Modelling Psychosocial Work Factors and Risky Driving in Ghana: Mediating Roles of Burnout and Engagement

Modelling Psychosocial Work Factors and Risky Driving in Ghana: Mediating Roles of Burnout and Engagement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modelling Psychosocial Work Factors and Risky Driving in Ghana: Mediating Roles of Burnout and Engagement Mustapha Amoadu, Edward Wilson Ansah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5711969/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Heavy goods vehicle (HGV) drivers face significant psychosocial work challenges that increase burnout and risky driving behaviours (RDB), affecting drivers’ well-being and road safety. However, these psychosocial work challenges have received limited research attention in the developing countries. This study explored the relationships between job demands (JD), job resources (JR), burnout, job engagement (JE), and RDB in Ghanaian HGV drivers. Method : This cross-sectional survey collected data from 1,575 HGV drivers (truck and tanker drivers) in Tema, Ghana. Data were collected through validated questionnaires and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) using Smart PLS. Results : High JD directly increases burnout and RDB among HGV drivers in Ghana, while burnout increases the occurrence of RDB and negatively affects JE. High JR reduces burnout and increases JE among the drivers. However, high JR did not significantly reduce RDB among the drivers. Mediation analyses revealed that burnout partially mediates the relationship between JD and RDB, and JR and RDB. JE partially mediates the relationships between JR and RDB. Moderation analyses show that JR significantly buffers the effects of JD on burnout and burnout on RDB. Conclusion : Data suggest that psychosocial factors strongly influence burnout and RDB among Ghanaian HGV drivers. Targeted efforts to balance job demands and enhance support systems are critical to improving the health, safety and well-being of these drivers, and ultimately reducing the on-the-road accidents. Job demands burnout risky driving behaviours Ghanaian HGV drivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heavy Goods Vehicle (HGV) drivers are indispensable to the global supply chain sector, facilitating the movement of goods across countries and regions. However, the occupational challenges these drivers face and associated road safety consequences remain a major public health concern [1,2]. HGV drivers often endure long hours, irregular schedules, minimal rest, poor remuneration, and inadequate access to healthcare services [1]. These harsh working conditions not only endanger their well-being but also increase the risk of road traffic crashes (RTCs), affecting public safety on a larger scale [2,3]. The World Health Organization (WHO) estimates that RTCs claim approximately 1.19 million lives annually globally, with human error often influenced by fatigue, stress, and other occupational factors being a leading cause [4]. In Africa, the RTC crisis is alarming, with approximately 26.6 deaths per 100,000 population [5]. In Ghana, the situation is even more concerning, with road traffic fatalities accounting for 24.9 deaths per 100,000 people, substantially higher than the global average of 18.2 deaths per 100,000 people [6]. HGV drivers disproportionately contribute to these statistics due to the inherent risks associated with their work. Crashes involving HGVs tend to be more severe than those involving smaller vehicles because of the high mass of HGVs [7]. These crashes often result in serious injuries or fatalities, particularly among occupants of smaller vehicles such as cars and motorcycles [6]. Notably, over 80% of fatalities in secondary vehicles are linked to collisions involving HGVs [8]. In Ghana, evidence shows that, on average, whenever a heavy vehicle driver dies in a RTC, three additional road users also lose their lives [9]. In developed countries, significant strides have been made in integrating contemporary occupational health and safety (OHS) practices into the road transport industry [10]. These efforts have facilitated evidence-based interventions that enhance the safety of road users [11]. For example, Europe and other high-income regions have substantially reduced occupational traffic crashes through the implementation of robust regulations, policies, and OHS research tailored to the road transport sector [10,12]. Conversely, developing countries, including Ghana, face worsening conditions in their road transport sector [13]. In Ghana, OHS practices remain underdeveloped, with the framework still in its infancy [14]. Consequently, road transport accidents, injuries, and fatalities are not only common but also increasingly challenging to address [14]. While efforts to reduce risky driving behaviours, such as over speeding or substance use, are important, they often overlook the broader work environment in which HGV drivers operate [14]. In Ghana, long driving hours, lack of job security, inadequate support from vehicle owners, poor remuneration, and limited rest stop facilities contribute to high stress among drivers [13]. These challenges not only harm drivers’ well-being but also increase the risk of errors and the resultant RTCs [12]. Additionally, the poor work environment hinders progress towards Sustainable Development Goals (SDGs), particularly Goal 8.5, which promotes decent, safe, and healthy workplaces, and Goal 3.6, aimed at halving RTCs by 2030. A recent review identified limited evidence from the African region on psychosocial work factors such as psychological demands and social support on risky driving and RTCs[3]. The review further reported that psychosocial work factors among hazardous transport drivers in low-and middle-income countries (LMICs) remain underexplored, with few studies [15,16] addressing this critical occupational group. This lack of research attention has contributed to a narrow understanding of the underlying causes of risky driving in LMICs. Without sufficient evidence on work-related stressors, interventions often fail to capture the organisational psychosocial dimensions that shape driver behaviour. Despite the critical role of HGV drivers in national and regional economies, there is limited research on their psychosocial work environment in Ghana. Consequently, current road safety interventions tend to focus predominantly on individual risky driving behaviours, while overlooking the broader systemic and workplace factors that contribute to these on-the-road behaviours [17]. This gap persists partly due to the dominant behavioural framing of road safety in Ghana, which tends to individualise responsibilities for occurrence of accidents [13]. As a result, systemic work-related stressors remain underexplored, limiting the development of holistic interventions that address the root causes of risky driving. Bridging this gap is essential for advancing theory and practice in road safety within the sub-Saharan African context [13,17]. This study aims to model the relationships between psychosocial work factors, burnout, work engagement, and driving behaviour among HGV drivers in Ghana. In this study, psychological demands were selected as the key job demands because HGV drivers in Ghana often face long working hours, tight schedules, creating mental fatigue, which can increase stress and risky behaviour [13]. Social support from supervisors and co-workers was chosen as a job resource because it is a practical and accessible form of support in the sector. These variables reflect common and relevant aspects of the drivers’ daily work experiences that influence their well-being and road safety. The findings provide knowledge useful in designing interventions that address root causes of risky driving, improving driver well-being and road safety. Thus, these findings have the potential to inform occupational health and transport policies by demonstrating the need to integrate psychosocial risk management into road safety strategies. They can guide employers, regulatory bodies such as the National Road Safety Authority, and other policymakers in developing context-specific interventions that prioritise supportive work environments alongside behavioural road safety measures, the aim is to protect the health, safety and well-being of the drivers and promote safety on the road. Theoretical model The Job Demand-Resource (JD-R) Model [18] underpins this study. The main argument of the JD-R theory is that JD lead to strain and negative outcomes (health erosion), while JR promote motivation and positive outcomes (motivation), with both processes influencing overall job performance and well-being of the worker. The model highlights two pathways: the health erosion hypothesis, where excessive JD without adequate JR lead to burnout, compromising health, the affect is likely increases in RDB. The other is the motivational hypothesis, where JR (in this study support from supervisors and co-workers) enhance job engagement (JE), fostering motivation and reducing unsafe driving behaviours. In this study, JD and JR serve as predictors, with burnout and JE acting as mediators, explaining the mechanisms through which these factors impact risky driving or driving behaviours. The JD-R model provides a practical framework for understanding the dual influence of stress-inducing demands and protective resources from the organisational and worker perspectives. This study extends the JD-R model by introducing job resources, specifically, social support from supervisors and co-workers as moderating variables in two key pathways: the relationship between job demands and burnout, and between burnout and RDB. Unlike traditional JD-R applications that primarily position resources as direct predictors or mediators of engagement [18], this study conceptualises downstream job resources (e.g., interpersonal support) as buffering mechanisms that mitigate the adverse effects of JD and burnout. Authors argue that by applying this extended framework in the understudied context of HGV drivers in Ghana, the study provides novel insights into how interpersonal support can dampen the health erosion process and reduce the likelihood of unsafe driving behaviours in these high-risk occupational settings. In a resource-limited setting and within a largely informal occupational group like long-distance HGV drivers in Ghana, downstream job resources such as supervisors and co-drivers supports are particularly crucial. These interpersonal forms of support often substitute for more structured organisational resources such as formal employee assistance programmes, occupational health services, and robust enforcement of labour protections, which are typically absent or poorly