Ride-sharing Service Providers Research Articles

Understanding the role of risk perception and health measures in ridesourcing usage in the post-COVID-19 era

The COVID-19 pandemic has had a significant impact on how people perceive health risks when using ridesharing services, as there is an increased risk of transmission. To mitigate this risk, individuals have implemented self-imposed preventive measures, while ridesharing service providers have introduced health measures to reduce the likelihood of transmission and encourage ridership. To understand the impact of COVID-19 on ridesharing usage and willingness to use it, we propose a framework to study the relationship between influencing factors (such as risk perception, vaccination status, and self-imposed or service-imposed non-pharmaceutical health measures like masks and/or sanitizing) and ridesharing-related decisions and intention. A web-based survey in China collected over 2300 responses, and Multiple Indicators and Multiple Causes models were estimated to investigate the influencing factors’ impacts on ridesourcing service usage at the later stage of the pandemic when China was still under Zero-COVID policy and their willingness to use it after the pandemic. The estimation results indicate that risk perception, self-imposed and service-imposed health measures, post-pandemic expectations of these measures are key influencing factors, along with certain sociodemographic and travel behavior factors. These findings can help decision-makers develop strategies to support the recovery of ridesharing services in the post-COVID-19 era.

Ride-sharing customers’ self-protection motivation in the post-COVID-19 world

PurposeThe COVID-19 pandemic has generated unprecedented public fear, impeding both individuals’ social life and the travel industry as a whole. China was one of the first major countries to experience the COVID-19 outbreaks and recovery from the pandemic. The demand for outings is increasing in the post-COVID-19 world, leading to the recovery of the ride-sharing industry. Integrating protection motivation theory and the theory of reasoned action, this study aims to investigate ride-sharing customers’ self-protection motivation to provide anti-pandemic measures and promote the resilience of ride-sharing industry.Design/methodology/approachThis study followed a two-phase mixed-methods design. In the first phase, the authors executed a qualitative study with 30 interviews. In the second phase, the authors used the results of the interviews to inform the design of a survey, with which 272 responses were collected. Both studies were conducted in China.FindingsThe present results indicate that customers’ perceived vulnerability of COVID-19 and perceived COVID protection efficacy (self-efficacy and response efficacy) are positively correlated with their attitude toward self-protection, thus leading to their self-protection motivation during the rides. Moreover, subjective norms and customers’ distrust appear to also impact their self-protection motivation during the ride-sharing service.Originality/valueThe present research provides one of the first in-depth studies, to the best of the authors’ knowledge, on customers’ protection motivation in ride-sharing services in the new normal. The empirical evidence provides important insights for ride-sharing service providers and managers in the post-pandemic world and promote the resilience of ride-sharing industry.

Understanding the Roles of Risk and Trust in the Context of Collaborative Consumption: A Test of Competing Models

Based on a survey of 266 ridesharing consumers in the United States, the study investigates relative impacts of trust and risk on intentions to continue using the service by comparing three alternative models via SEM analysis. We examine specific trust and risk sub-dimensions that are salient in the online-tooffline environment and identify a mechanism of their joint effects on consumer decision-making. Our findings show that risk does not directly affect intentions in this collaborative consumption context but is mediated by consumer trust. The results highlight the importance of trust-building efforts for ridesharing service providers.

Impact of environmental triggers on students' behavior to use ride-sharing services: the moderating role of perceived risk.

Ride-sharing services are a sustainable form of the transportation system that needs to be popularized among students by examining the crucial factors that determine the students’ behavior to use the innovative service. Therefore, this paper attempts to explore students’ behavior regarding the use of ride-sharing services by extending the “Technology Acceptance Model.” The expanded model includes the current TAM structures and integrates contextual stimuli that may or may not affect the ride-sharing service’s behavior. Moreover, the study focuses on determining the moderating role of perceived risk between the proposed relationships. The paper uses PLS-SEM to analyze the research model and determine the results of the hypotheses. The findings of this research are useful for ride-sharing service providers and policymakers who can promote the services among students by reducing the perceived risks and promoting the environmental benefits of ride-sharing. Furthermore, the limitations of this paper pose future research directions.

