Seller Competition Research Articles

Influencer marketing and product competition

With the rise of social platforms and digital media (e.g., videos, (live)streaming, and multi-media blogs), firms and brand owners increasingly depend on influencers to attract consumers, who care about both product quality and consumer-influencer interaction. Sellers thus compete in both influencer and product markets. We develop a theory to understand the influencer labor market and its interaction with product competition, under a plausible specification of influencers' value added. We find: (i) more powerful influencers sell better quality products for sellers with weaker direct-sale capacity, facilitating seller competition; (ii) advances in the intermediation technology can lead to non-monotonic changes in influencers' payoff and income inequality; (iii) style pluralism mitigates market concentration by horizontally differentiating the consumer experience but serves as either complements (intermediate style dispersion) or substitutes (small or large dispersions) to vertical product differentiation; and (iv) influencers may inefficiently under-invest in consumer outreach and unidirectional exclusivity can improve welfare in less competitive markets. Collectively, our findings highlight the novelty of the influencer economy and establish several theoretical baselines for future studies.

Platform investment and seller competition in two-sided markets

Platform investment and seller competition in two-sided markets

To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment

Demand uncertainty and seller competition are substantial challenges for online platforms. In “To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment,” Birge, Chen, Keskin, and Ward analyze whether and how an online platform should offer demand information or price incentives to the sellers participating on the platform. The authors show that, when facing uncertain demand, the platform could be better off by doing nothing—that is, not providing any information or incentives to the sellers. They also develop a strategic reveal-and-incentivize policy for the platform to choose when to start sharing information and offering rewards to coordinate the sellers’ pricing. They prove that the strategic reveal-and-incentivize policy achieves near-optimal profit performance for the platform.

Seller competition on two-sided platforms

Seller competition on two-sided platforms

Trust me: Communication and Competition in a Psychological Game

Abstract We study, both theoretically and experimentally, a communication game with and without seller competition and embed it in a psychological-game framework where players experience costs for lying, misleading others, and being disappointed. We derive the equilibrium predictions of this model, compare them to the setting without psychological payoffs, and test these predictions in a laboratory experiment, in which we induce both material and psychological payoffs. We find that the setting in which players have both material and psychological payoffs features more trade, trades goods of marginally better quality, and does so without welfare losses to either side of the market relative to the setting with material payoffs only. However, the introduction of competition counteracts this improvement and lowers welfare for both sides of the market. This happens due to a surge in dishonesty by sellers in the competitive setting and the buyers’ inability to detect this deception.

Building a reputation for trustworthiness: Experimental evidence on the role of the feedback rate

In 25 years, research on reputation-based online markets has produced robust evidence on the existence of the so-called reputation effect, that is the positive relation between online traders’ reputations and these traders’ market success in terms of sales and prices. However, there is an ongoing debate on what the size of the reputation effect means. We argue that the rate of truthful feedback that traders leave after completed transactions is negatively related to the size of the reputation effect. The higher the rate of truthful feedback, the quicker will untrustworthy traders be screened and disincentivized to enter the market. With mostly trustworthy traders entering the market, buyers will demand smaller price discounts from market entrants without a good reputation. We test this mechanism empirically in two laboratory experiments. In both experiments, we systematically vary the probability with which information about sellers’ behavior in an economic trust game is recorded and shown to future interaction partners of these sellers. In the second experiment, we introduce competition among sellers by allowing buyers to choose one of two sellers in each interaction. We find that sellers give discounts to buyers to build or repair their reputation and that sellers who give discounts or have a good reputation are trusted more. However, we do not find support for our hypothesis that a higher feedback rate significantly decreases sellers’ propensity to give discounts. We argue and show in exploratory analyses that this is likely due to the high level of unconditional trust buyers exhibit towards sellers without a reputation. Yet, seller competition increases the propensity to offer discounts among sellers without a reputation the most.

