Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes

Ji, Guojun; Yu, Muhong; Tan, Kim Hua; Kumar, Ajay; Gupta, Shivam

Abstract

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms’ big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms’ innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models.

Citation

Ji, G., Yu, M., Tan, K. H., Kumar, A., & Gupta, S. (2024). Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes. Annals of Operations Research, 333, 871-894. https://doi.org/10.1007/s10479-022-04867-1

• Journal Article Type: Article
• Acceptance Date: Jun 28, 2022
• Online Publication Date: Jul 20, 2022
• Publication Date: 2024-02
• Deposit Date: Aug 4, 2022
• Publicly Available Date: Jul 21, 2023
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 333
• Pages: 871-894
• DOI: https://doi.org/10.1007/s10479-022-04867-1
• Keywords: Management Science and Operations Research; General Decision Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/9587100
• Publisher URL: https://link.springer.com/article/10.1007/s10479-022-04867-1