Cross-network Effects Research Articles

Simulation Modeling and Analysis on the Value-Added Service of the Third-Party E-Commerce Platform Supporting Multi-Value Chain Collaboration

Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.

The role of platform ecosystem configuration toward performance bifurcation

The prevalence of digital ecosystems and platforms underscores the need for a deeper understanding of the factors that influence performance variations within these platforms. This study introduced a multidimensional model to examine the platform ecosystem characteristics that affect performance outcomes. To this end, it adopted the Lotka-Volterra complex system theory. The model was simplified into a one-dimensional framework, enabling the examination and prediction of the relationship between performance bifurcation and configurations of platform characteristics. This study provides practical insights and implications that extend beyond academia. The results reveal that high network threshold values indicate robust platform performance, suggesting resilience against collapse. Additionally, increasing the network effect influences platform performance by shifting competition strength toward prominent tipping-point locations, which is considered a desirable regime. The results further confirm that competition dampens a platform's exponential growth; however, cross-network effects enhance it. These insights have significant implications for investors, offering a practical vision that can inform managers' strategic decision-making. These findings provide a solid foundation for developing informed strategies to enhance platform performance. In a world in which digital ecosystems play a pivotal role, the implications of this study empower stakeholders to make data-driven decisions, fostering success in a dynamic and competitive digital business environment.

Cross-Group Network Effects of National Payment Gateway Implementation: Evidence from Indonesia’s Debit Card Industry

We use Indonesian debit card data Year 2015-2020 to propose a hypothesis of the existence and intensity of network externalities in Indonesia's two-sided payment card market. We find cross-network effect does not fully occur in Indonesia since only the number of electronic data capture (EDC) installed influenced the average transaction/card and average transaction/EDC. Even after regression analysis, we found that a number of installed EDCs significantly negatively affected average transaction/ EDC. From these results, we can propose that Bank Indonesia prioritizes increasing merchant acceptance rather than increasing the number of payment instruments issued.

Dynamic order allocation in a duopoly hybrid workforce of competition: A machine learning approach

We develop a continuous-time stochastic differential game model that aims to capture market demand and stochastic cross-network effects, and we seek to find equilibrium order allocation strategies between the firm and the platform. By solving the Hamilton–Jacobi–Bellman (HJB) partial differential equation system, we obtain the feedback equilibrium. For a simple scenario, we derive the analytical solution which indicates that the equilibrium expenditures depend only on the marginal market thickness, and that market thickness is consistently advantageous for the value function. For the complex scenario, we propose a machine learning approach based on the Deep Galerkin Method to solve high-dimensional nonlinear HJB systems, and we demonstrate its good convergence properties. Based on reliable parameter values, our simulation results show that: (1) For higher market thickness, value functions exhibit greater sensitivity to changes in cross-network effects. (2) For a given cross-network effect, the equilibrium acquisition and retention expenditures display significant sensitivity to market thickness and time, respectively. To show the interaction between the two platforms, we present the following two results. One is that for the platform adopting a high pricing strategy, its acquisition expenditures exceed those of its competitor, while retention expenditures are the opposite. The other is that as the cross-network effect of the platform increases, its maximum profit initially rises and then declines, and the time of occurrence of the maximum profit monotonically decreases. In contrast, the competitor’s maximum profit initially declines and then rises, and the time of occurrence of the maximum profit monotonically increases.

The diffusion of platform self-operation on reputation-based two-layer network

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.

Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities

Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

Research on E-Commerce Platforms’ Return Policies Considering Consumers Abusing Return Policies

Currently, major e-commerce platforms are competing to improve their return services, while merchants are suffering from consumers abusing return policies. We developed a dual oligopoly model consisting of two e-commerce platforms, one offering a lenient return policy and the other enforcing a stringent one, to investigate the effectiveness of lenient return policies in the presence of opportunistic consumers. We examine the impact of the proportion of opportunistic consumers, cross-network effects, gains from dishonest returns, and penalties on the scale of users and profits for both platforms. The findings indicate that: (1) As the proportion of opportunistic consumers increases, multi-homing merchants tend to be single-homing on a platform with a stringent return policy. This reduces the number of consumers on a platform with a lenient return policy and lowers the platform’s profit. Moreover, increased gains from dishonest returns worsen the situation. (2) Network effects on merchants from the consumer side significantly affect the effectiveness of lenient return policies. (3) Enforcement of penalties for dishonest returns could prevent an exodus of consumers and merchants from platforms that offer lenient return policies. However, it does not raise profits. In other words, its impact on the success of lenient return policies is limited.

