Ride-hailing Companies Research Articles

Driver Safety Protocols, Emergency Response Systems, Insurance Coverage, and Customer Feedback on Service Quality in Indonesia's ride-hailing industry

This study examines the impact of driver safety protocols, emergency response systems, insurance coverage, and customer feedback on service quality in Indonesia's ride-hailing industry. Utilizing a mixed-methods approach, quantitative data were collected from 400 ride-hailing users through a structured survey, while qualitative insights were gathered from 20 semi-structured interviews with drivers, customers, and company representatives. The findings indicate that emergency response systems have the most significant positive effect on service quality, followed by customer feedback and driver safety protocols. Although insurance coverage is also important, it demonstrated a comparatively lower impact. The study highlights the need for ride-hailing companies to enhance safety measures, improve communication regarding insurance, and develop robust customer feedback mechanisms to foster trust and satisfaction. These insights provide actionable recommendations for industry stakeholders and contribute to the theoretical understanding of service quality determinants in the ride-hailing context.

Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Selecting optimal locations for customized bus stops is crucial for enhancing the adoption rate of customized bus services, reducing operational costs, and, consequently, mitigating traffic congestion. This study leverages ride-hailing data to analyze the distance between passengers and bus stops, as well as the operational costs associated with establishing these stops, to construct a customized bus stop location model. To address the limited local search capability of conventional immune algorithms, we propose a Dual Population Adaptive Immunity Algorithm (DPAIA) to solve the bus stop location problem. Finally, we conduct simulation experiments using passenger travel data from a ride-hailing company in Chengdu to evaluate the proposed customized bus stop location model. Through simulations with Chengdu ride-hailing data, the DPAIA algorithm minimized the weighted cost to CNY 28.95 ten thousand, outperforming all counterparts. Although proposing 9–11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban congestion while boosting operator profits.

Strategies to Enhance Drivers Benefits and Establish an Intra-Corporate Social Business in Leading on-Demand Company (Ride Hailing)

Driven by social mission and mandate of government towards Sustainable Development Goals (SDG), one of the leading Indonesia ride-hailing company (INDOJEK) has launched and run a drivers’ welafe program named ProDrivers. The program is supposed to reducing drivers’ expenses. In this research, author attempts to crack the dilemma, specifically the ProDrivers program, to not only creating social impact, but also generating a sustainable social business that can provide unlimited resource to sustain INDOJEK social initiative to drivers. Author has used mixed-method in deploying this research, both qualitative and quantitative. Data collection was conducted to both drivers (as ProDrivers beneficiaries) and internal ProDrivers team (as strategist and executor of ProDrivers program), through survey, interview and focus group discussion (FGD). Using theories, such as 9 core elements of marketing and marketing mix (7Ps) which provide insight on how to improve ProDrivers program adoption, and internal marketing to understand to which extent ProDrivers program has delivered to drivers. Aside of it, author also uses some tools, such as New Wave of Marketing to facilitate marketing improvement. Revenue Model to find alternatives which ProDrivers can earn income and also Social Business Model Canvas to discover new business model

The influence of the ride hailing apps loyalty program on customer loyalty: A case study in Indonesia

Gojek is one of the most popular ride-hailing companies in Indonesia. It has recently started innovations in developing its loyalty program; therefore, analyzing its impact on customer satisfaction and loyalty is an area that has the scope and need for research. This study aims to determine whether Gojek’s loyalty program, GoClub, influences the loyalty of its customers. For this study, online questionnaires were used with 555 respondents. The data were then processed using partial least squares structural equation modeling. Interviews with 20 respondents were also conducted to understand in detail the reasons for the rejection of the hypotheses. The results of the data analysis showed that the customers’ perceived quality of information, service, system, and value of the Gojek app significantly affected their level of satisfaction in using the GoClub loyalty program. This study also found that customers’ attitudes, subjective norms, and perceived behavioral control also influenced their loyalty to Gojek app. Furthermore, no relation was found between customers’ satisfaction in using the GoClub loyalty program and their loyalty to the Gojek application where millennial loyalty on certain app in Indonesia is very low. The findings of this study can help ride-hailing companies understand the factors that increase customers’ loyalty to such companies.

