Dynamic Pricing and Demand Learning in Decentralized Supply Chains: Implications for Product Quality and Firm Profits

Abstract

In modern consumer technology industries, manufacturers make substantial upfront investments in product quality, despite facing significant demand uncertainty—particularly regarding the size of the high-valuation segment that values quality highly. Dynamic pricing, where retailers adjust prices based on demand learning in the early period, may help mitigate this uncertainty. We develop a game-theoretic model to examine how a retailer's dynamic pricing capability impacts a manufacturer's wholesale pricing and product quality investment and whether it benefits both firms. In our model, the retailer can learn the proportion of high-valuation consumers by setting a high early price to target only that segment. This demand learning opportunity affects the manufacturer's wholesale pricing strategy and its expected return on quality investment. We find that the retailer's dynamic pricing improves product quality when the market is moderately likely to be good but has no effect when a good market is nearly certain. Dynamic pricing can also lead to either win-win or lose-lose outcomes: it benefits both firms when it mitigates double marginalization and enables broader market coverage, but harms both when it induces inefficient premium pricing and reduces total demand. We extend our main model to several settings, including cases where both firms have dynamic pricing capabilities and where consumer valuations follow a continuous distribution. While our main qualitative insights remain robust, we uncover several new findings. For example, when both firms (rather than only the retailer) can adjust prices dynamically, dynamic pricing is more likely to enhance product quality and increase the manufacturer's profits.