网约车聚合平台

To address the compliance issues of emerging ride-hailing aggregators

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Pure self-management model, pure aggregation business model and Self-support + aggregation model are three commonly used business modes on ride-hailing platforms. We use an analytical model to study these three business models and give the optimal business model decision of the platform. The research shows that the heterogeneity ratio of drivers, the cost of the platform under the Self-support model, the franchise fee received by the platform under the aggregation model and the dissatisfaction of the original users on the platform play a key role in the selection of the platform’s business model. When the difference between the franchise fee under the aggregation mode and the platform cost under the Self-support mode fails to generate positive feedback on the platform profit, the platform should choose the pure Self-support mode. When riders are more sensitive to the heterogeneity of service quality of the platform and user stickiness can be ensured, the platform should choose the pure aggregation business model. When user stickiness can be guaranteed and the cost of the platform under the self-run model is controllable, the platform should choose the Self-support + aggregation business model.

Abstract: A Blockchain-based decentralized cab aggregator system that aims to revolutionize the traditional ride-hailing industry. The system leverages the transparency and security features of blockchain technology to create a thrustless environment where riders and drivers can connect without the need for intermediaries. The decentralized platform also eliminates the problem of surge pricing and ensures that drivers are paid fairly. In addition, the use of smart contracts ensures that all parties fulfil their obligations, thereby reducing the risk of fraud and disputes. The project promises to create a more equitable and efficient cab aggregation system that benefits all stakeholders involved.

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Practice- and Policy-Oriented Abstract This study analyzes whether a ride-hailing platform should adopt a closed business model, that is, serving riders only with platform-owned vehicles, or an open business model, that is, allowing private vehicles to utilize the platform to provide services. Analytical results reveal that the open business model leads to more service supply (supply-augmenting effect) and a lower price increase (price discrimination–hindering effect) in the surge period, both benefiting riders. It also reduces the number of idle vehicles in the normal period, thus resulting in more efficient utilization of vehicle resources. Interestingly, the platform does not always benefit from adopting the open business model. In sum, the open business model always benefits the riders, private drivers, and the society as a whole, but it may not be more profitable for the platform. This interesting result calls for policymakers to incentivize the platform so that it adopts the open business model instead of the closed one.

Problem definition: Motivated by the behavior of drivers on ride-hailing platforms (individual drivers decide whether to work based on the offered wage and where to locate themselves in anticipation of future fares), we examine how the introduction of autonomous vehicles impacts the strategic behavior of human drivers and driver welfare. Specifically, we consider a setting in which a ride-hailing platform deploys a mixed fleet of conventional vehicles (CVs) and autonomous vehicles (AVs). The CVs are operated by human drivers who make independent decisions about whether to work for the platform and where to position themselves when they become idle. The AVs are under the control of the platform. The platform decides on the wage it pays the drivers, the size of the AV fleet, and how the AVs are positioned spatially when they are idle. The platform can also make decisions on whether to prioritize the AVs or the CVs in assigning vehicles to customer requests. Methodology/results: We use a fluid model to characterize the optimal decisions of the platform and contrast those with the optimal decisions in the absence of AVs. We examine the impact of automation on strategic drivers and the ride-hailing platform. We show that, although the introduction of AVs can displace drivers and depress effective wages, there are settings in which the introduction of AVs leads to higher effective wages and more drivers being hired. We discuss how these results can, in part, be explained by the interplay of two counteracting effects: (i) the introduction of AVs provides the platform with an additional source of supply and renders human driver substitutable (displacement effect), and (ii) having access to and control over AVs enables the platform to influence the strategic behavior of CVs, thereby reducing the inefficiency from self-interested behavior (incentive effect). The relative strength of these two effects depends on the cost of AVs and the vehicle dispatching policy. Managerial implications: Our results uncover a new effect through which the introduction of AVs affects the welfare of human drivers (the incentive effect) and another mechanism to mitigate inefficiencies because of human drivers acting strategically. Our results have potentially broader applications to other areas in which automation is introduced and workers are strategic. Funding: This work was supported by the National Science Foundation [Grant SCC-1831140]. The Guangdong (China) Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [2023B1212010001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0416 .

A rideshare platform acts as an aggregator that connects riders with ride providers (drivers). The drivers are independent workers who share a part of their revenue with the principal, who owns the platform. While drivers have flexible schedules, the fairness of labor contracts and control exercised by the principal have come into question lately. Suggested options include treating the drivers as employees and/or safeguarding a minimum income for them. We study a rideshare platform with blended driver capacity: full time employees with a fixed wage rate, and independent drivers who are paid a share of revenue. We examine a scenario where the principal establishes the number of employee drivers, revenue sharing, and a base price for the platform; and the independent drivers then determine whether to join the platform. We identify economic equilibrium for two different demand rationing strategies: preference for employee drivers, and equal opportunity for all drivers (driver‐agnostic). We find that a blended platform capacity becomes viable if the wage rate is moderate, pool of independent drivers is large, and the ride‐seeker market is large. We show that the unpredictability of driver's reservation value motivates the principal to hire more employee drivers and to increase the base price. Our result that a driver‐agnostic demand rationing causes fewer independent drivers to join the platform is somewhat counterintuitive and is explained by how revenue sharing affects demand rationing. We find that the ride seekers prefer preferential demand rationing over driver‐agnostic rationing.

The aggregation of ride-sharing platforms has forced traditional ride-sharing platforms to decide whether to join or leave these emerging platforms. This study presents a stylized model analyzing the demand, supply, and profit of two self-operated platforms, C2C platforms (such as DiDi and Uber) and B2C platforms, considering aggregation platform awareness and commissions. The study investigates the conditions under which the self-operated platforms should employ the entry strategy based on the optimization method and Cournot game theory, as well as exploring the reasons why self-operated platforms choose to withdraw after joining. The results show that in order to avoid competition, B2C platforms adopt an entry strategy, while C2C platforms adopt a non-entry strategy. Only during the off-peak period, when the awareness of the aggregation platform is very high and the level of competition between the two types of platforms is very intense, will both types of platforms adopt an entry strategy, but C2C platforms may experience a significant loss of market share, leading to a decline in social welfare. Furthermore, even if the self-operated platform chooses to withdraw, social welfare will still increase if the two self-operated platforms adopt the best strategy. The study contributes to sustainable development by promoting efficient resource allocation, reducing redundant competition, and improving overall market efficiency, thereby fostering a more sustainable urban transportation system.

Applications based on the business model of the sharing economy, which rely on traditional technologies, have trouble ensuring information traceability, transparency, and trust between their participants. Blockchain technology and smart contracts can help in overcoming these problems and improve efficiency. In this paper, the benefits that this technology can provide in support of sharing economy applications are presented. It also addresses which existing problems and challenges it can solve. The paper includes a use case of blockchain technology using the example of a ride-sharing platform. Although the participants and transactions in the described model are simplified, they are an excellent demonstration of how these technologies can improve the efficiency of the described system.

The study presents a comparative analysis of regulatory responses to Uber's ride‑sharing services in Romania, Finland, and the Czech Republic, following the ECJ decision C‑434/15. It explores the nuanced approaches undertaken by each country, from Romania's strict regulations to Finland's more liberalized stance, and the Czech Republic's intermediary model. Throughout the analysis, the study discusses the concept of influence, particularly the ECJ's role in shaping these national regulatory frameworks. Despite differences, all responses aim to strike a balance between fostering innovation and addressing concerns related to fair competition and consumer rights. This study offers insights into the diverse regulatory landscapes within the EU and their implications for the future of ride‑sharing platforms like Uber.

With the rapid development of information technology and the widespread use of the Internet, the sharing economy has emerged as a major economic force in China. Among its various forms, the ride-hailing industry exemplifies the platform economys ability to diversify employment and improve public convenience. As of 2023, Chinas sharing economy surpassed one trillion yuan in market size, with over ten million registered ride-hailing drivers, highlighting its strong growth momentum and market potential. However, this rapid rise also brings legal and regulatory challenges, particularly in identifying labor relations. The highly flexible and non-standardized working arrangements between platforms and drivers often fall outside traditional labor law frameworks. Consequently, many workers lack adequate protection in areas such as social insurance, minimum wage, and working hours. This paper focuses on the challenges of labor relationship identification under the sharing economy, using the ride-hailing sector as a case study. By analyzing representative judicial cases and comparing domestic and international academic theories and legislative practices, the study aims to explore the limitations of existing legal standards, assess the relevance of foreign experiences, and identify unresolved theoretical issues. Based on this analysis, it proposes targeted legislative recommendations to better protect the rights of workers in new economic forms and promote the adaptive development of labor law in China.