Do buyer protection mechanisms help sellers? A model of seller competition in the presence of online reputation systems

In online markets, consumers rely on reputation systems to make purchases decisions. However, traditional accumulative-score type online reputation systems bring disadvantages to new sellers since they do not have sufficient transactions. In this paper, we investigate how do consumer decision making as well as seller competition change after introducing buyer protection mechanisms, such as escrow services and money-back guarantee. Do consumers rely less on online reputation systems after launching buyer protection mechanisms? Does introducing buyer protection mechanisms alleviate the competition pressure among sellers? Does a new seller benefit more than an existing seller? This paper proposes a theoretical model to discuss how online reputation systems affect sellers and consumers in the presence of buyer protection mechanisms. Contrary to the conventional wisdom that buyer protection mechanisms enhance buyer experiences and give low-reputation sellers advantages, we find that such scenarios occur only in the short term. In the long run, consumers rely more on online reputation systems as the effectiveness of buyer protection mechanisms increase. Both consumers and low-reputation sellers can be worse off. We also empirically assess these theoretical predictions.

Platform financing vs. bank financing: Strategic choice of financing mode under seller competition

Third-party sellers on online platforms primarily rely on banks to meet their financing requirements and are often constrained by the lack of sufficient working capital. Online platforms such as Amazon and Alibaba have ushered new dynamics in the e-commerce financing landscape by offering working capital loans to these sellers, thereby directly competing with banks and influencing market competition. This paper analytically studies how the competition between third-party sellers on platforms affects the strategic financing choices of sellers, namely, platform financing (P) vs. bank financing (B). We examine how the financing choice influences sellers’ pricing decisions in the competitive product market. Based on the sellers’ financing choices, four lending modes are possible: BB, PP, BP, and PB. Our analyses provide critical insights on the optimal financing choices and uncover the interplay of pricing, product substitution, referral fees, and production costs. We show that any seller deviating from PP to a hybrid mode (BP/PB) leads to a higher platform interest rate and a lower seller profit. We find that the competing sellers choose a hybrid lending mode when the unit production cost is low. However, the platform always prefers PP, leading to an all-win outcome in the supply chain when the production cost is high. Furthermore, for products with low referral fees and high substitution effect, BP/PB emerges as the equilibrium mode, while for products with low referral fees and low substitution effect, PP is the equilibrium outcome. Interestingly, we find that both sellers may choose bank financing when their working capital levels are high.

Marketplace Leakage

A key issue for the design of online marketplaces is addressing leakage. Buyers may use the marketplace to discover a seller or to obtain certain conveniences, but the seller may then want to take transactions off the marketplace to avoid transaction fees. Assuming buyers are heterogenous in their switching cost or inconvenience cost of purchasing directly, we provide a model in which there is partial leakage in equilibrium. We use the model to analyze the trade-offs associated with different strategies the marketplace can use to attenuate the effects of leakage: investing in transaction benefits, limiting communication, charging referral fees, using price-parity clauses, introducing seller competition on the marketplace, and hiding sellers that try to induce too much leakage. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4757 .

Simulation-Based IoT Stream Data Pricing Incorporating Seller Competition and Buyer Demands

The concept of sensor clouds has been populated for utilizing data from massive amount of IoT devices. In the sensor cloud, a large number of sensors and users are connected and sensor data are traded among them. A number of market frameworks for such data ecosystems have been proposed so far, most of which assumes multiple stakeholders and coordinates their interests using techniques such as the traditional economic theory and game theory. However, because of the duplicability of IoT data, designing a natural pricing scheme based directly on market principles, such as the balance between seller competition and consumer demands, is still a challenge. In this paper, we propose a new pricing scheme for IoT stream data, where prices are determined by the balance between seller competition and consumer demand. Unlike conventional methods, our method is based on simulation. By simulating the market and sellers’ pricing behaviors on the broker’s platform, fair pricing is achieved without causing undesirable phenomena such as price wars. The evaluation results show that the proposed pricing method has desirable characteristics for an IoT data market.

