The platform economy has enabled extensive information sharing among manufacturers, retailers, and e-commerce platforms. While existing studies primarily examine bilateral exchanges of demand or supply data, the rise of platform-provided customer analytics allows manufacturers to better understand consumers’ reference quality and refine their product quality decisions. Therefore, this study aims to investigate whether such analytics truly enhance customer satisfaction without prompting excessive or misdirected investments in product quality.
This study investigates a manufacturer’s optimal choice of information sources for product quality decisions by integrating game-theoretical modeling with numerical analysis. The model captures a multi-echelon supply chain consisting of a manufacturer, a retailer, a platform and end consumers.
The analysis shows that the manufacturer’s strategic choice of information source is jointly determined by its perception of the consumer's reference quality, the platform’s commission rate, and the retailer’s information service fee. Moreover, the manufacturer exhibits a stronger incentive to acquire customer analytics when it opens a direct distribution channel – selling directly on the platform rather than exclusively through the retailer. When platform commission rates are relatively low, the manufacturer is more inclined to align with the platform by adopting direct sales and procuring analytics services from the platform.
First, this research extends the information-sharing literature by shifting the focus from demand information to reference-quality information. Second, it fills an important gap by examining scenarios in which both an e-commerce platform and an online retailer offer customer analytics services to the manufacturer. Finally, it provides insights into best practices for leveraging big data analytics-enabled customer analytics in product quality design, thereby enriching supply chain research under vertical information asymmetry.
