Driving Sustainable Mobility: Adoption and the Willingness to Participate in Electric Ride-Hailing Service Among Driver Groups in Less-Developed Cities

Abstract

The decarbonization of urban transport is critical for achieving sustainable development goals, and the electrification of ride-hailing services offers one promising pathway. However, the acceptance of electric ride-hailing services (ERHS) in less-developed cities lags behind that in developed regions, and existing research lacks a systematic analysis. This study fills the gap by conducting a survey in Zhangzhou, China, and employing ordered and binary logit models to analyze the factors influencing the acceptance of ERHS and the willingness to participate in this sustainable program by drivers and potential drivers. The findings indicate the following: (1) For drivers, environmental awareness is an important driving factor for accepting ERHS. Drivers who worry about the potential health effects of EV radiation are less willing to adopt ERHS. Part-time drivers and those who receive operational subsidies are more likely to adopt ERHS. (2) Among potential drivers, males, individuals aged 36 to 45, and those who are insensitive to fuel price fluctuations show a lower willingness to adopt ERHS. Conversely, the perceived fairness of the commission rates of the platform, driving range, and driving experience significantly promote the acceptance. (3) For potential drivers, the willingness to participate in ERHS is significantly affected by recycling subsidies, education level, and the currently driven vehicle type. The results could provide a policy blueprint for accelerating the green transformation of the ride-hailing industry, and could also provide policymakers with the empirical evidence needed for differentiated intervention measures to promote sustainable and low-carbon urban transportation.

1. Introduction

Under the severe circumstances of ongoing global warming, the excessive carbon dioxide emissions have become a critical issue that needs urgent action. Transportation, as one of the major sources of carbon emissions, is facing unprecedented pressure [1]. In recent years, with the explosive growth of private car ownership in China, the energy consumption of urban transportation has continuously increased. This phenomenon has not only aggravated the greenhouse effect but also triggered a series of issues, such as traffic congestion and air pollution, which pose significant challenges to the sustainable and healthy development of cities [2]. In this context, electric ride-hailing vehicles (ERHVs), with their low carbon emissions, efficient energy utilization, and shared mobility features, are regarded as an important approach to promote the green transformation of urban transportation. However, the effectiveness of promoting electric ride-hailing services (ERHS) heavily depends on the acceptance and occupational decisions (i.e., the willingness to participate in the electric ride-hailing industry) of drivers, especially in less-developed cities, where such decisions are constrained by multiple factors, such as the infrastructure, economic incentives, and policy factors [3].

Although existing studies have discussed the influential factors of passengers’ adoption of electric vehicles (EVs) and the acceptance of ERHS, most of them focused on developed cities (such as Shanghai and Shenzhen). Globally, existing research [4] has reviewed the incentives for promoting EVs, including direct financial subsidies and tax breaks. However, a systematic analysis of the factors that influence the acceptance and occupational decisions of drivers in less-developed cities is still lacking, which hinders the understanding of the electrification trends in the ride-hailing industry in these areas. Compared with developed cities, less-developed cities exhibit significant differences in economic levels, infrastructure, and social environments. Drivers are more sensitive to income, and their risk perception of new technologies and career decision-making mechanisms may be completely different. This means that directly applying the policy experience of developed cities may lead to resource misallocation. Therefore, a thorough understanding of the behavioral patterns and occupational decision-making mechanisms in less-developed cities among drivers and potential drivers (i.e., the group that may join the ride-hailing platform) is key to devising differentiated promotional strategies that align with local conditions.

The objective of this study is to investigate the influence mechanism of multi-dimensional factors in less-developed cities on the acceptance levels of ERHS by drivers and potential drivers, as well as the occupational decisions of potential driver groups. The contributions can be reflected as follows: first, an ordered logit model is established to analyze the factors that influence the acceptance levels of ERHS by both existing drivers and potential drivers, and a binary logit model is adopted to explore potential drivers’ willingness to join the electric ride-hailing industry. Second, we innovatively introduce some factors that had received less attention in the past, including personal attributes (recognition of the importance of new energy vehicles in promoting environmentally friendly travel), policy and surrounding evaluation (desired government subsidies for ERHS, evaluation of local ride-hailing infrastructure, and attitude toward the role of EVs in urban public transport systems), ERHV functional attributes (driving operation sense and concern about the potential health impact of EV radiation), and attitudes toward the ride-hailing market (perception of the fairness of the platform’s commission structure). These factors are particularly important and cannot be ignored when evaluating drivers’ acceptance of ERHVs in the context of less-developed cities. Furthermore, taking the less-developed city of Zhangzhou as a case study, some beneficial policies and recommendations are proposed. The findings could provide the decision-making basis for the formulation of relevant policies and market practices for promoting ERHS in less-developed cities, and can support the achievement of green and sustainable transportation development goals in these regions.

The rest of this study is organized as follows. Section 2 reviews the relevant research regarding the adoption of electric vehicles (EVs) and ERHS, as well as the research on potential drivers’ employment in the ride-hailing industry. Section 3 introduces the data source. Section 4 elaborates on the research methods. Section 5 and Section 6 present the model results and relevant policy recommendations, respectively. Section 7 concludes and summarizes this research.

2. Literature Review

This section reviews the determinants of consumers’ adoption of EVs, the factors that influence the acceptance of ERHS, and the factors that affect the occupational decisions of potential drivers. These analyses will lay the foundation for further exploration of this study.

2.1. Factors Influencing the Adoption of EVs by Consumers

Investigating the factors influencing consumers’ adoption of EVs could accelerate the popularization of EV, support the formulation of relevant policies, and promote the sustainable development of the industry. Sang and Bekhet [5] investigated the factors that influenced the adoption of EVs by Malaysian consumers. They found that the social influence, performance attributes, economic benefits, environmental concerns, demographic characteristics, infrastructure readiness, and government intervention significantly affected consumers’ intention to adopt EVs. The contribution of this research lies in that it has constructed a comprehensive framework for analyzing the influential factors of EV adoption behavior in the context of developing countries. Ko and Hahn [6] used the Bayesian approach and the mixed logit model to analyze the factors that influenced South Korean consumers’ choice of EVs. The results revealed that consumers’ preferences for swappable batteries were stronger than for non-swappable ones, and charging infrastructure availability showed its importance in driving the development of the EV market. The key point of this study is to emphasize the importance of charging convenience for consumers when purchasing EVs. Li et al. [7] found that the factors that influenced the adoption willingness of EVs of potential consumers mainly include demographic characteristics, situational factors, and psychological factors. The results showed that consumers with higher educational levels and greater concern for environmental problems were more likely to accept EVs. The vehicle price and the availability of charging infrastructure were identified as the primary barriers; policy subsidies and the expansion of the charging network could be implemented to lower the entry barriers for potential consumers. Although this review is comprehensive, it also points out the directions where the research on psychological factors needs to be further deepened. Based on the discrete choice experiment data from German consumers, Hackbarth and Madlener [8] applied the mixed logit model to analyze the consumer preferences for alternative fuel vehicles. They discovered that a preference for vehicles with longer driving ranges, lower fuel costs, and reduced emissions was exhibited by consumers. Tax exemptions and free parking promoted the market penetration of EVs. Their research provides an important economic basis for the formulation of incentive policies. In addition to these direct incentives, other studies have highlighted the profound impact of non-financial regulatory policies. Using an advanced agent-based model to simulate the Beijing market, Zhuge et al. [9] found that the license plate lottery policy, which granted easier market access to EV buyers, could be an even more powerful driver for EV adoption than direct financial subsidies. This discovery highlights that in a specific market environment, institutional conveniences may be more effective than monetary incentives. These studies are mainly based on the background of developed economies, where the EV market and supporting infrastructure are more mature. The unique influential factors of less-developed cities have not yet been fully studied. Noppers et al. [10] investigated the joint influence of the instrumental and symbolic attributes of EVs on consumers’ adoption behavior through regression analysis. They concluded that although instrumental attributes such as price and performance, as well as environmental attributes such as eco-friendliness, were important, the influence of symbolic motivation was more predictive in the adoption decision. This article highlights the significant influence of symbolic motivation factors on consumers’ decisions to adopt EVs. However, the literature almost entirely adopts the viewpoints of ordinary consumers or passengers, for whom cars are mainly a means of personal transportation. This overlooks the fundamentally different decision-making calculus of professional drivers, for whom a vehicle is an essential tool for income generation.

2.2. Acceptance of ERHS

Exploring the acceptance of ERHS is crucial for driving the low-carbon transformation of urban shared transportation and optimizing relevant industry policies. Existing research has approached this topic from several key angles. Firstly, studies have leveraged big data and advanced analytical methods to establish the technical and economic viability of EVs for the high-mileage ride-hailing profession.

