1. Introduction

With the rapid development of the fair trade economy, ride-hailing services such as Uber and Lyft have dominated the market, providing drivers with an opportunity to work flexibly and earn a considerable income, bringing them great development prospects. Their user types are diverse and they do not have to worry about how to hail a taxi, which is completely different from the traditional taxi operation. According to reports, as of 2020, the market size of ride-hailing apps (RHAs) has reached a staggering $113 billion, becoming a new competitive field in the travel industry [1]. Although the cost of renting a car is cheaper than traditional methods, concerns about the level of service quality have not been resolved due to the sluggish market growth.

The huge demand for rides often outstrips the supply of qualified drivers, forcing platforms to relax hiring standards. This often results in uneven service quality, as platforms prioritize proximity over driver quality when matching drivers and passengers. As a result, passengers may experience inconsistent service levels, paying similar fares but receiving very different experiences. This problem is exacerbated if the rating system is unable to effectively identify and reward quality service, resulting in a uniform incentive structure that does not account for differences in quality. This failure of the rating system can lead to a situation of “milking” reputation, where drivers can maintain full-price incentives even if they provide lower-quality service, a clear manifestation of moral hazard.

This study proposes a novel application of Herzberg's Two-factor theory of organization management as a framework to rethink incentive structures within ride-hailing platforms. Rather than focusing on customer satisfaction directly, this approach emphasizes the provider-side factors that contribute to service quality. The "hygiene" factors ensure a stable baseline by addressing provider dissatisfaction and fostering respect and loyalty within the provider-platform relationship, thus reducing defections. The "motivation" factors introduce management tactics that incentivize high performance, potentially initiating a virtuous cycle of investment in quality by providers.

Our primary objectives are to elucidate the mechanisms by which rating system failures contribute to moral hazard among providers and to offer normative recommendations for platform incentive systems based on the two-factor theory. This approach diverts from traditional customer satisfaction models and focuses on structural incentives that indirectly enhance customer experiences by elevating service standards.

The structure of this paper is organized as follows:

•Chapter 2: Literature Review, providing an overview of relevant theories and research background.

•Chapter 3: Research Methodology and Design, detailing the prescriptive method and data analysis techniques.

•Chapter 4: Results and Discussion, presenting the findings and validating the hypothesis.

•Chapter 5: Conclusion, summarizing the insights gained from the study and the implications for the future of ride-hailing platforms.

By redefining the utility of rating systems as binary filters for basic quality standards and integrating two-factor management theories, this research aims to reshape the incentive frameworks of ride-hailing platforms, thereby mitigating moral hazard and fostering a sustainable model for high-quality service delivery.

2. Literature review

2.1. Ride hailing service

Since Uber launched in 2010, ride hailing services have been disrupting established transportation business models [2]. Unlike traditional car services, it is more widely used in densely populated areas, providing sharing (i.e. reducing traffic volume) advantages by matching customer needs (i.e. travel) with resources (i.e. cars) or promoting car sharing, thereby providing passengers with more convenient and personalized services [3]. Therefore, this field is widely welcomed and used by users.

However, ride hailing services still have many uncontrollable factors, one of which is the failure of traditional indicators such as ratings. The scoring system is a tool used to evaluate characteristics and performance, typically reflecting the quantification of indicators, and was first applied in the medical field [4]. The rating of ride hailing services is an important indicator for measuring the quality of ride hailing services, which directly affects passenger satisfaction, driver order volume, and company profits. Currently, research has found that the ratings suffer from issues such as inflation and bias [5,6]. Meanwhile, this factor comes from the subjective consciousness of users [7], which means that it is closely related to user loyalty and satisfaction but easily distorted, thus seriously threatening the quality of ride hailing services and employee motivation.

