Bundling strategies have been widely adopted by online retail platforms that use agency selling, where suppliers pay a commission fee to the platform to gain access and sell directly to consumers. We study the optimal bundling strategy for a retail platform through which two independent suppliers distribute their products. The suppliers first set their product prices, and, subsequently, the platform decides whether to offer the two products as a bundle and, if so, the price of the bundled product. By analyzing a two‐stage Stackelberg game, we find that the platform adopts the bundling strategy only when the commission rate and the product prices are sufficiently high. The existence of the bundling option does not affect the suppliers’ optimal pricing decisions if the product costs are either above a high threshold or below a low threshold. However, the suppliers may strategically raise the retail prices to induce the platform to offer bundled products when the marginal costs are in the medium range. The strategic interaction between the platform and suppliers leads to interesting insights into the bundling problem. For example, the retail platform may not prefer a high commission rate (i.e., a greater bargaining power) if the platform has the option to provide the bundled product. Additionally, the platform having even the possibility of bundling may hurt the platform's profit, but it always benefits the suppliers.

Popularity bias reflects the positive impact of popularity information on consumer choices. There are two common display formats for popularity information on online retail platforms: the total-based cumulative sales format, where the total sales of a product are displayed since its launch, and the period-based cumulative sales format, where only sales within a specific recent period are shown. Both types of popularity information are continuously updated. This paper focuses on the multiproduct dynamic pricing problem with popularity bias. We employ the widely used multinomial logit model to investigate the impact of popularity bias on consumer choices. In particular, we examine how popularity bias affects marginal revenue, pricing decisions, and market shares. Moreover, we highlight that ignoring popularity bias can lead to a suboptimal outcome. As the multiproduct dynamic pricing problem suffers from the curse of dimensionality, we propose a semi-myopic pricing policy, which is computationally tractable, and demonstrate its asymptotic optimality under both formats. Our numerical simulations further indicate that ignoring popularity bias can result in substantial revenue losses, while the semi-myopic pricing policy consistently outperforms other heuristics under both formats. Finally, empirical tests on real data provide a comprehensive procedure for identifying the most appropriate choice models, which offer practical insights for implementation.

Data have become a vital competitive asset for online advertising platforms, such as Facebook and Snapchat. These platforms often partner with data brokers to enhance targeting capabilities, which raises important consumer privacy concerns. This study develops a game-theoretic model to analyze the economic dynamics of partnerships between competing platforms and a data broker in a two-sided market. Our findings reveal that heightened consumer privacy concerns can encourage platforms to collaborate with data brokers rather than discourage them, as these concerns may strategically soften price competition on the advertiser side. However, when both platforms partner with a data broker, a prisoner’s dilemma may arise, even if the broker significantly improves targeting capabilities. From a policy perspective, such partnerships can harm consumer surplus in a purely ad-sponsored model, where users are not charged. Conversely, they may enhance consumer surplus in a mixed revenue model that combines advertising with subscriptions. These insights suggest that policymakers should consider regulations on platform-data broker partnerships to protect consumer interests, particularly regarding privacy protections that platforms may not prioritize under competitive pressures.