For example, based on the GPS trajectory data from 144,867 ride-hailing drivers in Beijing, Tu et al. [11] employed a machine learning model to evaluate the extent to which EVs can meet drivers’ daily travel demands. The pioneering nature of this research is that it uses large-scale real-world trajectory data, rather than traditional questionnaires, to quantitatively evaluate the feasibility of electric vehicles in the high-intensity operating scenario of ride-hailing. Similarly, demonstrating the power of this data-driven approach, Guo et al. [12] conducted a comprehensive lifecycle cost assessment using trajectory data from over 169,000 private battery electric vehicles (BEVs) in the megacity of Shanghai, utilizing an advanced Latent Dirichlet Allocation (LDA) model to identify distinct travel patterns. Research has confirmed the cost advantage of electric vehicles, but this does not cover the group of ride-hailing drivers who are profit-driven and have more frequent charging needs. Based on the driving data on the Lyft platform in 2019, Taiebat et al. [13] utilized unsupervised learning algorithms to identify groups of drivers with similar travel patterns. They discovered that 86% of drivers could meet their daily travel needs with a 250-mile-range battery electric vehicle on 95% of their driving days. For high-mileage drivers, the total cost of ownership of an EV was still lower than that of a gasoline vehicle even without subsidies. The results also demonstrate that policy subsidies and information interventions played crucial roles in promoting the adoption of EVs, and that the expansion of charging infrastructure should be prioritized rather than solely focusing on increasing vehicle range. The data from this study can alleviate market concerns about the battery life and cost of ERHVs. Recent research has begun to apply AI to optimize the operational behavior of ERHS fleets. For instance, Chen et al. [14] utilized a deep neural network trained on 2.14 million charging events of Beijing’s electric vehicle ride-hailing fleet to optimize charging strategies, and the results demonstrated how artificial intelligence could address the core operational challenges faced by professional drivers; however, its model and strategy are highly dependent on Beijing’s dense and diverse charging network. Sanguinetti and Kurani [15] used k-means clustering to analyze the survey data of EV drivers on the Uber platform in the United States. Their analysis identified that the primary motivation for drivers to choose EVs was economic benefits, and that drivers were motivated by savings on fuel and maintenance costs, which significantly increased their net income; however, this is also based on a relatively mature market and sound charging facilities. Although existing literature has analyzed ERHS using advanced methods, these studies are still limited to developed cities and may not be suitable for less-developed cities. For instance, Boateng et al. [16] found in Ghana that the introduction of ride-hailing services would lead to an increase in the total number of vehicles on the roads (often older, used cars) and created unstable working conditions for drivers, which indicated that the acceptance of drivers in less-developed cities was worthy of specialized study.

With regard to passengers’ acceptance of ERHS, based on the survey data from 418 households in Kolkata, India, Bhaduri and Goswami [17] revealed that perceived usefulness and the attitude towards riding were the main factors driving the potential users to use ride-hailing services, while social norms had a negative impact on the acceptance of ride-hailing services. This reveals that there are unique social and cultural backgrounds in developing countries. Based on the choice experiment data from an online survey, Sheldon and Dua [18] discovered that most respondents held a positive attitude toward EVs and hybrid cars, but had a relatively low acceptance for carpooling and autonomous vehicles. The main barriers were identified as concerns about the safety and the resistance to sharing rides with strangers. This study quantifies consumers’ willingness to pay for different modes of transportation, which is more in-depth than simple descriptive surveys. However, this demand-side focus represents only half of the equation for a sustainable ERHS ecosystem. The feasibility of ERHS also depends on the supply side, that is, the ability of drivers to provide services and the willingness of potential drivers to join the market to offer services. These factors cannot be captured by passenger-centered studies, and this is precisely the core focus of our study.

2.3. Factors Influencing Potential Drivers’ Occupational Decisions in Ride-Hailing Services

Henao and Marshall [19] utilized an ethnographic approach to study the income of ride-hailing drivers in the Denver area, and they identified that after various expenses were deducted, the net income of most drivers was lower than the state minimum wage level. Potential drivers were drawn to the platform by its flexible working hours and additional sources of income. Their actual earnings were significantly lower than the high-income expectations advertised, and many potential drivers were deterred by the high costs, including fuel and maintenance expenses, as well as the long waiting times for customers, which made them hesitant to engage in the profession. However, their research has not answered a key question: How do multiple factors influence potential drivers to join the ride-hailing market? Qiao et al. [20] applied the structural equation model to analyze the residential characteristics of Chengdu ride-hailing drivers. They concluded that drivers were primarily from low-income communities, and the work flexibility and low entry barriers were regarded as the main factors attracting the potential drivers to this profession. However, this analysis is still confined to the traditional fuel vehicle market. Lefcoe et al. [21] studied the multi-work phenomenon of ride-hailing drivers (drivers who work multiple jobs part-time) through multivariate analysis of variance. The results demonstrated that one of the motivations for potential drivers to choose to engage in ride-hailing service was the flexibility and the solution to financial pressures. Potential drivers might choose to give up joining this industry because of the fear of high stress and unsafe work environments. This study provides a reference for us to understand the career motivations and risk-averse behaviors of potential drivers in the context of North America. Bansal et al. [22] analyzed the decision-making process of ordinary people choosing to become drivers or passengers, or remain non-users, and found that for the majority of daily drivers (65%), the intention to serve transportation network companies was the main factor in their vehicle purchase decisions. Their research further identified the characteristics of drivers who were more inclined to switch to fuel-efficient vehicles, and suggested that vehicle choice and potential operational savings were key components of the occupational decision process. This reveals that for many potential drivers, the decision to purchase a car itself is driven by their willingness to work. However, these studies have almost entirely focused on the traditional non-electrified ride-hailing market. Potential drivers who want to enter the electric ride-hailing market have to weigh more factors, such as the availability and cost of charging, as well as the different subsidy structures in less-developed cities. Our research fills the gap in this area.

2.4. Summary of Existing Research

Although existing studies have examined the factors that influenced consumers’ adoption of EVs, the acceptance of ERHS, and the occupational decisions of potential drivers, these studies primarily focused on developed cities with well-established policy support and infrastructure. In contrast, in less-developed cities, the factors such as inadequate charging facilities and limited economic capabilities of drivers may result in different effects on the acceptance of ERHS. Furthermore, the current literature tends to emphasize macro-level policy effects. Vojković et al. [23] analyzed the impact of Croatia’s 2018 Act on Road Transport on the liberalization of the taxi passenger transport market and found that this top-down regulatory change had injected new vitality into the market. This method can clarify the changes in the macro institutional framework. However, the transformation of the transportation market was also a regulatory and behavioral problem. The existing literature pays less attention to micro-level factors, particularly the economic capabilities or behavioral patterns of individual drivers. As a result, there is a lack of comprehensive explanations for the acceptance behaviors of ERHS and the dynamic mechanisms of potential drivers participating in the electric ride-hailing market in different contexts. This study addresses these gaps by exploring the micro-level factors driving the acceptance of ERHS and occupational decisions of driver groups in less-developed cities, which can provide more detailed theoretical support and policy guidance to promote the widespread adoption and sustainable development of ERHS in these regions.

3. Data

3.1. Study Area

Zhangzhou is located in the south of Fujian Province; its geographical location is shown in Figure 1. By the end of 2023, Zhangzhou’s permanent population was 5.063 million, with 3.255 million urban residents. In 2023, Zhangzhou’s Gross Regional Product (GDP) was CNY 572.843 billion. The number of privately owned cars was 709,300, and there are 23 bus routes. In 2019, Zhangzhou actively responded to the national “Carbon peaking and carbon neutrality goals” and vigorously promoted the application of new energy vehicles. By 2024, all of the more than 1200 newly added or updated taxi (ride-hailing) vehicles in Zhangzhou City had been fully electrified. However, in 2023, despite 8924 individuals registering for the ride-hailing driver qualification examination, the missing exam rate reached 24.92% [24,25,26,27,28]. In this context, this research uses Zhangzhou, a less-developed city, as a case study, to examine the drivers’ acceptance of ERHS and the factors influencing potential drivers to participate in the electric ride-hailing industry (occupational decisions). The research findings could offer significant theoretical and practical implications for promoting ERHS in less-developed urban areas and refining relevant policies. The findings also could offer valuable experience for other similar, less-developed cities that are seeking to achieve green and sustainable urban shared transportation development.

