Exploring the dual effect of trust in GAI on employees’ exploitative and exploratory innovation

Xiaoyue Lin, Tiandong Wang () and Fan Sheng
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Xiaoyue Lin: Harbin Institute of Technology
Tiandong Wang: Harbin Engineering University
Fan Sheng: Harbin Engineering University

Palgrave Communications, 2025, vol. 12, issue 1, 1-14

Abstract: Abstract The deployment of generative artificial intelligence (GAI) has attracted substantial research attention, yet its impact on employee innovation remains debated. Based on Technology-Task Fit (TTF) theory, this study investigates how trust in GAI affects employees’ exploitative and exploratory innovation. Analysis of survey data from 302 Chinese employees reveals that trust in GAI is positively related to exploitative innovation, while demonstrating an inverted U-shaped relationship with both exploratory innovation and the complementarity of these two innovation types. Furthermore, employee-GAI fit amplifies the positive effect of trust in GAI on exploitative innovation and moderates the inverted U-shaped relationship with exploratory innovation by shifting the turning point to the right. Employee-innovation task fit weakens the inverted U-shaped relationship between trust in GAI and exploratory innovation. These findings advance original theoretical insights into the relationship between trust in GAI and ambidextrous innovation and offer actionable guidance for innovation management practitioners.

Date: 2025
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DOI: 10.1057/s41599-025-04956-z

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