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Consumer-to-consumer (C2C) platforms have become a major engine of growth in Internet commerce. This is especially true in countries such as China, which are experiencing a big rush toward e-commerce. The emergence of such platforms gives researchers the unique opportunity to investigate the evolution of such platforms by focusing on the growth of both buyers and sellers. In this research, we build a utility-based model to quantify both cross and direct network effects on Alibaba Group’s Taobao.com, the world’s largest online C2C platform (based in China). Specifically, we investigate the relative contributions of different factors that affect the growth of buyers and sellers on the platform. Our results suggest that the direct network effects do not play a big role in the platform’s growth (we detect a small positive direct network effect on buyer growth and no direct network effect on seller growth). More importantly, we find a significant, large and positive cross-network effect on both sides of the platform. In other words, the installed base of either side of the platform has propelled the growth of the other side (and thus the overall growth). Interestingly, this cross-network effect is asymmetric with the installed base of sellers having a much larger effect on the growth of buyers than vice versa. The growth in the number of buyers is driven primarily by the seller’s installed base and product variety with increasing importance of product variety. The growth in the number of sellers is driven by buyer’s installed base, buyer quality, and product price with increasing importance of buyer quality. We also investigate the nature of these cross-network effects over time. We find that the cross-network effect of sellers on buyers increases and then decreases to reach a stable level. By contrast, the cross-network effect of buyers on sellers is relatively stable. We discuss the policy implications of these findings for C2C platforms in general and Taobao in particular. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0976 .

PurposeThis paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.Design/methodology/approachA two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.FindingsThe evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.Research limitations/implicationsThis study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.Practical implications This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.Originality/valueInvestigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

PurposeDue to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.Design/methodology/approachThis study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.FindingsThe degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.Originality/valueThis study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

Broadcast TV is a well-known example of a two-sided platform where cross network effects on the viewer and advertising sides interact. Like many platforms, it is advertiser-financed. While the literature shows that viewers dislike advertising, we explore a unique data set and distinguish between paid and non-paid (informative) ads. Cross-network effects’ estimates show that the latter carry a positive network effect on viewership. We also explore a significant change in public interest for more information in TV content in Russia in 2022 to estimate structural changes to cross-network effects. The results indicate that negative paid ads’ cross-network effects on viewership demand become stronger while positive non-paid (information) ads cross-network effects­ become weaker, even conditional on TV programing changes. Symmetrically, on the other side of the platform, advertisers value viewership less after the preference change.

Nowadays, more and more transactions or interactions like online dating and shopping are completed on two-sided platforms involving two groups of agents. On these two-sided platforms, there often exist cross-network effects, i.e., the benefits that agents at one side receive are positively related to the number of agents at the other side, and vice versa. This paper considers such two-sided platforms, where the platforms offer a certain service to attract agents of both sides to join the platforms, and then charge agents who join the platforms a lump-sum fee to gain the profit. We present service and pricing strategies for both monopolistic and duopolistic platforms, respectively. We also investigate the impact of platforms’ life cycle on their service and pricing strategies. Some managerial implications are shown.

The pricing of ride-hailing platforms (e.g., Didi Rider and Uber) is heavily and simultaneously influenced by the cross-group network effect and congestion effect. To analyze the bilateral pricing of ride-hailing platforms under the influence of these two effects, in this paper we construct a game-theoretic model under four different scenarios and analyze the equilibrium outcomes. The results show that: (1) when both passengers and drivers are sensitive to hassle costs, if the cross-group network effect on the passenger side is higher than that on the driver side, then the platform’s pricing on both sides increases with the increase in the congestion effect, otherwise the prices on both sides of the platform decrease with the increase in the congestion effect; (2) when passengers are sensitive to hassle costs and drivers are sensitive to price, if the ratio for passengers’ and drivers’ different perceptions of price and hassle cost is greater than a certain threshold, then the platform’s pricing on the passenger side increases with the increase in the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect; (3) when passengers are sensitive to price and drivers are sensitive to hassle costs, if the ratio for passengers’ and drivers’ different perceptions of price and hassle costs is greater than a certain threshold, then the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side increases with the increase of the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect; (4) when both passengers and drivers are price-sensitive, if the cross-group network effect on the passengers’ side is larger than that on the drivers’ side, then the platform should decrease its pricing on both sides with the increase in the congestion effect, otherwise, if the cross-group network effect on the passengers’ side is less than that on the drivers’ side, the platform should increase its pricing on both sides with the increase in the congestion effect; (5) the platform is able to generate the highest profit in each scenario, and the results of the profit comparison between the four scenarios depends on the cross-group network effects and the congestion effects on both the passengers’ and the drivers’ sides.