3.2. Survey Design

To explore the acceptance of ERHS by drivers and potential drivers, as well as the potential drivers’ occupational decisions, the dependent variable, acceptance, is measured by five indicators (strongly accept = 5, relatively accept = 4, neutral = 3, relatively reject = 2, strongly reject = 1). The occupational decision (whether the potential drivers are willing to participate in the electric ride-hailing industry) was expressed by two indicators (yes = 1, no = 0). The questionnaire consists of four main parts:

(1) Personal information of respondents: occupation, gender, age, driving experience, education level, and monthly income.

(2) Respondents’ attitudes on the government’s subsidy policy for ERHS, the construction of charging infrastructure, and the role of ERHS in promoting environmentally friendly travel.

(3) Respondents’ attitude towards the performance and functional attributes of ERHVs, including driving range, charging efficiency and time, operation sense of driving, etc.

(4) Respondents’ feelings about fuel price volatility, platform-sharing regulations, and regional policy frameworks, as well as their acceptance of ERHS and their willingness to participate in the electric ride-hailing industry. The research framework of this study can be seen in Figure 2.

3.3. Data Source

The questionnaire survey was conducted from 13 November to 1 December 2024. The survey targeted ride-hailing drivers, taxi drivers, and potential drivers in Zhangzhou City, Fujian province. Through cooperating with the Fujian Provincial Transportation Bureau and other relevant departments, questionnaires were distributed in public places such as government service windows and ride-hailing examination halls. In the process of offline questionnaire distribution, we adopted a random intercept strategy at the distribution location. During the distribution of online questionnaires, we imposed technical restrictions on the geographical location of the respondents to ensure that all samples were from Zhangzhou City. At the same time, through ride-hailing driver communities on social platforms, potential drivers from various industries were recruited to fill in the questionnaire, which effectively expanded the coverage of the survey.

To achieve the effective integration of online and offline questionnaires, the same questions and options were set for the two survey forms to ensure data comparability and consistency. We developed standardized operation procedures: the online survey was accompanied by corresponding filling instructions, while the offline survey was guided by trained investigators. Moreover, we established a unified data management system, and used the same format and coding rules for data entry.

To ensure the standardization of the questionnaire filling process and the accuracy of the data, the investigators were trained before the formal investigation to help them better explain the problems and terms in the questionnaire to the respondents. At the same time, to optimize the questionnaire contents and estimate the filling time, the team first invited 15 driver volunteers to perform a pre-test. The results showed that the average filling time of the questionnaire was 4 min. During the data collection process, team members first provided participants with a detailed explanation of the survey’s objectives and background to ensure that respondents fully understood the questionnaire contents and its completion requirements. After the questionnaire was completed and reviewed by the team, eligible respondents received a reward of CNY 3. It should be noted that questionnaires with incentives may lead to response bias. During the team’s survey, respondents were clearly informed that incentives would only be provided to them after they had completed all the questions and passed the logical verification. This measure can prevent a small number of respondents from simplifying their responses in an attempt to quickly obtain incentives. In total, 724 questionnaires were recovered. To mitigate this risk and ensure data quality, we implemented a rigorous, multi-step data-screening process to identify and exclude invalid responses. First, we checked completion times; questionnaires completed in a time significantly shorter than the 4 min that was established during our pre-test were excluded. Then, we screened out questionnaires that were not filled out carefully, which included identifying straight-lining (where respondents select the same answer for all or most questions in a series) and illogical responses (e.g., the questionnaire set questions with different expressions but the same essence, and the options they chose before and after were different). After data cleaning and screening, 630 valid responses were obtained, accounting for 87.02% of the total sample. The quantity meets the minimum sample size required by statistics and the results are reliable [29,30].

3.4. Respondent’s Basic Attribute Analysis

The statistical analysis of respondents’ basic attributes of the valid samples is shown in

Table 1. Respondent’s attribute statistics.

Variable	Attribute	Drivers (%)	Reference (%)	Potential Drivers (%)	Total (%)
Gender	Male	76.39	80 1	59.60	71.11
	Female	23.61	20	40.40	28.89
Age	18–25	4.63	9.9 2	5.05	4.76
	26–35	62.04	69.3	13.64	46.83
	36–45	23.15	19.8	29.29	25.08
	46+	10.19	1.0	52.02	23.33
Education Level	Junior high school and below	8.80		13.64	10.32
	High school	32.18		23.23	29.37
	Associate degree	35.42		24.24	31.90
	Bachelor’s degree	21.30		35.86	25.87
	Master’s degree and above	2.31		3.03	2.54
Monthly Net Income (RMB)	0–2500	4.63	8.7 3	12.12	6.98
	2500–5000	35.19	91.2	37.37	35.87
	5000–7500	46.30		24.75	39.52
	7500–10,000	13.19		18.69	14.92
	10,000+	0.69	0.1	7.07	2.70
Local Hukou (Residence Status)	No	10.88	8.32 4	17.68	13.02
	Yes	89.12	91.68	82.32	86.98

¹ Global Taxi & Ride-Hailing Figures 2024. ² Traffic Crash Injuries and Disabilities: The Burden on Indian Society. ³ https://upup.li/tools/salary/report/%E6%BC%B3%E5%B7%9E/%E5%8F%B8%E6%9C%BA (accessed on 1 September 2025). ⁴ https://finance.china.com.cn/industry/wl/20160623/3780112.shtml (accessed on 1 September 2025).

. For gender, the proportion of male drivers is 76.39%, while female drivers are represented by only 23.61%. In both the ride-hailing and taxi industries, the dominance is held by male drivers. This is likely to be closely linked to the traditional gender roles and occupational preferences in less-developed cities. In terms of age distribution, more than half of the drivers (62.04%) are between 26 and 35. This indicates that the driver population in less-developed cities is mainly composed of young and middle-aged individuals. In contrast, drivers over the age of 46 account for only 10.19%, while in the potential driver group, this age group is represented by 52.02%. The education level of the interviewed drivers is generally low: 32.18% of drivers hold a high school diploma, 35.42% of drivers have an associate degree, 21.30% of drivers possess a bachelor’s degree, and only 2.31% of drivers have a master’s degree or higher. In contrast, among the potential driver group, 35.86% of drivers have a bachelor’s degree or higher, with a relatively larger proportion of individuals possessing the higher education level. In terms of income levels, only 0.69% of drivers have a monthly income exceeding 10,000 CNY. In contrast, this proportion is 7.07% among potential drivers, which indicates that the income level of potential drivers is generally higher than that of existing drivers. In terms of household registration, only 10.88% of drivers are registered with non-local households. This suggests that non-local residents may face difficulties in working as ride-hailing drivers in less-developed cities, which is likely because there are various restrictions in vehicle registration, operating permits, and other factors. It is worth noting that obtaining official, detailed demographic data for the specific driver population in Zhangzhou is challenging. We have introduced a reference column alongside our driver data. These reference data are drawn from large-scale studies on professional drivers in other comparable regions. The comparison reveals a strong similarity in key demographic structures. This consistency suggests that our sample effectively captures a demographic profile of typical drivers in less-developed urban contexts, thus supporting the validity of this study.

4. Research Methodology

4.1. Ordered Logit Model

The dependent variable in this study is the acceptance level of ERHS by both drivers and potential drivers, which is an ordinal categorical variable. The ordered logit model can handle the ordinal nature of the dependent variable, and accurately analyze the impact of independent variables on different levels of acceptance by estimating cumulative probabilities and using the logit transformation. Furthermore, by employing maximum likelihood estimation to determine the parameters, random errors arising from sample diversity and complexity are effectively controlled, which ensures the stability and reliability of the results. The ordered logit model with J levels of the ordinal dependent variable is expressed as:

$$\ln {\displaystyle \frac{P(Y\le j\left|X\right.)}{1-P(Y\le j\left|X\right.)}}=a_j+{\displaystyle \sum _{k=1}^K{\beta }_k{x}_k}$$

(1)

where $X$ is the set of independent variables; $Y$ is the set of dependent variables; $a_j$ is the intercept of the jth rank, j = 1, 2, …, J; ${\beta }_k$ is the regression coefficient of the kth independent variable; $x_k$ is the kth independent variable, k = 1, 2, …, K; $P(Y\le j\left|X\right.)$ is the cumulative probability, and ${\displaystyle \sum _{j=1}^{J}P(Y\le j\left|X\right.)}=1$.

The probabilistic model for the ordered logit model is:

$$P(Y\le j\left|X\right.)=\exp (a_j+{\displaystyle \sum _{k=1}^K{\beta }_k{x}_k})/\left[1+\exp (a_j+{\displaystyle \sum _{k=1}^K{\beta }_k{x}_k})\right]$$

(2)

4.2. Binary Logit Model

To further investigate the occupational intention of potential drivers, which is defined as two categories (Yes/No), the binary logit model is applied.

