The platform market, where a platform serves as both market maker and regulator, has received increasing attention in recent years. Despite great success, the inherent uncertainty of the platform market has hindered its overall development. Previous papers have studied uncertainty by focusing on either sellers or products, ignoring a platform's significant effects. To fill this void, we propose and validate a new dimension of uncertainty in the platform market: platform uncertainty. To examine the systematic dimensions of uncertainty in the platform market and reveal its consequences and antecedents, we utilize the perspective of information asymmetry. Structural equation modeling is used to build a theoretical framework, and we then conduct a survey among 352 consumers of a leading commerce platform in China to collect our data. We use the partial least squares (PLS) method to validate our hypotheses. Our empirical analysis provides the results that follow: First, we confirm that platform uncertainty is not only distinct from seller uncertainty and product uncertainty but also impacts both of these aspects. Consumers' perceived platform uncertainty positively influences their perceived seller uncertainty and product uncertainty. Consequently, these three dimensions of uncertainty negatively influence consumers' purchase intention. We also find that platform uncertainty negatively and indirectly influences consumers' purchase intention by impacting seller uncertainty. We demonstrate that consumers' concerns about a platform's hidden characteristics and opportunistic behavior contribute to platform uncertainty. These two significant antecedents explain much of the variance in platform uncertainty. 

Uncertainty in the platform market: The information asymmetry perspective

This paper investigates the impact of privacy concerns on customers’ purchasing decisions and service providers’ pricing strategies in Smart-X systems. We develop profit maximisation models to test...

Pricing strategy with customers’ privacy concerns in Smart-X systems

A systematic review of value-aware recommender systems

Motivated by the prevalent entry of store brands (SBs) and its potential impact on national brands (NBs) in the supply chain, this study develops game-theoretic models to examine the strategic interactions between the platform’s SB introduction strategy and the NB manufacturer’s innovative strategies under both the reselling and agency modes. We find that first, influenced by the manufacturer’s innovation strategy, the platform should not introduce an SB, even if the cost of doing so is negligible under certain conditions. Second, the manufacturer’s optimal countermeasure to the introduction of SB depends on various factors. Specifically, when consumer acceptance of SB is low, the manufacturer should upgrade the existing NB products if her innovation efficiency in developing new products is low under the reselling mode or if consumers’ preference for the newly developed NB products is low under the agency mode. In addition, the manufacturer should invest in developing a new category of NB products if both her innovation efficiency and consumers’ preference for that are sufficiently high, regardless of the operation mode. In this scenario, Pareto optimality is achieved, and the manufacturer can even benefit from the entry of an SB. Moreover, unlike the reselling mode, where the manufacturer’s optimal response to the SB is always to implement innovative measures, under the agency mode, the platform should employ a no-innovation strategy if consumers’ preference for SB products is high and both the commission rate and market value of the new NB products are low. Furthermore, by clarifying the interplay between the strategies of these two players, we find that the manufacturer can successfully prevent the platform from introducing an SB by upgrading the existing NB products under the agency mode, as long as certain conditions are met, but cannot successfully do so under the reselling mode. 

The Interactions between Online Platform’s Store Brand Introduction and Manufacturer’s Innovation Strategies: Reselling vs. Agency Modes

In the context of the digital economy, online retailers are faced with increasing competition and seek to improve sales and attract consumers through innovations such as recommender systems. This paper investigates the strategies for adopting recommender systems by online retailers, introducing a theoretical model to assess whether both incumbent and new entrant retailers should adopt these systems. The model conducts a comparative analysis, factoring in consumers' search costs and the strategic choices of competitors. It establishes equilibrium outcomes for four distinct scenarios, followed by an analysis of the interplay between the strategy selection decisions of the two types of retailers. The results show that if one retailer does not adopt the system, the other retailer should always adopt it. However, when one retailer has already implemented the system, the other retailer should only do so if the search cost is relatively low and the recommendation cost is high, but not when the search cost is moderate. Given two retailers' strategies, we identify the requirements that form two equilibriums: both retailers adopting the system, or only the incumbent retailer adopting it. Furthermore, the recommendation levels and product prices aren't necessarily in a monotonic relationship with the cost of the system. 

Strategic adoption of the recommender system under online retailer competition and consumer search

Many online marketplaces use “profit-based” personalized recommendation systems, recommending each consumer with the product that maximizes the marketplaces’ expected profit. We study how a marketplace’s profit-based recommendation system and its consumer profiling accuracy influence the marketplace, sellers, and consumers. Under the profit-based recommendation system, sellers strategically adjust their prices to compete for the marketplace’s recommendations. We find that when the profiling accuracy increases, the equilibrium price first decreases and then increases, and interestingly, the marketplace’s profits can decrease despite its more accurate predictions about consumer preferences. These results are driven by the interaction of three incentives of each seller when setting its price: to induce more recommendations from the marketplace, to extract surplus from high-fit consumers, and to compete for consumers who can find the products without the marketplace’s recommendations. Counterintuitively, the profit-maximization objective behind the profit-based system can backfire for the marketplace, and the marketplace can achieve a higher profit by excluding pricing information in its recommendation decisions. Although the profit-based system can prioritize more expensive products with higher commissions, regulations that prevent recommendation systems from considering profit margins can lead to higher prices and harm consumers.

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

In many markets, such as video streaming or information services, a consumer may purchase multiple competing products or services. The existing theoretical literature typically assumes that each consumer can buy only one product. This paper explicitly models the consumer’s multi-purchase behavior, and examines an upstream content creator’s content production and selling strategies, as well as competing downstream distributors’ content acquisition and pricing strategies. We find that in contrast to the case of single-product purchase, under multi-product purchase, only one distributor will acquire the creator’s new content in equilibrium. Furthermore, when the content distributors are not highly differentiated (each having a limited amount of unique content), the content creator will reduce new content production, leading to lower profits for both the content creator and the content distributors. By contrast, when the distributors are already highly differentiated with a substantial amount of unique content, the content creator will increase its content production, leading to higher profits for both the content creator and the distributors. We show that our main results and insights are robust to several alternative assumptions.

Competition of Content Acquisition and Distribution Under Consumer Multi-Purchase

This paper studies the impact of the best seller recommendation, a widely used popularity-based system, on consumers, sellers, and the online marketplace. In a two-period model, sellers decide whether to join the marketplace and consumers decide whether to make a purchase. Upon entry, competing sellers decide their prices, while consumers form their consideration set and search products within that set before making the purchase decision. Without a recommendation system, consumers’ consideration set will consist of sellers randomly selected in both periods. With the best seller recommendation, consumers still randomly search in the first period, but they will definitely include the best seller of the first period in their consideration set in the second period. We show that the best seller recommendation intensifies competition among sellers, resulting in a lower equilibrium price and decreased seller participation. Both the reduced price and the decreased seller participation hurt the marketplace’s commission revenue. When sellers’ pricing competition induced by the best seller recommendation is sufficiently intense, adopting the best seller recommendation system is not beneficial to the marketplace. We also identify conditions under which the best seller recommendation simultaneously benefits everyone in the ecosystem. In addition, we find that the main results are robust even if consumers engage in costly search and endogenously determine the size of their consideration set, or when the marketplace optimizes its commission rate. These results highlight the importance of accounting for the strategic response of the sellers before an online marketplace implements the best seller recommendation system.

How Does Best Seller Recommendation Shape the Ecosystem of an Online Marketplace?

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

Competition of Content Acquisition and Distribution Under Consumer Multi-Purchase

How Does Best Seller Recommendation Shape the Ecosystem of an Online Marketplace?