With Mobility-as-a-Feature (MaaF), transportation scholars propose an extension of the Mobility-as-a-Service (MaaS) concept. Leveraging the ongoing trend of platformization, MaaF intends to integrate mobility with unrelated services such as food delivery, grocery delivery, financial services

PurposeThis study aims to complement the literature disparity regarding the practice of technology in intermediary business models, which is still limited. The discussions of this study comprise (1) the comprehension of intermediary business models through building block modification and (2) the crystallization of blockchain adoption for intermediary business models.Design/methodology/approachThis study encourages the development of a new canvas through the iteration between theories and empirical evidence of intermediary business models, including using blockchains in this model. The new canvas was developed referring to the system complexity of the intermediate business model and confirmed using a single case study. The case studied was ALKO, which drives its business value by adopting blockchain technology. A few data sources were used to produce robust findings in this study.FindingsThe new canvas can elucidate the intermediary business model with designated case studies. Blockchain technology significantly contributes to the intermediary business model, where this technology can influence the entire activity system. The technology is being adopted as a “creation” for the firm to realize the “proposition” offered and “capture” value. In this typical business model, this technology is applied to implement shared values such as traceability, authenticity and integrity of information. This business model shows firm activities as coherent and cohesive relationships between blocks.Research limitations/implicationsBlockchain technology strengthens intermediary business models through its unique features. This study also describes the role of this technology in a particular system through the development of an intermediary business model canvas using a descriptive study. The intensity of this technology on a typical business model is clearly explained in this study.Originality/valueThis research brings a novel value in developing a canvas for intermediary business models and confirms the role of blockchain technology in this business model. The canvas design was carried out systematically, including explaining the contributions of blockchains in detail.

This study presents a combined revealed preference (RP) and stated preference (SP) survey to understand travelers’ mode choices under the influence of real-time information for different activity types and trip lengths. The D-efficient method is adopted to generate SP scenarios. The empirical data for this study came from a “Survey to understand the impact of ICT on transportation choices” (SUIT; ICT = information and communication technology), conducted in July 2023 in the Washington, DC metro area and the Charlotte metro area, North Carolina (NC), USA A combined RP–SP multinomial logit and mixed logit model (MxL) capturing the error components have been estimated based on the collected data. The model results reveal that daily parking costs significantly impact individuals’ mode choices and tend to discourage driving. Furthermore, real-time information such as the availability of parking spaces at workplaces and metro stations encourages people to prefer drive and park & ride modes. Conversely, information on flash flooding alerts, road closures, and road accidents discourages people from driving, riding as auto-passengers, or taking a transportation network company (TNC) (Uber/Lyft) for trip purposes. Lastly, information on reduced waiting time and disruption plays a significant role in selecting transit and park & ride modes. The results obtained from this study can be beneficial to policymakers when assessing or designing alternative sustainable modes in the presence of real-time information. As its policy finding, the study recommends that transit disruption should be handled carefully to retain loyal customers and achieve various sustainability goals. In the event of transit disruption, alternative sustainable transportation modes should be offered to transit riders.

Ride-on-demand (RoD) services such as Uber and Didi are becoming increasingly popular, and in these services dynamic prices play an important role in balancing the supply and demand to benefit both drivers and passengers. However, dynamic prices also create concerns. For passengers, the "unpredictable" prices sometimes prevent them from making quick decisions: one may wonder if it is possible to get a lower price if s/he chooses to wait a while. It is necessary to provide more information to them, and predicting the dynamic prices is a possible solution. For the transportation industry and policy makers, there are also concerns about the relationship between RoD services and their more traditional counterparts such as metro, bus, and taxi: whether they affect each other and how. In this paper we tackle these two concerns by predicting the dynamic prices using multi-source urban data. Price prediction could help passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is based on urban data from multiple sources, including the RoD service itself, taxi service, public transportation, weather, the map of a city, etc. We train a simple linear regression model with high-dimensional composite features to perform the prediction. By combining simple basic features into composite features, we compensate for the loss of expressiveness in a linear model due to the lack of non-linearity. Additionally, the use of multi-source data and a linear model enables us to quantify and explain the relationship between multiple means of transportation by examining the weights of different features in the model. Our hope is that the study not only serves as an accurate prediction to make passengers more satisfied, but also sheds light on the concern about the relationship between different means of transportation for either the industry or policy makers.

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As a very important passenger transportation model in the era of sharing economy, the online ride-hailing (ORH) has also caused new traffic management issues while improving resource allocation. Although regulations and policies have imposed macro-level supervision on the ORH market, they have not prevented some drivers from cheating on platforms’ subsidies and jeopardizing passengers’ safeties at the source. In order to realize the voluntary and sustainable ORH supervision, and enable relevant participants to actively supervise, report and comply with rules, this paper constructs an evolutionary game model among the platform, passengers and drivers. Based on the bounded rationality and expected benefits of the participants, the main factors determining the optimal strategies are analyzed. At the same time, the evolution path and the equilibrium state of the three game groups are studied by numerical simulation. The results show that important factors of realizing the benign supervision of ORH include minimizing the reporting costs of passengers, making penalties for drivers who violate the rules far greater than the illicit incomes, realizing the platform supervision costs less than the sum of penalty incomes and positive social effects. In addition, improving rewards for reporting can promote the continuity of passengers’ participation but increase the possibility of false reports. Therefore, the platform needs to consider the cost of identifying false information when designing the reward amount.

This case follows a protagonist-based narrative structure with Rubik Joshi positioned as the central decision-maker during Tootle’s post-crisis strategic inflection. The case is based entirely on secondary data. Sources include publicly available journalism, statements and social media posts from Tootle’s leadership team (particularly LinkedIn), and promotional content from Tootle’s official website and YouTube channel. These data were triangulated to construct an accurate and balanced depiction of the firm’s strategic decisions. No primary interviews were conducted, and no confidential or proprietary information was used in the preparation of this case. All characters and decisions are based on verifiable public material. As such, no institutional ethics review or human subjects’ approval was required, and no data have been disguised. In mid-2024, the leadership team at Tootle, Nepal’s first ride-sharing platform, faces a strategic decision following a brand relaunch under Zapp Services Pvt Ltd. Once celebrated for pioneering gig-based mobility in Kathmandu, Tootle had collapsed due to technical setbacks, regulatory ambiguity and rising competitive pressure. Now under the direction of Joshi as managing director, Shreyas Krishna Shrestha as chief executive officer and Keyush Shrestha as chief operating officer, the company has stabilized operations and re-entered the market with a rebuilt platform and a new subscription business. At the center of the case is whether the leadership should expand beyond Kathmandu into other Nepali cities, such as Pokhara or Bharatpur, or instead consolidate its position in the local market first. It is designed as a strategy teaching case, applying classic frameworks such as SWOT and strategic group mapping to examine platform competition in an emerging market context. It is suitable for use in strategic management, entrepreneurship and digital innovation courses. Students will have the opportunity to apply tools such as SWOT analysis and strategic group mapping to examine platform strategy in an emerging market context. This case is designed for use in upper-level undergraduate and graduate courses, including MBA programs. It is particularly well-suited for courses such as: It also aligns with curriculum modules on platform-based business models and scaling startups. The case is appropriate for various teaching modalities, including in-seat, hybrid and fully online formats. It has been classroom tested with MBA students in a strategic management course, where it was used to explore trade-offs in platform growth and competitive positioning in emerging markets.

The function of social media is now transforming to become source of information, even to support electronic word of mouth (e-WOM). Many companies, including ride-hailing service providers, can capture customers' opinions for the purpose of evaluating their products and services. Text mining can be useful to analyze great number of comments from ride-hailing customers in social media. Furthermore, by applying sentiment analysis, service providers can define the service categories which are good and still needing improvement. Customers' comments were taken from Twitter, and text classification method was used to classify the comments based on six predefined categories and their respective polarity. The accuracy of the classification model was 86% which was good to classify the text data. The output of this research is expected to give insight for ride-hailing service provider to understand customers' perspective about the services so that it will be easier to evaluate and improve their services based on the categories in this study.

ABSTRACT To design an effective MaaS bundles, the weights of attributes of MaaS bundles should be first identified. The object case of best-worst scaling (i.e. BWS case 1) method is adopted, and a factor representing the degree of mobility service satisfaction is introduced to modify the weights of attributes of MaaS bundles. Based on such a modification, latent classes exploded logit models are established and estimated using samples from three cities of China. The estimation results confirm the advantage of considering people’s satisfaction toward mobility services in the model and show that heterogeneous weights of the attributes of MaaS bundles are found not only in the samples from different cities but also in the sample from a same city. These findings confirm the validity of the modified model of BWS case 1 and suggest the MaaS providers to offer tailored mobility services for specific socio-demographic groups.

To date, there is limited evidence on how shared micro-mobility services are being used in Mobility as a Service (MaaS) trials. To add new evidence, this research investigated the MaaS trial data from the University of Queensland in Brisbane, Australia. Descriptive analysis and statistical models were employed to analyze participants’ usage behavior in shared micro-mobility services with MaaS bundles. From this analysis, several critical and interesting findings emerged. First, when compared with bundles including micro-mobility, basic mobility bundles with unlimited public transport services were more popular in the university community. Second, compared with the pay-as-you-go option, trial participants were more willing to adopt shared micro-mobility services with PASS options with daily travel budgets. Third, there was a substantial underutilization of shared micro-mobility budgets within existing mobility bundles. Fourth, multimodal bundle subscribers used shared micro-mobility services about two to three times longer per day, compared with basic bundle users. Finally, shared micro-mobility service providers seemed to experience positive externalities from other micro-mobility providers in the market. The findings could provide insights to optimize existing MaaS business models with micro-mobility and to help evaluate the impacts of MaaS on the travel behavior of university populations.