The model links the independent variables with the probability of the event occurring through a logit transformation, which avoids the issue encountered in ordinary linear regression where predicted values may fall outside the reasonable range when dealing with such variables, thus ensuring that the results are confined to the 0 to 1 probability interval. During the analysis, parameters are determined using maximum likelihood estimation, which allows random errors arising from sample diversity to be effectively controlled. Multiple independent variables, such as gender and age, are considered to accurately reveal their relationship with occupational decision-making. The basic form of the binary logit model is as follows:

The probability model of the potential driver’s willingness to participate in the electric ride-hailing industry can be expressed as:

$$P(y=1\left|X\right.)=\exp ({\displaystyle \sum _{k=1}^K{\beta }_k{x}_k})/[1+\exp ({\displaystyle \sum _{k=1}^K{\beta }_k{x}_k})]$$

(3)

The probability model of the potential driver who is not willing to participate in the electric ride-hailing industry can be expressed as:

$$\mathrm{P}(y=0\left|X\right.)=1/[1+\exp ({\displaystyle \sum _{k=1}^K{\beta }_k{x}_k})]$$

(4)

where $X$ is the set of independent variables; $Y$ is the 0–1 dependent variable; ${\beta }_k$ is the regression coefficient of the kth independent variable; $x_k$ is the kth independent variable, $k=1,2,\dots ,K$; $P(y=1\left|X\right.)$ is the probability when the event happens; $P(y=0\left|X\right.)$ is the probability when the event does not happen.

4.3. Variable Calibration

The calibration of independent variables can ensure the accuracy and interpretability of the model. It also helps to eliminate the influence of different dimensions and scale differences by standardizing or centralizing the variables, so that the model estimation results are more robust and comparable. In addition, the calibration can also improve the convergence speed of the model and make the coefficient estimation of the independent variable more reliable. The variable calibration in this study is shown in

Table 2. Variables calibration and notes.

Variables	Definition and Notes
Dependent Variable
Acceptance of ERHS	Strongly reject = 1, Relatively reject = 2, Neutral = 3, Relatively accept = 4, Strongly accept = 5
Intention to participate in the electric ride-hailing profession (occupational decisions)	Yes = 1, No = 0
Personal Attributes
Occupation	Full-time ride-hailing driver = 1, Part-time ride-hailing driver = 2, Taxi driver = 3, Other = 4
Gender	Male = 1, Female = 2
Age	18–25 years = 1, 26–35 years = 2, 36–45 years = 3, 46+ years = 4
Driving experience	0–3 years = 1, 4–6 years = 2, 7–9 years = 3, 10+ years = 4
Local residency status	Yes = 1, No = 2
Currently driving an electric vehicle	Yes = 1, No = 2
Previously used a fuel-powered vehicle	Yes = 1, No = 2
Education level	Junior high school or below = 1, High school = 2, Associate degree = 3, Bachelor’s degree = 4, Master’s degree and above = 5
Importance of new energy vehicles in promoting environmentally friendly travel	Strongly oppose = 1, Relatively oppose = 2, Neutral = 3, Relatively agree = 4, Strongly agree = 5
Work intensity	Less than 4 h = 1, 4–8 h = 2, 8–12 h = 3, More than 12 h = 4
Monthly net income (CNY)	<= 2500 = 1, 2501–5000 = 2, 5001–7500 = 3, 7500–10,000 = 4, >10,000 = 5
Policy and Surrounding Evaluation
Desired government subsidies for ERHS	Operating subsidy = 1, Recycling subsidy = 2, Vehicle purchase subsidy = 3, Charging subsidy = 4
Attitude toward the popularization of shared electric bicycles in the city	Strongly oppose = 1, Relatively oppose = 2, Neutral = 3, Relatively agree = 4, Strongly agree = 5
Attitude toward the role of EVs in urban public transport systems	Strongly oppose = 1, Relatively oppose = 2, Neutral = 3, Relatively agree = 4, Strongly agree = 5
Evaluation of local ride-hailing infrastructure (charging stations, repair points)	Very inadequate = 1, Inadequate = 2, Neutral = 3, Relatively adequate = 4, Very adequate = 5
ERHV Functional Attributes
Driving range	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Charging efficiency and duration	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Smart features (Autonomous driving)	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Safety	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Experience and comfort	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Maintenance and repair costs	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Insurance costs	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Driving operation sense	Very dissatisfied = 1, Dissatisfied = 2, Neutral = 3, Satisfied = 4, Very satisfied = 5
Concern about the potential health impact of EV radiation	Very concerned = 1, Relatively concerned = 2, Occasionally concerned = 3, Rarely concerned = 4, Not concerned at all = 5
Attitudes toward the Ride-Hailing Market
Perception of the fairness of the platform’s commission structure	Very unfair = 1, Unfair = 2, Neutral = 3, Relatively fair = 4, Very fair = 5
Impact of fuel price fluctuations on ride-hailing business	Very large = 1, Relatively large = 2, Some impact = 3, Little impact = 4, No impact = 5
Difficulty in obtaining the necessary ride-hailing certification	Very difficult = 1, Relatively difficult = 2, Neutral = 3, Relatively easy = 4, Very easy = 5

.

5. Model Results and Discussion

5.1. Factors Influencing the Acceptance of ERHS

The ordered logit model results for the factors influencing the acceptance of ERHS by driver groups and potential driver groups are presented in

Table 3. Model results for factors influencing the acceptance of ERHS by drivers.

	Estimated Value	Significance
Recognition of the importance of new energy vehicles in promoting environmentally friendly travel	0.575 ***	0.000
Belief that EV has a positive impact on the urban public transport system	0.533 ***	0.000
Support for the popularization of shared electric bicycles in the city	0.235 **	0.025
Satisfaction with intelligent attributes of ERHVs (e.g., autonomous driving)	0.194 *	0.087
Not concerned about the potential health impact of EV radiation	0.358 ***	0.001
Belief that the commission structure of the ride-hailing platform is reasonable	−0.259 **	0.033
Belief that obtaining the necessary ride-hailing certifications is easy	0.281 ***	0.009
Belief that society and government provide adequate support for ride-hailing drivers	0.415 ***	0.001
Occupation = Full-time ride-hailing driver	1.162 **	0.031
Occupation = Part-time ride-hailing driver	1.853 ***	0.001
Occupation = Taxi driver	0 a	-
Desire for the government to provide operational subsidies for ERHS	0.793 **	0.011
Desire for the government to provide charging subsidies for ERHS	0 a	-
Intention to enter the electric ride-hailing profession	0.518 *	0.086
No intention to enter the electric ride-hailing profession	0 a	-

Note: * p < 0.1 (marginally significant), ** p < 0.05 (statistically significant), *** p < 0.01 (statistically significant), ^a denotes that this category of the variable is redundant, therefore it is set to zero.

and

Table 4. Model results for factors influencing the acceptance of ERHS by potential drivers.

	Estimated Value	Significance
Recognition of the importance of new energy vehicles in promoting environmentally friendly travel	0.515 **	0.030
Support for the popularization of shared electric bicycles in the city	0.777 ***	0.002
Satisfaction with the driving range of ERHVs	0.511 *	0.085
Satisfaction with the driving experience of ERHVs	0.546 *	0.072
Belief that the commission structure of the ride-hailing platform is reasonable	0.494 **	0.018
Belief that fuel price fluctuations have no impact on the ride-hailing business	−0.493 ***	0.010
Optimism about the future development of the ride-hailing industry with the popularization of autonomous driving technology	0.471 **	0.035
Gender = Male	−1.286 ***	0.003
Gender = Female	0 a	-
Age = 36–45	−0.835 *	0.065
Age = 46+	0 a	-
Driving experience = 7–9 years	1.076 *	0.096
Driving experience = 10+ years	0 a	-
Currently driving an EV (including hybrid)	−0.965 ***	0.006
Currently not driving an EV (including hybrid)	0 a	-
Intention to enter the electric ride-hailing profession	1.355 ***	0.001
No intention to enter the electric ride-hailing profession	0 a	-

Note: * p < 0.1 (marginally significant), ** p < 0.05 (statistically significant), *** p < 0.01 (statistically significant), ^a denotes that this category of the variable is redundant, therefore it is set to zero.