Taking development for a ride: the World Bank’s research with ride-hailing companies

The World Bank’s International Finance Corporation has produced three research reports on gender equality and ride-hailing, in collaboration with ride-hailing companies. This article examines these reports in light of wider conversations about the growing corporate co-production of development knowledge. Focusing on research claims about the gender and development potential of ‘sharing economy’ firms like Uber, it argues that the shortage of women drivers has been successfully framed as a gender and development problem, rather than a labour supply problem to be resolved by ride-hailing companies themselves. Multi-stakeholder solutions are proposed, involving licensing actors, charities, government development agencies, banks, and insurance providers. Gender equality research contributes to a new development common sense involving expanding the numbers of indebted ‘independent contractors’ in the gig economy; reducing ‘regulatory burdens’ on platforms; and binding nongovernmental organizations (NGOs) ever tighter to corporate interests. This article shows the centrality of gender research to the Bank’s broader reorientation towards business-led development, and highlights some distinctive lessons of work on ride-hailing.

How do fares affect the utilization of ride-hailing services: Evidence from Uber Japan's experiments

Ride-hailing has been introduced and has become popular in many major cities worldwide. The service often relies on the use of dynamic pricing, in which fares are adjusted in real time. Therefore, understanding the impact of fare on demand is necessary for the operation of ride-hailing. The aim of this study is to empirically investigate the impact of fares on demand through price elasticities using the session data of Uber taxis from Uber Japan's experiments in two cities: Nagoya and Kyoto. A mixed logit model with a flexible mixing distribution was estimated to capture the taste heterogeneity among riders, which increased the reliability of the result. The estimation results indicated that most riders were price inelastic, with an average price elasticity of approximately −0.2 to −0.1. These findings are useful for ride-hailing companies and policymakers because they provide valuable information to enable the maximization of profit or benefit to customers.

Forward Chaining Method Implementation for AI-Powered Passenger Ojek Online and Drive Solutions

This research explores the application of artificial intelligence (AI)-based technology in the Grab application, an online passenger transportation service company in Southeast Asia. The main aim of this research is to improve service quality, safety and efficiency for Grab passengers and drivers, while solving traffic and location problems. The use of AI in this application opens up opportunities to optimize passenger pick-up and drop-off processes, improve navigation, and improve real-time traffic management. Special emphasis is placed on the application of the Forward Chaining method, an artificial intelligence technique that allows systems to make decisions based on predetermined rules. By implementing this method, Grab can predict passenger movements and arrange the best routes automatically, increasing time efficiency and reducing congestion. The implications of this research are very significant in the context of improving the quality of online transportation services, by providing a better experience for passengers and ensuring safety on every trip. The main contribution of this research is the development of an efficient and reliable AI system, as well as the application of Forward Chaining in the context of the Grab application. Additionally, this research provides an in-depth understanding of how AI technology can overcome traffic and location challenges, creating effective and reliable solutions for ride-hailing companies in the future. The results of this research provide a valuable contribution to the development of transportation technology, providing a basis for similar companies to explore the potential of applying artificial intelligence in their services. Thus, this research paves the way to greater innovation in the field of online transportation, creating a more efficient, safe and comfortable environment for users, and creating significant added value for the industry.

Transport Assessment Using Bayesian Method to Determine Ride-Hailing in Kula Lumpur: A Case Study

This research was designed to investigate the factors affecting the frequency of use of ride-hailing in a fast-growing metropolitan region in Southeast Asia, Kuala Lumpur. An intercept survey was used to conduct this study in three potential locations that were acknowledged by one of the most famous ride-hailing companies in Kuala Lumpur. This study used non-parametric and machine learning techniques to analyze the data, including the Pearson chi-square test and Bayesian Network. From 38 statements (input variables), the Pearson chi-square test identified 14 variables as the most important. These variables were used as predictors in developing a BN model that predicts the probability of weekly usage frequency of ride-hailing. According to the final model, the attitude of the commuters towards the speed of ride-hailing over hailing regular taxis was the most important and presented in all probability conditions. Several related studies also identified ride-hailing speed as one of the top reasons for using this travel option. The findings of this study imply that commuters still compare the ride-hailing services with the traditional taxis in Kuala Lumpur, especially in terms of complementarity to other modes, ease of payment, ease of access, and speed. It is critical to have a sustainable strategy for keeping commuters’ satisfaction at the highest level because if the ride-hailing services cannot meet the commuters’ expectations, they may switch back to conventional transport options.

