Ride-sharing Platform Research Articles

Scarcity and market design: How effective matching can promote the peer-to-peer sharing economy

Scarcity is an essential issue in service marketing. This study examined scarcity in a game-theoretic setting. It focused on the role of customer evaluations, through satisfaction ratings, on a technology-enabled peer-to-peer sharing economy platform in achieving stable provider–user matching. The model shows that an appropriate market design employing a user-proposing version of Gale and Shapley's (1962) deferred acceptance algorithm for a ride-sharing platform where many users approach a few providers can help avoid congestion and ensure stable matching. To manage scarcity, the model uses a customer satisfaction rating system that modifies the widely used batch matching algorithm by matching highly rated riders much faster than their low-rated peers. A platform's ability to offer time-bound stable matching for highly-rated users during scarcity is likely to help it maintain a competitive edge.

The IT‐driven ridesharing economy at the base of the pyramid: Unravelling the impact of uncertainty reduction on drivers' engagement in ridesharing

AbstractDespite the potential benefits for drivers at the base of the pyramid (BoP) of IT‐driven ridesharing businesses, drivers are susceptible to uncertainty owing to their low education and incomes, potentially compelling them to engage less in ridesharing over time. Although this important sociotechnical phenomenon has garnered increasing attention, IS studies on this topic are scarce. Drawing on uncertainty reduction theory, we explored mechanisms for reducing BoP drivers' uncertainty and building their trust to enable them to engage in ridesharing. We identified three means of uncertainty reduction at three levels: trust in passengers, trust in ridesharing platforms, and the effectiveness of ridesharing institutional mechanisms. Using a sequential, triangulated mixed‐methods approach, we conducted 30 semi‐structured interviews to explore trust building with drivers and used data provided by 303 ridesharing drivers to quantitatively test the proposed research model. We found that perceived empathy and information congruity increased BoP drivers' trust in passengers, whereas effective escrow services, perceived justice, matching accuracy, and a high level of privacy control increased BoP drivers' perceived trust in ridesharing platforms, motivating them to engage more frequently with ridesharing platforms. Furthermore, the results indicated that increasing trust in both passengers and ridesharing platforms and improving the effectiveness of ridesharing institutional mechanisms can enhance BoP drivers' engagement in ridesharing. We conducted 30 follow‐up interviews with BoP drivers to corroborate the quantitative results and draw robust inferences.

Real-time Multi-platform Route Planning in ridesharing

The increasing availability of mobile Internet and portable devices has led to the popularity of spatial crowdsourcing recently. The significant burgeoning of ridesharing services in spatial crowdsourcing has transformed urban mobility. However, due to the imbalance between supply and demand, some requests may be rejected or workers may keep idle for a long time. Fortunately, sharing tasks and workers across multiple platforms in a collaborative manner can diminish the negative effects of non-uniform distribution. In this paper, we propose a Multi-platform Route Planning in ridesharing problem (MPRP), which integrates route planning and task assignment in spatial crowdsourcing. We study how to improve the total revenue of platforms through coordination with multiple ridesharing platforms. To solve the problem, we propose the Greedy Multi-platform Route Planning algorithm (G-MPRP), which extends the dynamic programming insertion operation and assigns workers in a greedy manner. To overcome the shortcoming of G-MPRP, we further propose the Pack-based Multi-platform Route Planning algorithm (P-MPRP), which packs requests through a ranking-based function. Extensive experimental results shows the proposed algorithms can improve the revenues significantly.