Platform Financing Vs. Bank Financing: Strategic Choice of Financing Mode Under Seller Competition

Platform Financing Vs. Bank Financing: Strategic Choice of Financing Mode Under Seller Competition

Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces

The paper studies how personalized product recommendations and their accuracy affect seller competition in an online marketplace.

Platform Governance

Platforms that intermediate trades—such as Amazon, Airbnb, and eBay—play a regulatory role in deciding how to govern the marketplaces they create. We propose a framework to analyze a platform’s nonprice governance design and its incentive to act in a welfare-enhancing manner. We show that the platform’s governance design can be distorted toward inducing insufficient or excessive seller competition, depending on the nature of the fee instrument employed by the platform. These results are illustrated with micro-founded applications to a platform’s control over seller entry, information provision and recommendations, quality standards, and search-design choices. (JEL D21, D83, L15, L23, L81, M37)

Service Investment and Pricing Strategies in E-Commerce Platforms With Seller Competition

With the development of the two-sided market, many platform enterprises classify their users into different types and cooperate with them with different strategies. The extant literature mainly explores the pricing and investment decisions for the platform, but pays little attention to the classification of sellers when making decisions. This paper investigates the investment of value-added service and pricing strategies for an e-commerce platform with competing sellers. Specifically, this paper considers a two-sided platform that is composed of an e-commerce platform, buyers and sellers. Sellers with high performance requirement and with low performance requirement compete for the buyers in the platform. This paper assumes that each buyer will choose the sellers’ type immediately after entering the platform and buy a unit of product in the platform. Through theoretical analysis the authors show that, the platform will gain more profits by investing in value-added services for type-A sellers and it will obtain the optimal profit when the transaction fee is moderate.

Information Sharing in the Online Marketplace when Sellers Incur Nonlinear Costs

We study information dissemination in an online platform where multiple sellers distribute their partially substitutable products. The platform has superior information about consumer demand and determines whether, and with how many sellers, to share the information. An important feature of our study is that the sellers have a nonlinear cost structure. We investigate the effect of and incentives for information sharing using a unified model that incorporates three types of nonlinear cost structure: constant marginal cost, increasing marginal cost, and decreasing marginal cost. We show that the cost structure alone determines the direction of the impact of information sharing on revenue and cost, while the scope of information dissemination (the number of sellers who receive information) affects only the magnitude of that impact. The online platform has incentives to voluntarily share demand information with sellers if and only if the cost structure exhibits diseconomy, linearity, or moderate economy of scale. When the economy of scale is too large, sharing information with sellers will reduce the total revenue and thus the platform's earned commission. When sellers compete in quantity, the online platform shares information with fewer sellers if their products are more similar. The platform tends to share information with more sellers if they compete in price, as long as the cost structure has a diseconomy or moderate economy of scale. The commission rate also impacts the platform's optimal scope of information sharing, depending on the cost structure. The platform's information sharing policy should be made based on the product category and the nature of seller competition. If the product category has a large economy of scale, the platform should not share information with any seller. Otherwise, the platform should share information with a subset of sellers if they compete in quantity, or with all sellers if they compete in price.

Managing competition on a two‐sided platform

AbstractOn many two‐sided platforms, users on one side not only care about user participation and usage levels on the other side, but they also care about participation and usage of fellow users on the same side. Most prominent is the degree of seller competition on a platform catering to buyers and sellers. In this paper, we address how seller competition affects platform pricing, product variety, and the number of platforms that carry trade.

Deceptive Products on Platforms

Many products sold on online platforms have additional features. Platforms can deliberately shroud these features from consumers, e.g. by revealing them only late in the purchase process. For example, platforms often reveal shipping- and handling fees, or upgrades like luggage or hotel services only late in the purchase process. We study when a two-sided platform discloses (a.k.a unshrouds) additional fees when some buyers naively ignore shrouded fees. We uncover a novel mechanism to explain why platforms shroud: platforms shroud or unshroud to manipulate cross-group externalities between buyers and sellers. Exploring this mechanism, we highlight two results suggesting online marketplaces lead to more shrouded features. First, we ask when a platform shrouds seller fees on its marketplace. Driven by cross-group network externalities to attract buyers and appear cheap, the platform has stronger incentives to shroud seller fees than sellers themselves. Second, we investigate when the platform shrouds its own additional fees and uncover a perverse effect of seller competition: fiercer competition between sellers encourages the platform to shroud its own fees. Both results hold even though the platform earns no commission to shroud seller fees and does not sell its own brands, so banning these practices will not induce a transparent marketplace. We discuss further policy implications and connect to common practices like drip pricing, steering, and rebate design.