R2V platform's business model reconstruction in the metaverse era: Based on network effects and bundling

This paper aims to investigate the motivations behind the reconstruction of the business model for Real-to-Virtual platforms in the metaverse era. By developing a pricing model for metaverse platforms that consider both social network effects and cross network effects, we analyze the mechanism of network effects on the bundling strategies of Real-to-Virtual platforms. These strategies, in turn, give rise to different business models for metaverse platforms. We find that network effects have a crucial impact on the bundling strategy and business model of Real-to-Virtual platforms. When the social network effects are weak, platforms choose between mixed bundling and independent sales to accommodate both real and virtual businesses, resulting in what we term Digital Twin Platforms. When the cross network effects are relatively weak, platforms may prefer mixed bundling to encompass both types of products, leading to what we term Digital Collaboration Platforms. However, when the two types of network effects are comparable, platforms will adopt a dynamic approach and switch between three strategies to maintain their market position according to the strength of the two network effects and competitive market environment, which is classified as Real-virtual Interconstruction Platforms. Thus, network effects and bundling constitute important aspects that distinguish different Real-to-Virtual platforms, playing a crucial role in platform performance.

An evolutionary game-theoretic analysis of cooperation strategy between SMEs and cross-border e-commerce platforms considering the cross-network effect

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.

플랫폼 경쟁과 멀티호밍 제한: 교차 네트워크 효과의 비대칭성과 구매자 유형의 불완비정보

Purpose - This paper aims to analyze competition between two platforms with asymmetric cross indirect network effects under hidden buyer sensitivity. Platforms are differentiated by indirect network effects toward buyers that are also differentiated privately with sensitivity to the effects. Design/Methodology/Approach - We set up a platform competition model based on a signaling game and thus characterized a perfect Bayesian Nash equilibrium. A nature selects two types of buyer with a certain probability. Each type of buyer chooses one platform (single-homing). Sellers can choose either single-homing or multi-homing with price setting and complete trade with buyers. Findings - First, the leading platform with stronger cross network effects does not always mandate exclusive homing for sellers. Second, in a two-sided market where the small platform is significantly weak, a leading platform does not mind seller multihoming if buyers sensitive to indirect network effects are numerous enough. However, price discrimination under seller multihoming lowers social welfare. Third, when platforms are close enough in indirect network effects, a leading platform always wants to impose mandatory exclusive homing on sellers. A seller can raise prices and narrow trades to buyers with high willingness-to-pay only responding to mandatory exclusive homing, which lowers social welfare. Research Implications - Asymmetry between platforms in indirect network effects and buyer hidden sensitivities to the effects must be scrutinized to design effective policy instruments to promote competition in the platform market.

Platform Competition with Asymmetric Access Costs

We analyze competition between platforms under asymmetric access costs. Agents that belong to one platform do not pay access costs, but agents that participate in other platforms pay a positive access cost. An example is competition between application stores for mobile smart phones, because application users can pay for additional access costs to download and install third-party application stores instead of using preinstalled application stores. The equilibrium prices are much more complicated than those of symmetric access cost case which is analyzed by Armstrong (2006). It is clear that asymmetric access costs will reduce the equilibrium market shares of the platform with the disadvantage on both sides. However, the effects of asymmetric access costs on the equilibrium prices of both sides are ambiguous and rely mainly upon the relative levels of two different cross network effects.

Profit or Growth? Dynamic Order Allocation in a Hybrid Workforce

Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is the system (comprising the firm and its platform) sustainable in the long run? We investigate these questions in the context of last-mile delivery. We develop a discrete-time, stochastic dynamic program that captures the system’s profit from serving demand and the platform’s growth dynamics. The dynamic model incorporates a service constraint for the platform and a simple version of a stochastic network effect. We find that the answers to our research questions critically depend on two key parameters: the mean and variance of the cross-network effect. We conduct a numerical study with data from a last-mile delivery firm in Vietnam to illustrate our findings. This paper was accepted by Vishal Gaur, operations management.

Pricing strategy for own shipping service of E-commerce platform using Two-sided market theory

• Examining how the cross-network effect influences the price decision and profit of a two-sided market platform. • Developing a two-sided market pricing model to consider the ancillary services such as shipping service as well as intermediation services. • Suggesting how to allocate the e-commerce platform’s shipping fees and usage fees for maximizing profits based on the two-sided market theory. • Building up two-stage price allocation model considering cross-group externality to explain the activities of e-commerce platform operator, buyer and seller. • Providing implications for pricing strategies for the platform to apply, according to the degree of buyers’ sensitivity on the shipping fees and usage fees. • Providing influence of pricing strategies on the individual usage fees paid by sellers and buyers. Recently, many e-commerce platforms are trying to provide their own shipping services. In this context, this study suggests how to allocate the platform’s usage fees and shipping fees for maximizing profits based on the two-sided market theory. Our research assumes a monopolistic e-commerce market and presents sellers' and buyers’ demand of the platform as reduced-form equations considering cross-group externalities. Then, we divide a price allocation model into two stages using these demand functions. First, we determine the usage fees of each group that maximize the platform profits when total usage fees and shipping fees are constant. Second, we decide how to allocate total usage fees and shipping fees when the total price level (total usage fees + shipping fees) is constant. As a result, this study proposes that the platform can apply two different pricing strategies according to the degree to which buyers are sensitive to usage fees and shipping fees and also shows how these pricing strategies influence the individual usage fees paid by each group.