SISTEM INFORMASI CUSTOMER RELATIONSHIP MANAGEMENT PADA RESTORAN DI KOTA MATARAM

Internet technology has a significant impact, especially for online restaurant businesses that require a restaurant management information system, known as CRM (Customer Relationship Management) system. The case study used in developing the CRM system for Valia's Kitchen. Valia's Kitchen is an online restaurant business that has been operating in the culinary industry since 2019, located in the city of Mataram. Valia's Kitchen has partnered with a ride-hailing company and provides its own application. However, there are limitations and shortcomings in the operational management provided, such as the inability to display customer data such as names and contact numbers due to privacy and security concerns, the inability to back up all order data, lack of inventory management for products and raw materials, and the absence of a cash flow record for expenditure and income over a specific period, leading to limitations in operational management features. To address these issues, the researchers designed a CRM Information System for Restaurants in the city of Mataram to improve operational efficiency and facilitate transactions with customers. The system is web-based, built using PHP programming language with a MySQL database, following the Personal Extreme Programming (PXP) development methodology, and tested using black box testing approach. The implementation of CRM in the system is manifested in features such as customer data management, menu management, raw material management, promotions management, partner management, raw material purchasing, restaurant expenses management, cash flow recording, transaction reports, restaurant statistics, and detailed reviews. The Black Box Testing results showed that the system features were functioning well without any errors. Furthermore, the User Acceptance Testing (UAT) results indicated that the average assessment scores for three admin respondents were 82%, while the average assessment scores for eleven respondents on the customer side were 86%.

The Profitability Puzzle of Digital Labor Platforms

By examining digital labor platforms that connect customers with gig workers, we attempt to solve the puzzle concerning why these labor platforms, such as ride-hailing companies and food delivery companies, continue to suffer weak profitability even though they promise a more efficient way to mobilize and utilize the platform’s labor force. Stylized facts on the profitability of the top ten global labor platforms and a descriptive analysis of their cost structures are presented. We further argue that the modes of production and competition inherent in digital labor platforms are critical to this profitability puzzle because they determine the upper limit of surplus value extraction in production and the heavy financial burden of profit generation for labor platforms. JEL Classification: P12, E24, D40

Surge pricing and consumer surplus in the ride-hailing market: Evidence from China

Surge pricing leads to efficient operations of ride-hailing services, but its implications on consumer welfare are difficult to estimate owing to endogeneity biases resulting from the simultaneity between passenger demand and surge pricing. We address this issue and present a comprehensive study on consumer surplus estimation in the ride-hailing market using multi-day data of around two hundred thousand daily ride-hailing trips in Nanjing, China. We estimate a relationship between surge level and demand to obtain the elasticity of passenger demand relative to the surge level. Potential endogeneity issues are addressed by applying the dynamic panel generalized method of moments estimation. The price elasticity estimates are then used to compute the consumer surplus. Our results provide three important insights. First, the magnitude of the surge elasticity of demand during peak hours is the lowest compared to other periods. Second, the magnitude of elasticity is higher in urban areas during the off-peak than in suburban areas. Third, the consumer surplus per kilometer is lower in urban areas due to longer trip lengths at higher surge levels than in suburban areas. The consumer surplus generated by a ride-hailing company per yuan (Y) ranges between Y0.47 and Y0.89. Our findings are relevant for both ride-hailing companies and policymakers. While ride-hailing companies can decide areas, time of the day, and the extent of surge pricing based on the spatiotemporal heterogeneity in price elasticity, government agencies can use consumer surplus as a yardstick to devise pricing regulations.

Sustainable development goals and quality practices: a winning combination for customer loyalty in ride-hailing companies

ABSTRACT Sustainable development goals (SDGs) are both a guide for growing more sustainably and a useful tool for engaging in corporate social responsibility (CSR) that can boost sustainable economic models. Hence, in this paper, we shed light on how companies’ application of SDG practices affects perceived quality (PQ) and consumer loyalty based on the resource-advantage theory of competition (RAT). Exploratory analysis was used to create a scale of SDG practices, which was confirmed through confirmatory analysis. Structural equation modeling was used to analyze the mediation between the constructs. Our findings indicate that PQ mediates the relationship between SDG practices and loyalty, confirming a previous result on CSR. Hence, PQ retains paramount importance; only through PQ can SDG practices affect loyalty. We also propose the theoretical foundations for conceptualizing SDG practices among ride-hailing companies and empirically demonstrate the mediation effect of PQ.