, respectively. The significance (p-value) determines whether the influence of a factor is real or may be caused only by random contingency. In the logit model results, when the significance value of a variable is less than 0.05, we believe that its impact is statistically significant. This means that we have at least 95% confidence that this factor does have an impact on the dependent variable. When 0.05 ≤ p < 0.1, this variable is marginally significant and will be interpreted as indicating a trend. For the statistically significant independent variables, their coefficient estimates reveal the direction and relative strength of their influence on the dependent variable. In the logit model, when the estimated value of the coefficient is positive, the higher the value, the more likely the dependent variable is to enter a higher level. When the estimated value is negative, the greater the absolute value, the more likely the dependent variable is to fall into a lower level. The significance of the parallel line test was 1.000 (greater than 0.05), which proves the validity of the models. The pseudo-R-square is used to measure the degree to which the independent variables of the model explain the variation of the dependent variable. We examined three of the most commonly used pseudo-R-squares. Cox and Snell values reflect the proportion of improvement in the goodness of fit of our model compared with the zero model, and the higher the value, the better. The values obtained by our two models are 0.703 and 0.512, respectively, which are at a high level. Nagelkerke is the modified R-square of Cox and Snell. The higher the value, the better the fitting effect. The Nagelkerke values obtained by the two models are 0.739 and 0.564, respectively. McFadden generally believes that when its value is between 0.2 and 0.4, it represents a very good fit of the model. The values obtained by our two models are 0.402 and 0.3, respectively. The chi-square test is used to evaluate the statistical significance of the entire model. The chi-square values of the two models are 524.673 and 142.064, respectively; the p-values are far less than the significance level of 0.05, which indicates that the selected variables have a significant impact on the model results at the 95% confidence level. The −2*log-likelihood value reflects the amount of information that the model fails to explain. The −2*log-likelihood values of the two models are 779.434 and 331.159, which are much smaller than their respective zero models (1304.107 and 473.224), indicating that the independent variables we included greatly improve the fitting effect of the model.

As for the model results of the factors influencing the acceptance of ERHS by driver groups, as shown in Table 3, in the dimension of environmental awareness, the variable of the importance of new energy vehicles in promoting green travel has a significantly positive impact on the acceptance of ERHS by drivers (estimated value 0.575, significance 0.000). It is indicated that, with all other factors held constant, for every unit increase in the recognition of this variable, the ordered logarithmic occurrence ratio of the dependent variable with a higher level of acceptance increased by 0.575. Similarly, the variables of drivers’ recognition of the positive role of EVs in urban public transportation systems and their support for the popularization of shared electric bicycles in cities also show significant positive effects. These variables all reflect the environmental awareness of the driver group to some extent, and the results further prove that the improvement of environmental awareness plays an important role in promoting the acceptance of ERHS. This is consistent with the conclusions of broader research on shared mobility. For instance, Tang et al. [31] also found in their study that environmental awareness, as a perceived social return, can significantly enhance users’ willingness to adopt Mobility as a Service (MaaS). This indicates that, whether as passengers or service providers, a sense of responsibility towards the environment is a key psychological driving force for the development of sustainable transportation models.

The variable of the drivers’ satisfaction with intelligent attributes of ERHVs, such as autonomous driving, shows a marginally significant positive impact on the acceptance of ERHS by drivers. This may be because the intelligent function is often regarded as the manifestation of the advanced technology of modern EVs. As Song et al. [32] demonstrated, L2-level autonomous driving on highways can reduce the driver’s operation frequency by 34%. Drivers who are satisfied with these intelligent functions tend to view electric ride-hailing vehicles as a more convenient and comfortable working device to operate, thereby increasing their acceptance.

Regarding health concerns, drivers who are not worried about the potential health impacts of EV radiation are more likely to adopt ERHS (estimated value 0.358, significance 0.001, p < 0.01). This means that for professional drivers who use EVs as their main workplace, the concerns over the health impacts of electromagnetic radiation will significantly reduce their willingness to accept them. Similarly, Materia et al. [33] found that public concern over the possible negative health effects of exposure to electromagnetic waves is a key obstacle hindering the adoption of new technologies. Therefore, in the process of promoting the electrification transformation of transportation vehicles, eliminating the health perception risks of high-intensity practitioners can accelerate the market acceptance.

Surprisingly, the variable of drivers who perceive the commission rules of their platform as reasonable has a significant negative impact on the acceptance of ERHS by drivers (estimated value −0.259, significance 0.033, p < 0.05). This phenomenon may be explained as market inertia based on economic rationality. The research of Zha et al. [34] pointed out that the operational purpose of the platform and its drivers is to maximize the common profit. The commission rules regarded as reasonable imply that the current market has reached a stable balance in this profit-sharing arrangement. In less-developed cities, some drivers have deeply adapted to the operation mode and commission rules of online ride-hailing services. Their reliance on the existing balance and their aversion to the risk of disrupting it make them reluctant to transition to ERHS. Another explanation is that drivers who consider it reasonable are very likely to be those who have successfully mastered the operational model of gasoline-powered ride-hailing. They know about the cheapest gas stations and other knowledge, but a large-scale shift to ERHS will require them to spend time learning again.

At the policy and driver characteristic level, drivers who perceive the acquisition of ride-hailing licenses as relatively easy are more inclined to accept ERHS (estimated value 0.281, significance 0.009, p < 0.01). This finding suggests that in less-developed cities, the simplification of administrative procedures and the reduction in entry barriers may contribute to a higher level of participation in the electric ride-hailing industry, which could improve overall market acceptance. Moreover, sufficient support for retraining and career transitions provided by society and the government has a significant positive impact on the acceptance of ERHS by drivers (estimated value 0.415, significance 0.001, p < 0.01). The importance of government support policies, particularly those related to career transition training, is thus underscored in the development of the electric ride-hailing market.

From the perspective of driver identity, compared with taxi drivers (the reference group), both full-time ride-hailing drivers (estimated value 1.162, significance 0.031, p < 0.05) and part-time ride-hailing drivers (estimated value 1.853, significance 0.001, p < 0.01) exhibit significantly higher acceptance of ERHS, and part-time drivers demonstrate an even stronger preference. This is because part-time ride-hailing drivers, who generally treat this work as a supplementary income source with flexible hours and lower intensity, tend to focus more on immediate financial benefits. As the charging price is lower than that of the fuel, the operating cost per trip for EVs is lower, which aligns with their short-term profit demands. This distinct operational pattern of part-time drivers has been empirically verified. Ma et al. [35] analyzed the distribution of drivers’ working hours, and showed that there are two peaks of less than 5 h and 10–15 h for online car-hailing drivers, while traditional taxi drivers have only one peak of long-term work. At the same time, they point out that the reason why part-time drivers are efficient is that they tend to enter the market at the peak of market demand to supplement the capacity. In contrast, full-time ride-hailing drivers, with longer daily operating hours, place greater emphasis on the stability of long-term income and cost balance. GOH et al. [36] analyzed Malaysian ride-hailing drivers and found that, after considering all operating expenses, the net profit of full-time drivers might be lower than the national minimum wage. Furthermore, the full life cycle costs and potential risks of EVs, such as the loss of operation duration due to the charging, to some extent offset the benefits brought by low electricity prices and undermine full-time drivers’ long-term income and cost balance. Consequently, they tend to be slightly more cautious about adopting ERHS than part-time ride-hailing drivers.

Regarding subsidy policies, compared with the charging subsidies, drivers who receive operational subsidies from the government are more inclined to accept ERHS (estimated value 0.793, significance 0.011, p < 0.05). This is because the effectiveness of charging subsidies is constrained by the insufficient coverage of charging infrastructure in less-developed cities, which limits the potential to significantly reduce operational costs. In contrast, operational subsidies allow these costs to be directly lowered, which effectively compensates for the relatively high initial investment and possibly low expected returns of ERHS. As a result, drivers’ confidence in the long-term economic benefits of ERHS is strengthened. The appeal of this policy is particularly pronounced in less-developed cities. This discovery is consistent with the study by Liu et al. [37], in which operational subsidies were found to have a stronger effect on improving drivers’ willingness to adopt EVs than non-monetary incentives.

Drivers who intend to participate in the electric ride-hailing profession show a marginally significant higher acceptance of ERHS (estimated value 0.518, significance 0.086, p < 0.1). This trend might suggest that after weighing the various benefits and challenges, drivers who decide to enter the industry may psychologically embrace this matter more positively. This also reflects the consistency of behavioral intention and the attitude to a certain extent.

To further quantify the actual effects of each influential factor, we calculated the marginal effect of the ordered logit model. The results (see

Table A1. The marginal effect table of driver acceptance.