2.2. Two-factor theory

A theory that can effectively reduce or avoid this situation is the two-factor theory, also known as the motivation-hygiene theory, first proposed by American psychologist Herzberg in 1959. He divided enterprise related factors into dissatisfaction (hygiene) and satisfaction (motivation) factors, which can be well reflected in ride hailing services [8]. Among them, health factors refer to the basic working conditions provided to employees (drivers), which can eliminate drivers' dissatisfaction at work and ensure the normal operation of business, but can’t motivate employees to provide higher quality services. For example, eight health factors for preventing driver fatigue have been derived through this theory, ensuring that drivers are in good working condition to obtain good user evaluations [9]; Zhang, from the perspective of consumers, effectively summarized the health factors of consumers' willingness to continue using, namely reputation and platform mechanisms, through this theory, and pointed out that if platforms attach importance to these two factors, they can reduce consumers' distrust[10]. On the other hand, motivational factors refer to factors that can stimulate employee motivation and significantly improve company performance. Taylor found that improving the efficiency of shared ride hailing services will encourage platforms to increase personal ride hailing services, while also increasing driver wages, thus viewed as a positive cyclical phenomenon[11]. At the same time, SSP equilibrium (high price strategy) is also an incentive factor, where platforms stimulate consumer desire to consume by setting higher initial prices, while using high returns to attract more drivers into the market[12]. Based on the above research, this study speculates that the two-factor theory can identify relevant factors for improving driver motivation and optimizing service quality in the case of ineffective ratings on ride hailing platforms.

2.3. Incentives mechanism

Leaving aside the previous definition of two-factor theory, incentives are crucial to aligning the interests of service providers with those of the platform and its users [13]. Theoretically, incentives are based on behavioral economics, which suggests that individuals are motivated by rewards that are aligned with their personal goals [14]. Ofori et al. elaborated in 2022 that on service-based platforms, such as car-hailing services, incentives can be categorized into monetary forms (e.g. bonuses, payments) and non-monetary forms (e.g., badges, recognition) [15]. The two-factor theory, first emerging from Herzberg's motivation-hygiene theory, provides a framework for understanding how these incentives affect service quality. According to this theory, certain factors (motivational factors) lead to higher satisfaction and performance, while others (hygienic factors) prevent dissatisfaction but do not necessarily improve performance [16]. In the case of taxi platforms, motivational factors may include performance-based bonuses, while hygienic factors may include basic operating conditions.

2.4. Moral hazard

Nevertheless, every issue has dual aspects, and the implementation of effective measures is frequently susceptible to moral hazard. Dutta and Radner describe moral hazard as the potential for individuals to engage in behaviors that are harmful to others or to the collective, due to their inability to fully bear the repercussions of their actions [17]. This phenomenon often arises in contexts characterized by information asymmetry, where one party possesses more information than another, enabling them to make risky or irresponsible decisions while shifting the resultant costs onto others or the broader community [18]. For instance, within an insurance contract, a policyholder might become less cautious, knowing that the insurance will cover damages in the event of an accident. An example is an individual with comprehensive vehicle insurance who may become more negligent about driving safety, as the insurance company will absorb the financial impact of any accident. Avalos supports this theoretical framework and further contends that moral hazard can emerge when the connection between an individual's or institution's interests and the consequences of their actions is either ambiguous or tenuous [19]. For example, in financial markets, banks or other financial institutions might engage in high-risk investments under the assumption of implicit or explicit government guarantees, believing that the government will provide a bailout if necessary. This excessive risk-taking can ultimately precipitate systemic financial crises. When losses are absorbed by a third party—such as society, the government, or an insurance company—actors are prone to become less vigilant, thereby increasing the likelihood of irresponsible behavior. In corporate settings, employees might exhibit reduced productivity or make detrimental decisions, knowing that the company will absorb a portion of the error or loss [18]. The platform economy, with its dependence on intermediary technology and data-driven management, is particularly susceptible to moral hazard. If the rating system does not effectively differentiate performance, service providers may believe that their rewards are not affected by performance, thereby reducing their efforts or lowering the quality of their services. In addition, service providers may exploit system loopholes, such as manipulating ratings or gaming the system, to provide inferior services while still receiving higher rewards [20]. Moral hazard is thus a complex and pervasive issue in economic activities, impacting market efficiency and leading to broader social and economic repercussions. Therefore, understanding and managing moral hazard is crucial for both businesses and policymakers.

