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The purpose of this study is to explore the coopetition relationships between platform owners and complementors in complementary product markets. Drawing on the coopetition theory, the authors examined the evolutionary trends of the coopetition relationships between platform owners and complementors and explore the main influence factors.

The authors used Lotka–Volterra model to analyze the coopetition relationship between platform owners and complementors, including the evolutionary trends as well as the results. Considering the feasibility of sample data collection, simulation is used to verify the effects of different factors on the evolution of coopetition relationships.

The results show that there are four possible results of the competition in the complementary products market. That comprises “winner-take-all for platform owners,” “winner-take-all for complementors,” “stable competitive coexistence” and “unstable competitive coexistence,” where “stable competitive coexistence” is the optimal evolutionary state. Moreover, the results of competitive evolution are determined by innovation subjects’ interaction parameters. However, the natural growth rate, the initial market benefits of the two innovators and the overall benefits of the complementary product markets influence the time to reach a steady state.

The study provides new insights into the entry of platform owners into complementary markets, and the findings highlight the fact that in complementary product markets, platform owners and complementors should seek “competitive coexistence” rather than “winner-takes-all.” Moreover, the authors also enrich the coopetition theory by revealing the core factors that influence the evolution of coopetition relationships, which further enhance the analysis of the evolutionary process of coopetition relationships.

Information Systems (IS) research has built up a considerable understanding of digital platform ecosystems, while policymakers worldwide are aiming to introduce platform regulations that seek to erode fundamental mechanisms of digital platforms. This viewpoint article provides an introduction to how platform regulation affects our current understanding of digital platform ecosystems and suggests opportunities for future research.

A detailed analysis of the effects of the European Union (EU) Digital Markets Act (DMA) on current findings of organizational, technical and economic IS platform research.

Government regulations of digital platforms such as the DMA likely affect the central mode of operation of platforms in the scope of the regulation. The authors preconceive a major impact on platform openness, governance, steering the platform supply-side, modularity, nestedness, network effects, pricing and single-/multi-homing. In addition, the authors present opportunities for future research in each of these IS platform research streams.

Landmark regulations implemented in the past, such as the General Data Protection Regulation (GDPR), caused paradigm changes that fertilized research opportunities in IS and beyond. This viewpoint article aims to nudge studies that examine the changed mode of operation of platforms following platform regulation.

For an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.

Based on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.

This study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.

This study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

This paper aims to explore the promotion decisions of heterogeneous sellers on a decentralized platform under competitive conditions and analyze how seller behaviors impact platform profit, seller revenue, buyer surplus and social welfare.

This paper considers a Cournot model consisting of a platform charging a commission rate and two sellers with different conversion rates and browsing costs. Promotion efforts by sellers can increase traffic, but they also incur promotion costs for sellers. The sellers decide on promotion effort by weighing these two effects. The authors also explore the equilibrium when the platform charges a fixed usage fee.

The seller’s profit improves as its conversion rate increases and worsens as browsing costs increase. Also, increasing the commission rate charged by the platform makes the seller invest less in promotional efforts. Therefore, the platform must consider this trade-off to determine an optimal rate. The analysis shows that the seller with a high conversion rate and high browsing cost plays a greater role in generating more overall revenue. When the market favors such a seller, the platform tends to charge less in order not to impair its profitability.

This paper incorporates conversion rate, buyer’s browsing cost, unit promotion cost and the fee charged by the platform into the model to study sellers’ promotion decisions on decentralized platforms.

This study explores how app store awards shape the behavior of leading digital entrepreneurs, focusing on their engagement in technological innovation through feedback and re-signaling mechanisms. It aims to reveal the nudging effect of award signals on entrepreneurial behavior.

In this study, data from 349 leading mobile app entrepreneurs in the UK Google Play Store were collected over an extended period from various sources. Functionality novelty and explorative behavior were assessed by analyzing app release date and permission technologies in comparison to both their own prior apps and those of their closest competitors. Hypothesized relationships were tested using accelerated failure time parametric models.

This study extends the literature on signaling by showing that (1) the top developer award signal served to nudge entrepreneurs to improve the functionality novelty of their apps and those who succeeded were less likely to switch to another product category, (2) the award signal created a window of opportunity for non-award entrepreneurs to respond and those who released new apps around the midpoint of a normal app development cycle significantly improved the likelihood of winning the award in a subsequent round of award-giving, and (3) the effect of functionality novelty on winning the award was more pronounced when non-award entrepreneurs pursued more explorative than exploitative behavior in app development.

The results offer novel insights into an understudied area, specifically the influence of online award signals on nudging entrepreneurs to pursue technological innovation. The research also highlights the crucial role played by the app store as an intermediary signaler.

The purpose of the article is to explore the perceptions of Brazilian game developers about the power relations between them and the sponsors of digital game platforms. It also aims to identify forms of collective action that developers can use to counteract the asymmetry of power.

The research employed an abductive approach, seeking empirical evidence that would challenge consolidated theory. To achieve this, semi-structured interviews were conducted with 25 Brazilian developers. The data were analyzed qualitatively using NVivo software. The aim was to resolve theoretical ambiguities identified in the literature review and to explore unexpected findings.

The study explores Brazilian game developers' perceptions through interviews, revealing their experiences within the industry’s concentrated structure and their use of collective action to navigate power dynamics.

The study's focus on Brazil limits the generalizability of its findings to the broader game development industry.

The study suggests Brazilian game devs can leverage collective action to counteract power imbalance with platforms, collaborate through events and projects and facilitate internationalization of their games.

The study suggests collective action could empower developers to challenge platform dominance and foster a stronger community among Brazilian game developers.

The article’s value lies in examining Brazilian devs' experiences within their specific industry context and highlighting collective action as a potential strategy for developers.

This study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’ person-goods matching process and how the platform firm’s similar strategies moderate the effects of TPSs’ strategies.

Using data collected from the top ten TPSs from a Chinese e-commerce platform, the fixed effect model is used to validate the conceptual model and hypotheses.

The study results show that both market detection strategy and matching optimization strategy can help large TPSs improve their sales performance. Moreover, the similar market detection strategy applied by the platform firm weakens the effect of large TPSs’ customer acquisition strategies, while the similar matching optimization strategy applied by the platform firm strengthens the effect of large TPSs’ customer acquisition strategies.

This study provides firsthand evidence on the performance of large TPSs’ and the platform firm’s strategies. It demonstrates the effectiveness of large TPSs’ market detection strategy and matching optimization strategy, which can be adopted to meet consumers’ search and evaluation motivations in their person-goods matching process respectively. Moreover, it identifies the role of platform firms by showing the moderating effect of similar strategies adopted by the platform firm on the effect of large TPSs’ customer acquisition strategies.

Due 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.

This 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.

The 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.

This 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.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

The purpose of this paper is to investigate how complementors’ marketing capability and technology capability affect their performance. Drawing on social capital theory, the authors examine platform network centrality as a mediator and platform reputation as a moderator of the relationships between these two capabilities and complementor performance.

This study collects data by questionnaire from 154 Chinese firms adopting e-commerce platforms. Hierarchical multiple regression is used to test the hypotheses of this study.

This study finds that complementors’ marketing capability and technology capability positively affect performance by increasing their platform network centrality. Moreover, platform reputation positively moderates the relationship between platform network centrality and complementor performance, and it strengthens the mediating role of platform network centrality.

This paper emphasizes the critical role of marketing capability and technology capability on complementor performance. It explores the improvement path of complementor performance from the perspective of network position, which is a key element for complementors to effectively leverage their capabilities to build competitive advantage.