Which is better? Matching effect on feedback content and source: Gen AI vs. human leaders in driving work engagement

Purpose Generative artificial intelligence (Gen AI; e.g. ChatGPT, DeepSeek) is increasingly being integrated into performance feedback systems in service organizations, operating alongside human leaders as a dual-source feedback mechanism. Yet the effects of Gen AI feedback on employee work engagement remain unclear. This study addresses the central question of whether and how the matching between feedback source (Gen AI vs. human leaders) and feedback content (positive vs. negative) differentially influences employee work engagement? Design/methodology/approach We report three scenario-based experiments with a pooled sample of 736 valid responses to test our hypotheses. The study examines the matching effects between feedback source (Gen AI vs. human leaders) and feedback content (positive vs. negative), and investigates the psychological mechanisms through which these effects influence employee work engagement by considering regulatory focus (promotion vs. prevention) and self-efficacy. Findings The results reveal a significant matching effect: Gen AI paired with negative feedback and human leaders paired with positive feedback form complementary pairings that more effectively enhance employee work engagement. Further analyses indicate that the mediating role of promotion-focused and prevention-focused regulation, as well as the moderating role of self-efficacy. Originality/value By integrating signaling theory and regulatory focus theory, and considering the dehumanized nature of Gen AI, this study uncovers the differentiated strengths and complementary relationship of Gen AI and human leaders as feedback sources. The findings advance theoretical understanding of human-AI collaboration in organizational contexts and offer practical guidance for designing dual-source feedback systems that optimize employee engagement.