An integrated ride-matching and vehicle-rebalancing model for shared mobility on-demand services

Shared mobility on demand (MoD) services are receiving increased attention as many high-volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to benefit from these developments as AVs enable a centrally operated and fully connected fleet. There are two fundamental tasks for a shared MoD service: ride-matching and vehicle-rebalancing. Traditionally, these two tasks are optimized sequentially and independently. The paper formulates an integrated ride-matching and vehicle-rebalancing problem for shared MoD services which simultaneously optimizes these two tasks. We propose a graph-based methodology to solve the integrated ride-matching and vehicle-rebalancing problem with a novel rebalancing cost term quantifying supply contributions of vehicle scheduling to zonal supply deficit balances (deviations from the desired supply level) in the network. The integrated model performance is validated using a large-scale empirical shared MoD dataset by comparing with state-of-the-art sequential models. Generally, the integrated model improves the level of service and sustainability performance compared to the sequential model. The detailed analysis shows that the vehicle rebalancing in the integrated model is replaced by a more effective ride-matching and penalizing singly served trips in the integrated model can further improve its sustainability performance.

Understanding Ride-Hailing Sharing and Matching in Chicago Using Travel Time, Cost, and Choice Models

Ride-hailing data is sparingly available throughout the U.S., which limits researchers’ understanding of the mode. Chicago is one of a few cities that have mandated ride-hailing companies to submit detailed trip data to their local transportation agency. The dataset is one of the few to contain trip-level attributes such as fare, travel time, and trip length. Most research using the Chicago dataset has focused on understanding why people use ride-hailing. This study focuses on why ride-hailing passengers choose shared over private trips and what influences the shared trips to be matched. Trips to/from airports are less likely to be shared. Trips to/from low-income areas are more likely to be shared. Longer shared trips are more likely to be matched, shared trips to/from dense areas are more likely to be matched, and shared trips between areas with a high number of shared trips are more likely to be matched. Matching an additional shared trip with another adds approximately 4 min to a trip. Ride-hailing users’ value of time is found to be $48.23 per hour. Understanding travel behavior is important for all modes of transportation including ride-hailing. The results of this paper can be applied to guide polices aiming to promote more sustainable transportation modes.

Intuitionistic Fuzzy AHP and WASPAS to Assess Service Quality in Online Transportation

Indonesia is currently entering a new normal era; this requires people to adapt to the clean-living habit in accordance with health standards in order to carry out normal activities. At the same time, online transportation services have reopened for activity. The service quality provided by online ride-hailing companies (i.e., ojek) such as Gojek, Grab, and Maxim must now consider matters relating to user safety. This study proposes Multi Criteria Decision Making (MCDM) as a method for assessing the service quality of online transportation service providers and uses the Pandemic-SERVQUAL 4.0 model. Pandemi-SERVQUAL 4.0 model adds two new criteria, namely "pandemic" and "industry 4.0". The addition of two new criteria that are more relevant to the current circumstances will increase the accuracy of the research. This study aims to propose the integration of Interval Valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIF-AHP) to determine the criteria weight and Interval Valued Intuitionistic Fuzzy Weighted Aggregated Sum-Product Assessment (IVIF-WASPAS) to assess the service quality of several online transportation service providers based on the obtained criteria weights. From the results of the service quality assessment using the integration of IVIF-AHP and IVIF-WASPAS, the ranking of online transportation service providers during the new normal era were Grab-car, Go-car, and Maxim-car.

Rightful resistance: How do digital platforms achieve policy change?