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The last decade has witnessed the introduction and rapid growth of emerging mobility services. They are expected to further grow in the near future through the aid of developing information and vehicle technologies. With this rapid growth in services and as travelers begin to perceive mobility as a service, offering diverse types of services, such as discounted bundled pricing, may better meet the demand of travelers and, in return, secure demand for providers. In this paper, we formulate a service bundle sizing and pricing problem for mobility services. We account for the well-known travel demand property that reduced cost of travel induces more travel. We also account for trip-based service cost, which comes from complex fleet operations. In the proposed formulation, demand and fleet operations are derived as a function of the decision variables Bundle Sizing and Pricing. This model uses a traveler target mileage probability density function (DTTM-PDF) as an input and derives service bundle choice probability (and therefore, demand) as well as operational metrics. We specify DTTM-PDF and demand increase functions that can describe the data appropriately and also output a convex optimization formulation. A case study of New York City is presented.

With growing awareness of sustainability and convenience expectations, customers are increasingly demanding integrated and seamless mobility in the form of mobility-as-a-service (MaaS). However, as centralized MaaS platforms have thus far failed to integrate a critical share of mobility service providers (MSPs), travelers lack opportunities to efficiently combine the various mobility services required for seamless end-to-end itinerary coverage. Particularly, MSPs often refuse to collaborate by devolving control over customer interfaces or sensitive data owing to threats of market power concentration. While alternative blockchain-based approaches aim to provide equal market access, they cannot sufficiently align competing business goals and face substantial problems resulting from the replicated processing of sensitive data. Both researchers and practitioners have recently suggested decentralized digital identity management enabled by digital wallets as a promising mechanism to exchange verifiable identity attributes while mitigating problems related to data aggregation. Following a design science research approach, the article accordingly explores how digital wallets can address the shortcomings of existing approaches to MaaS. It contributes a novel IS architecture and principles for a design at the nexus of centralized and decentralized solutions to mitigate tensions between cooperation and competition. Further, the findings indicate that when building decentralized solutions, one should also consider components beyond blockchain and smart contracts.

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Sustainable mobility is one of the main challenges on a global level. In this context, the emerging Mobility as a Service (MaaS) plays an important role in the mobility of people. This paper investigates the main enabling factors for implementing the MaaS paradigm, with a specific focus on the level of acceptance of this new technology. To achieve this objective, the proposed methodology for measuring the potential MaaS acceptance is based on a set of pilot surveys. The methodology integrates motivational surveys with Stated and Revealed Preference (SP, RP) and Technology Acceptance Models (TAM). The collected data are processed to obtain indicators that measure the potential level of MaaS acceptance. The main results of the two pilot experiments are illustrated by referring to urban and extra-urban mobility with or without physical barriers. The results obtained show that the level of MaaS acceptance grows with the increase in generalized transport costs perceived by the users.

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The phenomenon of servitization paired with digitalization has resulted in an emerging research stream referred to as digital service innovation (DSI). DSI has attracted much interest in academia and industry, given its increasing economic importance. However, extant innovation research still exhibits a strong product innovation focus and limited attention to the end of the innovation process, especially the determinants of innovation success. We investigate the role of market entry strategies for DSI success in digital service and platform settings. Drawing on a novel sample of 325 DSIs in the mobility sector, we examine the role of three types of market entry strategies, that is, market entry order, brand extensions, and home country advantage on the success of the DSI. The results indicate that early market entry and individual brands rather than extensions of existing brands are relevant determinants of DSI success, and that this impact depends on the level of business model innovativeness and competitive intensity. By examining the uniqueness of market entry strategies for DSI success, this paper adds novel insights for DSI and their success factors to the digital and service innovation literature. In doing so, we also suggest essential strategies that help practitioners advance DSI performance. Graphical Abstract

Purpose: This study explores the challenges and opportunities of Mobility as a Service (MaaS) in meeting the needs of tourists in urban environments, aiming to provide insights into its potential for enhancing urban tourism mobility. Methodology: The research employs a mixed-methods approach, combining a systematic literature review, comparative case study analysis of four European MaaS schemes, and a conjoint choice experiment with 500 international tourists. Results: The study reveals that MaaS offers diverse mobility services catering to various tourist needs, with integrated transport modes and competitive pricing being the most valued attributes. Tourists demonstrate willingness to pay for enhanced MaaS features, with preferences varying across demographic segments. Key challenges identified include regulatory barriers, data sharing issues, and the need for stakeholder collaboration. Theoretical contribution: This research extends the understanding of MaaS in tourism contexts, addressing a significant gap in the literature. It provides a conceptual framework for analyzing MaaS in urban tourism and offers empirical evidence on tourist preferences and willingness to pay for MaaS attributes. Practical implications: The findings offer valuable insights for MaaS providers, urban planners, and policymakers in developing and implementing MaaS solutions tailored to tourist needs. The study highlights the importance of flexible package designs, stakeholder collaboration, and addressing regulatory challenges for successful MaaS implementation in urban tourism contexts. Sustainable Development Goals (SDGs): SDG 11: Sustainable Cities and Communities; SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action

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The positive effects that Mobility-as-a-Service (MaaS) is envisioned to have on transport can only be reaped if people are using MaaS. Yet, the understanding of the user perspective on MaaS is incomplete and primarily based on experiments with non-users. To address this shortcoming, this paper reports user experiences from a trial of a high-level MaaS service in Sydney, Australia. Based on questionnaires and interviews, it analyses who participated in the trial and why, and whether the trial experience satisfied their motives. The contribution to the literature on MaaS is three-fold. Firstly, most of the people that participated in the trial were frequent users of both public transport and private cars. This supports the notion that multi-modal travellers are likely early adopters of MaaS and contradicts the fear that MaaS does not appeal to private car users. Secondly, a desire to contribute to innovation and curiosity about MaaS were the main motives for signing up for the trial, which highlights the important role an inviting setting for experimentation, such as a trial, can play in stimulating MaaS adoption. Thirdly, many participants struggled with making the trialled service work for them and on average they seemed to value the support and feedback functions higher than other service features. This underscores the novelty of MaaS, compared to existing service models, and reiterates the notion that more than an app and a few subscription plans is needed to make MaaS useful for users.

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The growing number of modes of transportation with diverse characteristics and situational suitability would allow a multifaceted mobility behavior. Unfortunately, the usage of a combination of heterogeneous modes of transportation – specifically during a complex travel chain with multiple changeovers – is hindered in various ways. Users have to query, compare, combine, book and use multiple specialized mobility service individually which results in inefficiencies both on demand and supply side. Centralized mobility service platforms can form a technological bridge to deliver service interoperability. In cross section between competition and cooperation, the need for suitable, profitable, and sustainable market forms to provide complex service configurations arises. As a result of interdisciplinary workshops with domain experts, we describe a role relationship model and identify relevant market forms. To do so, we present a conceptional tool to analyze, characterize and differentiate various mobility service platforms and apply it to set of platforms currently beeing developed.

ABSTRACT Mobility as a Service (MaaS) has emerged as a transformative concept in urban transportation, integrating multiple modes of transportation through digital platforms to provide seamless travel experiences. By utilizing smartphone applications, MaaS simplifies trip planning, real-time information access, and consolidated payment systems, offering convenience to users. However, MaaS research has primarily focused on developed countries with well-established transport systems, making it crucial to explore its potential and challenges in developing cities of the global south, such as Noida in India. In this study, Noida, a satellite town of NCT Delhi, was chosen as only case study to gather data on user behaviour and preferences regarding app-based mobility services. A comprehensive survey collected information on socio-economic factors, personal vehicle ownership, commuting patterns, public transport usage, and attitudes towards digital ecosystems and app-based mobility services. Principal Component Analysis and K-means Clustering techniques were applied to identify distinct user types and categories, providing insights into user preferences and expectations. The analysis of the collected data revealed user clusters and their respective characteristics. The sustainability aspects of on-demand mobility services were evaluated, comparing user perceptions with private vehicle usage. The study also examined the impact of app-based mobility services on public transport and identified barriers and constraints specific to different user clusters, contributing to a better understanding of the feasibility of implementing MaaS. The findings will provide valuable insights for policymakers and transportation authorities, enabling the development of strategies and interventions to enhance urban mobility and foster MaaS adoption. By addressing the specific needs and preferences of users, MaaS can play a significant role in improving the efficiency and sustainability of transportation in complex urban environment cities like Noida in India.

Background Ecosystems aim to create joint value that is higher than the sum of the value added of the single companies combined. However, for Mobility as a Service (MaaS) ecosystems, the economic potential is not yet proven. This concurs with the definition of MaaS ecosystems and the debate about who should be the orchestrator – a private or a public entity. Purpose This article therefore delivers a first approach to quantify the joint value of publicly and privately orchestrated MaaS ecosystems. Methodology The value estimationations are based on potential user preference analysis combined with market simulation and different volume discounts granted to a private orchestrator in the agency. Findings The results show that due to the high costs of all ecosystem actors in this asset-heavy industry, no profits are made in all constellations. The least value is destroyed when a private orchestrator receives 2% discount. Thus, added value must be created, for example through data analysis and advertising. Cities and governments must hence reallocate subsidies and support all MaaS actors to build a viable ecosystem.

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Mobility‐as‐a‐Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on‐demand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID‐19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two‐class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge–reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived two‐class BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real‐world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems.