To effectively mitigate moral hazard in the platform economy, it is necessary to develop and implement incentive structures that are both robust and transparent, thereby reducing the risk of service providers exploiting system loopholes. A key strategy is to improve the accuracy of the rating system to more accurately reflect service quality dimensions [21]. To do this, complex algorithms and multidimensional rating frameworks that go beyond simple assessments can be adopted [22][23]. In addition, it is critical to create a transparent system that provides clear and actionable performance feedback to service providers. This transparency enables service providers to understand how their actions affect their rewards, thereby aligning their behavior with expected performance outcomes [23]. Moreover, designing incentives that balance immediate rewards with long-term performance indicators is critical to minimizing the possibility that service providers will manipulate the system for short-term gain [21]. By integrating these strategies, platforms can better align service provider incentives with customer satisfaction and service quality, thereby effectively addressing moral hazard and enhancing the overall integrity of the platform's operational mechanisms.

2.5. Research gap

Existing literature has studied the general role of incentives and moral hazard in various contexts but often lacks specific insights into the subtleties of platform economies. This study fills this significant research gap by investigating the intersection of rating system failures and service quality on ride-hailing platforms. Through the analysis of secondary data and the proposal of enhancements to incentive structures, this research contributes to a more nuanced understanding of how to effectively manage moral hazard. The study offers practical recommendations for platform managers to refine incentives and elevate overall service quality. By linking theoretical models of incentives in the platform economy with their practical implementation, the application of two-factor theory in designing systems that can better differentiate between high- and low-quality service providers offers valuable guidance.

3. Analysis

3.1. Research goals

One of the purposes of this study is to identify the deficiencies in the rating system of ride hailing platforms, confirm the potential causes of the failure of the rating system through literature support, and demonstrate the negative impacts that may arise. Secondly, in the case of system scoring failure, the two-factor theory is adopted to study the related influencing factors, focusing on the combination of case studies and influencing factors to draw conclusions and develop platform strategies for driver incentive systems while improving customer satisfaction and reducing supplier moral hazard.

3.2. Research plan

This study explores the failure of the rating system of ride-hailing platforms, where subjective and biased evaluations by passengers lead to unfair driver ratings and increased moral hazard. This research will use secondary data to analyze the measures implemented by Uber and Lyft, such as Uber Pro, Lyft rewards, and safety features, which aim to reduce moral hazard through incentive systems and driver support. Based on Herzberg's two-factor theory, prescriptive recommendations will be made to link incentive strategies (e.g., recognition, transparent compensation) to driver performance. These recommendations aim to improve customer satisfaction by minimizing the failure of the rating system and to strengthen performance Responsibility through a fairer, data-driven evaluation system.

4. Methodology—two factor theory application

The rating of ride hailing services is based on subjective evaluations from passengers, which are easily influenced by emotions or personal biases, rather than evaluations of the driver's actual service quality [24]. This will lead to some drivers having lower ratings for illogical reasons, reducing their income and hindering career development [25]. Also, from the perspective of ride hailing drivers, they overly rely on high ratings and may make unreasonable concessions in order to obtain high ratings, such as ignoring safety regulations or driving norms in certain situations [26], or even catering to passengers' unreasonable demands, thereby increasing driving risks. Therefore, the psychological factors of both parties mentioned above will exacerbate the failure of the rating system.

There may be a disconnect between the driver's actual performance and the passenger's evaluation. For example, when a passenger gives a negative evaluation based on traffic conditions or platform algorithm problems, this has nothing to do with the driver's actual service, but has an adverse impact on his or her rating [27]. In addition, since income and career prospects are linked to ratings, drivers may increase psychological pressure due to concerns about receiving low ratings, which may affect work efficiency and customer experience in the long run [28]. These external unconfirmed factors have led to the ineffectiveness of the scoring system, resulting in failures.

