On sellers' cooperation in hybrid marketplaces

Abstract

Hybrid marketplaces, such as Amazon's and Zalando's stores or Apple's and Google's app stores, which distribute their own products and services in competition with those of third-party sellers, play a significant and growing role in the Internet economy. This paper shows that, other things equal, such platforms would maximize their profits if they lowered the fees charged to sellers and the prices charged to consumers in response to cooperation agreements between third-party sellers: horizontal mergers or collusive agreements. It also shows that such cooperation can be pro-competitive when the platform is a vertically integrated gatekeeper, adopts the agency business model, is a close competitor to the third-party sellers it hosts, and observes (or correctly anticipates) the third-party sellers' agreement. The discussion here is of significant policy relevance, since third-party sellers in online marketplaces may find it easier to collude and may respond to the bargaining power of certain gatekeeper platforms by merging their activities.

Behavior-based algorithmic pricing

“…The results derived in this analysis rely on the possibility for the firms to commit to a targeting strategy. As mentioned earlier, the possibility of such a strategic commitment is a growing consideration of the literature on algorithmic pricing (Salcedo, 2015;Klein, 2018;Bisceglia and Padilla, 2023;Loots and den Boer, 2023;Brown and MacKay, 2023). Scholars and practitioners have identified two main ways for firms to implement such a commitment in practice.…”

“…Hence, for each firm the revelation of its targeting strategies has the value of a credible commitment to use this strategy in the remaining of the game. This specification is increasingly common in a vast strand of the literature on algorithmic pricing (Klein, 2018;Bisceglia and Padilla, 2023;Loots and den Boer, 2023). 16 Overall, these articles argue that, for most algorithmic pricing systems, the learning process and the design of the optimal strategy occur offline, as it is assumed in this article since the firms first design the optimal targeting strategy, and then engage into targeted pricing online.…”

“…Instead, with algorithmic price discrimination, firms can choose for each past customer whether to charge a personalized price or to use a flexible third-degree price discrimination by pool-ing different consumers who are charged homogeneous prices. On the other hand, there is a growing recognition in the literature that firms can use algorithms as devices to commit to a specific pricing strategy (Salcedo, 2015;Klein, 2018;Bisceglia and Padilla, 2023;Brown and MacKay, 2023;Loots and den Boer, 2023). This article embeds these two new elements in a model of behavior-based algorithmic pricing, to analyze how the ability for firms to target past customers strategically shapes their decision to collect consumer data, and impacts the competitive structure of data-driven industries.…”

“…Besides allowing firms to design sophisticated pricing strategies, pricing algorithms also offer a simple way for firms to implement such a strategic commitment. The use of pricing algorithms as devices to commit a specific strategy is indeed the topic of a growing literature (Salcedo, 2015;Klein, 2018;Bisceglia and Padilla, 2023;Brown and MacKay, 2023;Loots and den Boer, 2023). In these models, the use of a pricing algorithm is observable by a firm's competitor and provides credible information on the ability of the firms to commit to a pricing strategy.…”

Information sharing in an e‐tailing supply chain for fresh produce with supplier encroachment

“…With the speedy progress of the Internet, e‐commerce is widespread in the retail industry (Chen et al., 2017; Bisceglia and Padilla, 2022). In the fresh produce industry, fresh produce e‐commerce is viewed as the last “piece of cake” of the e‐commerce industry and is developing rapidly (He et al., 2019).…”