Purpose Digital platforms (DP) are transforming service delivery and affecting associated actors. The position of DPs is impacted by the regulations. However, emerging economies often lack the regulatory environment to support DPs. This paper aims to explore the regulatory developments for DPs using the multi-level perspective (MLP). Design/methodology/approach The paper explores regulatory developments of ride-hailing platforms (RHPs) in India and their impacts. This study uses qualitative interview data from platform representatives, bureaucrats, drivers, experts and policy documents. Findings Regulatory developments in the ride-hailing space cannot be explained as a linear progression. The static institutional assumptions, especially without considering the multi-actors and multi-levels in policy formulation, do not serve associated actors adequately in different times and spaces. The RHPs regulations must consider the perspective of new RHPs and the support available to them. Non-consideration of short- and long-term perspectives of RHPs may have unequal outcomes for established and new RHPs. Research limitations/implications This research has implications for the digital economy regulatory ecosystem, DPs and implications for policymakers. Though the data from legal documents and qualitative interviews is adequate, transactional data from the RHPs and interviews with judiciary actors would have been insightful. Practical implications The study provides insights into critical aspects of regulatory evolution, governance and regulatory impact on the DPs’ ecosystem. The right balance of regulations according to the business models of DPs allows DPs to have space for growth and development of the platform ecosystem. Social implications This research shows the interactions in the digital space and how regulations can impact various actors. A balanced policy can guide the paths of DPs to have equal opportunities. Originality/value DP regulations have a complex structure. The paper studies regulatory developments of DPs and the impacts of governance and controls on associated players and platform ecosystems.

Based on two-sided market theory, this paper has studied the pricing problem of ride-hailing platforms with a combination of inter-group network externality and inner-group network externality. Two scenarios of user structure are considered. In scenario 1, both travellers and drivers are single-homing. In scenario 2, travellers are single-homing while drivers are multi-homing. Moreover, time sensitive factors and driver’s commission rate are introduced to reflect the characteristics of transport industry. Finally, the impact of network externality, time sensitivity, driver’s commission rate and entry cost on ride-hailing platform pricing, user scale and profits are analysed. The results show that inter-group network externality and inner-group network externality have a negative effect on platform prices charged to both travellers and drivers. However, when travellers are multi-homing, the price charged to travellers is positive with respect to the inter-group network externality from drivers. In the relationship between travellers’ scale and inter-group network externality, inner-group network externality is positive. Further, in both scenarios, the network externalities from the two sides affect platform profits negatively.

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The paper explored the collaborative governance between government departments and stakeholders in the ride-hailing market to address to the problems of government regulation failure and self-regulation without enforcement powers. The governance of ride-hailing market involves elements such as market behaviour, security, reputation, credit and data. To effectively manage and control these elements, a multi-party collaborative governance framework was proposed. The framework incorporates government, industry, platform, driver, and passenger as the governance participators and manifests their differentiated governance capabilities and advantages. Through setting up the collaborations between government and industry, government and platform, industry and platform, as well as platform and driver/passenger, a workable collaborative governance pattern will be achieved to prompt a compliant and healthy ride-hailing market.

ABSTRACT Ride-hailing platforms such as Uber offer service suppliers more flexible work conditions but lower security than permanent jobs. This study examines the preferences of ride-hailing drivers when they are offered contract and platform design options related to flexibility, financial security, and information features. The sample design and the discrete choice experiment approach we use permit the exploration of differences in the preferences of sub-groups of suppliers, including active and inactive drivers, drivers with and without other jobs, and those who multi-home. We find that the utility and willingness of drivers to work for ride-hailing platforms generally increase when the ride-hailing company provides a minimum wage guarantee, a company-sponsored benefit plan, and information features that protect drivers’ privacy and allow them to screen for undesired passengers. These attributes are important enough for drivers so that they are willing to sacrifice scheduling flexibility by committing to minimum working hours in exchange for those attributes. We identified significant heterogeneity in driver preferences. For drivers with a primary job or jobs other than ride-hailing, the perception of working conditions in the primary job affects their willingness to restart ride-hailing services. Preferences also vary depending on whether a driver participates in one or more platforms. These results suggest that offering a menu of contract options could provide novel tools for ride-hailing companies to improve the stability and predictability of supply. It may also alleviate some of the concerns about driver working conditions that have led to increasing calls for the regulation of ride-hailing services to protect drivers.

The increasing popularity of ride-hailing applications has given rise to a new channel in which ride-hailing platforms are bundled into aggregation platforms to earn additional orders by charging commissions and slotting fees. Such bundled channels, unlike traditional reseller electronic ones, may flutter prices, service levels, market demands, and then further affect their profits. These divergent attitudes raise an interesting and key question about whether and under what conditions bundled channels should be introduced to ride-hailing platforms. In this paper, we provide an analytical framework for ride-hailing and aggregation platforms in unbundled and bundled scenarios, respectively. We build a Stackelberg game model in which ride-hailing and aggregation platforms as leaders obtain prices by constructing Nash equilibria, while drivers as followers determine service levels given to two platforms. Drivers’ best responses in terms of service levels for two platforms, as well as platforms’ optimal pricing strategies and profits are achieved. To capture access conditions of the ride-hailing platform and the profit contention between two platforms, we further conduct sensitivity analysis on cost coefficients of service levels, price and cross-price substitutions, service level and cross-service level substitutions, revenue-sharing ratio, cost, as well as commission and slotting fee. Based on numerical examples and analysis of the results, some interesting managerial insights about bundling strategies are gained for ride-hailing platforms.

In the case of the low-density city, empirical evidence continuously demonstrates that transit investment is not a magic bullet. Desirable outcomes are not guaranteed and are often dependent on development density and other urban characteristics. Mobility-as-a-service (MaaS) presents a new approach: a digital platform providing access to multi-modal travel alternatives and totally comprehensive integrated trip-making, planning, and payment services. Review of the literature highlights shortcomings in traditional transportation planning by examining aspects of multi-modal planning such as adoption, parterships, operations, integration, capacity implications, and impact analyses. To enhance the practice of multi-modal planning, the following experiment evaluates various performance measures and inter-modal interactions on International Drive in Orlando, Florida, U.S., via D- and I-optimal experimental designs in a simulated MaaS network. Alternative scenarios are developed comparing varied modal shares across five travel modes: personal vehicles, transit, ridesourcing (or ride-hailing), micro-mobility, and walking. The modal effects are analyzed to highlight the strengths and weakness of each mode under a variety of congestion conditions. While transit enjoys the lowest impact per person, ridesourcing demonstrates adverse effects across all measures. Based on the novel interactions of transit and ridesourcing with directional demand, strategies are outlined for optimizing ridesourcing-transit integration to reduce route travel time, queuing, and overall network delay. The performance impacts of curbside facilities are also discussed for improved multi-modal integration at the street level. These findings are applied to propose a framework for effective planning and implementation of mobility services in low-density cities, focused on operations, city-level connectivity, and curbside management.

This research explores the innovations and challenges associated with managing online cab aggregators, focusing on how these platforms are transforming urban mobility. As the demand for ride-hailing services grows, cab aggregators face plenty of operational challenges, including regulatory compliance, driver management, customer satisfaction, and competition. Innovations in technology, such as real-time data analytics, artificial intelligence, and mobile applications, play a crucial role in enhancing service efficiency and user experience. The study examines case studies from leading cab aggregator platforms, highlighting successful management strategies and identifying potential barriers to effective operation. By understanding the interplay between technological advancements and management practices, this research aims to provide insights for industry stakeholders to navigate the evolving landscape of online cab aggregation effectively. Key Words: Cab Aggregators, Urban Mobility, Operational Challenges, Technological Innovations, Ride-Hailing Services, Management Strategies

This study aims to explore how small and medium-sized platforms in the sharing economy can gain a competitive advantage in a market monopolized by giants. Taking China’s ride-hailing industry as an example, the k-means clustering method was employed to compare and analyze the characteristics and differences of 207 small and medium-sized platforms based on the framework of the business model canvas. An empirical typology comprising four representative successful business models of small and medium-sized ride-hailing platforms is extracted from the dimensions of key partners, value propositions, customer segmentation, and cost-revenue structure: high-end business platforms, Minibus-hailing platforms, Intercity carpool platforms, and Aggregation mode platforms. The results show that small and medium-sized platforms in the sharing economy can rely on their own characteristics and advantages to seek new potential development directions and gain development space by constructing value propositions and operating models that are different from industry giants. Specifically, it is necessary to further implement refined market identification and segmentation to continuously develop differentiated competitive advantages. In particular, more effective and open business strategies should be explored and practiced to greatly expand the network effects and business boundaries of small and medium-sized platforms, thereby significantly enhancing their competitiveness in the ride-hailing market. This study provides a new perspective on the reconstruction of market exchange in the sharing economy through the prism of small and medium-sized digital platforms and has certain important theoretical significance for supplementing and advancing existing research.