implemented in such contexts. The extension of the JD-R model to include social support as a moderator may help explain why these lower-level resources are effective in reducing the impact of job stress on risky driving behaviour, offering a more context-sensitive understanding of how drivers cope with high job demands and burnout. Hypotheses development Health erosion hypothesis Within the JD-R framework, JD are theorised to trigger a health impairment process, whereby sustaining the exposure to excessive workload, time pressures, and psychological strain that progressively deplete energy reserves [19]. This depletion compromises cognitive functioning, induces emotional exhaustion, and undermines drivers' capacity to make sound decisions, ultimately heightening the risk of unsafe driving behaviours [20–22]. JD, such as excessive working hours, high workload, and time pressures, drain physical and mental resources of these drivers, leading to burnout [18,23]. Prolonged exposure to these demands exacerbates stress and exhaustion, making individuals (drivers) vulnerable to burnout [24]. Among commercial drivers, studies reveal that high JD significantly elevate burnout levels [25,26]. Additionally, high JD contribute to RDB by inducing fatigue and stress, which compromise decision-making and reduce adherence to safety protocols while on the road [27,28]. Professional drivers facing time pressures or heavy workloads are more likely to engage in unsafe practices, such as over speeding and or ignoring traffic rules [26]. In contrast, high JR mitigate burnout by fostering resilience and providing support for workers [26]. Resources such as supportive supervision and flexible schedules reduce burnout and help drivers cope with JD effectively [23,25]. For drivers, these resources strengthen physical and psychological resilience, thereby lowering burnout [26]. The Challenge is that burnout impairs cognitive functioning and decision-making, which further increase RDB [28]. Research among drivers has shown that burnout is strongly associated with unsafe driving practices [21]. Evidence further shows that JD and JR affect driving performance through burnout and work engagement [10]. Based on this evidence it is hypothesised that: H 1 : JD have positive and significant relationship with burnout. H 2 : Burnout have positive and significant relationship with RDB. H 3 : JR have negative and significant relationship with burnout. H 4 : JD have positive and significant association RDB. H 5 : Burnout significantly mediates the association between JD and RDB. H 6 : Burnout significantly mediates the association between JR and RDB. Motivation hypothesis In the JD-R model, JR activate the motivational process by enhancing individuals’ capacity to remain committed, focused, and psychologically resilient in the face of occupational challenges [19]. In the transport sector, supportive interactions with supervisors and peers serve not only to bolster engagement, but also to buffer the negative effects of high demands, thereby promoting adherence to safe driving behaviours and reducing the propensity for risk-taking [25,28,29]. JR, such as social support from supervisors and colleagues enhance engagement by fostering motivation and resilience among workers [18,19]. Dollard et al. [30] found that access to JR positively influence energy and dedication at work. While the link between JR and JE among drivers is underexplored, existing studies indicate a positive relationship between these variables [31,32]. JR serve as protective factors against unsafe driving behaviours by providing drivers with motivation and support to follow safety standards while on the road [21]. The connection between JR and driving performance is well-documented [12,21]. Burnout diminishes engagement by draining the emotional and physical resources [33], leading to disengagement and decreased motivation among workers [19,33]. However, this relationship is extensively examined among professional drivers. Furthermore, JR influence organisational and employee outcomes through work engagement [19,34]. Hence, these hypothesises: H 7 : JR have positive and significant association with JE. H 8 : JE have negative and significant association with RDB. H 9 : JR have negative and significant association with RDB. H 10 : JE significantly mediates the association between JR and RDB. H 11 : Burnout has a negative and significant association with JE. Buffering effect of job resources Demerouti et al. [18] demonstrated that JR mitigate the adverse effects of high JD on employee well-being. This buffering effect protects employees from experiencing excessive burnout by providing the necessary resources to cope with high JD effectively [35]. The buffering effect of JR means that in a high JR work setting, the effect of high JD on burnout or the effect of burnout on job performance could be reduced significantly. The moderating role of JR in the health erosion pathway has been explored in the general working population [35,36] but isamong HGV drivers in Africa. A review shows that previous studies have consistently applied the JD-R model in a more conventional manner [19], often without exploring alternative roles of job resources in settings with limited infrastructure and institutional support. This consistency, while valuable, points to an opportunity to expand and test the model’s