Understanding How Leadership Can Help Reduce Occupational Stress Faced by Gig Economy Workers: Case Study of Ride Sharing Services in the US

1. INTRODUCTION

Over the past several years, the so-called gig economy of digitally mediated, on-demand work has grown tremendously in the United States (Woodcock, 2021). This change can be exemplified by ride-sharing platforms like Uber and Lyft, which give workers control over their schedules and work intensity while offering passengers a convenient alternative to traditional transportation services (Zhang, 2022). Gig work has become part of the work lives of over one-third of US workers, contributing to a significant overhaul of the traditional labor system, as suggested by (Adler, 2021) as well. Although it is flexible, ride-share drivers face many stress-inducing factors, such as unequal pay, safety concerns, a lack of job protection, and continuous performance appraisals based on algorithms (Malik & Raziq, 2022). It was discovered that the vast majority of drivers with a crash experience reported it as the result of stress and fatigue, as argued by (Shannon, 2024), which portrays severe occupational health outcomes. The stressors have resulted in dissatisfaction, high turnover, and poor service delivery in the industry which is indicated by (Cropanzano et al., 2023) as well.

The second issue is that, despite the nature of ride-sharing companies promoting flexibility and autonomy, drivers remain to face high work pressure because of unstable revenue, the lack of formalized support measures, and the all-over nature of the algorithmic management. Compared with traditional employment systems, where managers can supervise and assist employees directly, gig workers can only communicate with automated systems, resulting in unclear expectations, pressure on performance, and a lack of psychological safety. This paper aims to analyze this burning issue, which is how leadership mechanisms platform, organizational, and emergent human leadership can alleviate these stressors in the U.S. ride-sharing setting.

The lack of traditional leadership relationships has become one of the industry’s main organizational problems. Ride-sharing work is governed by algorithmic systems that perform all task assignments, behavior tracking, and deliver automated communication and rewards instead of human managers (Ali & Sivasubramanian, 2024). This leaves a leadership gap, as drivers often view themselves as unassisted and dehumanized (Kuhn et al., 2021). The absence of cross-functional leadership in gig work limits opportunities to obtain guidance, recognition, and social support, which are usually acquired within the manager-employee relationship, as (Keith & Long, 2020) also claim.

In this respect, past studies such as (Haque & Yamoah, 2021) mostly focus on traditional leadership in the standard employment environment, and there is a gap in the relevance of leadership concepts in the gig economy. There is a lack of empirical evidence on the impact of platform-mediated leadership indicators, organizational regulations, and ad hoc peer support on occupational stress among ride-sharing drivers. Moreover, the literature tends to ignore the relationship between autonomy and income stability and the role of work environment support, especially in the U.S., where market volatility and the presence of algorithms create greater pressure, leading to market stress that requires a cohesive examination and detailed analysis.

To fill this conceptual gap, this paper explores the importance of leadership mechanisms in reducing occupational stress among ride-sharing employees in the U.S. gig economy by investigating the effects of platform-based, organizational, and emergent leadership indicators on drivers’ stress. The conceptualization of leadership in the study encompasses the leadership integrated in the algorithmic systems, platform policies, and support structures that define the demands of the work and the resources available to fulfil it, not what the traditional understanding of the leadership as a managerial role holds which is also supported in the arguments of (Mohhlmann et al., 2021) as well. The study aims at describing how leadership-related practices can reduce uncertainty, perceived support, and coping in gig workers by making use of stress-oriented theoretical perspectives. In order to assess perceptions of leadership, it is proposed that to evaluate the behaviors of human leaders, the Multifactor Leadership Questionnaire (MLQ) (adapted to this study) is applied on the platform, as (Watson et al., 2021) recommend. The restatements of the objects are done to grasp how drivers view the platform communication, feedback systems and policy signals as leadership influences. This approach can be compared to the arguments put forward by (Silva & Nyobe, 2023), according to which gig services fulfil a functional leadership task with the help of design and communication which is reflected by (Moorman et al., 2024) in their analysis.