In the two-sided market for online streaming content, the platform’s co-opetitive strategy has been wildly discussed, where the platforms cooperate in sharing the broadcasting right of content and meanwhile compete for both subscribers and advertisers. Although platform co-opetition in practice can be easily captured, the impacts of cross-side network effects on pricing strategy are contingent upon the participation decision of both sides, including single-homing and multi-homing. Therefore, we examine the optimal co-opetitive strategy of duopoly platforms using a Hotelling model to capture user behaviors and investigate the equilibriums of pricing decisions and profits in three scenarios: single-single, multi-single, and multi-multi. The main findings are: (1) Advertisers choose multi-homing only when subscribers are also multi-homing, and the broadcasting cost is relatively low. (2) With single-homing advertisers, the primary broadcasting platform earns more profit than the re-broadcasting one. (3) With multi-homing advertisers, the primary broadcasting platform’s profit increases with the broadcasting rights cost. (4) Platforms should focus on building strong cross-side network effects with multi-homing advertisers. Alternatively, they would be better off contracting with single-homing advertisers if the effects are relatively low.

PurposeBoundary decision is an important but underexplored theme in digital platform research. The boundary decision of digital platform enterprises (DPEs) differs from traditional organizations because of cross-side network effects (CNEs). This study intends to investigate whether transaction cost economics (TCE) and resource-based view (RBV), as classical organization boundary mechanisms, are still applicable for DPEs.Design/methodology/approachTo unfold the research problem, this study conducts a fuzzy-set qualitative comparative analysis (fsQCA) on the samples of 21 platform business units.FindingsThe results show that the classical boundary decision theory still applies in the context of DPEs, but the cross-side network effects will affect boundary decision of DPEs.Originality/valueThis study provides a new framework – integrates TCE, RBV and CNEs – to analyze boundary decision of DPEs. This paper also contributes to research on both organization boundary decision and platform governance.

Considering a two-sided software platform with software developers on one side and software users on the other, we study whether the platform should adopt a penetration pricing strategy or skimming pricing strategy on the developer side. We propose a two-period analytical model with asymmetric cross-side network effects to analyze the platform’s optimal pricing strategy. Our analysis reveals that the platform should adopt a penetration pricing strategy if the user-to-developer network effect is strong and a skimming pricing strategy otherwise. If the platform does not charge users an access fee, the platform should consider subsidizing developers’ access in the first period only. However, when the platform charges users an access fee, subsidizing developers’ access in both periods can be viable for the platform. Charging the software user an access fee incentivizes the platform to subsidize developers in the first period if the user-to-developer network effect is weak. Finally, this study reveals that the optimal access fee charged or subsidy provided to developers in the two periods is determined by several key factors: developers’ basic expectations about the revenue to be gained from the platform (optimistic or pessimistic), intensities of cross-side network effects, the lengths of the two periods, and the access fee charged to users.

In this study, we examine the versioning strategy for two-sided dating platforms. We assume a monopoly dating platform facing two sides of users (male and female) with different levels of willingness to pay across and within the same side. The platform knows the user type distribution on each side but does not know the exact types of the individual users. The platform needs to decide whether or when to offer different versions of its services to the different types of users. The main findings are as follows: (1) the platform should always serve both types of users on each side; (2) the platform should choose to conduct versioning when the proportion of low-type users is small and/or their taste for quality is markedly different from that of high-type users, and choose not to do so otherwise; (3) when versioning is optimal, the platform should degrade the low-quality version as much as possible; and (4) versioning is never socially optimal. The necessary condition for versioning to improve social welfare is that high-type users benefit more from quality improvement than low-type users suffer from quality reduction.