HOW DOES AI CAPABILITY ENABLE DIGITAL PRODUCT INNOVATION? A MIXED METHODS DESIGN

Shuwen Li et al.

International Journal of Innovation Management2025https://doi.org/10.1142/s1363919625500173article
AJG 2
Weight
0.44

What the paper says

Despite the fact that Artificial Intelligence (AI) in innovation management has been a topic of interest for several decades, little is known throughout the literature about how and why AI capability creates product value. In this work, we proposed a dual process model to explore the effects of AI capability on digital product innovation and tested it using quantitative and qualitative methods. In quantitative analysis, based on AI–Open Innovation matrix and dynamic capability theory, we tested the model using a total of 314 managers from 127 firms in the Chinese mainland. We found that AI capability enables digital product innovation by enhancing online value co-creation (a process of external asset) and digital resilience (a process of internal asset). Moreover, socio-cognitive sensemaking strengthens the mediation process of digital resilience but has no significant moderating effect on the mediation process of online value co-creation. The qualitative analysis enables us to better interpret the reasons why sensemaking plays different roles in mediation processes and suggests that it strengthens the effects of online value co-creation and digital resilience on digital product innovation through the external loop (time effect) and the internal loop (interactive effect), respectively. Our findings provide insights into how firms can scale digital product innovation using AI, with important implications for management.

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https://doi.org/https://doi.org/10.1142/s1363919625500173

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@article{shuwen2025,
  title        = {{HOW DOES AI CAPABILITY ENABLE DIGITAL PRODUCT INNOVATION? A MIXED METHODS DESIGN}},
  author       = {Shuwen Li et al.},
  journal      = {International Journal of Innovation Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s1363919625500173},
}

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Evidence weight

0.44

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

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
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.