Understanding experiences of adverse digital incorporation of ridesharing drivers in Bangladesh

PurposeBangladesh recently experienced frequent demonstrations by drivers of ridesharing applications. Since the drivers are not excluded from the technology environment, rather they are a part of the digital ecosystem, these protests may point toward the existence of unequal interactional outcomes for different stakeholders afforded by the digital system within the country’s social and cultural contexts. This research is an attempt to unveil the reasons behind value inequality experienced by drivers of ridesharing applications in Bangladesh and understand how power asymmetries influence adverse digital incorporation that can result in the emergence of resistance.Design/methodology/approachWe obtain the data by conducting interviews with 91 drivers of ridesharing platforms in Dhaka, Bangladesh and analyze our data using thematic analysis. We propose an integrated framework unifying adverse digital incorporation (ADI) with the “powercube” model to illuminate our inquiry.FindingsWe find the existence of all three drivers to ADI – ignorance/deceit, direct compulsion and exclusion – exclusion being the most prevalent – that are experienced by the drivers of ridesharing applications in Bangladesh. We also find support for the four causes behind value inequality – design inequality, resource inequality, institutional inequality and relational inequality with the respondents placing the highest emphasis on relational inequality. There are visible, hidden and invisible forms of power involved in how the drivers are incorporated into the ridesharing platforms. The forms of power in the platform environment are exercised primarily in closed spaces and the invited spaces for the drivers are very few. The drivers in response to the closed spaces of power create their own space (claimed space) through the help of social media and other messaging apps. We also find that the power over the drivers is exercised at global, national and local levels.Practical implicationsOur research identifies norms specific to the social and cultural contexts of Bangladesh and can help decision-makers to make more informed choices during the formulation of future digital platform guidelines. Based on the research findings, the paper also makes short-term and long-term policy recommendations.Social implicationsThis research has implications for creating a decent work environment for ridesharing drivers which broadly falls under the Sustainable Development Goal 8 (SDG 8).Originality/valueTo the best of the authors’ knowledge, this is the first paper that integrates the ADI model with the “powercube” framework to reveal that the drivers working on the ridesharing platforms in Bangladesh are adversely incorporated into the digital system where value inequalities are operating within the power dimensions.

A meaning-making perspective on digital ridesharing platforms in underdeveloped markets

PurposeThe digital platform-based sharing economy has become ubiquitous all over the world. In this paper, we explore how market actors’ conflicting interpretations of digital platforms’ business models give form and shape value co-creation and capture practices in contexts marked by weak institutions and underdeveloped markets.Design/methodology/approachIntegrating insights from the broader literature on digital platforms and the contemporary turn to “meaning-making” in social theory, we adopt a problematization method to unpack the collective contest over the interpretation of value co-creation and capture from ridesharing platforms in contexts marked by weak institutions and underdeveloped markets.FindingsCollective contest over the interpretation of digital business models may give rise to competing meanings that may enable (or impede) digital platform providers’ ability to co-create and capture value. We present an integrative framework that delineates how firms caught up in such collective contests in contexts marked by weak institutions and underdeveloped markets may utilise such conditions as marketing resources to reset their organising logic in ways that reconcile the conflicting perspectives.Practical implicationsThe paper presents propositions constituting a contribution to a meaning-making perspective on ridesharing digital platforms by offering insights into how digital business models could potentially be localised and adapted to address and align with the peculiarities of contexts. It goes further to present a theoretical model to extend our understanding of the different sources of contestation of meaning of digital platforms.Originality/valueThe meaning-making perspective on digital platforms extends our understanding of how the collective contest over interpretations of value co-creation and capture may offer a set of contradictory frames that yield possibilities for ridesharing platform providers, and their users, to assimilate the organising logic of digital business models into new categories of understanding.

A Comprehensive Overview of Deep Learning for Algorithmic Pricing in Ride-Sharing Platforms

A Comprehensive Overview of Deep Learning for Algorithmic Pricing in Ride-Sharing Platforms

Intercity Travel Solution: Web Application

Amidst the rise of urbanization and environmental consciousness, our research paper presents a pioneering solution to modern transportation challenges. Unlike traditional ride-sharing platforms, our innovative approach focuses on routes as the fundamental units of collaborative travel. Users create route profiles, facilitating the sharing and offering of rides with like-minded commuters along chosen routes. This unique system fosters dynamic networks and promotes community engagement, while prioritizing environmental responsibility. Our platform features route discovery, collaborative travel arrangements, dynamic pricing, and route communities. By prioritizing routes over profiles, we revolutionize the carpooling experience, creating a vibrant ecosystem for users to connect, share, and positively impact their daily commutes. This paper explores the transformative potential of our route-centric model in reshaping urban mobility landscapes, offering a sustainable alternative for transportation needs. Top of Form Keywords— Urban Mobility, Sustainable Transportation, Route-Centric Approach, Ride-Sharing, Community Engagement, Environmental Responsibility, Dynamic Networks, Collaborative Travel, Route Discovery, Dynamic Pricing, Community-Centered Solutions, Transportation Innovation, Social Cohesion, Environmental Stewardship, Mobility Justice.