These moral risks indicate that the current evaluation system needs to be improved to reduce the negative impact on drivers while ensuring the transparency and fairness of the platform. The following is a model diagram based on existing secondary data (Table 1), which will be analyzed in detail in the below section.

4.1. Prescriptive recommendation

Platform security is one of the hygiene factors, providing drivers with a safer working environment and condition. Even in emergency situations, relevant measures can be taken to ensure the personal and property safety of drivers as well as the success rate of transactions, effectively stabilizing their work psychology [29]. Both Lyft and Uber adopt similar security technology support. They minimize the negative impact of this factor by designing nine safety features on the platform, including emergency assistance, information support and privacy protection services, to minimize potential risks. As each transaction is generated, the platform provides insurance for people, vehicles, and injury protection [30]; At the same time, Uber has established a partnership with safety brand ADT to provide drivers with safety agency services [11], minimizing potential losses. It is worth noting that the establishment of the Safety Advisory Committee provides drivers with a more standardized online safety plan. The above measures not only meet a safe working environment for drivers but also provide safe services for passengers, which can enhance psychological positivity from both perspectives. Therefore, achieving platform security can fundamentally ensure the effectiveness of the rating system.

Another pivotal hygiene factor is driver compensation fairness, which influences both market competitiveness and internal stability, mainly reflected in the driver's work enthusiasm and loyalty [31]. According to the official websites of Uber, driver income depends on the content, location, duration, and frequency of driving, and the cost details can be queried through the "fare details" section for each transaction. For price fluctuations, Uber pointed out that it will adopt a method of taking the difference and making up for the lower to allow drivers to receive booked income; In June 2023, Lyft improved its prepaid payment function, with the "Revenue Summary" displaying fare allocation and company financial information, ensuring transparency and effectively increasing Lyft driver income. In addition, both companies adopt a minimum wage standard of $32.50 per hour since 2024, and in some states, they continue to strengthen their position as independent contractors for drivers and improve their benefits [32]. Therefore, ensuring fair compensation can achieve reasonable ratings from the perspective of drivers.

Uber's focus on improving driver experience aligns with Herzberg’s Two-Factor Theory, particularly in enhancing work conditions and driver support. Uber ensures that drivers have optimal working conditions by providing tools that enhance the ease of accessing and completing jobs. A key feature is the real-time earnings tracker, which allows drivers to view their earnings after each trip. This transparency helps drivers make decisions on when and where to work, optimizing their schedules to match peak demand times. The app also integrates navigation tools and heatmaps, which guide drivers toward high-demand areas, reducing the time spent searching for rides and helping them maximize their efficiency [33,34]. The working conditions can achieve efficient operation of ride-hailing services; therefore, when this factor is met, the rating system will reduce the risk of failure.

Driver support is also a focus, with Uber providing 24/7 phone support, in-app messaging, and chat features to quickly resolve any issues drivers encounter. In emergencies, the Safety Incident Reporting Line offers immediate assistance, while the in-app Help Center allows drivers to troubleshoot common problems independently [35]. Therefore, driver support can also stabilize their psychology and reduce the possibility of scoring system failure.

By addressing these key hygiene factors—maintaining good work conditions through efficient tools and providing comprehensive driver support—Uber and Lyft create a work environment where dissatisfaction is minimized, enabling drivers to perform effectively without unnecessary disruptions, greatly reducing the possibility of scoring system failure.