The taxi aggregator industry in India, driven by companies such as Ola and Uber, has massively disrupted the state of urban commute via affordable and tech-enabled transport. As competition escalates, operators are positioning on price, user experience, and additional safety features. This paper studies different pricing policies and their impacts on customer behavior and the market. The roadmap includes a focus on things like fare transparency, driver quality, response times, and app performance, as well as safety features designed with the users in mind. The paper also looks at gender-sensitive transportation options, including women-run cabs for women passengers that not only increase safety but also include women in the economic process. One of the key contributions of this work is the conceptualization, implementation, and deployment of CabEase, a mobile app that provides real-time fare comparisons, integrated SOS alerts, ride tracking, and a gender-aware ride-matching process. The app uses location-based services and usability to increase the trust and decision-making process of commuters. By the survey data, experimental comparisons, and feature validation, the research sheds light on customer satisfaction and operational improvements in the Indian taxi aggregator ecosystem. The results contribute to a better understanding of the design of shared, secure, and fair ride booking services in response to changing urban dynamics.

With the rapid expansion of ride-hailing services, it has gradually become a new travel choice for urban residents. Various research studies have focused on market relationships and platform strategies from the perspective of platform competition. However, little research has been studying issues related to the platform integration of ride-hailing services from the corporate perspective. Based on an analysis of integration modes and travellers’ behavioural factors, we established an evolutionary game model to study travellers’ choice behaviour under the integration of ride-hailing platforms. Furthermore, this study employed methods of model deduction and numerical study. The findings indicate the following. (1) When the travel risk associated with platform integration is high, travellers are less likely to choose ride-hailing services, and the integration strategy of ride-hailing platforms will not be pursued. (2) Ride-hailing platforms tend to interconnect with larger-scale platforms. (3) As the negative effect of perceived sacrifice decreases, ride-hailing platforms are more likely to interconnect with other platforms, and travellers are more inclined to choose ride-hailing services. (4) A higher cost of platform integration will decrease the probability of ride-hailing platforms adopting an integration strategy, but it will not significantly impact travellers’ behaviour.

In the ride-hailing market, platforms such as Didi Chuxing are increasingly collaborating with taxi services through the integrated channel, like Simultaneous Ride-Hailing Request (SR). SR allows riders to submit a single request that includes both platform-based and taxi services, with the platform allocating each request to one of the two services and charging the corresponding price. To investigate the implications of SR, we develop an analytical model of a ride-hailing platform that integrates a platform-based service with self-scheduling drivers and a taxi service with a fixed supply. Riders differ in their service preferences and sensitivity to driver availability. We find that although integrating the taxi service into the platform intensifies competition for riders between the two services, SR can yield a win-win outcome by improving both platform profitability and taxi utilization. In particular, SR enables the platform to serve additional riders without lowering the price and helps the taxi service alleviate demand loss caused by offline search frictions, thereby mitigating price competition across services. Moreover, we identify the sensitivity of self-scheduling drivers to rider availability as a key performance determinant driving these results. When self-scheduling drivers are sufficiently sensitive, SR adoption is profitable by enhancing supply–demand coordination. Finally, we confirm the robustness of our results by relaxing two key assumptions: allowing the platform to charge the commission fee on the taxi service and permitting platform-based riders to also use SR. Overall, this study provides new insights into platform–taxi cooperation and contributes to the broader understanding of sustainable ride-hailing markets.

The proliferation of ride-hailing aggregator platforms presents significant growth opportunities for ride-service providers by increasing order volume and gross merchandise value (GMV). On most ride-hailing aggregator platforms, service providers that offer lower fares are ranked higher in listings and, consequently, are more likely to be selected by passengers. This competitive ranking mechanism creates a strong incentive for service providers to adopt coupon strategies that lower prices to secure a greater number of orders, as order volume directly influences their long-term viability and sustainability. Thus, designing an effective coupon strategy that can dynamically adapt to market fluctuations while optimizing order acquisition under budget constraints is a critical research challenge. However, existing studies in this area remain scarce. To bridge this gap, we propose FCA-RL, a novel reinforcement learning-based subsidy strategy framework designed to rapidly adapt to competitors'pricing adjustments. Our approach integrates two key techniques: Fast Competition Adaptation (FCA), which enables swift responses to dynamic price changes, and Reinforced Lagrangian Adjustment (RLA), which ensures adherence to budget constraints while optimizing coupon decisions on new price landscape. Furthermore, we introduce RideGym, the first dedicated simulation environment tailored for ride-hailing aggregators, facilitating comprehensive evaluation and benchmarking of different pricing strategies without compromising real-world operational efficiency. Experimental results demonstrate that our proposed method consistently outperforms baseline approaches across diverse market conditions, highlighting its effectiveness in subsidy optimization for ride-hailing service providers.

In this paper, we focus on the business model of online ride-hailing platform and analyze the fundamental factors that determines the economic performance of online ride-hailing business model. We define the business model of platform economy in the perspective of organizational economics, and based on this definition, two types business model of online ride-hailing market are conceptually constructed. Then, we analyze the economic determinants that influence the adoption of the business model of the online ride-hailing platform. We find that, when the network effects is weak and the passengers' heterogeneity degree in service quality is low, the marketplace model dominates the online ride-hailing market and the market competition level is relatively low; when the network effects and passengers' heterogeneity degree are moderate, the marketplace model remains dominant, while the integration model will emerge in the market, and the proportion of two models depend on the level of network effects and passengers' heterogeneity degree; when the network effects and passengers' heterogeneity degree are all high, the marketplace model and the integration model will coexist, and the market competition is relatively high.

PurposeDrawing on institutional entrepreneurship theory, this study examines (1) how ride-hailing service markets are created and legitimized and (2) how the involved stakeholders (acting as institutional entrepreneurs) co-evolve to improve city connectivity.Design/methodology/approachEmploying qualitative methods, this study engages thirty-one interviewees, including drivers, users, policymakers, and influencers. In addition to interview data, this study uses visual methods, online documents, and participant observation.FindingsThe findings shed light on policymakers’ struggles in managing the emerging digital technology-induced industry and illuminate the co-evolution of various actors in legitimizing the ride-hailing industry, delineating four distinct phases: introduction, local validation, expansion, and general validation.Originality/valueThis research enriches the institutional entrepreneurship theory by demonstrating the fluid boundaries between economic, political, and policy entrepreneurs, revealing that roles are more interconnected and overlapping than previously acknowledged. It introduces the concept that collective and adaptive actions by various actors are crucial for driving institutional change, moving beyond the traditional focus on individual entrepreneurs. By examining how ride-hailing platform operators navigate and shape multiple institutional orders to their advantage, the study highlights the dynamic interplay between enabling conditions and entrepreneurial actions within institutional theory.

Autonomous ride-hailing services, as an innovative solution in the shared mobility sector, have sparked intense competition with traditional ride-hailing platforms. This study examines a traditional large-scale ride-hailing platform and an autonomous ride-hailing platform, constructing profit models for both platforms under competitive and cooperative scenarios. The impact of these scenarios on the platforms’ optimal profits is analyzed using a game-theoretic framework. The study identifies passenger trust in the autonomous platform and the commission rate as critical factors influencing the strategic choices of the two platforms. Surprisingly, irrespective of variations in passenger valuation coefficients and commission rates, there is no scenario where both platforms simultaneously prefer cooperation, which contradicts intuitive expectations. Furthermore, the findings suggest that when passenger trust and valuation differences are relatively low, the autonomous platform can maximize profits by adopting a high-pricing strategy. However, as passenger trust and valuation differences increase, the autonomous platform must adjust its strategy, shifting toward cost optimization and price competition. The study also explores the role of transfer payments as an incentive mechanism for traditional platforms to encourage cooperation from autonomous platforms, providing a robust theoretical foundation for fostering collaboration between traditional and autonomous ride-hailing platforms.

The Fourth Digital Revolution has transformed Indonesia's informal transportation sector, particularly traditional motorcycle taxi drivers transitioning to digital ride-hailing platforms. This shift represents movement from location-bound, passive income generation to algorithm-driven, flexible employment. Despite widespread Grab adoption across secondary cities, limited research examines multidimensional impacts on drivers' socio-economic well-being beyond metropolitan areas. This study investigates how digital transformation affects the income and social well-being of former traditional motorcycle taxi drivers in Kendari City, a representative secondary urban area. The research employed a qualitative exploratory case study design, utilizing in-depth interviews with eight primary informants who transitioned to the Grab platform, supplemented by community leaders and driver representatives. Data collection used methodological triangulation, combining semi-structured interviews, participant observation, and document analysis over three months. Analysis followed Braun and Clarke's thematic framework, integrating Digital Transformation Theory, Platform Economy Theory, and Social Impact Theory. Findings reveal substantial positive impacts across multiple dimensions. Economically, drivers experienced significant income improvements, enhancing financial stability and predictability. Beyond monetary gains, digitalization strengthened social well-being through improved access to children's education and healthcare, greater work-life balance, and enhanced psychological well-being through reduced income uncertainty. However, challenges emerged regarding platform commission fees and technological dependence. Digital transformation through ride-hailing platforms serves as a powerful economic empowerment instrument, significantly improving income security and multidimensional social well-being. The transition constitutes a structural transformation reducing informal sector uncertainties while providing occupational autonomy, offering evidence-based insights for inclusive digital transformation policies.

This research investigates the ethical implications of algorithmic management (AM) on workers of ride-hailing platforms, with a focus on two-wheeled taxi riders (ojek) in Indonesia. The algorithmic control built into mobile applications automatically evaluates workers' performance under precarious working conditions. This study examines the intersection between technology adoption and social issues by asking: How do platform workers perceive algorithmic control, considering the risks they may encounter? Built on the literature on perceived risks, this study employed a qualitative approach, conducting focus group discussions and interviews. The results of this study identified the risks encountered by riders and elucidated the mechanisms by which algorithms control workers. Overall, this study contributes to the literature on algorithmic management by focusing on its dual function: as a tool for risk reduction and surveillance among its riders.