Fair and efficient ride-scheduling: a preference-driven approach

ABSTRACT Smart mobility and, in particular, automated ridesharing platforms promise efficient, safe, and sustainable modes of transportation in urban settings. To make such platforms acceptable to the end-users, it is key to capture their preferences not in a static manner (by determining a fixed route and schedule for the vehicle) but in a dynamic manner by giving the riders the chance to get involved in the routing process of an upcoming journey. To that end, this work provides a toolbox of multiagent methods that enable different forms of active preference-awareness in ridesharing services. We capture riders’ preferences (as end-users of a ridesharing service), preserve their privacy by avoiding expecting them to share preferences with other riders, and show the efficacy of the presented ridesharing algorithms using agent-based simulation and illustrating their utilitarian and fairness properties.

Truthful mechanisms to maximize the social welfare in real-time ride-sharing

Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers’ desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare.

Mobility-as-a-Feature (MaaF): Why and how ride-sharing platforms have evolved into super apps

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, or e-commerce and shopping. In this research, I show that some ride-sharing platforms basically already provide MaaF functionality. Uber, Grab, Didi Chuxing, Bolt, and others have transformed into super apps, offering a wide range of services beyond their core ride-sharing business. The findings of this study offer valuable insights into which ride-sharing platforms are actively pursuing the super app strategy and shed light on the motivations driving their expansion into diversified service offerings. While the phenomenon of super apps is already established in the global South, it is now gaining interest in Europe and the U.S. This trend could initiate a significant shift in how people access mobility and other services, emphasizing the necessity of including super apps in future lines of transportation research.

Competition between P2P ride‐sharing platforms and traditional taxis

Over the past decade, the surge of peer‐to‐peer (P2P) ride‐sharing has significantly cut the market share and profitability for taxis, but taxis remain a major service provider in the personal transportation service industry. This paper analytically examines a market with two segments of consumers based on their travel distances, where a P2P platform and a traditional taxi company have different inconvenience costs and compete for customers through pricing. Our analysis shows that consumers’ inconvenience costs and the relative size or travel–distance heterogeneity of the two consumer segments play an important role in determining the firms’ equilibrium targeting and pricing decisions. We find that the taxi's inconvenience cost can have non‐monotonic effects on firms’ prices. An increase in the taxi's inconvenience cost can reduce both firms’ profits because it can induce both firms to lower their prices. In an extension, we show that distance‐based price discrimination (charging different unit prices based on the consumer's travel distance) can lead to win–win or lose–lose outcomes for both firms. Our results have useful managerial and regulatory implications.

WIN THE COMPETITION BY LISTENING TO CUSTOMER VOICES: AN ANALYSIS OF RIDESHARING BUSINESS IN SURAKARTA

Ridesharing has attracted worldwide attention due to its contribution to the environment, notably in reducing the number of privately owned vehicles, alleviating traffic congestion, and mitigating air pollution arising from private transportation. Currently, Indonesia’s ridesharing platform is a prominent player within the realm of popular e-commerce businesses. The use of technology has increased public interest in adopting ridesharing services. The goal of this study is to assess the competitive landscape among ridesharing companies operating in Surakarta, Indonesia. The participants are customers with experience with three ridesharing platforms: Grab, Go-Jek, and Maxim. This study implemented the Analytic Hierarchy Process to evaluate the competitiveness of ridesharing platforms. The benchmarks for platform selection include service quality, price, and promotional activities. Within the service quality domain, Parasuraman proposed five dimensions to serve as sub-criteria. The price criteria include three sub-criteria: low price, discounts, and affordability. Meanwhile, the promotional criteria encompass advertisement, vouchers, and digital influence as the sub-criteria. This study’s findings reveal a preference hierarchy among ridesharing alternatives from the customer’s perspective as follows: Grab (0.402), Go-Jek (0.365), and Maxim (0.226). Grab ranks highest in eight sub-criteria, whereas Go-Jek ranks highest in three sub-criteria. Conversely, Maxim consistently occupies the lowest rank. Notably, service quality emerges as the foremost priority, closely followed by price and promotion. Within the service quality criteria, assurance takes precedence, affordability leads the price criteria, and vouchers have the highest priority among promotional criteria.