In the competitive world of ride-hailing, performance, and safety have been several critical factors that influence the welfare of both drivers and customers. One way to improve all of these areas is implementing driver rewards programs, which “recognizes and motivates drivers, leading to increased satisfaction, enhanced performance, and improved safety” (The Power). Such a program has already been proven to be effective in not only boosting driver productivity but also performance by motivating them to achieve their best. According to the study done by the Incentive Research Foundation, “drivers who participated in recognition programs outperformed their counterparts by up to 34%. Also, Companies that implemented driver recognition and rewards programs experienced an average 18% increase in performance among their drivers” (The Power). Another crucial factor is safety. Through offering incentives to drivers who perform well, ride-hailing platforms manage to encourage drivers to take customers’ safety as a priority. Incentives cover the money obtained through making lots of driving, which requires drivers to drive at a high speed that probably leads to traffic accidents, A study done by the Network of Employers for Traffic Safety indicates that “companies with driver rewards programs experienced a 35% reduction in accidents compared to those without such programs” (The Power). Then, how can drivers meet the standard of obtaining the rewards? Ride-hailing platforms have provided drivers with plenty of opportunities to help them navigate to success. First, both Uber and Lyft provide drivers with detailed data and analytics on their performance, including earnings breakdowns, ride statistics, and peak times. These insights help drivers understand their performance and optimize their driving patterns to achieve their financial targets. Second, Uber and Lyft use heat maps in their apps to indicate areas where demand for rides is high, enabling drivers to position themselves for more ride requests and higher earnings. Thus weakening, to some extent, the failure of the rating system. Second, Uber and Lyft use heat maps in their apps to show areas of high ride demand, enabling drivers to position themselves to receive more ride requests and higher earnings, thus somewhat undermining the failure of the rating system.

In terms of fostering Growth Opportunities and Responsibility, both Uber and Lyft have introduced structured incentive programs to improve driver engagement and performance to reduce risk. For example, Uber's "Uber Pro" program provides benefits such as priority support, health care discounts, and fuel discounts by earning different levels of points based on driver performance. In terms of promoting “growth opportunities and responsibilities”, both Uber and Lyft have introduced structured incentive programs to increase driver engagement and performance, thereby reducing risk. For example, Uber’s “Uber Pro” program provides benefits such as priority support, health care discounts, and fuel discounts by awarding different levels of points to drivers based on their performance [36]. Providing this tangible reward to incentivize drivers to maintain high service standards can alleviate the low morale that may be caused by rating differences and effectively address the inhibitory factors caused by the rating system's inability to accurately reflect driver performance. Providing such tangible rewards to motivate drivers to maintain high standards of service can alleviate the low morale that may result from rating differences and effectively address the disincentive factors caused by the rating system’s inability to accurately reflect driver performance.

Similarly, Lyft has taken important measures through its’ Reinforcement Learning Platform, which provides comprehensive training courses in both online and offline formats, which not only enables drivers to master basic service skills and business knowledge, but also enables them to handle complex and changing situations at work Similarly, Lyft has also taken important steps to provide comprehensive training courses in both online and offline formats through its “enhanced learning platform”, which not only enables drivers to master basic service skills and business knowledge, but also enables them to cope with complex and changing situations at work [37]. In addition to promoting skill growth, the career development achieved through this program mainly promotes personal potential career advancement or job-hopping development, thereby creating long-term engagement and responsibility. In addition to promoting skill growth, the career development achieved through this program is mainly to promote individuals’ potential career advancement or job-hopping development, thereby building a long-term sense of engagement and responsibility [38].

Additionally, Lyft’s "Lyft Rewards" and "Destination Mode" features promote higher earnings by allowing drivers to prioritize rides in specific directions [39,40]. This feature helps drivers optimize route planning and income, provides them with greater control over their work, and reduces the risk of low motivation or dissatisfaction [40,41]. Similarly, Uber’s "Uber Express Pool" also optimizes ride efficiency, offering drivers better control over their routes and improving time management [42]. Through these programs, Uber and Lyft have reduced operational inefficiencies and, to a certain extent, addressed the problems caused by the failure of the rating system. Table 2 below is a summary of the actual application of Uber and Lyft to avoid the failure of the rating system.