	1	2	3	4	5
Recognition of the importance of new energy vehicles in promoting environmentally friendly travel	−0.004	−0.048	−0.087	0.096	0.043
Belief that EV has a positive impact on the urban public transport system	−0.002	−0.02	−0.036	0.039	0.018
Support for the popularization of shared electric bicycles in the city	−0.004	−0.044	−0.081	0.089	0.04
Satisfaction with intelligent attributes of ERHVs (e.g., autonomous driving)	−0.001	−0.016	−0.029	0.032	0.015
Not concerned about the potential health impact of EV radiation	−0.003	−0.03	−0.054	0.06	0.027
Belief that the commission structure of the ride-hailing platform is reasonable	0.002	0.022	0.039	−0.043	−0.02
Belief that obtaining the necessary ride-hailing certifications is easy	−0.002	−0.023	−0.043	0.047	0.021
Belief that society and government provide adequate support for ride-hailing drivers	−0.031	−0.069	0.063	0.035	0.003
Occupation = Full-time ride-hailing driver	−0.018	−0.16	−0.096	0.225	0.048
Occupation = Part-time ride-hailing driver	−0.022	−0.209	−0.201	0.322	0.111
Occupation = Taxi driver (base outcome)
Desire for the government to provide operational subsidies for ERHS	−0.006	−0.068	−0.117	0.131	0.06
Desire for the government to provide charging subsidies for ERHS
Intention to enter the electric ride-hailing profession	−0.004	−0.045	−0.077	0.088	0.038
No intention to enter the electric ride-hailing profession (base outcome)

Note: The columns labeled 1 through 5 correspond to the five levels of the ordinal dependent variable ‘Acceptance of ERHS’, where 1 = Strongly reject, 2 = Relatively reject, 3 = Neutral, 4 = Relatively accept, and 5 = Strongly accept.

in Appendix A) provide more in-depth evidence for the findings in the model, and clearly reveal the specific probability influence of key variables on drivers’ choices of different acceptance levels. In terms of key factors, the results are highly consistent with our previous discussion. For example, when the recognition of the environmental importance of new energy vehicles increases by one unit, the probability of choosing “relatively accept” and “strongly accept” increases by 9.6% and 4.3%, respectively. Similarly, when the variable of not being concerned about EV radiation increases by one unit, the probability of choosing “relatively accept” and “strongly accept” increases by 6.0% and 2.7%, respectively. The influence of occupation type is particularly significant. When the probability of occupation being a part-time ride-hailing driver increases by one unit, the probability of choosing “strongly accept” increases by 11.1%, while, when the probability of occupation being a full-time ride-hailing driver increases by one unit, the probability of choosing “strongly accept” increases by 4.8%, which quantitatively confirms that part-time drivers have the strongest acceptance. Conversely, when the variable of the belief that the platform’s commission is reasonable increases by one unit, the probability of choosing “relatively accept” and “strongly accept” decreases by 4.3% and 2.0%, respectively. In conclusion, the marginal effect analysis not only verifies the directional conclusion of the model, but also reveals the actual intensity of the influence of each factor through specific probability changes, which provides strong quantitative evidence for us to understand the decision-making mechanism of drivers’ acceptance of ERHS.

As for the model results of the factors influencing the acceptance of ERHS by potential driver groups, as shown in Table 4, in terms of environmental awareness, the potential drivers’ recognition of the importance of new energy vehicles in promoting environmentally friendly travel, as well as their support for the popularization of shared electric bicycles in cities, show significant positive effects on their acceptance of ERHS. This finding is similar to the conclusion of Du et al. [38] that environmental awareness is positively associated with the growth in new energy vehicles.

The driving range and driving experience of ERHVs appear to be factors that influence the acceptance of ERHS by potential drivers. The satisfaction with the driving range of ERHVs has a marginally significant positive impact on potential drivers’ adoption (estimated value 0.511, significance 0.085, p < 0.1). In less-developed cities, where charging infrastructure is relatively weak, the driving range is regarded as a key factor that influences potential drivers’ decisions on whether to participate in the electric ride-hailing profession, as suggested by the findings of Timmons et al. [39] in developing countries. Furthermore, there is suggestive evidence indicating that the satisfaction with the driving experience of ERHVs positively affects adoption (estimated value 0.546, significance 0.072, p < 0.1). The results of Tarei et al. [40] are similar: consumers’ perceptions of EV performance significantly influence their adoption.

In terms of economic and market factors, potential drivers who consider that the platform’s commission rules are reasonable are more likely to accept ERHS (estimated value 0.494, significance 0.018, p < 0.05). This result also confirms the findings of dynamic labor supply models [41]: a lower commission rate effectively attracts potential drivers to register and join. Interestingly, this variable was found to have a significant negative impact on driver acceptance in the study of the previous model. This is because the driver group is an insider in the passenger transport market. They believe that the reasonable commission rules reinforce their inertia to remain in the current ride-hailing market that is not yet fully electrified, thereby reducing their acceptance of ERHS. Potential drivers, on the other hand, are outsiders to the passenger transport market. What they perceive is the overall commission rules in the transformation of the ride-hailing market. Reasonable commission rules are a positive market access signal for potential drivers, which encourages them to adopt more cost-effective new tools, such as ERHVs, to enter the attractive electric ride-hailing market, thereby enhancing the acceptance of ERHS. However, for potential drivers who believe that fuel price fluctuations have no impact on the ride-hailing business, the acceptance of ERHS is reduced (estimated value −0.493, significance 0.010, p < 0.01). This suggests that in less-developed cities, the fuel-saving advantages of ERHVs in the context of fuel price fluctuations may not be fully recognized by potential drivers, and their low sensitivity to fuel price changes results in a decreased willingness to accept ERHS.

Optimism about the future development of the ride-hailing industry with the popularization of autonomous driving technology has a significant positive impact on potential drivers’ adoption of ERHS (estimated value 0.471, significance 0.035, p < 0.05). Autonomous driving technology is expected to effectively improve the work efficiency of ride-hailing drivers, which will stimulate potential drivers to accept ERHS to a certain extent.

From a demographic perspective, the acceptance of ERHS is significantly influenced by gender and age. In this study, the results show that male potential drivers, compared with females, exhibit lower acceptance of ERHS (estimated value −1.286, significance 0.003, p < 0.01). It indicates the substantial role that gender plays in the acceptance of ERHS. This contrasts with some studies on passengers. For instance, Vivoda et al. [42] found that men are more likely than women to understand and use ride-hailing services, which is to some extent attributed to higher technological acceptance. The opposite trend we observed among potential drivers may indicate that when the role shifts from passenger to professional provider, and the technology shifts from familiar gasoline vehicles to new electric vehicles, the acceptance results also change. For potential male drivers in our sample, adopting a new technology as a major income-generating tool could make them perceive unknown risks and responsibilities, leading them to be more cautious. Regarding the age, there is a marginally significant trend indicating potential drivers between the ages of 36 and 45 have lower acceptance of ERHS (estimated value −0.835, significance 0.065, p < 0.1). This is likely because this group was at the peak of the development of gasoline vehicles when they got to know the vehicles, and they have a strong brand loyalty to certain car brands. Moreover, the economic levels and technological acceptance in less-developed cities are generally lower, which may further reduce their acceptance of ERHS.

In addition, there is suggestive evidence indicating that potential drivers with 7–9 years of driving experience have a higher acceptance of ERHS. The current potential drivers of EVs or hybrid vehicles have a relatively low acceptance of ERHS, which may be due to the fact that the existing driving experiences of current car owners have to some extent affected their willingness.

Marginal effect analysis for potential drivers also illustrates the impact of key factors. The analysis results are shown in

Table A2. The marginal effect table of potential driver acceptance.

