The impact of co-intelligence on digital innovation: A perspective based on knowledge digitization and human-AI collaborative knowledge innovation
Qiang Cheng et al.
Abstract
Amidst the new wave of technological revolution and industrial transformation, collaboration between humans and artificial intelligence (AI) is fundamentally reshaping organizational practices, making digital innovation a crucial source of competitive advantage for enterprises. However, scholarly research on the impact of co-intelligence on digital innovation, as well as the mediating role of knowledge digitization and human-AI collaborative knowledge innovation, remains underdeveloped. To address this gap, this study surveyed 282 Chinese enterprises that implement AI scenario applications. Employing the partial least squares structural equation modeling (PLS-SEM) approach, we investigated how co-intelligence enhances digital innovation, with a specific focus on the mediating effects of knowledge digitization and human–AI collaborative knowledge innovation. The analysis reveals three key findings: (1) Co-intelligence has a significant positive effect on digital innovation. (2) The relationship between co-intelligence and digital innovation is mediated by knowledge digitization and human–AI collaborative knowledge innovation. (3) These two mediators form a significant chain mediation pathway between co-intelligence and digital innovation. Theoretically, this study elucidates the influence mechanism of co-intelligence on digital innovation through a chain-mediated model. Practically, it provides managers with insights for leveraging co-intelligence and refining knowledge management practices to advance digital innovation, offering a foundation for effectively using AI to drive innovation.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.