The theoretical approaches used in the paper include Job Demands-Resources (JD-R) and Conservation of Resources (COR) as the perspectives that can be used to analyse how platform leadership may contribute to occupational stress. According to (Mohhlmann et al., 2021), supportive leadership signals also signify job resources that introduce clarity, reassurance and perceived fairness, thus safeguarding the emotional and mind resources of the drivers. Conversely, cues of unsupportive leadership augment job requirements such as pressure and job insecurity. This theoretical prism highlights how the perception of leadership in gig work environments can alleviate or increase stress which was found by (Krzywdzinski & Gerber, 2020) also. Since the advancement of society towards increased use of ride-sharing is a significant issue, the ability to create this knowledge is central to the development of sustainable, people-centered conceptualizations of gig work in the United States.

It helps in the expansion of theory as it takes the research on leadership in the gig economy, which has not been the central topic of the traditional organizational and management research before. The empirical data of the study demonstrates the results of the investigation on the ride-sharing services in the United States and the direct and indirect relationships between work environment support networks, leadership styles, income, and worker autonomy and reduced occupational stress in drivers. The findings add to the understanding of employee welfare in non-traditional working settings. Within the identified research gap and problem, the current study examines how the leadership mechanisms, income stability, worker autonomy, and work environment support have any effect in reducing occupational stress in ride share drivers in the United States. It also analyses the moderating nature of worker autonomy in the association between these predictors and workplace stress. Placing these aims in the context of the specifics of the gig economy, the research article makes it possible to formulate the study subject. It makes sure that the empirical analysis is based on meaningful and policy-relevant questions.

In spite of the fact that the presence of ride-sharing platforms are increasing at high rates in the United States, empirical research on the effects of leadership processes in platform systems on the issue of occupational stress among gig workers, is lacking. Although previous studies such as (Asfahani et al., 2023; and Gerber, 2020) investigated stress and leadership in traditional work and hierarchical contexts, little attention has been paid to algorithmically mediated environments where managers interact indirectly and where management is realized through digital technologies, policies, and visual signals. Being employed by a ride-sharing service means working by demand, without transparency, and being subject to performance assessment programs that can create stress patterns not typical of traditional work environments (Oruh et al., 2021). Nevertheless, little has been done to determine how platform-based leadership cues, organizational policies, and support systems help reduce or increase occupational stress levels. Such a gap limits theoretical development and policy making. Thus, an empirical study is essential to clearly understand how the factors related to leadership operate within U.S. ride-sharing platforms and determine the outcomes of occupational stress among workers in the gig economy in modern labor markets.

The objectives of the study are stated as:

• To examine the impact of leadership style on occupational stress among ride-sharing drivers in the U.S. gig economy.
• To investigate the relationship between income stability and occupational stress among ride-sharing drivers.
• To assess how worker autonomy influences occupational stress in the U.S. ride-sharing sector.
• To evaluate the effect of work environment support on occupational stress among gig workers.
• To explore the moderating role of worker autonomy in the relationships between leadership, income stability, work environment support, and occupational stress.