E-Hailing Service Satisfaction: A Case Study of Students in a Higher Education Institution in Perlis, Malaysia

E-hailing is an online transportation administration recognised as the travellers’ top pick in the transportation industry, including collaboration among clients and service providers. As clients now are more informed and can decide to choose any service provider they prefer, the e-hailing companies are obligated to fulfil and offer better support to their numerous clients, particularly for e-hailing administration. Keeping up consumer loyalty is vital for e-hailing’s specialist organisation to support upper hands. This research aims to investigate the role of safety and security, price, convenience and accessibility and whether they affect e-hailing service satisfaction. This research adopted a quantitative research methodology using a survey approach. Data was collected from 276 e-hailing services customers from higher learning institutions using an online survey. Data were analysed using the Statistical Package of Social Sciences (SPSS), and a few analysis were carried out, such as descriptive analysis, correlations and regression analysis. The results have shown that all factors have somehow affected e-hailing service satisfaction. This result will provide understanding to ridesharing service providers and can be used to improve their services by looking at the most and least influencing customer satisfaction. In the future, researchers could include diverse variables to study the customer satisfaction of e-hailing services in Malaysia.

Passenger satisfaction and loyalty for app-based ride-sharing services: through the tunnel of perceived quality and value for money

PurposeThe purpose of this study is to investigate the passengers' perception of app or application-based ride-sharing service in Bangladesh. The research directly measures the passengers' perception of perceived quality and value for money of using app-based ride-sharing services in Bangladesh and how it impacts passengers' satisfaction. The study also measures the indirect relationship of perceived quality and value for money and passengers' loyalty through the mediating effects of passenger satisfaction in app-based ride-sharing services.Design/methodology/approachThe authors used an online self-administered survey questionnaire to collect data from the respondents who have experienced app-based ride-sharing services in Bangladesh. In this study, 400 questionnaires were distributed to the respondents online (Google form) and received 281 useful responses that give a 70.25% response rate. The survey data were analyzed based on construct validity, convergent validity and structural equation modeling by using Smart PLS 3.FindingsThe research findings indicate that perceived quality and value for money positively and significantly influence passengers' satisfaction. The passengers' satisfaction has a direct and significant relationship with passenger loyalty. The research findings also indicate that perceived quality and value for money have an indirect relationship with passenger loyalty through the mediating effect of passenger loyalty in app-based ride-sharing services.Practical implicationsBoth perceived quality and value for money have been the key drivers of passenger satisfaction and loyalty. Thus, the ride-sharing service providers should emphasize enhancing passenger value perception and quality service to reinforce passenger satisfaction and loyalty by increasing communication with the passengers about their apps. Besides, the service providers need to keep track of passengers' satisfaction levels and adopt necessary initiatives to ensure satisfied passengers toward loyalty.Originality/valueLimited studies have investigated the impacts of perceived quality and value for money on passenger satisfaction and loyalty to app-based rideshare service. It is a suitable time as the research findings will help ride-sharing service providers enhance their quality performance to compete healthily. Simultaneously, passengers can enjoy improved, and value-added services due to increasing competition among the app-based service providers. Regulators can also emphasize passenger quality services and the importance of money as a proposition while formulating policy and regulations toward the management of ride-sharing companies.

Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential

Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing ofcarjourneys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO2 is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers.

RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User’s QoE

Nowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users’ quality is a tangible novel evaluation parameter. Consequently, this study treats the use of quality of experience (QoE) in the ridesharing service context, proposing a recommendation system (RS) for ridesharing services (RsRS), which considers user profile information extracted from online social networks (OSN) and user preferences. Thus, the main objective of the proposed RsRS is to improve users’ QoE. To this end, the users’ profile for the ridesharing service is built based on OSN data, which includes group of users with similar characteristics in the same trip, thus avoiding users with opposite preferences. First, subjective tests are carried out to obtain information on users’ preferences and the results are analyzed via machine learning algorithms to determine the various user profiles. The experimental results demonstrate that the random forest algorithm has the best performance, considering OSN data and explicit preferences saved in the proposed solution and only OSN data, for average F-measures of 0.92 and 0.91, respectively. Additionally, a ranking containing a list of recommended users to share a ride is determined using a similarity function, and the results demonstrate that 94.2% of assessors agree with the proposed recommendations. Furthermore, the RsRS has a modular configuration and its integration with a real ridesharing service providers is also discussed.

