Pricing of delivery services and the emergence of marketplace platforms

This paper studies the pricing of delivery services and its impact on the market structure in the-commerce sector. We focus on one of the ongoing trends, namely the development of marketplaces. A retailer may not just sell its own products; but also provide a marketplace for other sellers, offering a variety of services including delivery. Marketplaces create a "secondary" market which undermines the delivery operator's abilityto differentiate prices. We study the subgame perfect equilibrium of a sequential game with two operators where retailer 0 may potentially develop a marketplace. The delivery operator and retailer 0 bargain over the delivery rate. Then, retailer 0 chooses the per-unit rate and the fixed fee at which it is willing to sell its delivery service to the other retailer. Finally, retailer 1 chooses its delivery option: either it directly patronizes the independent delivery operator, or it uses the services offered by the marketplace, and the corresponding subgame is played. Analytical results are completed by numerical simulations and lead to three main lessons. First the equilibrium nearly always implies a discount to the "leading" retailer, even when the profit maximizing operator has all the bargaining power. Second, the delivery operator cannot avoid the emergence of a marketplace even though this decreases its profits. Third, the market power of the delivery operator cannot be assessed solely by considering its market share

Pricing of delivery services and the emergence of marketplace platforms

This paper studies the pricing of delivery services and its impact on the market structure in the-commerce sector. We focus on one of the ongoing trends, namely the development of marketplaces. A retailer may not just sell its own products; but also provide a marketplace for other sellers, offering a variety of services including delivery. Marketplaces create a "secondary" market which undermines the delivery operator's abilityto differentiate prices. We study the subgame perfect equilibrium of a sequential game with two operators where retailer 0 may potentially develop a marketplace. The delivery operator and retailer 0 bargain over the delivery rate. Then, retailer 0 chooses the per-unit rate and the fixed fee at which it is willing to sell its delivery service to the other retailer. Finally, retailer 1 chooses its delivery option: either it directly patronizes the independent delivery operator, or it uses the services offered by the marketplace, and the corresponding subgame is played. Analytical results are completed by numerical simulations and lead to three main lessons. First the equilibrium nearly always implies a discount to the "leading" retailer, even when the profit maximizing operator has all the bargaining power. Second, the delivery operator cannot avoid the emergence of a marketplace even though this decreases its profits. Third, the market power of the delivery operator cannot be assessed solely by considering its market share

Transparency and Control in Platforms for Networked Markets

In this work, we analyze the worst case efficiency loss of online platform designs under a networked Cournot competition model. Inspired by some of the largest platforms today, the platform designs considered tradeoffs between transparency and control, namely, (i) open access, (ii) controlled allocation and (iii) discriminatory access. Our results show that open access designs incentivize increased production towards perfectly competitive levels and limit efficiency loss, while controlled allocation designs lead to producer-platform incentive misalignment, resulting in low participation and unbounded efficiency loss. We also show that discriminatory access designs seek a balance between transparency and control, and achieve the best of both worlds, maintaining high participation rates while limiting efficiency loss. We also study a model of consumer search cost which further distinguishes between the three designs

Control Mechanisms for Assessing the Quality of Handmade and Artistic Products in e-Marketplace Platforms

Selling handmade and artistic goods online is challenging since buyers need to be able to assess product quality before purchase. This study aims to explore how control mechanisms aid the assessment of the product quality of handmade and artistic goods. We do so by extracting control mechanisms for e-marketplace platforms from existing literature and discussing to what extent these are suitable for handmade and artistic goods. We found that existing literature mainly focuses on reputation systems. We reshaped the findings by conducting desk research to identify how control mechanisms are applied in a number of e-marketplaces. Our results show that in e-marketplaces that focus on selling handmade artistic products, a reputation system is not sufficient to ensure product quality in an online environment. Thus, it is critical to apply other control mechanisms which are more effective in increasing the trustworthiness of the seller of artistic and handmade goods. Last, we also suggest alternative control mechanisms to be explored in future research

Provable Guarantees for General Two-sided Sequential Matching Markets

Two-sided markets have become increasingly more important during the last years, mostly because of their numerous applications in housing, labor and dating. Consumer-supplier matching platforms pose several technical challenges, specially due to the trade-off between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences. In this work, we study a general version of the two-sided sequential matching model introduced by Ashlagi et al. (2019). The setting is the following: we (the platform) offer a menu of suppliers to each consumer. Then, every consumer selects, simultaneously and independently, to match with a supplier or to remain unmatched. Suppliers observe the subset of consumers that selected them, and choose either to match a consumer or leave the system. Finally, a match takes place if both the consumer and the supplier sequentially select each other. Each agent's behavior is probabilistic and determined by a regular discrete choice model. Our objective is to choose an assortment family that maximizes the expected cardinality of the matching. Given the computational complexity of the problem, we show several provable guarantees for the general model, which in particular, significantly improve the approximation factors previously obtained

Two-sided Adverse Selection and Bilateral Reviews in the Sharing Economy

Online peer-to-peer platforms match service providers with consumers. Both providers and consumers derive heterogeneous payoffs depending on whom they are matched with. To ensure that providers and consumers identify the most valuable matches, many of these platforms elicit relevant information from and also disclose the information to the market participants by adopting bilateral review schemes. Although the bilateral review scheme has its own merits in reducing information asymmetry and possibly enabling better matches, its impact on the various stakeholders in online peer-to-peer platforms remains unexplored. We show that, in equilibrium, the bilateral review scheme intensifies price competition among service providers to attract low-cost consumers and consequently reduces the platform\u27s profit. Interestingly, service providers may be better off with more intense price competition and lower prices when the proportion of low-cost consumers is sufficiently high. More importantly, we find that social welfare is not always higher under the bilateral review scheme compared to either the unilateral review scheme or no reviews. Our findings demonstrate that even though the bilateral review scheme eliminates the information asymmetry and adverse selection on both sides of the market, it does not necessarily enhance market efficiency when competing providers strategically respond to reviews by adjusting their prices

Online Assortment Optimization for Two-sided Matching Platforms

Motivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers, and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests she has received and, based on her preferences, she chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers’ arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers’ preferences are described by the Multinomial Logit and Nested Logit choice models. We develop new forms of balancing algorithms, which we call preference-aware, that leverage structural information about suppliers’ choice models to design the associated discount function. In certain settings, these algorithms attain competitive ratios provably larger than the standard “barrier” of 1 − 1/e in the adversarial arrival model. Our results suggest that the shape and timing of suppliers’ choices play critical roles in designing online assortment algorithms for two-sided matching platforms