2. LITERATURE REVIEW

2.1. Leadership and Occupational Stress

Leadership style has also been identified as a significant factor that defines occupational stress outcomes. In the study, (Oruh et al., 2021) found that compassionate leadership alleviated stress among employees in crises by mitigating job insecurity and emotional burden; nevertheless, the qualitative emphasis in the traditional sectors of Nigerian economies limits generalization to an algorithmic environment such as ride-sharing. In contrast, a study by (Krzywdzinski & Gerber, 2020) established the same association, showing that the negative influence of stress on performance was mitigated by servant leadership. However, they found that supervisor-worker relationships are absent on gig platforms. To change the leader measurement to a platform situation, the Multifactor Leadership Questionnaire (MLQ) is used by reevaluating leader behaviors in terms of platform cues such as communication tone, performance feedback, and policy-based guidance, so that respondents evaluate platform leadership rather than an actual supervisor. Guided by the theory of Job Demands-Resources, supportive leadership is expected to improve the resources and alleviate occupational stress. Although previous research by (Oruh et al., 2021; and Krzywdzinski & Gerber, 2020) emphasizes the stress-relieving effects of compassionate and servant leadership, it is mostly limited to traditional workplaces where employees are managed and where qualitative or supervisor-focused indicators are used. The difficulty is that there is no empirical literature on the mechanisms of leadership operating through platform-mediated signals in gig economies, including algorithmic feedback, policies, and communication. This gap constrains knowledge of the effectiveness of leadership in mitigating occupational stress among ride-sharing drivers, which explains why the present study is relevant. Therefore, the study hypothesizes that;

H1: Leadership style is statistically significantly associated with occupational stress among ride-sharing drivers in the United States.

2.2. Income Stability and Occupational Stress

Income stability is one of the most critical factors determining gig-based employment stress. (Ali & Sivasubramanian, 2024) studied an algorithmic pay structure and, conceptually, posited that increasing drivers’ stress through the unpredictability of their earnings further increases their occupational stress; however, their results were not empirically tested. (Zhu et al., 2024) examined the volatility of gig workers’ incomes, making empirical connections between changing earnings and reduced well-being. Nevertheless, generalizability to the US ride-sharing arena is constrained by cultural and regulatory differences. Conversely, (Asfahani et al., 2023) focused on financial anxiety in digitally mediated work but used descriptive data, which limited causal interpretation. Collectively, these studies indicate that income stability is a stressor, though they provide piecemeal evidence, typically not in a US context or with behavioral consequences. Based on JD-R and COR theories, income stability is a resource that conserves a psychological capacity and reduces strain. Whereas current research has indicated that income instability is a cause of occupational stress, available data remains either disjointed, context-dependent, or methodological. There are a limited number of empirical studies that test the effect of algorithmic pay on stress within the U.S. ride-sharing industry, and most do not measure the behavioral implications of algorithmic income volatility. This creates an acute gap in understanding the role of income stability as a job resource in the JD-R and COR model in facilitating occupational stress among drivers in the gig economy. Thus, this research hypothesizes:

H2: Income stability is statistically significantly associated with occupational stress among ride-sharing drivers in the United States

2.3. Work Environment Support and Occupational Stress

The work environment support is a critical job resource that may counteract the impact of occupational stress. (El-Bourkadi, 2023) found that supportive organizational practices can alleviate stress by increasing psychological safety; however, the research was based on traditional employment models, in which employees have direct access to managerial support. Instead, (Tassinari & Maccarrone, 2020) demonstrated that ride-sharing platforms lack a formal support framework, and drivers have an unstructured communication system and few options for resolving conflicts, which increases occupational stress. (Meijerink & Keegan, 2021; Dolber et al., 2021) emphasized that the priorities of algorithmic management are more focused on productivity than on well-being, eliminating the possibility of Leader-Member Exchange and minimizing the availability of emotional or procedural support.

By utilizing the JD-R and COR theories, resource gain is limited due to insufficient support, and resource loss is hastened, making gig work more susceptible to stress, as argued by (Caza et al., 2022). Thus, providing a work environment is still crucial in digitally mediated work. Although (El-Bourkadi, 2023; and Tassinari & Maccarrone, 2020) affirm that work environment support alleviates stress, they are more concerned with traditional employment settings in which managers interact directly with employees. There are few studies on the workings of supportive structures in algorithmically controlled gig economies, where communication is unstructured and procedural or emotional support is limited. This gap limits insight into the functioning of work environment support as a buffer resource to reduce occupational stress among ride-sharing drivers in digitally mediated, U.S.-based gig work. These arguments emerging from the literature review led to the formulation of the fourth hypothesis of the study, which is:

H3: Work environment support is statistically significantly associated with occupational stress among ride-sharing drivers in the United States.

