Managing Market Thickness in Online Business-to-Business Markets

Abstract

We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders. This paper was accepted by Vishal Gaur, operations management.

Scalability in Platforms for Local Groceries: An Examination of Indirect Network Economies

“…This phenomenon has been observed in platforms involving inventory liquidation auctions (Bimpikis et al. 2020) as well as in platforms for ride‐hailing services (Kabra et al. 2016).…”

“…Our study is relevant to the literature at the intersection of operations management, information systems, and marketing. Part of this research focuses on studying the design and growth of online platforms specialized in the sale and/or rental of durable goods in the media, electronics, automotive, home furnishings, and toys/games categories (e.g., Fraiberger and Sundararajan 2015, Parker and Van Alstyne 2005, Zhu and Liu 2018) as well as in secondary markets (Bimpikis et al 2020, Dhanorkar 2018. Another part focuses on the design and growth of online platform operations involving services in the lodging and passenger-transportation industries (e.g., Allon et al 2018, Zervas et al 2017.…”

Predicting transaction outcomes under customized pricing with discretion: A structural estimation approach

“…Methodologically, we use structural estimation method to predict transaction outcomes (i.e., whether trade takes place and, if so, the final price) using field data. Structural estimation method is gaining significant traction in operations management (see, e.g., Akşin et al., 2013; Bimpikis et al., 2020; Li et al., 2014). We contribute to this line of research by expanding the use of structural estimation method to predicting transaction outcomes in the context of customized pricing with discretion.…”

An analysis of operating efficiency and policy implications in last‐mile transportation following Amazon's integration

“…Our paper also contributes to the growing literature on online platform design. Prior research in this literature has investigated critical design factors that affect online platform performance, such as market thickness and matching efficiency (Bimpikis et al 2020; Li and Netessine 2020; Nikzad 2017), pricing mechanisms (Bai et al 2019; Cachon et al 2019), subsidy policies (Allon et al 2018; Benjaafar et al 2021), and governance (Jiang et al 2011; Parker and Van Alstyne 2018; Zhu and Liu 2018). We focus on platforms in the Internet retail industry.…”