Online ride-hailing has become an important travel way. During the ride-hailing, different passengers have different expectations for the order price. Traditional platform pricing strategies cannot make reasonable pricing for different passengers’ expectations, and they cannot make adaptive responses to the changes. To solve the problem, in this paper we design a dynamic pricing strategy to maximize the long-term profits of the platform by meeting passengers’ expectations. Specifically, we first collect drivers’ and passengers’ information, simulate the bidding strategy of them, and obtain the order price that drivers and passengers are satisfied with. In the process of studying the bidding strategy, we use double auction to match drivers with passengers. Based on the satisfied price of passengers in different time periods, we model the pricing problem of the online ride-hailing platform as a Markov decision-making process, and use deep reinforcement learning to design a Deep Deterministic Policy Gradient-Prioritized Experience Replay (DDPG-PER) algorithm to solve the pricing strategy. The experimental results show that DDPG-PER can effectively set prices according to passengers’ expectations and improve the platform's long-term profits.

Today, ride-hailing platform operations are popular. Facing pandemics (e.g., COVID-19) some customers feel unsafe for the ride-hailing service and possess a “safety risk-averse” (SRA) attitude. The proportion of this type of SRA customers is unfortunately unknown, which makes it difficult for the ride-hailing platform to decide its optimal service price. In this article, understanding that blockchain technology (BT) based systems can help improve market estimation for the proportion of SRA customers, we conduct a theoretical study to explore the impacts that the BT-based system can bring to the platform, customers, and drivers. We consider the case in which the platform is risk-averse (in profit) and serves a market with both SRA and non-SRA customers. We analytically prove that using BT, the optimal service price will be increased and BT is especially helpful for the case with a more risk-averse ride-hailing platform. However, whether it is more or less significant for the more risk-averse SRA customers depends on their degree of risk aversion. We uncover that when the use of BT is beneficial to the customers, it will also be beneficial to the drivers, and vice versa. We derive in closed-form the analytical conditions under which the use of BT can be beneficial to the ride-hailing platform, customers, and drivers (i.e., achieving “all-win”). When all-win cannot be achieved automatically, we explore how governments can provide sponsors to help. We further extend the analysis to consider the general case in which BT incurs both a fixed cost as well as a cost increasing in demand. We prove that the main conclusion remains robust. In addition, we reveal that the required amount of government sponsor to achieve all-win is the same between the two different costing models explored in this article.

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Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and fairness issues in ride-hailing have been neglected. Fairness issues in ride-hailing, including significant earning differences between drivers and variance of passenger waiting times among different locations, have potential impacts on economic and ethical aspects. The recent studies that focus on fairness in ride-hailing exploit traditional optimisation methods and the Markov Decision Process to balance efficiency and fairness. However, there are several issues in these existing studies, such as myopic short-term decision-making from traditional optimisation and instability of fairness in a comparably longer horizon from both traditional optimisation and Markov Decision Process-based methods. To address these issues, we propose a dynamic Markov Decision Process model to alleviate fairness issues currently faced by ride-hailing, and seek a balance between efficiency and fairness, with two distinct characteristics: (i) a prediction module to predict the number of requests that will be raised in the future from different locations to allow the proposed method to consider long-term fairness based on the whole timeline instead of consider fairness only based on historical and current data patterns; (ii) a customised scalarisation function for multi-objective multi-agent Q Learning that aims to balance efficiency and fairness. Extensive experiments on a publicly available real-world dataset demonstrate that our proposed method outperforms existing state-of-the-art methods.

The article provides an argument that the platform is the site of Burawoy’s workplace games. The game observed on the platform used a pattern quite similar to one diagnosed by Burawoy, successfully employing coercion and consent to control the workforce. Control on the platform has a general nature which combines technological, organisational and normative aspects. Work on the app is coordinated by adopting a co-optation strategy, reducing conflicts by enabling mobility, and remuneration from the platform is based on a piece-rate system. Yet, the modern game, labelled in the paper as Ride-Pass, is different from the one described by Burawoy. Due the game is taking place in the service sector, the article argues that it is structured around two mutually connected stakes: working time and self-recognition. The article contributes to the Labour Process Theory, supporting its conclusions with a long-lasting study among Polish platform workers (53 interviews with Uber’s drivers).

Ride-hailing drivers (e-drivers) are among the platform workers who successfully embrace the worker-platform co-operative model's emergence to counter capitalistic ride-hailing platforms and their complex ecosystems, which align with fairwork principles of pay, condition, contracts and (platform) management. This study was conducted to determine the extent of ride-hailing drivers' preferences for satisfactory working conditions, fair pay-profit distribution and willingness to form and join worker-platform co-operatives in Kenya. The study was grounded in the social exchange theory, affective theory of social exchange, and utility theory. The researcher applied critical-realism research philosophy and multimethod research designs of discrete choice experiments and sequential explanatory mixed method research designs. Using the Yamane sampling formula, the study used quantitative data from 497 respondents from sampled 600 of the 20,000 e-drivers in the Nairobi Metropolitan Region. The quantitative data was analysed using multinominal logistic regression. Moreover, the study used qualitative data from 11 participants, which was analysed using thematic analysis. Results indicated that the two fairwork principles were statistically significant to e-drivers’ willingness to form and join worker-platform co-operatives. fair pay-profits distribution (FPD) principle, in particular, was a highly preferred fairwork principle in terms of e-drivers’ willingness to form and join the worker-platform co-operative model (Coeff=1.564, SE = 0.234, Z = 6.67, p< .001), compared to the satisfactory working conditions (SWC) principle (Coeff=0.783, SE = 0.156, Z = 5.012, p< .001). This study brings practical and theoretical contributions to improve the e-drivers’ benefits from the ride-hailing sector. Policymakers, promoters, and platform workers like e-drivers could understand how best to promote worker-platform co-operatives to succeed in the country’s societal context.

The rapid expansion of two-wheeler ride-hailing platforms has transformed urban mobility, offering convenience and cost-effectiveness. However, the unregulated pricing strategies of these services pose challenges for both consumers and drivers. This study explores the urgency to implement pricing regulations and identifies the primary beneficiaries and those in need of such measures. Through a comprehensive review of existing literature, stakeholder interviews, and market analysis, it becomes evident that price regulation can stabilize fares, ensuring affordability for riders while providing fair compensation for drivers. Riders, particularly from low and middle-income groups, benefit most from regulated pricing as it guarantees predictable and reasonable fares, enhancing their access to reliable transportation. Drivers, often facing income volatility due to fluctuating ride demand and pricing schemes, also stand to gain significantly from a regulated system that ensures fair wages and job security. Furthermore, regulatory frameworks can foster competitive equity among ride-hailing companies, preventing monopolistic practices and encouraging healthy market competition. Therefore, the regulation of pricing for two-wheeler ride-hailing platforms is crucial not only for consumer protection but also for safeguarding driver welfare and promoting a balanced market ecosystem. The findings advocate for policymakers to urgently address this regulatory gap, ensuring that the benefits of ride-hailing services are equitably distributed across all stakeholders.

The rapid expansion of ride-hailing services has led to significant challenges in maintaining efficient and scalable customer support systems. Ensuring platform reliability is critical to handling surges in customer support requests, particularly during peak hours, major events, and service disruptions. This paper explores the methodologies, technologies, and frameworks that enable ride-hailing platforms to scale customer support while maintaining service reliability. Case studies from leading ride-hailing companies, including Lyft, Uber, Grab, Didi, and Bolt, demonstrate best practices in implementing artificial intelligence (AI), machine learning (ML), predictive analytics, and automation to optimize support operations. The paper further examines the impact of surge pricing models on support demand, the role of AI-powered chatbots in enhancing customer experience, and strategies for managing workforce scalability. A discussion on infrastructure resilience and disaster recovery strategies provides insights into maintaining operational efficiency under high-demand conditions. The research concludes with future trends in AI-driven customer support and recommendations for ensuring sustained scalability and reliability in the ride-hailing industry.

ABSTRACT The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately.

Under the guidance of the "double carbon" target, the electrification of ride-hailing vehicles has become a trend. This study considers a system consisting of an electric vehicles (EVs) manufacturer, a B2C ride-hailing platform, and a service provider in the selection of charging and battery swapping stations, and proposes four strategies based practical experience. We found that the network efficiency of battery swapping stations is an important factor affecting the strategy choices of the three parties. The manufacturer and the ride-hailing platform tend to choose the strategy of self-building. The cost of producing charging/swapping EVs indirectly affects the decision of the service provider. In the future, contracts design can be used to coordinate the interests of various parties.