2.4. Worker Autonomy and Occupational Stress

Worker autonomy is generally accepted as providing employees with an environment in which to escape occupational stress. Experimentally, (Zeuge et al., 2023) showed that technology-based autonomy decreased stress, ensuring that employees did not feel forced to work overtime, depending on the leadership style. Still, the short-term simulation limits the ecological validity of the intervention in real working conditions. By applying a large longitudinal sample, (Clausen et al., 2022) confirmed the positive association between autonomy and psychological well-being; however, they relied on a different context of traditional employment and stability of the income, whereas in the case of an algorithmically controlled gig employment, perceived autonomy can be limited by platform systems. Autonomy has been described by (Jaaron et al., 2023) as an advantage in ride-sharing. Still, COR and JD-R theories indicate that autonomy can only help reduce stress when the autonomy is combined with adequate resources, including fairness, control, and decision latitude, as (Jain et al., 2021) argue. Autonomy can be oppressive or lonely without encouraging signals from leadership. Therefore, based on these literature findings, the third hypothesis H3 of the study is formulated.

H4: Worker autonomy is statistically significantly associated with occupational stress among ride-sharing drivers in the United States.

H4a: Worker autonomy significantly moderates the association between the work environment and occupational stress among ride-sharing drivers in the United States.

H4b: Worker autonomy significantly moderates the association of income stability with occupational stress among ride-sharing drivers in the United States.

H4c: Worker autonomy significantly moderates the association of leadership style with occupational stress among ride-sharing drivers in the United States.

3. METHODOLOGY

This study uses a quantitative research design to examine the role of leadership in minimizing occupational stress among ride-sharing service workers in the gig economy. The quantitative approach is applicable in this research, as it allows for structured and systematic data collection, measurement, and analysis of statistical and numerical datasets, and for further testing, formulating, and exploring relationships between variables (Fischer et al., 2023). The data are collected using a primary data collection approach, with different instruments containing quantifiable, structured, and closed-ended questions (Appendix 1). For instance, (South, 2022) suggested that a structured online questionnaire on digital channels will be used to collect data. Finally, the questionnaire will use a 5-point Likert scale to capture responses for the variables: Leadership perceptions, occupational stressors, and degree of worker autonomy. Moreover, standardized and validated instruments are adapted for this study, including the Leadership Style Transformational and Supportive Leadership, measured using the Multifactor Leadership Questionnaire (MLQ), as guided by (Batista-Foguet, 2021). However, it was adapted to fit into the gig economy context.

The MLQ was also appropriate in the current study, as leadership cues continue to emerge in managerial policies and in algorithmic decision-making perceived as leader behavior, as argued by (Cameron et al., 2022). Feedback, task assignment, and performance monitoring are examples of platform signals that act as the so-called digital leadership cues, which drive motivation and stress in the same way as human supervision (Meijerink & Keegan, 2021; Tassinari & Maccarrone, 2020). Earlier studies on platforms have adapted the leadership scales by paraphrasing the questions to indicate indirect or technologically mediated control (Abkhezr & Tang, 2024), so that respondents can evaluate perceived leadership behaviors rather than individuals. Further, as (Shafie, 2024) suggested, occupational stress was measured with the Occupational Stress Indicator (OSI). Also, for Worker Autonomy, a scale adapted from prior organizational behavior literature was used to measure it.