	1	2	3	4	5
Recognition of the importance of new energy vehicles in promoting environmentally friendly travel	−0.001	−0.001	−0.084	0.011	0.075
Support for the popularization of shared electric bicycles in the city	−0.001	−0.002	−0.127	0.017	0.113
Satisfaction with the driving range of ERHVs	−0.001	−0.001	−0.083	0.011	0.074
Satisfaction with the driving experience of ERHVs	−0.001	−0.001	−0.089	0.012	0.079
Belief that the commission structure of the ride-hailing platform is reasonable	−0.001	−0.001	−0.081	0.011	0.072
Belief that fuel price fluctuations have no impact on the ride-hailing business	0.001	0.001	0.080	−0.011	−0.072
Optimism about the future development of the ride-hailing industry with the popularization of autonomous driving technology	−0.069	−0.010	0.077	0.001	0.001
Gender = Male	0.002	0.002	0.195	0.003	−0.203
Gender = Female (base outcome)
Age = 36–45	0.002	0.002	0.141	−0.027	−0.117
Age = 46+ (base outcome)
Driving experience = 7–9 years	−0.001	−0.001	−0.120	−0.092	0.214
Driving experience = 10+ years (base outcome)
Currently driving an EV (including hybrid)	0.002	0.002	0.160	−0.027	−0.137
Currently not driving an EV (including hybrid) (base outcome)
Intention to enter the electric ride-hailing profession	−0.002	−0.002	−0.186	−0.044	0.234
No intention to enter the electric ride-hailing profession (base outcome)

Note: The columns labeled 1 through 5 correspond to the five levels of the ordinal dependent variable ‘Acceptance of ERHS’, where 1 = Strongly reject, 2 = Relatively reject, 3 = Neutral, 4 = Relatively accept, and 5 = Strongly accept.

in Appendix A. When the variable of support for the popularization of shared electric bikes increases by one unit, the probability of a potential driver choosing “strongly accept” increases by 11.3%. Conversely, demographic factors present significant barriers to acceptance. When the probability of gender being male increases by one unit, the probability of a potential driver choosing “strongly accept” decreases by 20.3%. Similarly, when the probability of age falling within the 36–45 age group increases by one unit, the probability of a potential driver choosing “strongly accept” decreases by 11.7%. These data clearly quantify the significant psychological barriers existing in specific male and middle-aged groups when accepting ERHS, which is completely consistent with our model results. In conclusion, marginal effect analysis provides crucial quantitative insights for the study of potential driver groups.

5.2. Factors Influencing the Willingness of Potential Drivers to Enter the Electric Ride-Hailing Profession

Through the analysis of potential driver sample data, as shown in

Table 5. Analysis of the relationship between acceptance and occupational decisions.

Acceptance	Enter Profession (%)	Not Enter Profession (%)	Total (%)
Strongly Reject	0.00	100.00	1.52
Relatively Reject	0.00	100.00	1.01
Neutral	12.07	87.93	29.29
Relatively Accept	28.21	71.79	39.39
Strongly Accept	47.37	52.63	28.79
Total	28.28	71.72	100.00

, only 1.52% and 1.01% of potential drivers exhibit low acceptance levels toward ERHS (“strongly reject” and “relatively reject”). A total of 29.29% of potential drivers hold a neutral attitude. In contrast, 39.39% and 28.79% of potential drivers demonstrate positive acceptance (“relatively accept” and “strongly accept”). Regarding occupational decision-making, 28.28% of potential drivers have the intention to join the electric ride-hailing market, while 71.72% currently have no such plan.

Among the potential drivers whose acceptance levels toward ERHS are “strongly reject” or “relatively reject”, the decision to participate in the electric ride-hailing profession is 0%. For potential drivers whose acceptance levels toward ERHS are “relatively accept” and “strongly accept”, their occupational decision rate to participate in the electric ride-hailing profession significantly increases. Among the potential drivers whose acceptance levels toward ERHS are “relatively accept”, 28.21% of them choose to enter the electric ride-hailing profession. Among those whose acceptance levels toward ERHS are “strongly accept”, although 52.63% of the drivers have decided not to enter the profession, 47.37% of them still show a willingness to do it.

The binary logit model results of the factors that influence the willingness of potential drivers to participate in the electric ride-hailing profession are shown in

Table 6. Model results for factors influencing potential drivers’ willingness to enter the electric ride-hailing profession.

	Estimated Value	Significance
High acceptance of ERHS	1.219 ***	0.001
Gender = Female	−2.549 **	0.017
Driving experience = 0–3 years		0.088
Driving experience = 4–6 years	−4.036 **	0.041
Driving experience = 7–9 years	−3.132 **	0.024
Currently not driving an EV (including hybrids)	−0.953 **	0.045
Age = 18–25	0 a	-
Age = 26–35	2.703 *	0.084
Education level = Junior high school or below	0 a	-
Education level = Bachelor’s degree	−1.423 *	0.060
Desire for the government to provide operational subsidies for ERHS	0 a	-
Desire for the government to provide recycling subsidies for ERHS	2.018 **	0.036
Satisfaction with the charging efficiency and duration of ERHVs	−0.543 *	0.057
Satisfaction with the safety of ERHVs	0.644 *	0.055
Optimistic about the future development of the ride-hailing industry with the popularization of autonomous driving technology	0.865 ***	0.004
Gender = Male * Driving experience = 0–3 years	0 a	-
Gender = Female * Driving experience = 7–9 years	6.283 ***	0.001

Note: * p < 0.1 (marginally significant), ** p < 0.05 (statistically significant), *** p < 0.01 (statistically significant), ^a denotes that this category of the variable is redundant, therefore it is set to zero.

. Cox and Snell values reflect the proportion of improvement in the goodness of fit of our model compared with the zero model. Nagelkerke is the modified R-square of Cox and Snell. The Cox and Snell value is 0.379 and the Nagelkerke value is 0.545 (greater than 0.5). They show that the model has a strong explanatory power. The Hosmer–Lemeshow test evaluates the calibration of the model by comparing the observed results with the predicted probabilities. The significance of the Hosmer–Lemeshow test is 0.899 (greater than 0.05), which means that the fit is good. The prediction accuracy rate of the regression model is 84.3%. The higher the percentage, the better the prediction ability of the model. Furthermore, the significance level of the final model is less than 0.05, indicating that at the 95% confidence level, the selected variables have a significant impact on the model results.

The potential drivers with the high acceptance of ERHS show a significantly higher willingness to enter the market (estimated value 1.219, significance 0.001, p < 0.01). A high level of acceptance deeply reflects the potential drivers’ high recognition of the multiple advantages of ERHS, which will promote them to join the electric ride-hailing industry to a certain extent. This shift from a positive attitude to behavioral intention is consistent with the research of Breschi et al. [43], who found that individuals with a high initial propensity for EVs (similar to our concept of high acceptance) have a lower adoption threshold.

Female potential drivers are significantly less willing to join the electric ride-hailing market (estimated value −2.549, significance 0.017, p < 0.05). This also confirms the objective reality that the proportion of female drivers in the ride-hailing market is relatively small (23.61%). In less-developed cities, women’s travel demands often have more stringent requirements for stability and safety. As an emerging means of transportation, ERHVs may not have successfully established a foundation that can make them feel sufficiently trusted in these key aspects [44].

Driving experience has a significant negative effect on the employment decision of potential drivers. Specifically, compared with the novice drivers with a driving experience of 0–3 years, potential drivers with a driving experience of 4–6 years and 7–9 years are significantly less inclined to enter the electric ride-hailing profession.

Potential drivers who currently do not drive EVs (including hybrid vehicles) are less inclined to join the electric ride-hailing market compared with those who drive EVs (estimated value −0.953, significance 0.045, p < 0.05). This is mainly because potential drivers who currently drive fuel vehicles are generally unfamiliar with the use of ERHVs. At the same time, they may have concerns about the long-term economic benefits and practical issues such as the charging of ERHS. This is consistent with the research findings of Budiman et al. [45]: drivers who are unfamiliar with EVs and charging infrastructure tend to be more resistant to switching to ERHS.

There is suggestive evidence indicating that potential drivers aged 26–35 are more inclined to join the electric ride-hailing market compared with the group aged 18–25 (estimated value 2.703, significance 0.084, p < 0.1). A possible explanation is that drivers in this age group are in the crucial step of their career development and have a more urgent need for stable income and long-term economic benefits. ERHVs, with their relatively low operating costs, can meet the core demands of drivers in this age group.

The results also provide suggestive evidence that potential drivers with a bachelor’s degree are less inclined to enter the electric ride-hailing profession (estimated value −1.423, significance 0.060, p < 0.1) compared with those with a lower educational level (junior high school and below). A study of Iranian taxi drivers by Rad et al. [46] found that drivers with a bachelor’s degree are more likely to experience mental stress from job dissatisfaction, which stems from their expectations for higher positions. Moreover, in less-developed cities, the number of drivers with a bachelor’s degree is relatively small. Under the pressure of social expectations, potential drivers with a bachelor’s degree often tend to pursue occupations with high social prestige to avoid career dissatisfaction in advance, which may reduce their willingness to enter the electric ride-hailing profession.

The recycling subsidy policy provided by the government can significantly increase the willingness of potential drivers to enter the electric ride-hailing profession (estimated value 2.018, significance 0.036, p < 0.05) compared with the operation subsidy policy. The recycling subsidy policy provides practical economic compensation for the recycling of old fuel vehicles, effectively reducing the financial pressure faced by drivers when switching to ERHS. This policy precisely meets the actual situation in less-developed cities, as drivers in these areas generally rely heavily on traditional fuel vehicles and are particularly concerned about the possible economic burden during the transition process. As the study of Simoiu et al. [47] showed, economic incentives foster driver behavior change towards sustainable energy use.