Ride-hailing worker-platform co-operatives had emerged as part of and for at least fairwork principles. Moreover, studies on e-drivers have recommended the formation of worker-platform co-operatives in Kenya. The actual worker-platform co-operatives by e-drivers are yet to be established. It was against this backdrop that a study was conducted to determine the extent of ride-hailing drivers’ preference for fairwork principles of trustworthy labour-platform management, greater worker-autonomy and willingness to form and join worker-platform co-operatives. The study was grounded in the social exchange theory, affective theory of social exchange, and utility theory. The researcher applied critical-realism research philosophy and multimethod research designs of discrete choice experiments and sequential explanatory mixed method research designs. Using the Yamane sampling formula, the study used quantitative data from 497 respondents from sampled 600 of the 20,000 e-drivers in the Nairobi Metropolitan Region. The quantitative data was analysed using multinominal logistic regression. Moreover, the study used qualitative data from 11 participants, analysed using thematic analysis. Results indicated that the two fairwork principles were statistically significant to e-drivers’ willingness to form and join worker-platform co-operatives. The trustworthy platform management (TPM) principle, in particular, was a highly preferred fairwork principle in terms of e-drivers’ willingness to form and join worker-platform co-operative model (Coeff=2.62; SE = 0.400, Z = 6.54, p< .001), compared to the greater worker-autonomy contract (GAC) principle (Coeff=1.565, SE = 0.202, Z = 7.74, p< .001). This study brings practical and theoretical contributions to improve the e-drivers’ benefits from the ride-hailing sector. Policymakers, promoters, and platform workers like e-drivers could understand how best to promote worker-platform co-operatives to succeed in the country’s societal context.

With the rapid development of the times, new economic forms emerge endlessly, and more and more people are seeking work and rewards through the platform economy. The concept of "ride-hailing" has quickly entered people's sight due to its speed and high comfort level. However, due to the imperfections of the platform economy and the virtual nature of the internet, issues faced by "ride-hailing drivers" continue to emerge: lack of signed labor contracts, difficulty in guaranteeing rest time, inadequate labor laws, etc., naturally causing certain negative impacts on the new economic industry. In this article, we will discuss the issues of labor relations under the platform economy from the perspective of "ride-hailing drivers" and propose reasonable governance measures.

Many ride-hailing service platforms have launched a surcharge policy to schedule long-distance drivers to meet demand during regionally peak demand periods. When someone is unwilling to wait in line until there exists an idle local driver to match, he/she would choose the surcharge policy to quickly match long-distance drivers and get fast service through paying a surcharge. With heterogeneous congestion-sensitive customers and reservation rates of local and long-distance drivers, this paper explores the role of the surcharge policy for a ride-hailing service platform's profit, consumer surplus and driver surplus. We find that when the potential demand increases, the platform sets a lower surcharge to shift the demand to those customers with the surcharge policy. Influenced by network externalities, increasing the number of long-distance drivers causes the platform to initially increase and then decrease the surcharge. There exists a threshold of the number of local drivers, below which the platform under the surcharge policy can gain a higher profit than the situation without the surcharge. However, only the surplus of customers who choose to wait for local drivers is beneficial, while other customers and drivers suffer losses, indicating that the proposed policy cannot result in an all-win outcome for all parties.

This article investigates the dynamic pricing problem of ride-hailing platform. It employs optimal control theory to construct a dynamic pricing model for ride-hailing platform with the aim of maximizing platform profit by adjusting the supply-demand relationship within the system through dynamic pricing. The research findings indicate that the optimal dynamic pricing for ride-hailing platform varies with changes in demand within the system. Specifically, as demand increases, the optimal dynamic prices gradually rise, while with decreasing demand, the optimal dynamic prices exhibit a gradual decline. Compared to static pricing strategies, dynamic pricing strategies effectively regulate the supply-demand relationship within the ride-hailing system, thereby achieving maximum platform profit.

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This study investigates how algorithmic governance, a core feature of modern e-commerce platforms, impacts the consumption behavior of its service providers—specifically, ride-hailing drivers’ preference for high-calorie food. From an e-commerce ecosystem perspective, the dynamic interaction between platforms and their service providers is critical for long-term value co-creation and platform sustainability. By examining how algorithmic control mechanisms spill over into drivers’ off-platform behaviors, this research offers crucial insights for designing more sustainable and human-centric platform business models. Analyzing 710 survey responses from ride-hailing drivers in China via PLS-SEM, our findings reveal that algorithmic tracking evaluation and behavioral constraints are positively associated with high-calorie food consumption, with emotional exhaustion acting as a key mediator. Notably, standard guidance algorithms showed no significant effect. These results contribute to the e-commerce literature by demonstrating how platform-centric control can inadvertently lead to adverse externalities that may undermine service quality and provider well-being, ultimately posing a risk to the platform’s brand reputation and operational stability. We offer practical recommendations for e-commerce platform managers on optimizing algorithmic strategies to foster a healthier and more sustainable gig worker ecosystem.

This study investigates the relationship between belief structures (perceived compatibility, self-efficacy, and subjective norm), behavioral adaptation, satisfaction, and continuance intention of a platform-based ride-hailing service called “Grab” in Vietnam. After applying a questionnaire-based survey method and convenience sampling for data collection, the study collected 439 responses from the current users of Grab. A structural equation modeling procedure was used to verify the hypotheses. The results revealed that, except for the relationship between perceived compatibility and behavioral adaptation, all belief structures positively affect behavioral adaptation and satisfaction. Furthermore, both satisfaction and behavioral adaptation positively affect the continuance intention. Our study also demonstrated the partial and full mediating role of behavioral adaptation between self-efficacy and continuance intention, and between subjective norm and continuance intention, respectively. Our study broadens the current understanding of the relationship mechanism between belief structures, behavioral adaptation, and continuance intention. Theoretically, this study is the first research into the relationships between belief structure, behavioral adaptation and continuance intention, and it also identifies the mediating effects of behavioral adaptation on continuance intention in the platform-based context. Additionally, our study offers insightful implications for firm managers to retain users effectively by boosting the factors contributing to continuance intention.

Abstract. This study investigates how descriptive norms and perceived product attributes (utilitarian and hedonic benefits) influence the intention of young consumers to continue to adopt ride-hailing apps. We developed a conceptual model drawing upon the socio-technical theory by involving 333 Indonesian users of ride-hailing apps and estimating the model using the statistical tool of PLS-SEM-based software. The findings showed that social aspects (descriptive norms) and technical aspects (utilitarian and hedonic benefits) significantly influence young consumers to adopt ride-hailing apps. Intriguingly, a positive perception of the apps’ benefits is instrumental for encouraging young consumers to continue to adopt ride-hailing apps when descriptive norms appear, when peers in their social circle widely adopt similar apps. This study highlights the unique behavior of young consumers in adopting ride-hailing apps by accentuating the role of descriptive norms and perceived product attributes from the perspective of socio-technical theory. This study provides practical recommendations for digital platform providers, particularly TNCs (Transportation Network Companies), which offer ride-hailing services to address the young consumer segment with a community-based marketing approach to maintain their continued adoption. Keywords: Continuation intention, platform economy, digital platform, ride-hailing services, socio-technical theory, young consumers

Purpose This study aims to understand the mechanism by which the value of ride-sharing services influences consumers’ continuance intention. Design/methodology/approach The authors collected data from 484 Chinese ride-sharing respondents and analyzed them using partial least squares structural equation modeling. Findings The results show that hedonic value, social connection value and environmental value positively affect consumers’ cognitive fit and emotional fit, while utilitarian value has no significant effect on either cognitive fit or emotional fit. In addition, both cognitive fit and emotional fit significantly affect consumers’ satisfaction and continuance intention. Furthermore, satisfaction mediates the effects of cognitive and emotional fit on continuance intention. Practical implications Ride-sharing practitioners should have a clear understanding of all the value dimensions of ride-sharing services, which would subsequently increase customers’ continuance intention. Originality/value This study defines and divides the dimensions of ride-sharing value and demonstrates the significant impact of environmental value on the sustainability of ride-sharing services. This study extends fit theory by dividing it into two dimensions.

Background: Ride-sharing services have emerged as an alternative mode of urban transportation in Kathmandu Valley, aiming to address issues such as traffic congestion and limited public transport options. However, user satisfaction with these services remains uncertain due to varying service quality and operational challenges. Purpose: The purpose of the study is to assess the user's satisfaction with the ride-sharing service in Kathmandu Valley. Specifically, the study assesses the current status of ride-sharing services in the Kathmandu Valley to examine the impact of service quality on user satisfaction with ride-sharing services in the Kathmandu Valley, to identify the various challenges faced by ride-sharing services, and to propose potential solutions to address these challenges. Design/methodology/approach: This study adopted an explanatory research design. Expectation confirmation theory (ECT) is used for the study because SERVQUAL is based on the expectancy-disconfirmation paradigm, which states that service quality is defined as the degree to which consumers' pre-consumption expectations of quality are confirmed or contradicted by their actual perception of the service experience. The primary data for this study were collected from 417 respondents, using a non-probability convenience sampling method. Structured questions were administered through the KOBO toolbox to gather the necessary information. The collected data were then analyzed using descriptive and inferential statistics in MS Excel and SmartPLS 4.0. Findings: Tangibility, reliability, responsiveness, and empathy directly influence users' satisfaction in using ride-sharing services in the Kathmandu Valley; however, assurance had no direct influence on users' satisfaction. In the context of using ride-sharing services, Nepali customers are not yet accustomed to using ride-sharing services compared to developed countries. Besides, the major challenges faced by ride-sharing service users include long wait times, safety concerns, unavailability of rides, unfriendly or unprofessional driver behavior, pricing issues, vehicle cleanliness, payment options, and difficulty using the app. The major solutions to the challenges are short wait times, enhanced safety measures, sufficient ride availability, friendly or professional driver behavior, better pricing models, improved vehicle maintenance, more driver training programs, easy-to-use apps, and more payment options. Conclusion: This study concludes that tangibility, reliability, responsiveness, and empathy have a positive and significant relationship with users' satisfaction, whereas assurance is insignificantly correlated with user satisfaction. Keywords: Ride-sharing Service, Users' Satisfaction, SERVQUAL Dimension, Service Quality, Kathmandu Valley

