How and when employee–generative AI collaboration contributes to digital performance: a knowledge acquisition approach

Purpose This study aims to explore whether collaborating with generative AI can facilitate employees to acquire different types and sources of knowledge, which, in turn, enhances their digital performance, and whether the effectiveness of this knowledge acquisition process is influenced by situational factors represented by task interdependence. Design/methodology/approach Based on social learning theory, this study proposes a research model and adopts the partial least squares structural equation modeling to analyze 297 questionnaire data from Chinese software development industry employees. Findings The results indicate that employee–generative AI collaboration (EA) positively influences knowledge acquisition, which, in turn, improves digital performance. Explicit knowledge acquisition has a larger positive effect on digital-enabled task performance; tacit knowledge acquisition, including tacit knowledge acquisition from own work life and other’s work life, has a larger positive effect on digital-enabled innovative performance. Task interdependence strengthens the effect of EA on explicit knowledge acquisition, but weakens the effect of EA on tacit knowledge acquisition from own work life, and does not moderate the effect of EA on tacit knowledge acquisition from other’s work life. Originality/value This study broadens the understanding regarding the pathways of knowledge acquisition by extending the target of employee knowledge acquisition from traditional real people to generative AI and by revealing the moderating role of task interdependence. Moreover, this study also reinforces the perception regarding the functions of knowledge acquisition in the current digital workplace by distinguishing variations in the influence of various types and sources of knowledge acquisition on digital performance.