The determinants of customer loyalty in the Indonesian ride-sharing services: offline vs online

Purpose This study aims to analyze the effect of service quality on trust, satisfaction and loyalty by adopting two models, namely, conventional service quality model from Parasuraman and information systems (IS) success model from Delone and McLean. Design/methodology/approach Respondents of this study were users of shared-motorcycle services who filled out a complete questionnaire totaling 507. This research used a second-order structural equation model. All question items had quite high reliability and validity based on the result of confirmatory factor analysis with a value of average variance extracted and composite reliability which was higher than 0.70. The goodness of fit was quite good with the values x2/df = 2.493, incremental fit index = 0.921, Tucker-Lewis index = 0.917, comparative fit index = 0.921 and root-mean-square error of approximation = 0.054. Findings Online and offline ride-sharing services reveal a strong and positive influence on trust and satisfaction. Trust reveals a strong and positive influence on satisfaction and loyalty. Finally, satisfaction reveals a strong and positive influence on loyalty. The research in general shows that the quality of offline service is more influential than the quality of online service in the case of ride-sharing service provided by two companies in Indonesia. Research limitations/implications The sampling frame of the research was diverse, including students of various collages and junior high schools, various private company workers and government employees. So, the results cannot be generalized to all populations especially to all Indonesian customers. It is recommended to increase the number of samples by focusing on the community groups of customers of public motorbikes, so that these groups can be compared. Next, the research finds that both service quality based on IS and service quality models reveal a strong and positive influence on loyalty both directly and indirectly. Originality/value The research uses respondents who use motorcycle services both online and offline. The findings of the research are important for online and offline ride-sharing motorbike service providers. They have to maintain their excellent services to the customers.

Ride-sharing service planning based on smartcard data: An exploratory study

This manuscript summarizes/reviews the current trends of ride-sharing services, which can be a supplement to or replacement of conventional transit services. It argues that smartcard data can be utilized to identify low-demand transit routes, where emerging ride-sharing services such as Uber and Lyft can play a role as a supplement to, or replacement of transit services along those routes. It outlines transferrable procedures and methods that can be used to process smartcard data and to define, identify and visualize low-demand transit routes. In an empirical study, it shows that some low-demand transit routes can probably be replaced by Uber at a lower level of overall costs. But the empirical study also indicates that the replacement can be challenging in reality given issues such as unwillingness or difficulties of local transit agencies and ride-sharing service providers in sharing financial information regarding their services, latent demand for private ride-sharing services, social equity and political/legal concerns over the introduction of private ride-sharing services into the transit-service market and usage of public funding for residents/riders to use private ride-sharing services.

Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers

Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.

Factors Affecting Customer Satisfaction of the Ride-sharing Industry in Bangladesh

This paper summarizes the arguments and counterarguments within the statistical analysis on the issue of customer satisfaction with the ride-sharing industry in Bangladesh. The objective of this study is to explore and assess the factors that affect the satisfaction level of ride-sharing users in Bangladesh. Systematization of literary sources and approaches for solving the problem of customer dissatisfaction indicates that there are real problems with the services of the ride-sharing companies. The relevance of the decision of this statistical analysis is that speed, cost, and convenience of ride influence the level of customer satisfaction. As the ride-sharing companies store the personal data while using the application, they were accused of using those data illegally and without users’ consent which goes against the business ethics principles. Investigation of the topic proves that if the ride-sharing companies can reduce the cost of the ride, increase the speed and convenience of the ride and run their business ethically customers will be more satisfied than before. Analysis in the paper is carried out in the following logical sequence: the statistical data available are analyzed, and then a survey is done, which reveals some factors that affect customer satisfaction. Methodological tools of the research methods were analysis and synthesis of the available data, survey, and statistical methods: hypothesis testing through binary logistic regression analysis with Hosmer-Lemeshow test which proves that there are significant relationships between some factors. The object of the research is factors that affect the customer satisfaction of the ride-sharing industry in Bangladesh. The study found speed of ride as the most significantly influential factor (regression coefficient) ([Coef.] =2.707, Odds Ratio [OR] =14.9899, p<0.01) followed by cost of ride (Coef. =2.291, OR=9.8883, p<0.01) and convenience of ride (Coef. =1.969, OR=7.1641, p<0.01). However, the security of ride, waiting time for the car and behavior of drivers were found insignificant. The model explains 78.44% deviance (Coefficient of determination, R2 =0.7844) in the response variable with its constructs. And the “Hosmer-Lemeshow” goodness-of-fit score (1.00) is also above the standard threshold (0.05), which indicates the data fit well with the model. The research empirically confirms and statistically proves that reducing the cost, increasing the speed and convenience of the ride can significantly impact customer satisfaction along with some other factors including maintaining business ethics, the confidentiality of personal data, improving vehicle fitness, service availability, and safety driving. The findings of this study are expected to be helpful for ride-sharing service providers to gain a better understanding of the factors that impact the customer satisfaction of the ride-sharing industry in Bangladesh. Keywords: Bangladesh, Business-ethics, Congestion, Customer-satisfaction, Ride-sharing.