The study examines the impacts of ride sharing apps among the youths of the Kathmandu Valley. Attitude towards using ride sharing apps is selected as the dependent variables. The selected independent variables ease of use, privacy concerns, time management, price and convenience. The primary source of data is used to assess the opinions of respondents regarding ease of use, privacy concerns, time management, price and convenience and attitude of youths towards using ride sharing apps. The study is based on primary data of 163 respondents. To achieve the purpose of the study, structured questionnaire is prepared. The correlation and multiple regression models are estimated to test the significance and impacts of ride sharing apps among the youths of the Kathmandu Valley. The study showed that ease of use is positively correlated to attitude towards using ride sharing apps. It indicates that ease of use leads to an increase in attitude towards using ride sharing apps. Likewise, privacy concern is positively correlated to attitude towards using ride sharing apps. It indicates that privacy concern has a positive impact on attitude towards using ride sharing apps. Further, time management is positively correlated to attitude towards using ride sharing apps. It indicates that time management leads to an increase in attitude towards using ride sharing apps. Additionally, price and convenience also demonstrate positive correlations with attitude towards using ride sharing apps., suggesting their significant roles in shaping the use of ride sharing apps in the Kathmandu Valley.

In the face of escalating urbanization and traffic congestion, the emergence of ride-sharing applications has triggered a transformative shift in transportation and the gig economy, with “khep” (a new form of contractual rides) emerging as a distinctive and popular ride-sharing phenomenon in Bangladesh. Having a lack of prior research on “khep” phenomenon in the context of HCI and ICT4D, we conduct comprehensive research involving a two-phase data collection to unravel the intricacies of khep. We present the context from the perspectives of both companies and commuters, examining factors influencing the preference for contractual rides over conventional ride-sharing apps. Cultural norms, trust dynamics, affordability, and accessibility are scrutinized alongside the role of technology literacy, marketing strategies, and regulatory frameworks in shaping the adoption landscape. Through a blend of surveys and interviews in each phase, the study provides nuanced insights into utilization patterns, preferences, and challenges within the Bangladeshi ride-sharing industry, with a specific focus on the khep phenomenon. Aligned with HCI labor work, the study’s implications transcend Bangladesh, offering valuable insights for ICT4D and HCI researchers, policymakers, transportation companies, and technology developers seeking a holistic understanding of the factors shaping the preference for contractual rides within the unique context of the khep phenomenon.

The Department of Veterans Affairs’ (VA’s) transportation system plays an important role in ensuring access to transportation services for veterans, particularly those in rural or underserved areas. However, concerns remain regarding the effectiveness of collaboration among the various VA transportation stakeholders. Persistent transportation challenges hinder veterans’ access to essential healthcare services and resources. Electric autonomous ride-sharing services (ARSSs) offer a promising opportunity to enhance transportation access; however, their current limitations and the perspectives of VA transportation personnel must be considered. This study explored the current perspectives of the VA transportation system and assessed ARSSs as an innovative and sustainable alternative through interviews with eight VA transportation stakeholders representing seven transportation sectors. Our findings revealed the VA’s strengths, including personalized service, flexible accommodations, and collaborative care models, but also identified challenges, including limited funding, staff shortages, volunteer constraints, and restrictive eligibility criteria. The introduction of ARSSs was identified as an opportunity to alleviate some of these constraints by reallocating human resources and improving access to essential services, although concerns remain regarding ARSSs’ ability to accommodate veterans with disabilities and address rural route complexities. Effective communication strategies and streamlined coordination were key recommendations for improving service delivery and expanding transportation access for veterans.

This study presents a comprehensive statistical analysis of factors influencing dynamic pricing and service quality in ride-sharing. Leveraging historical data, we employ regression models, including simple and multiple linear regressions, as well as logistic regression, to examine the relationships between trip duration, passenger count, driver availability, and customer loyalty on ride costs and service ratings. Results reveal that trip duration significantly predicts ride costs, while customer loyalty and location are key determinants of service quality. These findings provide actionable insights for enhancing dynamic pricing strategies and service quality optimization in ride-sharing, supporting data-driven decision-making in a competitive market.

Purpose: This paper aims at assessing the factors that determine the customer satisfaction of ride sharing service in Bangladesh. The motive of this study lies in exploring the influential factors that shape the customer satisfaction of ride sharing service, specifically Pathao users in Dhaka city. Additionally, the paper examines how much the Bangladeshi people are clued up the use of ride sharing service and to what extent they are taking this service. Methodology: This study is quantitative in nature and based on both primary and secondary data. This study employed 120 participants comprising females (30%) and males (70%) aged between 20 and 50 years. Nonprobability convenience sampling technique has been used in this study. Multiple regression analysis has been employed to analyze data and SPSS software has been used to process data. Findings: The study unveils that comfort, price and discount are the most influential factors of satisfaction. Moreover, attitude toward new technology influences the customer satisfaction level to a great extent. Practical Implications: This study offers a direction to the practitioners (ride sharing service providers) by which they can design strategic schemes to ensure customer loyalty and attract more customers to use their service. Originality/Value: Findings from this quantitative study can extensively contribute to the enhancement of fundamental knowledge regarding the factors affecting satisfaction with ride sharing service. Further this study potentially easing the ability of future studies to examine other related factors such as perceived risk, environmental benefits and involvement. Limitations: The study has not looked into the interconnection effect between three precursors including perceived service quality, perceived sales promotion and lifestyle and attitude on satisfaction.

With the in-depth development of the big data era, artificial intelligence (AI) technology, relying on its powerful data processing and pattern recognition capabilities, has become a core driver for advancing business analysis towards intelligence and automation. This paper aims to systematically explore the specific application models, significant advantages, and potential limitations of AI technology in the field of data analysis. First, from the perspective of technological development, the paper outlines the key technical components of current cutting-edge business analysis and explains the inevitability of AI becoming an indispensable element therein. Second, taking Uber's ride-sharing algorithm as a typical case, it deeply analyzes its core principlethe "Trip Chain"and working mechanism, revealing how AI achieves accurate resource matching and route planning in a highly dynamic environment to improve overall system efficiency and user experience. Finally, the paper dialectically analyzes the advantages and disadvantages of AI applications and concludes that AI technology significantly enhances the scientific nature and intelligence level of business decisions by improving the efficiency, scale, and depth of data analysis. However, its widespread application still needs to address multiple constraints in terms of technology itself and social acceptance. Technology is a lever, while humans remain the fulcrum. The future of AI-driven data analysis belongs to practitioners who skillfully wield these tools without succumbing to technological determinism.

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Electric vehicles (EVs) has tiny environmental impact and will constitute a major mean of urban transportation in the future. Shared EV is quickly becoming a new business model under the sharing economy initiatives, providing easy access for commuters who possess no private cars. In this paper, we consider a carsharing company that owns EVs and some parking lots. Passengers can hire an EV at one parking lot and drive it to another one and pay for the service at a certain price determined by the company. A dedicated EV mobility model is proposed to capture the spatial transportation of energy without tracking every single vehicle. Price elasticity is described by a linear demand-price function. The company schedules the aggregated charging of unoccupied EVs in each parking lot, aiming at maximizing the total profit. Parking lots possess relatively large capacity and have to participate in a distribution power market; energy consumption is paid at the locational marginal price. The decision-making problem of the company is formulated as a bilevel program. The lower level simulates the distribution market clearing, and the upper level represents the pricing and charging scheduling problem faced by the company. Starting from a global polyhedral approximation of the power flow model, we develop an equivalent mixed-integer program based on primal-dual optimality condition and integer algebra technique, together with a warm-start strategy which accelerates computation remarkably. Case studies demonstrate that the proposed business model can reshape the load profile by shaving the peaking and filling the valley without harming the profit of the company.

Abstract Ride-hailing services provide not only alternative transportation for passengers but also job opportunities for potential drivers, resulting in both negative and positive effects on new car purchases. Our study assesses the impact of ride-hailing platforms’ market entry on new car purchases in the presence of platform competition. Our data is a monthly panel data on new car registration plates from 2013 to 2015, during which two leading ride-hailing platforms (Didi Chuxing and Uber) rolled out their services across select cities in China. We find that, while the entry of a single ride-hailing platform led to a decline in new car purchases, platform competition mitigated the negative impacts of platform entries. Our explanation is that the two competing platforms may have provided subsidies to drivers such that more people purchased new cars in order to sign up as drivers. By leveraging brand heterogeneity, our analysis finds supporting evidence that platform competition has resulted in increased sales of those car brands that are commonly adopted by ride-hailing drivers. Our study contributes to the literature on pricing strategies and subsidy allocation for two-sided markets by providing empirical evidence from the ride-hailing market. It suggests that companies’ competitive strategies need to account for consumer expectations and learning in the presence of strong network effects.