Coordinating Demand and Supply for Crowd Logistics Platforms with Network Effect

The crowd logistics platforms connect stochastic demand with uncertain delivery supply which is provided by independent service providers. Considering direct-network effects and cross-network effects between the demand and supply side, a dynamic surge pricing model for crowd logistics platforms is built. The pricing strategy is derived to coordinate the supply with demand to equilibrium. Furthermore, the pricing strategy minimizing cumulative delivery orders is analyzed. The numerical simulation results show that the dynamic surge pricing strategies can stimulate the uncertain delivery supply for maximizing platforms’ revenue. And, direct-network effects pose a positive impact on the dynamic surge pricing strategy. In contrast, the cross-network effects have a negative impact on the pricing strategy. However, direct-network effects and cross-network effects negatively influence platforms’ revenue.

Development of Social Support Networks by Patients With Depression Through Online Health Communities: Social Network Analysis.

BackgroundIn recent years, people with mental health problems are increasingly using online social networks to receive social support. For example, in online depression communities, patients can share their experiences, exchange valuable information, and receive emotional support to help them cope with their disease. Therefore, it is critical to understand how patients with depression develop online social support networks to exchange informational and emotional support.ObjectiveOur aim in this study was to investigate which user attributes have significant effects on the formation of informational and emotional support networks in online depression communities and to further examine whether there is an association between the two social networks.MethodsWe used social network theory and constructed exponential random graph models to help understand the informational and emotional support networks in online depression communities. A total of 74,986 original posts were retrieved from 1077 members in an online depression community in China from April 2003 to September 2017 and the available data were extracted. An informational support network of 1077 participant nodes and 6557 arcs and an emotional support network of 1077 participant nodes and 6430 arcs were constructed to examine the endogenous (purely structural) effects and exogenous (actor-relation) effects on each support network separately, as well as the cross-network effects between the two networks.ResultsWe found significant effects of two important structural features, reciprocity and transitivity, on the formation of both the informational support network (r=3.6247, P<.001, and r=1.6232, P<.001, respectively) and the emotional support network (r=4.4111, P<.001, and r=0.0177, P<.001, respectively). The results also showed significant effects of some individual factors on the formation of the two networks. No significant effects of homophily were found for gender (r=0.0783, P=.20, and r=0.1122, P=.25, respectively) in the informational or emotional support networks. There was no tendency for users who had great influence (r=0.3253, P=.05) or wrote more posts (r=0.3896, P=.07) or newcomers (r=–0.0452, P=.66) to form informational support ties more easily. However, users who spent more time online (r=0.6680, P<.001) or provided more replies to other posts (r=0.5026, P<.001) were more likely to form informational support ties. Users who had a big influence (r=0.8325, P<.001), spent more time online (r=0.5839, P<.001), wrote more posts (r=2.4025, P<.001), or provided more replies to other posts (r=0.2259, P<.001) were more likely to form emotional support ties, and newcomers (r=–0.4224, P<.001) were less likely than old-timers to receive emotional support. In addition, we found that there was a significant entrainment effect (r=0.7834, P<.001) and a nonsignificant exchange effect (r=–0.2757, P=.32) between the two networks.ConclusionsThis study makes several important theoretical contributions to the research on online depression communities and has important practical implications for the managers of online depression communities and the users involved in these communities.

Online Healthcare Platform Evolution: The Interplay of Bargaining and Network Effects

Healthcare needs have been growing rapidly in many countries as populations age. Recently, ecosystems with online platforms at their center have emerged to address this need. This is because these platforms can scale rapidly, increasing healthcare access and equity. The speed of scaling depends on the network effects across healthcare providers and consumers on the platform. The network effects also impact the bargained prices for healthcare providers, which in turn impact the platform's profits and growth rate. We develop a novel framework that combines theories of network effects and bargaining to examine the evolution of online healthcare platforms as they balance the conflicting objectives of growth and profitability. We apply our framework to data from a major Chinese online healthcare platform that links hospitals and consumers for preventive health exams. Our results show that hospitals that provide a stronger (weaker) inducement to consumers to join the platform, i.e., exhibit higher (lower) cross-network effects, extract more (less) economic value from the platform. Using a series of simulations, we provide insights for the platform in terms of resource allocation to balance growth and profitability. Consumers participating in this ecosystem also see a monetary benefit equivalent to two hours of minimum wages.

