Information sharing decision of retail platform: platform’s risk aversion and competing suppliers
An increasing number of retail platforms are adopting blockchain technology to mitigate information asymmetries and share data with upstream suppliers, thereby reducing demand uncertainty. However, these platforms often engage with multiple upstream suppliers of varying product quality. This study constructs a game-theoretic model within a supply chain framework, featuring a risk-averse retail platform and two upstream suppliers of different quality levels. As the core leader of the game, the retail platform decides whether to share demand information with the two competing suppliers after implementing the technology. Using mean-variance theory, this study addresses a key question: Which types of suppliers should be included in information sharing on a retail platform? The results show that when the unit cost of information sharing is low, allowing both suppliers to share demand information is most beneficial. Conversely, when the unit cost is high, only high-quality suppliers should be included. Notably, as the unit cost of information sharing and the intensity of competition between high-quality and low-quality products increase, low-quality suppliers are excluded from information sharing. Additionally, the model indicates that the greatest social welfare can be achieved whether both high-quality and low-quality suppliers are authorized to join the information sharing network, or only high-quality suppliers are included.
Authors
- Jianbiao Ren
- Zhenzhong Guan (ORCID: https://orcid.org/0000-0001-9768-9622)
- Xiaofei Wang (ORCID: https://orcid.org/0009-0005-3021-1073)
Institutions
- Shanghai Jiao Tong University (CN)
- Southwest Jiaotong University (CN)
Publication Details
- Journal
- RAIRO. Operations research
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1051/ro/2024206
- Citations
- 1
- Primary Topic
- Consumer Retail Behavior Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.44
Funders
- National Social Science Fund of China