Vertical Relationships within Platform Marketplaces

In two-sided markets a platform allows consumers and sellers to interact by creating sub-markets within the platform marketplace. For example, Amazon has sub-markets for all of the different product categories available on its site, and smartphones have sub-markets for different types of applications (gaming apps, weather apps, map apps, ridesharing apps, etc.). The network benefits between consumers and sellers depend on the mode of competition within the sub-markets: more competition between sellers lowers product prices, increases the surplus consumers receive from a sub-market, and makes platform membership more desirable for consumers. However, more competition also lowers profits for a seller which makes platform membership less desirable for a seller and reduces seller entry and the number of sub-markets available on the platform marketplace. This dynamic between seller competition within a sub-market and agents’ network benefits leads to platform pricing strategies, participation decisions by consumers and sellers, and welfare results that depend on the mode of competition. Thus, the sub-market structure is important when investigating platform marketplaces.

Enabling customer-provided resources for cloud computing: Potentials,challenges, and implementation

Recent years have witnessed cloud computing as an efficient means for providing resources as a form of utility. Driven by the strong demands, industrial pioneers have offered commercial cloud platforms, mostly datacenter-based, which are known to be powerful and effective. Yet, as the cloud customers are pure consumers, their local resources, though abundant, have been largely ignored. In this paper, We present SpotCloud , a real working system that seamlessly integrates the customers’ local resources into the cloud platform, enabling them to sell, buy, and utilize these resources. We also investigate the potentials and challenges towards enabling customer-provided resources for cloud computing. Given that these local resources are highly heterogeneous and dynamic, we closely examine two critical challenges in this new context: (1) How can the customers be motivated to contribute or utilize such resources? and (2) How can high service availability be ensured out of the dynamic resources? We demonstrate a distributed market for potential sellers to flexibly and adaptively determine their resource prices through a repeated seller competition game. We also present an optimal resource provisioning algorithm that ensures service availability with minimized lease and migration costs. The evaluation results indicate it as a flexible and less expensive complement to the pure datacenter-based cloud.

Management of competition among sellers and its performance implications for business-to-business electronic platforms

Purpose – This paper aimed to analyze the short- and long-term effects of the breadth and depth of seller competition on the performance of platform companies, and investigated the underlying mechanisms of customers’ two-sided marketing tactics on the structure of the competition between sellers. Design/methodology/approach – A longitudinal research design was adopted by gathering daily market objective data on e-commerce platforms for 250 days, and the dynamic evolution effects was analyzed by using a vector autoregression model which compared the differences between the short- and long-term effectiveness of different customer relationship management (CRM) strategies. Findings – The breadth of competition amongst sellers improves the performance of platforms, whilst the depth of competition among sellers has a positive effect on the short-term performance. However, it has a negative effect on the long-term performance of their platforms. In both the short and long terms, advertising tactics that attract new buyers contribute more to increases in the breadth of seller competition than those that attract existing buyers do. Subsidies for new sellers decrease the depth of seller competition more than those for old sellers. Research limitations/implications – Further research could be undertaken to investigate the validity of marketing tactics other than advertising tactics, and thus expand the time windows of the available data. Practical implications – It is imperative for platform companies to implement effective control over seller competition to balance the interests of the sellers and of themselves. Originality/value – The dyadic paradigm of CRM research has been extended by considering the perspective of the electronic platform company, how the tactics of exploitation and exploration of two-sided customers impact upon seller competitive structures have been delved into and why new customers have a unique value to platform companies has been identified.