Uncovering the innovation effects and multiple‌ antecedents of employee role orientations in human-generative AI collaboration: a social information processing perspective

Abstract

In human-generative AI collaboration (HGAIC), employees can adopt different role orientations to collaborate with GenAI, but it remains unclear how employee role orientations affect their innovation and how they are shaped. Based on social information processing theory (SIPT), this study examines the effects of passive and co-production role orientations on GenAI-enabled employee innovation, the multiple antecedents of these orientations, and the moderating role of employee opportunism. This study used partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyze survey data from 409 employees. The results indicate that passive and co-production role orientations positively affect GenAI-enabled employee innovation. GenAI intellectual stimulation and task urgency positively influence passive role orientation, whereas task innovativeness negatively influences it. GenAI feedback richness, GenAI intellectual stimulation, and task innovativeness positively affect co-production role orientation. Opportunism only amplifies the positive effect of passive role orientation on GenAI-enabled employee innovation. Additionally, the fsQCA results reveal three configurations that lead to high levels of GenAI-enabled employee innovation. First, this study reveals the relationship between different role orientations in HGAIC and GenAI-enabled employee innovation. Second, this study identifies the antecedents of passive and co-production role orientations from the perspectives of GenAI feedback and task requirement cues. Finally, this study reveals a counterintuitive finding: employee opportunism can act as a positive catalyst for innovation under specific HGAIC conditions.