A study on online platforms’ price discrimination behavior and the same-side network effects

Purpose The purpose of this paper is to whether competition between platforms can be effective, thus leading to efficient allocations. Design/methodology/approach Based on the classic linear Hoteling model, this paper builds a two-period competition model for two competing platforms using two variants, namely, a discrimination pricing model and a unified pricing model. Findings In the case of the discrimination pricing model, the competition is moderate, and the two platforms split the market evenly in the first stage, while both platforms tended to offer preferential treatment to new users and set higher prices for regular customers in the second stage. Compared to the unified pricing model, in the first stage, the platform can provide a higher price that depends on the cross-network effect when it implements discrimination, and thus, obtains higher profits. However, in the second stage, fierce competition leads to the release of benefits, new and regular customers obtain lower prices and the platforms lose higher profits. In the long-run, discriminatory pricing is not the best option due to lower total profits. The two platforms will implement cooperative pricing or one platform becomes dominant. Originality/value Instead of focusing on the cross-network effects, this paper emphasizes the role of the same-side network effect on price discrimination regarding the platforms’ competition. The same-side network effects are investigated in relation to a discrimination pricing strategy and compared to a unified pricing strategy. Another innovative aspect is the study of these network effects in a dynamic setting based on a two-period competition model for two platforms.

Конкуренция на рынках с перекрестными сетевыми эффектами: теоретические основания (Cross Market Competition: Theoretical Foundations)

Russian Abstract: Повсеместное внедрение цифровых технологий привело к смене моделей бизнеса: сетевые эффекты начинают оказывать значимое влияние на функционирование многих компаний и рынков. В работе рассмотрены базовые и расширенные модели функционирования рынков с перекрестными сетевыми эффектами. Так оказывается, что при моделировании конкуренции на рынках с перекрестными сетевыми эффектами ключевую роль играют такие факторы, как структура цен, характеристика спроса и возможность множественной адресации. English Abstract: The widespread adoption of digital technology has led to a change in business models: network effects are beginning to have a significant impact on the functioning of many companies and markets. The paper considers basic and advanced models of market functioning with cross network effects. It turns out that when modeling competition in markets with cross-network effects, key factors are played by factors such as price structure, demand characteristics, and the possibility of multiple addressing.

How does customer orientation (in)congruence affect B2B electronic commerce platform firms' performance?

Business-to-business (B2B) electronic platforms have become important channels for transforming traditional modes of transaction. The success of these platforms relies heavily on the platform firms' customer orientation (CO) practices, which are designed to attract both sellers and buyers. This study draws on the cross network effect theory to explore whether and how a B2B e-commerce platform firm's (in)congruent CO strategic initiatives toward sellers or buyers affect the firm's performance. In addition, the moderating effects of seller-side and buyer-side demand uncertainty on the relationship between CO (in)congruence and platform firm performance are investigated. The analysis of data collected from 185 B2B electronic platform firms in China reveals that CO incongruence is more beneficial to firm performance than CO congruence. Furthermore, when seller-side demand uncertainty is high, an increase in seller-focused CO incongruence (i.e., higher seller orientation than buyer orientation) or buyer-focused CO incongruence (i.e., lower seller orientation than buyer orientation) improves or impedes a B2B e-commerce platform firm's performance, respectively. However, when buyer-side demand uncertainty is high, an increase in either type of CO incongruence does not improve firm performance. These findings contribute to the literature on and practices of B2B e-commerce and customer orientation.

Profit or Market Thickness? Dynamic Order Allocation Mechanisms with a Hybrid Workforce

Modern digital technology has enabled the emergence of the hybrid workforce in service organizations, where a firm uses on-demand freelancers to augment its traditional labor supply of employees. Freelancers are typically supplied by an electronic platform. How should demand be allocated between employees and freelancers? Under what conditions is the system (comprising the firm and its platform) sustainable in the long run? We investigate these questions in the context of last-mile delivery. We develop a discrete-time, stochastic dynamic program that captures the system's profit from serving demand and the platform's growth dynamics. The dynamic model incorporates a service constraint for the platform and a simple version of a stochastic network effect. We find that the answers to our research questions critically depend on two key parameters: the mean and variance of the cross-network effect. We conduct a numerical study with both synthetic data and data from a last-mile delivery firm in Vietnam to illustrate our findings.