Potential drivers who are optimistic about the development of the ride-hailing industry after the popularization of autonomous driving technology have a significantly increased willingness to enter the electric ride-hailing profession (estimated value 0.865, significance 0.004, p < 0.01). Autonomous driving technology can greatly assist in reducing the operational pressure and occupational fatigue of drivers, and improve work efficiency and safety. Potential drivers who are confident in this technological progress are more inclined to join the electric ride-hailing market.

The interactive analysis of gender and driving experience presents thought-provoking results. Female potential drivers with a driving experience of 7–9 years are more likely to enter the electric ride-hailing profession compared with male potential drivers with a driving experience of 0–3 years (estimated value 6.283, significance 0.001, p < 0.01). Male novice drivers may show more hesitation and uncertainty when facing ERHS due to their lack of sufficient driving experience. However, female drivers with long-term driving experience in less-developed cities, with their sensitivity to local market changes, can be more actively involved in this market with great potential.

6. Policies and Recommendations

To improve the acceptance of ERHS among drivers, encourage potential drivers to participate in the electric ride-hailing industry, and accelerate the popularization of ERHS in less-developed cities, the following recommendations are proposed:

(1) Customized Multi-Dimensional Subsidy and Incentive Package: The model results show that operational subsidies can significantly enhance drivers’ acceptance of ERHS. This indicates that for drivers already operating in the market, measures to directly reduce their daily operating costs are more effective. Mileage is the most direct metric for a vehicle’s operational intensity and its contribution to green transportation. Therefore, we suggest the government implement a “Green Mileage Subsidy”, which would provide financial support based on the actual operational mileage of ERHVs. In this way, drivers’ long-term economic viability can be improved, and the environmental benefits of the policy can be maximized.

For potential drivers, we have found that the recycling subsidy significantly increases their willingness to enter the electric ride-hailing industry compared with the operation subsidy. Therefore, vehicle recycling subsidies for old fuel-powered vehicles can be further increased by the government, so that drivers’ financial burdens can be alleviated and more of them can be incentivized to enter the electric ride-hailing profession. Although the exact amount of vehicle recycling subsidies should be based on the financial capacity of less-developed cities, the scale of past policy incentives can be referred to. The Chinese government still gave CNY 18,000 to 25,000 for each EV in 2019 [48]. Then, for most less-developed cities today, vehicle recycling subsidies of CNY 10,000 are a feasible and influential starting point.

(2) Building an Intelligent Charging Service Network Platform: Our research has found that the satisfaction with the driving range of ERHVs positively affects the acceptance of potential drivers, and the dissatisfaction with the charging efficiency and duration of ERHVs is a significant negative factor influencing their willingness to enter the industry. Given the weak charging infrastructure in less-developed cities, an intelligent charging service network platform could be jointly built by the government and enterprises. This platform will integrate various charging facilities within the city, and big data and artificial intelligence technologies could be utilized to provide services such as real-time charging station location queries, charging reservations, and intelligent navigation to charging stations for drivers. At the same time, the usage of charging facilities should be monitored by the platform, and the feedback should be provided to the relevant authorities for necessary maintenance and upgrades. This will help improve the utilization rate and reliability of the charging infrastructure, thereby enhancing potential drivers’ confidence in the driving range of ERHVs and increasing their acceptance. To maximize the utilization rate of existing charging stations, a private charging station sharing program can be implemented through the platform, ensuring that charging demand and available facilities are efficiently matched and the use of charging resources is optimized.

(3) Precision Promotion and Acceptance Enhancement Plan: The model results consistently indicate that environmental awareness is one of the most important factors promoting drivers and potential drivers to accept ERHS. Large-scale environmental awareness campaigns could be led by the government and relevant departments to increase public understanding and promote green travel options. A series of popular science videos and promotional brochures could be produced that highlight the environmental advantages of new energy vehicles and the contributions of ERHVs to urban sustainable development. These materials could be widely distributed through social media, public transportation hubs, community activity centers, and other channels to ensure that the information reaches a broad audience. At the same time, targeted promotional strategies could be developed for different demographic groups. For the potential drivers aged 36 to 45 with lower acceptance in our study, we found that the possible reason might be that they are more loyal to fuel vehicle brands. It is recommended that the government and EV manufacturers launch test drive activities to break their brand loyalty to traditional fuel vehicles by personally experiencing the advantages of EVs. Our study found that for drivers who have reduced their acceptance due to health concerns about EV radiation, the government can carry out health science popularization on EV radiation, reduce the anxiety of drivers, and overcome the psychological barriers for the promotion of ERHS. Through precise communication and interactive engagement, public environmental awareness could be strengthened, and the social recognition and the acceptance of ERHS could be further enhanced.

7. Conclusions

This study applies the ordered logit model to analyze the acceptance of ERHS among driver groups and potential driver groups in less-developed cities. Based on this, a binary logit model is utilized to analyze the willingness of potential drivers to engage in this sustainable program. The specific conclusions are as follows:

(1) Among drivers, environmental awareness is the important factor that influences their acceptance of ERHS. This reveals that in less-developed cities, promoting sustainable transportation models requires enhancing the environmental awareness of practitioners through publicity and education. Drivers who are worried about the potential health impacts of EV radiation are less likely to adopt ERHS. It indicates that eliminating the perceived health risks brought by new technologies can accelerate adoption among practitioners. Both full-time and part-time ride-hailing drivers show higher levels of acceptance of ERHS compared with taxi drivers. Additionally, compared with the charging subsidies, drivers who receive operational subsidies from the government are more inclined to accept ERHS.

(2) Male potential drivers, those aged 36–45, and those who currently drive an EV (including hybrid) have lower acceptance of ERHS. This indicates that targeted promotion strategies need to be formulated for potential drivers with different characteristics to reduce psychological barriers. On the economic front, those who believe that the platform’s commission rules are reasonable are more likely to accept ERHS, as such rules act as a positive market access signal, whereas those who feel that changes in fuel prices have no impact on ride-hailing businesses are less likely to accept it.

(3) For potential drivers, the education level, the government’s recycling subsidy policy, and the optimism about the development of autonomous driving technology in the ride-hailing industry are the important factors that influence their occupational decision-making. The effectiveness of the recycling subsidy policy stems from its direct alignment with the economic realities of less-developed cities, reducing the initial financial pressure of potential drivers in vehicle replacement. Potential drivers aged 26–35 are more inclined to pursue a career in ERHS. Potential drivers with longer driving experience (4–9 years) show a significantly lower willingness to engage in the electric ride-hailing profession.

This study is not without limitations, and the following points suggest directions for future research. Firstly, we acknowledge that the study’s sample size is a limitation. The relatively modest sample size, particularly when subdividing between current and potential drivers, may limit the statistical robustness of the model estimations. Due to financial constraints and difficulties in finding drivers, we faced many rejections at the beginning of the investigation. It was only with the help of the local transportation bureau that we were able to successfully collect questionnaires. Future research can, under the condition of more abundant human and financial resources, adopt more appropriate methods to conduct expanded sample surveys involving a wider range of people. In addition, if someone makes public the dataset of ride-hailing drivers in the future, we will conduct more objective research using the open-source dataset.

Secondly, this study is based on a single-city case study of Zhangzhou, just as most other questionnaire surveys are conducted in one city [17,30,45]; this approach enables us to conduct a more in-depth analysis of the influential factors in a specific environment and provides valuable experience for other similar less-developed cities to achieve green and sustainable urban shared transportation development. However, we still suggest that future research should conduct comparative studies among multiple cities to test the universality of our findings and identify key regional differences.

Thirdly, the ordered and binary logit models used in this study are classic methods to analyze individual choice behavior. Their advantages lie in the robustness of the models and the intuitive interpretability of the results. However, we must also admit the inherent limitations of this method. The core assumption of these standard logit models is that the parameters are fixed, but there are significant differences between individuals. Drivers with different personal characteristics may respond very differently to the same policy, and standard logit models cannot directly capture these unobserved individual differences. Advanced methods such as machine learning are very powerful in identifying complex, nonlinear relationships and overcoming this limitation, but they often lack the direct interpretability of traditional econometric models and sometimes act like a “black box”. Therefore, a promising direction for future research is to combine the two methods to investigate the acceptance of ERHS among driver groups in less-developed cities, comparing ERHS development models under varying economic development levels, urbanization degrees, and transportation infrastructure conditions.