4.2. Findings

Based on the findings in Part III, it can be learnt that platform safety and driver compensation are important hygiene factors that affect driver satisfaction on ride-hailing platforms. Safety measures, such as Uber’s and Lyft's nine safety features, emergency assistance, insurance, and cooperation with safety brands such as ADT, help stabilize drivers' psychological state, ensure personal safety and smooth transactions. These platforms provide a safer working environment and standardized online safety programs to minimize risks [12,30]. In terms of compensation, transparency and fairness play a vital role. Uber and Lyft provide drivers with real-time income tracking and fare details and implement a minimum wage of $32.50 per hour starting in 2024. Uber's fare adjustment ensures that drivers meet the minimum income standard, while Lyft's prepayment feature increases income transparency [31]. Both companies strengthen drivers' status as independent contractors and improve financial stability [33]. To further increase driver engagement and satisfaction, platforms offer reward programs such as Uber Pro and Lyft Rewards to motivate drivers for performance, service quality, and safety to ensure fairness. In addition to this, training programs improve drivers’ service skills and provide career development opportunities, helping them manage operational inefficiencies, increase income, and reduce dissatisfaction [38]. These measures not only improve safety and performance but also address driver morale and participation in the “gig economy”, avoiding rating system failures as comprehensively as possible.

4.3. Comparison with previous studies

The research background of this paper and previous cases are the context of the exact sharing economy environment, and fully demonstrate the upward trend of ride hailing platforms since their establishment [1,3,4]. But what previous studies did not consider was rating failure, because in this situation, the negative impact of ride hailing platforms will be infinitely amplified. Therefore, this paper studies how to reduce moral hazard in the dual context mentioned above.

The research framework of this article differs from previous studies. Firstly, the purpose of this study is to reduce the moral hazard of drivers, which has been proven to be a common phenomenon [17,18,20]. Compared with previous cases focusing on user satisfaction and market demand, there are differences in the focus; in other words, the research object is the supply side of the ride-hailing market rather than the demand side [2,11]. Secondly, the research methods are different. This article applies the two-factor theory for qualitative analysis, surpassing the complex algorithms and multidimensional rating frameworks of simple evaluations [20,23]. Although some research conclusions have been reflected in the two-factor theory, they are not based on this theory, but rather on regression models and other theories.

The conclusion of this study satisfies the discovery of the two-factor theory [9] and is highly consistent with the conclusions of previous cases [10,12-14], proving the rigor and effectiveness of this study.

In addition, this study suggests mainly providing strategies for the quality dimension of ride hailing services, which is an integration and improvement of previous strategies [25,27,28].

4.4. Theoretical development contributions and practical application possibilities

The development of safety and compensation strategies on the Uber and Lyft platforms has made valuable contributions to Herzberg’s two-factor theory, especially in the area of hygiene factors. These platforms minimize dissatisfaction by addressing issues such as working conditions, safety, and financial stability, ensuring a supportive environment for drivers to focus on job performance without worrying about other things. By providing real-time income tracking, comprehensive safety measures, and a fair compensation model, these companies provide the necessary foundation for driver satisfaction. In addition, programs such as Uber Pro and Lyft Rewards go beyond hygiene factors to motivate drivers through recognition and reward systems, which coincides with Herzberg’s motivational factors. These structured incentives boost driver engagement, reduce risk, and promote career development, demonstrating how addressing both hygiene and motivation factors simultaneously can improve performance and long-term loyalty. Ultimately, these innovations strengthen the application of Herzberg’s theory to the gig economy, demonstrating how a balanced focus on both factors can improve the worker experience.

This research is a reflection of objective facts, and various influencing factors have been demonstrated through practical cases. Therefore, the relevance of this study to reality is high. Meanwhile, the research also confirms that the eight factors proposed in this article can effectively improve the quality of driver services and reduce moral hazard, including the overall strength and competitiveness of the supply side. Therefore, the research recommendations are applicable to the development of ride hailing platforms. In addition, there is no technical bottleneck in reducing moral hazard through this study, and the social acceptance is high with low risk; The only uncertainty is economic feasibility. Overall, this study has a high applicability in the ride hailing market.

5. Limitation

This study faces several key limitations, particularly in its methodology, data source, and relativity. The research relies on secondary data to evaluate the effectiveness of platforms such as Uber and Lyft. This limits the direct application of findings, as the study depends on previously published data, which may not fully capture current trends or the nuances of driver experiences. The study also uses case studies to analyze specific elements like Uber Pro and Lyft Rewards, but these may not generalize to all contexts or geographical regions, as different markets may have varying regulatory frameworks and rider behaviors.