This study targets ride-sharing service workers in the United States, e.g., drivers working for Uber and Lyft, as the target population. Participants were recruited through a non-probability convenience sampling method, using online forums, social media, and driver networks to reach a diverse sample of 600 respondents. However, only 280 participants responded to the questionnaire, yielding a response rate of 46.6%. Missingness was assessed using the MCAR test, which indicates that missing data were not random and that imputation should not be used when there are significant gaps. Responses that had more than 10% non-response rates, that had patterned non-response, or extreme outliers based on +-3.29 z-scores and Mahalanobis distance were dropped to prevent biases in SEM as suggested by (Krzywdzinski & Gerber, 2020) as well. This left a combined sample of 200 cells (28.6%), which was valid and met the recommended data-quality criteria for SEM model reliability. According to partial least squares structural equation modeling (PLS- SEM) guidelines, a minimum sample size of 200 respondents is finalized, which is calculated using the G*Power model, as a sample size large enough to obtain statistical power is required, which is suggested by (Maragheh, 2024) as well.

Multiple regression with a maximum of eight predictors (a =.05, power =.80, medium effect f² =.15) a priori power analysis (e.g., G*Power) would indicate that the sample should have at most 109 cases, though to estimate the latent variables and the interaction terms reliably, much larger samples are needed to do so. Given 8 latent constructs (five primary constructs and three interaction terms), with about 24 indicators and numerous paths, a target of N = 200 was selected as a conservative sample size to achieve stable parameter estimates and model fit, as reflected by (Möhlmann et al., 2021).

The primary data collection also raises substantial issues, such as selection bias and non-response bias, which warrant emphasis. Therefore, in the current study, the selection bias is addressed by recruiting participants from professional networking platforms such as LinkedIn and the official social media pages of ride-sharing services. On these platforms, riders and drivers with full-time and part-time contracts with ride-sharing services, from diverse demographics, were approached, allowing for diverse responses. On the other hand, non-response bias was addressed by following the suggestions of (Minderop, 2023), who identified the significance of the difference between late respondents (n1 = 30) and early respondents (n2 = 30), with an insignificant difference indicating no non-response bias.

The data collection method in the study followed Partial Least Squares Structural Equation Modelling (PLS-SEM), using the software application SMARTPLS, as suggested by (Sarstedt, 2021), to precisely measure direct and indirect relationships among variables by examining mediation and moderating effects. The measurement model was applied using the CFA approach in the first step. Composite Reliability and Cronbach’s Alpha were evaluated against the 0.7 threshold to assess validity and reliability. It is followed by an analysis of convergent validity using Average variance extracted (AVE), with a threshold of 0.5 as suggested by Shrestha (2021). Further, as guided by (Roemer, 2021), discriminant validity was assessed using the HTMT matrix to determine whether the variables are distinctive and separate, with no conceptual overlap. Lastly, path coefficient analysis was conducted to analyze direct and indirect relationships among the variables.

The application of PLS-SEM is justified by the study’s emphasis on prediction-oriented analysis and by the extension of theory across the developing research on mechanisms of leadership in the gig economy. Unlike covariance-grounded SEM, which requires rigid normality in large samples, PLS-SEM is robust with multifactored models and even functions with smaller sample sizes. Its application is also justified by the need to analyze multiple interaction effects, as simple regression-based approaches cannot concurrently estimate measurement and structural models with comparable reliability and precision.

4. RESULTS

4.1. Demographics Analysis

The statistical results depicted in Table 1 demonstrate the demographic profile of the participants. As shown in Table 1, among the total participants (n = 200), 71.50% were male and 28.50% were female. On the other hand, by age bracket, 20% were 18-25 years old, 32.5% were 26-35 years old, 20% were 36-45 years old, and 27.50% were 46-55 years old. Among all participants, 60% were riders and 40% were drivers, 66.50% served in ride-sharing services, and 33.50% worked in delivery or parcel services.

Table 1. Demographic profile.

4.2. Measurement Model Using CFA

The validity and reliability of the measurement model were assessed using CFA approaches. Reliability and validity were assessed using Cronbach’s alpha and Composite reliability, with values above 0.7 indicating acceptable levels. Convergent validity was assessed using the Average Variance Extracted Method, with a cutoff of 0.5.

