Pricing Strategy in the C2M Supply Chain Considering Overconfidence

This paper develops a C2M supply chain model involving a C2M manufacturer, a brand manufacturer, and an e-commerce platform. The platform collects consumer data and provides demand information to the C2M manufacturer, who adjusts production accordingly. We incorporate overconfidence biases into this framework, where the C2M manufacturer overestimates the effectiveness of data services and the brand manufacturer overestimates market demand. Four scenarios are considered: rr (both manufacturers are rational), or (C2M manufacturer is overconfident), ro (brand manufacturer is overconfident), and oo (both manufacturers are overconfident). The results show that overconfidence has non-monotonic and asymmetric effects on supply chain outcomes. First, the C2M manufacturer’s overconfidence intensifies price competition and may reduce overall supply chain profit; only moderate overconfidence within a certain threshold improves its performance. Second, the brand manufacturer’s overconfidence exhibits an inverted U-shaped effect: while excessive overconfidence harms its own profit, moderate overconfidence increases not only its profitability but also that of the C2M manufacturer and the e-commerce platform, thereby promoting a “win-win-win” outcome. Third, a higher data service level provided by the platform raises the retail prices of both products, enabling the platform to obtain higher data service fees and commission revenue. Overall, our findings highlight the important role of behavioral bias and platform strategy in shaping equilibrium decisions and profit distribution in C2M supply chains.