Additionally, the focus on two-factor theory may not encompass all the factors affecting driver satisfaction and performance. This presents a limitation in its relativity to other motivational or economic models, which could provide alternative explanations for the issues surrounding driver incentives and performance evaluation. The complexity of the ride-hailing platform’s ecosystem may require a more holistic, multi-disciplinary approach to fully understand the implications.

6. Prescriptive recommendations and future direction

The study showed that one of the most constructive suggestions for the ride-hailing industry is to provide more comprehensive benefits, such as health insurance, retirement savings options, and paid vacations, which can help attract and retain drivers. In addition, introducing more different bonus programs and loyalty rewards for different driving modes and preferences will also increase participation. In this promising industry, companies like Uber and Lyft can remain competitive by focusing on accessibility and inclusion. Strengthening services to cater to a wider range of people, including the elderly, people with disabilities, and low-income people, maybe an insightful future direction to help companies succeed. For example, developing more accessible vehicle options, establishing partnerships with healthcare providers, and providing tailored services for these groups can all expand the customer base. In addition, to improve the reliability of the rating system, a multi-dimensional rating method can be introduced to combine passenger ratings with driver performance indicators such as acceptance rate, completion rate, driving safety, and customer complaints. This can reduce the impact of subjective ratings on the overall evaluation. In addition, the automatic rating system can monitor driver behavior through platform algorithms, track factors such as speed, emergency braking, route selection, etc., provide more objective data, and reduce reliance on passenger feedback. Incorporating time and environmental weights, such as taking into account peak hours or bad weather, will further ensure that service ratings are adjusted according to external conditions and prevent unfair evaluations. A feedback review mechanism can be implemented to allow drivers to object to unfair evaluations, which can then be verified and corrected by the platform. Finally, the importance of accurate ratings should be promoted to passengers, and transparent evaluation criteria should be provided to guide passengers to make fair and reasonable evaluations and minimize the phenomenon of arbitrary low ratings.

7. Conclusion

The results of this study clearly demonstrate that it is critical to address the motivational issues that influence drivers’ behavior and performance on ride-hailing platforms. By focusing on factors on the driver side, particularly through the two-factor theory, this study contributes significantly to understanding how to design better incentive mechanisms to reduce moral hazard and improve service quality without directly involving customer satisfaction indicators.

Hygiene factors such as platform safety, fair compensation, and driver support lay the foundation for stable and satisfactory working conditions. By ensuring that these factors are well managed, ride-hailing platforms can reduce driver dissatisfaction. On the other hand, incentive factors such as recognition, growth opportunities, and increased responsibility will encourage drivers to bring better performance beyond standard expectations. Therefore, platforms can promote the provision of excellent and high-quality services.

The normative recommendations for the development of more comprehensive and less exploitable rating systems are particularly pertinent. Implementing multidimensional rating systems that go beyond simple star ratings and incorporate detailed performance indicators can reduce the occurrence of rating bias and more accurately reflect the quality of drivers. In addition, improving the transparency and responsiveness of these systems will ensure that drivers are fairly rewarded and incentivized to maintain high standards.

In summary, this study highlights the need to focus on drivers as the main lever for improving the quality of ride-hailing platform services. By applying hygiene and motivational factors, platforms can foster a more satisfied and motivated driver base, which in turn inherently improves overall service quality. Future research should continue to focus on these factors and explore new ways to support drivers to ensure that the ride-hailing industry remains competitive and fair. This driver-centric approach not only benefits drivers, but also indirectly improves the customer experience by ensuring that customers receive service from highly motivated and satisfied drivers.

Acknowledgement

Kunying Xin, Lingwen Kong, Lintao Zhong and Siyu Chen contributed equally to this work and should be considered co-first authors. We sincerely thank all those who have provided us with assistance in this paper.