The measurement model was evaluated by applying Confirmatory Factor Analysis (CFA) to analyze the validity and reliability of the constructs as depicted in Table 2. The findings reveal that all the factor loadings satisfy the threshold of (0.7) which means that each of the latent variables has required item representation. In the case of Income Stability, loadings are high, with Cronbach’s alpha (0.822) and Composite Reliability (0.833), which provide validation of internal consistency. Leadership Styles has strong loadings (0.881-0.917), Cronbach’s alpha (0.882), and Composite Reliability (0.882), demonstrating strong reliability and convergent validity. Likewise, Occupational Stress has high factor loadings (0.887-0.912), Cronbach’s alpha (0.878), and Composite Reliability (0.881). The Worker Autonomy also shows high reliability in loading (0.842-0.925), alpha (0.875), and Composite Reliability (0.902). Finally, there is a satisfactory internal consistency of Work Environment Support (loadings 0.857-0.907; alpha = 0.865; CR = 0.869). The Average Variance Extracted (AVE) values for all constructs are above 0.7, indicating high convergent validity.

Table 2. Measurement model.

4.3. Discriminant Validity

Discriminant validity was assessed using the Heterotrait-Monotrait Ratio (HTMT), following the approach suggested by (Sarstedt, 2021). The HTMT criterion allows for the guarantee that every construct is empirically distinct and separable. As Table 3 indicates, all HTMT values are below the conservative threshold of 0.85, indicating that discriminant validity is observed across all latent variables.

Table 3. Discriminant validity.

As indicated in Table 3, the highest HTMT value is observed between Work Environment Support and Occupational Stress (0.780), which is a moderate and acceptable level of conceptual overlap and separability, consistent with the theoretical expectation that environments that support influence perceived stress. The Leadership Style and Work Environment Support (0.678), Income Stability and Work Environment Support (0.654) also show acceptable inter-construct distinctiveness. In general, these findings indicate that the constructs are distinct and that there is no conceptual overlap, suggesting that the measurement model is adequate for use in the structural path analysis.

4.4. Path Analysis

The findings illustrated in Table 4 are valuable for understanding the interconnections among the independent variables that influence the level of occupational stress among employees in the gig economy ride-sharing sector. The most statistically significant and the strongest predictive relationship with occupational stress was Work Environment Support (β = -0.400, p < 0.001). In response to this, it is self-evident that a conducive workplace is highly essential for alleviating stress, and platforms are required to provide resources, safety, and positive feedback to employees. Lastly, Leadership Style is negatively and significantly associated with Occupational Stress (β = -0.202, p = 0.001), suggesting that transformational and supportive leadership practices have a strong impact on reducing stress, particularly in terms of employees’ emotional and mental health. In contrast, income stability is found to negatively but insignificantly affect occupational stress (β = -0.1, p = 0.06).

Table 4. Path coefficients.

Note: *Significance at 10%; **Significance at 5%; ***Significance at 1%

The other predictor of occupational stress is Worker Autonomy (β = -0.192, p < 0.001). The fact that the workers possess a certain level of control over the order and time of their work, in fact, contributes to the notion that the increased levels of autonomy, however, can be the primary source of satisfaction in a job and reduced levels of stress provided that it integrates with the healthy leadership and support structures.

Results for Worker Autonomy interaction effects with other independent variables are insignificant. Worker Autonomy and Leadership Style interaction on occupational stress are negative but insignificant (β = -0.012, p = 0.820), indicating that autonomy neither strengthens nor weakens the relationship between leadership and stress. The study similarly finds insignificant interactions between Worker Autonomy and Income Stability (β = -0.054, p = 0.203) and between Worker Autonomy and Work Environment Support (β = -0.046, p = 0.424). These results imply that, in this context, Worker Autonomy has an insignificant moderating effect on other variables and is independently significant in reducing stress levels.