boundaries. Hence, we hypothesised that: H 12 : JR significantly moderate the association between JD and burnout. H 13 : JR significantly moderate the relationship between burnout and RDB. Methods Designs and population This cross-sectional survey involved 1,575 HGV drivers in Ghana, consisting of 910 truck (haulage) drivers and 665 tanker drivers. The sample size represents 26.6% of the estimated target population of 5,312 HGV drivers. This population includes 3,240 haulage drivers operating at the Tema Port truck terminal and 2,072 tanker drivers working at seven bulk storage terminals in the same area. In this study, long-distance driving refers to drivers who travel at least 140 km per trip to their destinations. The truck drivers are registered members of the Ghana Haulage Truck Drivers Association, they operate from the Tema Port truck terminal. Similarly, the tanker drivers are members of the National Petroleum Tanker Drivers Union and the Liquified Petroleum Gas Tanker Drivers Union. All the drivers included in the study were full-time workers and held professional licenses issued by the Driver and Vehicle Licensing Authority (DVLA) of Ghana. Measures The drivers provided information about their age, highest educational attainment, marital status, years of work experience as a professional truck/tanker driver, daily driving hours and weekly working days. The summary are presented in Table 1. Insert Table 1: Details of measures Procedures and Ethics The study recruited drivers with the assistance of their union executives and company administrators. Drivers waiting for their next load were conveniently sampled for the study. The questionnaire was translated into Twi, a widely spoken local language, and back-translated into English to ensure accuracy by a language expert from the University of Cape Coast. Data collection was conducted with the help of eight trained field assistants through survey interviews lasting 25 to 45 minutes. The survey interviews took place over three months, from June to August 2023. Drivers were informed of their right to withdraw from the study at any time and were assured of confidentiality. For drivers who could not read or understand the English language, field assistants explained the consent form before obtaining their written consent. No financial or material incentives were provided for participation in this study. This study received ethical approval from the Institutional Review Board of the University of Cape Coast, Ghana (ID: UCCIRB/CES/2022/82). Prior to data collection, a pre-test involving 91 heavy goods vehicle (HGV) drivers in the Takoradi Metropolis was conducted to evaluate the psychometric properties of the survey instruments. Analytical procedures Hypotheses (paths) in the proposed model were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM) using Smart PLS version 4.1.0.9. The procedures proposed by Hair et al. [37] in analysing path models were followed. Model specification and assessment of outer model The predictors (JD and JR), mediators (Burnout and JE), and the outcome variable (RDB) were quantitative latent variables measured with quantitative indicators and reflectively modelled (see Figure 1). Composite reliability (CR) with acceptable values ≥ 0.70 [37] was used to establish the internal consistency of constructs in the path models. Average variance extracted (AVE) values ≥ 0.50 [37] were used to assess convergent validity. To assess the discriminant validity of the constructs, the criteria by Fornell and Larcker [38] and Heterotrait-Monotrait ratio of correlation (HTMT) values (< 0.90) were applied (See Table 2 and 3). Indicators with outer loadings < 0.70 were deleted, and the analysis continued until the desired outer loading (≥ 0.70) was achieved. This led to the deletion of two items measuring social support (JR5 and JR8) due to outer loadings < 0.70. The outer loadings, CR, Cronbach’s alpha (a), and AVE of constructs in the path model are presented in Table 4. Insert Figure 1: Structural model based on the JD-R model Insert Table 2: HTMT ratio of correlations Insert Table 3: Fornell and Larcker (1981) Criterion Insert Table 4: Outer loading, a, CR and AVE of constructs Assessment of inner model The assessment of the inner model started with the assessment of the multiconllinearity. The Fornell and Larcker’s criterion was used, we found that issues of multicollinearity did not exist in the path model (See Table 3). This was further confirmed using the variance inflation factor (VIF) (See Table 5), which were between the acceptable range (VIF >0.10 and < 5) [37,38]. Satndardised root mean square residual (SRMR) was then used to assess the model fit with a criterion of £ 0.10 (Hairt et al., 2021). The SRMR of 0.10 of the inner model was acceptable. The adjusted R ² ( R 2 adj ) was used to determine predictive ability of the model. JD, JR, burnout and JE explained 78.5% of variance in RDB ( R 2 adj = 0.785). JD and JR explained 61.4% of variance in burnout ( R 2 adj = 0.614) and JR and burnout explained 66.6% of variance in JE ( R 2 adj = 0.666). See the details in Figure 2. Cross-Validated Redundancy ( Q 2 ) was used to assess the predictive relevance of the inner model using the Stone-Geisser criterion of Q 2 >0 [37]. The three endogenous constructs, burnout, JE and RDB had Q 2 values of 0.608, 0.664 and 0.691 respectively, the higher it is