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Ridesharing platforms have gained a strong foothold as an alternative transportation option to vehicle ownership for consumers while being contested for causing widespread market disruption. They continue to foster business model innovation and unveil new opportunities for delivering goods and services within the broader sharing economy. However, relatively little is known about the comparative value of services provided by the numerous ridesharing platforms available today. We, therefore, analyze three exemplars within the broader sharing economy: Uber®, BlaBlaCar®, and Zimride®. We find that these ridesharing platforms are unique service systems with different designs for facilitating peer-to-peer service interactions, which are reflected in their technology features, affordances, and constraints. Our analysis offers researchers and platform owners new ways to conceptualize and understand these two-sided, digital markets with a range of participants, user goals, and service experiences. In particular, we demonstrate that platforms can be designed to cultivate entrepreneur dependency or enable prosumer communication and collaborative consumption. Given pending legislation to regulate platform-based work, platform owners should be mindful about creating an asymmetrical power imbalance with providers given assumptions about service interactions and technology features. Furthermore, researchers should account for service design differences, as well as the technology affordances and constraints, of platforms.

The increasing number of internet users and the adoption of cloud-based technology have significantly contributed to the growth of ridesharing across the world. Previous research in developed countries reveals that, despite many criticisms, the rise of ridesharing offers extra income to drivers, and provides an environmentally-friendly solution to society. However, there is less research on ridesharing in developing countries. Therefore, this research will study adaptation to ridesharing in developing countries, particularly in India and Thailand, by exploring it from the perspectives of four main interest group – passengers, local businesses, ridesharing drivers and government – as well as ridesharing platform operators. With the research purpose above, the research question is: 'How are India and Thailand adapting to the rise of ridesharing?'. The topic is examined through thematic analysis of empirical data sourced primarily from news articles. A comparative case study is applied as the main research methodology to compare how the two countries are adapting to the rise of ridesharing. The key findings of this research are as follows. From the point of view of passengers, passengers in India are more sceptical of ridesharing services than those in Thailand. Safety concerns are more prominent in India, whereas in Thailand ridesharing services are more highly appreciated due to dissatisfaction with the local taxi services. From the point of view of local businesses, in India some taxi companies have adapted their business strategies to gain market share, whereas in Thailand local taxis in particular have not adapted and protest against the ridesharing services because they consider their income to be stolen in unfair competition and the sector not adequately regulated by the government. Ridesharing drivers in both countries are concerned about the deteriorating benefits and income. In India, the ridesharing drivers have responded by protesting against the ridesharing operators since 2017. In contrast, the drivers in Thailand do not seem to be as concerned with this issue and are viewed by other interest groups as those who benefit the most. While ridesharing is regulated in some parts of India, it is still completely unregulated in Thailand. Both national and local governments in both countries are working on reviewing laws and regulations for the benefit of all interest groups. However, the local taxi operators in Thailand are protesting against the ridesharing legalisation plan. In response to the protests of ridesharing drivers in India and the local taxi operators in Thailand, ridesharing platform operators have apologised to their passengers for the inconvenience caused and have in Thailand advised their drivers on how to avoid conflict with the local taxi operators. However, they have refused any long-term solutions to address the underlying causes of the strikes. In summary, the interest groups have responded differently to the challenges in the two countries. The findings contribute to the ridesharing literature and reveal in particular the efforts of governments to regulate ridesharing. However, there seems to be no single model on how developing countries can best adapt to the rise of ridesharing.

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.

AbstractThis article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.

Research on ridesharing platforms under the gig economy has focused much on the incentives and barriers of users, leaving many gaps in understanding drivers' intention to provide ridesharing services. This paper aims to explore, from the perspective of driver-partners, motives that encourage them to continue being gig workers. Data for the study are based on a cross-sectional survey of ridesharing drivers in three metropolitan areas in three regions (North, Central, and South) of Vietnam, conducted from June to July 2022. The paper regresses behavioral intention to continue being a gig driver on their demographic characteristics and self-estimation of economic benefit, time preference, and enjoyment of being a gig driver via ordered probit models. For all three regions, the result suggests that economic benefit, time preference, and enjoyment are good predictors of drivers' intention to provide the services. Specifically, the probability of remaining in gig work among drivers decreases with their educational and economic status. Higher economic benefit does not predict a higher intention of drivers to stay longer in gig work. Similarly, those with higher levels of enjoyment of traveling and vehicles have a lower intention to remain in this sphere. In the North, the interaction terms between time preference and enjoyment level are significant, suggesting that the effect of enjoyment levels becomes less damaging with an increase in time preference. In other words, time preference is vital in keeping gig drivers in this type of work.

New models of peer governance are emerging from online communities in the Global South. This is visible in an understudied case of ridesharing "platforms" created on social media communities and materializing in Latin American cities. In this article, I investigate these online communities in different cities of Colombia and how they develop peer governance models. A particular focus is paid to developing organization forms that do not follow the typical structure of firms. In these communities, I study the relationships between members, community managers, and the governance rules they create, while illuminating the hierarchies present, the accountability of their administrators, and its legitimacy. The emerging literature on platform cooperativism, platform urbanism, and peer governance is used to structure a way to understand this new phenomenon with its "southern" particularities. Moreover, in-person and online qualitative research methods are incorporated to engage with the elusive nature of these structures. This will be one of the first studies engaging with the peer governance dilemmas emerging from online communities in the Global South. An analysis on what the platform literature and the institutional ecosystem in developing countries can harness from the particularities of these community-platforms as they evolve in these contexts is also included.