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Human-artificial intelligence interaction, knowledge sharing and R&D team innovation performance

M.-J. Chen et al.

Journal of Knowledge Management2026https://doi.org/10.1108/jkm-02-2025-0226article
AJG 2ABDC A
Weight
0.41

Abstract

Purpose Previous research has mostly examined how human–artificial intelligence interaction (HAI) affects individual workers’ cognitive abilities, paying little attention to how well R&D teams collaborate with AI to achieve innovative performance. Based on theories such as social roles and knowledge management, this paper aims to explore differential impacts of partner and steward HAI on the innovation performance in the R&D team. In addition, to the mediating role of knowledge sharing (KS), and further validated the moderating roles played by knowledge protection regulation (KPR) and digital organizational culture (DOC). Design/methodology/approach This study used SPSS and AMOS to empirically examine a data sample of R&D teams from 60 Chinese firms with high R&D activities and strong innovation capabilities. Findings This study found that partner HAI is positively related to R&D team innovation performance and KS, whereas steward HAI is negatively related to these two outcomes. In addition, KS has a partial mediating role in the effect of HAI on innovation performance. The moderating role of KPR and DOC was found. Originality/value First, it broadens the meaning and application of HAI by reorienting the study toward the various roles that it plays in R&D teams’ innovation performance. Second, this research validates that KS is a significant driving path, highlights the significance of KS in HAI and innovation performance and adds to the body of knowledge management literature. Finally, it extends social role theory to the role relationship between people and AI, improving its application in the field of HAI.

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https://doi.org/https://doi.org/10.1108/jkm-02-2025-0226

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@article{m.-j.2026,
  title        = {{Human-artificial intelligence interaction, knowledge sharing and R&D team innovation performance}},
  author       = {M.-J. Chen et al.},
  journal      = {Journal of Knowledge Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jkm-02-2025-0226},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 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.