Overall, the results suggest that leadership and work environment support are the most important factors in reducing occupational stress among gig economy workers, with income stability secondary. Autonomy of workers is validated as a key direct factor, but does not have significant moderation effects in the model. These findings imply that gig platforms need to focus on leadership and supportive work environments, and to further exploit autonomy as an independent stress-management factor.

4.5. Model Explanatory Power

The model’s explanatory power is measured by R-square and Adjusted R-square, which indicate the percentage of variance in the target variable explained by its predictors. The results of explanatory power are explained in Table 5.

Table 5. Model explanatory power.

The results in Table 4 indicate that income stability, leadership styles, worker autonomy, and work environment support predict 56.5% of the variance in occupational stress. These results indicate that overall working conditions, leadership, and motivational factors in the ride-sharing sector can account for a considerable share of occupational stress.

5. DISCUSSION

The results, while accepting H1, indicate that leadership processes are significant in reducing the occupational stress of ride-sharing employees in the United States, especially in settings with uncertainty and lower organizational identification. The findings are aligned with the ones of (Oruh et al., 2021), who proved that caring leadership lowers job insecurity and emotional stress, however, their support was founded on classical sectors. In similar vein, (Krzywdzinski & Gerber, 2020) found that the negative effect of stress on performance is offset by servant leadership. However, in contrast to the works that are conducted on a direct supervisor-employee relationships, U.S. ride-sharing leadership can be provided on the platform policy, algorithmic cues are the communications and a sense of procedural fairness. Such a distinction is possible due to the fact that gig workers are controlled through algorithms, as opposed to humans. These leadership cues enhance transparency of resources, equitable and emotional certainty, and therefore lower the level of stress, as (Cropanzano et al., 2023) also find to be the case with the JD-R model. Practically, open communication and fair policy-making is institutionalised on the U.S. platforms to improve the welfare of drivers. This perspective is also favoured by (Cropanzano et al., 2023), who declare that the perceived organisational care is capable of reducing emotional exhaustion despite constant structural constraints. Hence, the efficacy of leadership in ride-sharing is not stabilisation of income per se, but the mitigation of psychological pressure on the basis of symbolism reassurances, clear guidelines, and redundancy of signalling.

The results showed that the relationship between income stability and occupational stress was statistically insignificant yet negative resulting in the rejection of H2. This means that the issue of income stability does not play a significant role in the alleviation of stress among U.S. ride-hailing drivers, which is to say that only income does not influence stress. It is the opposite of the research of (Zhu et al., 2024), which explained the volatility of incomes by poor well-being, and (Asfahani et al., 2023), which utilised the concept of financial anxiety in the digitalized workplace. Such distinction can exist as a result of the conditions of surge-pricing and the demand variability undergone by ride-sharing drivers in the U.S. According to the JD-R and COR theories, the income stability is a work resource. Nevertheless, autonomy, leadership indicators, and work environment assistance are other tools that drivers utilise in order to cope with the levels of occupational stress.

Regarding the Job Demands-Resources model, income stability is regarded as a potential job resource, though, the buffer effect of this resource is contingent on the expectations and adaptive plans of the workers. (Silva & Nyobe, 2023) argue that when work design is structurally unstable, people recalibrate their coping patterns, making the role of income predictability less prominent in stress-related outcomes. Current evidence supports this point, showing that the drivers of U.S. ride-sharing companies may have generalized the risk of income as an intrinsic aspect of the job rather than a changeable stressor. However, financial transparency is still applicable. (Cropanzano et al., 2023) demonstrate that predictable rewarding systems improve wellbeing by reducing feelings of uncertainty and unfair treatment. The lack of such a system in the competitive, poorly regulated U.S. ride-sharing market may mute the effects that could be observed.