from zero, the better the predictive relevance of the construct. Finally, the model’s path coefficients and its significance (See Figure 3) and effect size (See Table 5) using Cohen f 2 were assessed using the bootstrapping process (Table 5). A path is significant if t-value is >1.96 at p<0.05. Cohen f 2 of 0.02, 0.15 and 0.35 imply small, moderate and large practical, respectively [39]. Evaluation of mediation and moderation models To evaluate the mediation role of burnout and JE, the direct paths need to be significant to establish the basis for assessing the mediation roles to be partial or full mediation (Hair et al., 2021). In full mediation, direct paths is no more significant in the presence of the mediators. In a partial mediation, the direct paths are still significant but reduced when the mediators are present in the model (Hair et al., 2021). The moderating roles of JR was examined using the two-stage approach [37]. The significance of the interaction effects (H 12 and H 13 ) were assessed using the bootstrapping process, which is supported when the t-value is > 1.96 at p< 0.05. The f 2 of the interaction effect were evaluated using criterion .005. 0.01 and 0.25 for small, moderate and large effects respectively [37]. Slop plots were then presented. Insert Table 5: VIF and f 2 of the path coefficients Insert Figure 2: Path coefficient, outer loading and R 2 adj of the path model Insert Figure 3: Path coefficients, p-values and outer weights of the path model Results Socio-demographic characteristics of the HGV drivers The study included 1,575 drivers, predominantly males (94.7%), with a mean age of 39.2 years and an average HGV driving experience of 13.2 years. Educational levels varied, with 39.7% having basic education and 11.4% attained tertiary education. Half of these drivers were single (50.2%), and the average monthly salary was $92.13, about 1,500 Ghana cedis Testing hypotheses Hypothesis 1 suggests that JD have a direct influence on burnout (r = 0.556, t = 29.609, p < 0.001), which is supported. Hypothesis 2 proposes that burnout has a direct impact on RDB, and this is also confirmed (r = 0.185, t = 10.333, p < 0.001). Hypothesis 3 states that JR have a negative association with burnout, it is supported (r = -0.263, t = 13.018, p < 0.001). Hypothesis 4 posits that JD are directly associated with RDB, and this is confirmed (r = 0.272, t = 12.093, p Burnout -> RDB) is significant (t = 7.409, p < 0.001), suggesting that JD increase RDB (r = 0.077) indirectly through increased burnout. Hypothesis 6 predicts that JR mediate the association between burnout and RDB. This is confirmed, with the specific indirect effect (JR -> Burnout -> RDB) being significant (t = 6.615, p < 0.001), meaning JR reduce RDB (r = -0.028) by decreasing burnout. Hypothesis 7 is that JR have a positive and significant association with JE, which is supported (r = 0.690, t = 47.134, p < 0.001). Hypothesis 8 proposed a negative association between JE and RDB, and this is also supported (r = -0.504, t = 22.310, p < 0.001). Hypothesis 9 posited a negative association between JR and RDB, but this is not supported (r = -0.042, t = 1.557, p = 0.120). Hypothesis 10 proposed that JR mediate the association between JE and RDB, and this is confirmed (t = 21, p < 0.001), with JR reducing RDB (r = -0.416) through increased in JE. Hypothesis 11 suggested that burnout has a negative association with JE, and this is supported (r = -0.180, t = 13.068, p < 0.001). All mediation effects were partial, as both the direct and indirect paths remained significant in the presence of the mediators. Hypotheses 12 and 13 proposed that JR play significant moderating role in the association between JD and burnout and between burnout and RDB. These hypotheses were confirmed indicating a significant moderating role of JR in these paths (See Figure 3). The f 2 for the moderating effects JR x JD-> Burnout and JR x Burnout -> RDB were 0.031 and 0.046 respectively, indicating moderate effect sizes [37] (see Table 5). Slope plots for the hypothesis 12 (JR x JD-> Burnout) is presented in Figure 4, which shows that at low levels of JR, JD and burnout rise significantly (red line). At high levels of JR, the slope is weak (green line). Thus, increased JR reduces the impact JD has on burnout, reducing the likelihood of burnout even as JD rise. The slope plot for Hypothesis 13 (JR x Burnout -> RDB) is presented in Figure 5. This slope plot shows that at low levels of JR, burnout and RDB increase significantly but as JR increase the slope becomes flat (green line) indicating that higher JR reduces the impact burnout has on RDB. Insert Table 6: Mediation effects in the path model Insert Figure 4: Slope plots for the moderating role of JR on the path JD->burnout Insert Figure 5: Slope plots for the moderating role of JR on the path burnout -> RDB Discussion Summary of findings The findings confirm that high JD directly increase the level of burnout and RDB among HGV drivers in Ghana, while burnout increases the occurrence of RDB and negatively affects JE. High JR reduce burnout and increase JE among the drivers. However, high JR did not significantly reduce RDB among the drivers. Mediation analyses reveal that burnout partially reduces the effect of JD on RDB, and JR on RDB, while JE partially mediates the effect of JR on RDB. Moderation analyses show that JR significantly