Path-based Dynamic Pricing for Vehicle Allocation in Ridesharing Systems with Fully Compliant Drivers

Abstract Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.

Agent-based approach to analyzing the effects of dynamic ridesharing in a multimodal network

An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid “abuse” of the system by short trips and accelerate the market penetration.

Why do people rideshare? An experimental study

Enabled by mobile technologies and fueled by the economic downturn, ridesharing has emerged in recent years as a private transportation facet of the shared economy. Our study investigates the motives for participation in situated ridesharing. We propose a theoretical model that includes economic benefits, time benefits, transportation anxiety, trust, and reciprocity either as direct antecedents of ridesharing participation intention, or mediated through attitude towards ridesharing. We conduct a scenario-based survey, with 300 participants. Our findings indicate that, in situations where transportation anxiety is high (e.g. construction on the road), if people can trust the ridesharing service providers and participants, in the presence of economic and time benefits, they will have a strong intention to participate in ridesharing.

A New Labour Ecosystem in the Sharing Economy: A Platform for Growth?

The role of the traditional labour market is being challenged by globalisation and modern technology, particularly the unprecedented and generalised use of smartphones. Online platforms facilitating the on-demand economy are radically changing the prospects for the jobs of the future, which will be less regulated and more inclusive. This paper focuses on online ridesharing platforms, particularly Uber as international market leader, and their potential for combating social inequality and developing the transport industry. Online ridesharing platforms assign private drivers to rides booked and paid for by passengers through an app, using their credit card or PayPal account. The platform’s drivers work as freelance business owners or “moonlight” workers to top-up the income from their regular work. Among these ridesharing service providers are amateur riders who were unable to cope with the tight restrictions of a heavily regulated professional passenger transportation sector, high priced licences, inefficient work-life balance policies and lack of safety guarantees. These barriers to the professional passenger transportation services increase unemployment, and they increase the price of personal transportation for consumers by limiting the market. On-demand ridesharing platform services have been heavily criticised for infringements of workers’ rights. Such arguments are based on the traditional employer-employee relationship, which is being reshaped in these Web 2.0 times. Debate rages as to whether the right to statutory working hours and the right to a stable work site (among others) should still be enforced today. Supporters argue that if pension allowances and work injury benefits must still be granted, this may be done if such platforms are given space to thrive. It is argued that the conditions offered by platforms to drivers encourage entrepreneurship across all segments of society and foster the decentralisation of economic growth. On-demand ridesharing platforms adapt drivers’ income to the market, providing for prime time pricing to meet increased demand. By allowing a fully flexible schedule, they promote work-life balance, and by integrating technology they ensure a more efficient provision of services and a safer one too, with passenger registration, money-free rides and GPS locations an integral part of the operation. This paper stresses the potential of on-demand ridesharing platforms to combat social inequality. Drivers who are excluded from traditional systems due to restrictive language requirements, for example, can openly access these platforms in an intuitive way, often with no other requirements than a driving licence and a reliable car. On-demand ridesharing platforms also absorb certain groups that the labour market tends to resist, such as older workers, for whom ridesharing may top-up their pensions, as well as increase social participation and reduce isolation. On-demand ridesharing platforms have the potential to create economic growth, provided the regulatory burden is reduced. Active participation of citizens and stakeholders in the lawmaking process would ensure that these platforms are aligned with the Better Regulation agenda of the Commission and its strategic policies, while also helping to create conditions for such entrepreneurial operations to thrive.