Antecedents of Intention to Use Ride-Sharing Platform

The Ride-Sharing Platform (RSP) implementation in Indonesia has been emerging recently. The use of this platform is increasing significantly year by year. This study aimed to identify the factors influencing the attitudes and intentions of RSP users in Indonesia. This research discussed the role of customers in service innovation and the relationship between Perceived Usefulness, Perceived Ease-of-Use, Word-of-Mouth (WOM), and Intention to Use RSP from the perspective of TAM and TPB. This research used 208 data originating from 219 samples acquired through questionnaire distribution. The obtained data were then analyzed using the SmartPLS Program. This research shows that Perceived Usefulness, Perceived Ease-of-Use, and word of mouth positively affect the Intention to Use RSP mediated by Customer Attitude toward Service Innovation. This research could contribute to the RSP industry and those who want to join RSP as one of the service providers. This study contributes considerably to the breadth and rigor of the RSP literature from the perspectives of TAM and TPB.JEL Classification: M31, M37, L92

When Sharing Economy Meets Traditional Business: Coopetition Between Ride-Sharing Platforms and Car-Rental Firms

Coopetition has been a common practice, especially among emerging markets. The coopetition relationship between a ride-sharing platform and a car-rental firm is distinct in that they operate under two different business models. Although the platform controls both its demand and supply by setting rider prices and driver wages, the car-rental firm operates under the traditional model with a fixed supply and cost structure. Both the platform and car-rental firm compete for riders seeking transportation. If the two cooperate, a driver is allowed to rent from the rental firm and drive for the platform; otherwise, only those owning personal vehicles are allowed to drive for the platform. We show that such supply-side cooperation intensifies demand-side price competition and decreases total revenue. Therefore, coopetition is mutually beneficial only when it leads to a significant decrease in supply costs. We find that the two firms are likely to form a coopetition relationship when the total rider market size is not high, the degree of rider substitutability between the two firms is low, and the platform has a significant market-size advantage over the rental firm. Coopetition between the platform and the rental firm benefits riders and hurts drivers, but it benefits society overall.

Algorithm Aversion: Evidence from Ridesharing Drivers

The low rate of adoption by human users often hinders AI algorithms from achieving their intended efficiency gains. This is particularly true for algorithms that prioritize system-wide objectives because they can create misalignment of incentives and cause confusion among potential users. We provide one of the first large-scale field studies on algorithm aversion by leveraging an algorithmic recommendation rollout on a large ridesharing platform. We identify contextual experience and herding as two important factors that explain ridesharing drivers’ aversion to an algorithm that is designed to help drivers make better location choices. Specifically, we find that drivers are less likely to follow the algorithm when the algorithmic recommendation does not align with their past experience at a given location-time unit and when their peers’ actions contradict the algorithmic recommendations. We discuss the managerial implications of these findings. This paper was accepted by Catherine Tucker, Special Issue on The Human-Algorithm Connection. Funding: The research at Shanghai Jiaotong University was supported by the National Natural Science Foundation of China [Grants 72202135, 72110107001, 72231003]. S. Zhang acknowledges the support of Shanghai Pujiang Program [Grant 21PJC070], and Special Fund for Creative Research Groups [Grant 72221001]. Y. Zhu acknowledges the support of National University of Singapore [Grant WBS A-8000489-00-00]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.02475 .

