When Less Confidence Reaps More AI Benefits: A Compensatory Pattern Through Future Work Self-Salience
Year: 2026
Type: article
Study design: Observational Study
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.
Source: Behavioral Sciences
DOI: 10.3390/bs16091585
Authors: Xiaobo Dong, Yanlong Zhang, Qi Li, Zhiyong Han
Institution: Anhui University of Finance and Economics
Language: English
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W7211872087