The relationship between worker autonomy and occupational stress was also found to be statistically significant and negative, indicating support for H3 and revealing that autonomy leads to decreased levels of occupational stress. Although (Zeuge et al., 2023) found that technology-enabled autonomy reduces stress by preventing overwork and reducing workers’ workload, the short-term experimental design limits its application to dynamic gig-based contexts. In similar vein, (Clausen et al., 2022) found positive correlations between autonomy and psychological well-being in conventional working environments where the income stability is experienced. However, in U.S. ride-sharing, autonomy operates within algorithmic systems of control and performance-measuring frameworks, as (Kuhn, 2021) points out. This disparity arises from the conditional freedom of gig workers, who are not given full control over platform algorithms. In line with the JD-R theory, autonomy reduces stress only when sufficient resources are available, suggesting that a lack of structural fairness and transparency implies that perceived autonomy both empowers and strains drivers in practice. In line with JD-R and COR theories, autonomy as a job resource can only be effective when it is supplemented with adequate levels of support, fairness, and decision discretion, as indicated in the study by (Jain et al., 2021). These results indicate that although autonomy is empowering, U.S. ride-sharing platforms should balance it with supportive systems and clear policies to address occupational-related stressors. Transformational Leadership Theory, on the other hand, proposes the inverse: that autonomy lowers stress when supported by considerate leadership that offers advice and appreciation, as reflected by (Asfahani et al., 2023) as well. Further the absence of such leadership in platform work implies that autonomy may become isolated. In the US cultural setting, where independence is valued and self-reliance is a dream, drivers see stress as a matter of personal failure rather than a systemic problem, as argued by (Cropanzano et al., 2023).

The research established that workers’ autonomy did not significantly moderate the relationships among occupational stress and other variables, such as income stability or support for the working environment and autonomy, leading to the rejection of H3a, H3b, and H3c. This result contrasts with the usual expectations, which hold that autonomy normally enhances the beneficial effects of favorable environments and reliable income. The reason could be due to illusory nature of worker autonomy in the gig economy. (Malik & Raziq, 2022) argued that the concept of autonomy in gig environments is frequently an illusion because of algorithmic governance, which restricts workers’ real freedom of choice. Along similar lines, (Keith & Long, 2020) stated that leadership models based on human interaction are not applicable when algorithms perform the manager’s role. The US cultural context favors competitive individualism, which may undermine a group-based coping strategy and reduce the moderating influence of autonomy. Incorporation of aspects of transformational leadership, including empathy-oriented communication, trust-building, and empowerment through sincere dialogue between the employee and employer, can result in reduced occupational stress among employees in the ride-sharing sector of the US.

Another predictor of occupational stress was found to be work environment support which showed statistically significant relationship with occupational stress in the US gig economy sector indicating acceptance of H4. Timely communication, safety procedures, and access to feedback are properties of a supportive environment that enable drivers to cope with the emotional demands of gig work. According to (Cropanzano et al., 2023; Ray et al., 2024), perceived organizational support creates a sense of belonging and well-being, reduces burnout, and minimizes emotional exhaustion. From the perspective of the Conservation of Resources (COR) theory, work environment support offers necessary psychological and instrumental resources, including safety assurance, responsive communication, and recognition, that drivers attempt to gain and maintain, as argued by (Cameron et al., 2022). If such resources are available, workers are less likely to lose them, which reduces stress and burnout.

In a similar vein, (Kuhn et al., 2021) found that workers’ morale can be boosted even by limited interpersonal connections in algorithmic systems. Nonetheless, (Malik & Raziq, 2022) noted that in the ride-sharing sector, the support process is usually procedural or even automated and not particularly empathetic. This difference is crucial in the US ride-sharing setting, since the notion of support generally refers to the responses the system generates, rather than human support. The industrial reliance on algorithmic management and the cultural focus on individuality or autonomy undermine support. From the perspective of the Transformational Leadership Theory, this is a lack of personalized consideration, which, as per (Bernhardt & Kresge, 2023), is one of the important factors that can aid employees in overcoming stress. Incorporating transformational aspects, including customized feedback, appreciation, and genuine interest. It can make support more valuable and efficient in alleviating occupational stress.