Characteristics of BlaBlaCar as one of the world’s ridesharing leaders

The article addresses one of the relevant global trends – the sharing economy or participation economy. The authors explore its features and benefits (time and resources saving, rational consumption, use of communication technologies etc.). The publication considers the driving forces, namely peering social networks, real-time technologies, the global economic crisis, environmental issues and a renewed belief in the importance of communities. The authors argue that BlaBlaCar is one of the world leading online ridesharing platforms. The article defines the essence of BlaBlaCar as a new model of shared consumption tracing the history of its inception and development. The authors examine characteristics and peculiarities of BlaBlaCar in terms of a modern information network; reveal the behavioral traits of BlaBlaCar users worldwide and in Ukraine. The article attempts to anticipate the prospects of BlaBlaCar development.

Impact of ridesharing platforms on hospitals' emergency department admissions

This paper examines the impact of information technology-based ridesharing platforms on hospitals' emergency department (ED) admissions. Employing a difference-in-differences design, we analyze patient-level data for 42 million ED visits in Florida. We find that the availability of ridesharing positively affects ED arrivals, indicating enhanced accessibility. Our analysis of the heterogeneous impact of ridesharing suggests the most significant effect applies to young, middle-aged, low-income, and non-critical patients. Additionally, we find a significant increase in ED service time for patients with less critical conditions and no change in visit length for critical patients. The rise in ED demand due to ridesharing entry can have two distinct implications. On the one hand, ridesharing entry addresses the access barrier when the patient's condition is non-critical, but ED usage is necessary. This is a positive impact of ridesharing services availability, especially since it helps low-income communities the most. Therefore, hospitals and ridesharing companies can collaborate to address the healthcare access challenge. On the other hand, ridesharing entry may exacerbate already overcrowded EDs by facilitating non-critical and unnecessary ED usage, which can be an adverse effect. In this case, effective patient triage to identify those with urgent needs may become even more essential after ridesharing entry.

Renewable-based charging in green ride-sharing

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users’ willingness to ride-share affects the charging strategy and the quality of service.

Nurturing resource availability of ridesharing platforms by priming goal-oriented operational effectiveness

Ridesharing platforms test incentive schemes with variable commissions where drivers are compensated proportionate to their inputs. The operational effectiveness implications of these incentives, however, remain largely unexplored. We test the differential effects of operational effectiveness parameters of drivers’ service quality, service failure, and monetary value on drivers’ referral and churn behaviors. At a process level, we demonstrate the beneficial effects of goal-oriented (versus informational) feedback on drivers’ monetary value and service performance outcomes. Further, we investigate the moderating role of drivers’ organizational justice perceptions on these relationships. Using exogenous data from a cluster-randomized field experiment with 1,698 ridesharing drivers across 8 cities, we show that goal-oriented feedback improves operational effectiveness parameters. Additionally, these parameters improve the platform’s resource availability with drivers’ higher referrals and lower churn. Comprehensively, platforms can derive benefits in operational effectiveness and resource availability by priming drivers using goal-oriented feedback in tandem with a variable commission.

Price competition in ride-sharing platforms: A duopoly supply chain perspective

A ride-sharing platform with a price-setting power mechanism exists in practice. However, it is unclear how supply chain transactions (ride price) and yields are affected due to a commission fee on ride-sharing platforms. To better understand, this study explores the impact of the service provider’s price-setting (Model S) and service enabler’s price-setting (Model E) power mechanism on supply chain profit under the influence of price competition in a sharing economy. We consider duopoly supply chains, wherein in one supply chain, a service provider delivers service through a service enabler, and in another, service providers offer direct customer service. Mathematical models are developed to analyze the difference in price-setting power mechanism effect, considering marketing efforts under different competitive scenarios. Results show that the supply chain profit is highest when the service provider sets the transaction price in Model S. Furthermore, when the commission fee exceeds a certain threshold (0.5), the total profit in Model E increases, but in Model S it decreases. In ride-sharing platforms, the service provider loses with a rise in commission fees, while the service enabler profits from it. The study contributes to the growing interest in the intricacies of sharing economy businesses.