environments, understanding how EAC drives employee innovative behavior (EIB) has become a critical issue in organizational psychology. Drawing on social cognitive theory (SCT) and complementarity theory, this study proposes an “Environment-Cognition-Behavior” framework to elucidate how employee–AI collaboration (EAC) drives EIB, specifically by assessing the mediating effect of future work self-salience (FWS) and the moderating effect of core self-evaluation (CSE). Methods: A three-wave time-lagged questionnaire survey was employed, and 457 valid employee responses were collected via the Credamo platform. The moderated mediation model was tested using regression-based path analysis and bootstrapping procedures. Results: EAC significantly and positively predicted EIB. FWS significantly mediated this relationship. CSE moderated the effect of EAC on FWS, yielding a significant moderated mediation effect. Notably, among employees with low CSE, the indirect pathway from EAC to EIB via FWS was stronger. Discussion: These findings extend SCT by identifying a pattern of person–environment interaction that is consistent with a compensatory interpretation in human–AI collaboration contexts. They also clarify the cognitive mechanism and individual-level boundary condition underlying AI-enabled innovation and provide practical implications for differentiated AI management strategies.