Many digital platforms have disrupted not only the market but also public policies. Platform companies have been shown to employ various political strategies such as lobbying and petitions to change restrictive policies. However, previous literature has failed to consider the unique nature of digital platforms, which involve actors across multiple sides of the platform, and its influence on platforms companies’ political tactics. This paper aims to address how the multi-sided nature of digital platforms impacts the way they achieve policy change. This paper conducts a case study of how Chinese ride-hailing companies facilitated the enactment of a new, favourable national policy in 2016. Drawing on the literature of contentious politics, it finds that policy change can be achieved via rightful resistance, through which digital platforms leverage divisions within the multi-sided digital platform and within the government to push the frontiers of what is politically permitted. In the context of digital platforms, rightful resistance means (1) seeking support from powerful third parties, (2) using the rhetoric of the central policies, and (3) co-opting the incumbents.

Do ride-hailing drivers' psychological behaviors influence operational performance?

PurposeThe proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.Design/methodology/approachThe authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.FindingsRide-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.Practical implicationsThe results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.Originality/valueThis study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.

Conceptualizing Halal Product in the Context of Culinary in the Digital Era: E-Commerce and Ride-Hailing Based

This study aims to analyze halal culinary development in the digital Era based on e-commerce and ride-hailing orders. The conceptualization related to the development process needs to be explored. The method used in this research is a critical assessment of the literature review. By analyzing the patterns that have been done by the SMEs, a proposed framework could be conceptualized. By using e-commerce and ride-hailing appropriately in this digital era, it is able to increase the development of the halal culinary business where the majority of Indonesian citizens are Muslim and realize that consuming halal food is an obligation, not a habit. Thus, the proposed model expectedly will give impact on three sectors i.e., 1) SMEs could take advantage of the increasing demand for halal food products, lifestyle, and the advancement of digitalization, and expand the business potential; 2) the e-commerce and ride-hailing company to implement halal food products assurance by displaying some additional requirements; 3) the consumers, need to be aware when ordering food and beverages in e-commerce and ride-hailing. Keywords: Digitalization, Halal Food, E-Commerce, Ride-Hailing, Indonesia

Safety Warning! Decentralised and Automated Incentives for Disqualified Drivers Auditing in Ride-Hailing Services

Since 2011, the private ride-hailing companies Didi (2019), Uber (2019) and Lyft (2021) have expanded into more and more cities. These ride-hailing services (RHS) bring convenience to our life; however, at the same time they, also raise security concerns for users. For example, several recent news items show that a considerable number of registered drivers whose licenses have been revoked are still taking RHS orders on the respective platforms; this phenomenon directly leads to insecurity on part of its users and the bad reputation of the ride-hailing service provider (SP). The traditional solution to solve this problem is to periodically check the validity of the drivers’ licenses; however, it is a considerably time-consuming and costly process since the SPs have to manually interact with the governing authorities. Therefore, in this paper, we have presented an auditable self-sovereign identity system (named AudiSSI), which provides an efficient approach for the SPs to manage their registered drivers’ qualifications in a decentralized and automatic manner. Further, using smart contract technology, we propose a safety guarantee insurance in the form of an auditing contract to enable the RHS rider to check their driver's qualifications before the trip starts and get incentives once they detect a disqualified driver. We designed an incentive mechanism and have provided a game theoretical analysis. Finally, we implemented a prototype of AudiSSI and deployed it on Hyperledger Indy and Fabric to show that self-sovereign identity system for RHS driver with qualification auditing is efficient and technically feasible.

The gender productivity gap in the ride-hailing market

The gender productivity gap in the ride-hailing market has not been well studied due to the difficulty in obtaining driver demographics and trajectory data. To address this gap, we use six-day long trajectory data of around 20,000 ride-hailing drivers from China, infer their gender from their names, and estimate gender disparity in productivity (daily earnings or passenger-carrying distance per unit working time) using the panel fixed effect regression models. The results indicate that the gender gap in productivity of ride-hailing drivers does exist, but it disappears if we compare male and female drivers with the same driving behavior (i.e., same driving speed), average trip lengths, and vehicle fuel type. The Blinder-Oaxaca decomposition shows that most productivity gap is driven by average driving speed and average trip length. These results suggest that although the productivity gap exists in the ride-hailing market, the reasons behind the gap are not worrisome for ride-hailing companies. Thus, these companies can encourage the participation of female drivers without compromising on operational performance, which could help sustain exclusive services like matching female riders with only female drivers and increase demand by making a female-centric safe brand.