buffer the effects of JD on burnout, and burnout on RDB, reducing their impacts. Discussion of findings The study confirms that JD significantly increase both burnout and RDB among HGV drivers in Ghana. This is consistent with the health erosion hypothesis of the JD-R model. High JD, such as long driving hours, tight delivery deadlines, and limited rest, are prevalent among Ghanaian HGV drivers, as found in the current study and also in the previous ones [17]. Burnout among drivers in this context arises from chronic exposure to these driving demands. The influence of JD on burnout and RDB are reported in studies in China [26] and the United States of America [25]. These studies reported that JD elevate stress and worker burnout, thereby impairing decision-making, which increases risky behaviours among drivers [25,26]. Ghana's unique road transport challenges, such as poor road infrastructure, long distances between rest stops, and inadequate enforcement of rest breaks [17], likely amplify these effects. The significant link between JD and RDB reemphasises that Ghanaian HGV drivers who drive under high pressure may engage in unsafe practices like over speeding or driving under high level of fatigue [26], or even under the influence of drugs. These behaviours are not merely individual lapses but stem from systemic issues in the work environment that demand immediate attention, unfortunately, they lead to crashes with attendant injuries, fatalities and their economic costs, both to the individual and the nation. The mediation analysis reveals that burnout partially mediates the relationship between JD and RDB. This means that while JD directly increase RDB, a significant portion of this effect operates through burnout. Burnout impairs cognitive, including reduce concentration and judgement, and physical functioning ability, which are critical for safe driving [21]. Previous studies conducted among professional drivers in China reported similar findings [27,28]. These studies highlight the role of burnout in fostering unsafe driving practices. In Ghanaian setting, the compounding effect of burnout on RDB may be particularly severe due to the absence of appropriate occupational health support systems for these drivers [14]. Unlike drivers who benefit from mental health programmes and stress management resources like those from Spain [10], Ghanaian drivers often lack these safeguards, creating a cycle where burnout reinforces risky behaviours, which further endanger drivers and other road users. Also, in the Ghanaian context, peer support and informal check-ins could serve as a culturally relevant and cost-effective approach to alleviating driver burnout, especially where resource-constrains limit formal mental health infrastructure and provision [14]. Such grassroots-level strategies may help reinforce coping mechanisms and build resilience, ultimately reducing the risk of unsafe driving behaviours. The study demonstrates that high JR, such as supervisor and co-worker supports significantly reduce burnout and enhance JE among the drivers. This supports the motivational hypothesis of the JD-R model, which argues that JR buffer the effects of JD by fostering motivation and resilience of workers [18,19]. Previous studies conducted in China [23] and Colombia [21] among professional drivers corroborate these findings, showing that supportive supervisors and colleagues mitigate stress and promote positive workplace attitudes. Also, among cross-sectoral workers in Belgium, JR was found to reduce burnout while improving JE [40]. In the transport sector in Ghana, however, the effectiveness of JR may be constrained by systemic limitations. For instance, while support from supervisors can alleviate some stress, structural issues such as inadequate wages and lack of proper training might limit the overall impact of these resources [17]. The enhancement of JE through JR also highlights that engaged drivers are less likely to succumb to burnout and its associated risks [23]. In developing countries like Ghana, where formal mental health services are often lacking in the transport sector [14], these informal yet contextually relevant support mechanisms could serve as a cost-effective means to bolster engagement and prevent burnout, especially when complemented with clear policies that reinforce supportive leadership practices. Interestingly, the findings reveal that JR did not directly and significantly reduce RDB among the drivers. This is contrary to evidence reported in previous studies [26,41]. This finding also diverges from a prior study [21] that suggests that JR can directly improve safety outcomes. While JR improve engagement and reduce burnout among the drivers, these effects may not translate into safer driving behaviours. [21]The lack of direct impact of this relation among HGV drivers in Ghana may stem from contextual factors such as external pressures on drivers, including unrealistic delivery schedules and financial insecurities. These pressures may overshadow the protective benefits of JR, forcing drivers to prioritise how to meet the requirements of the job over safety despite supportive environments, from immediate supervisors and co-workers [17]. Also, the absence of a direct impact of JR on risky driving may reflect a mismatch between the type of support provided and the practical needs of the drivers. In high-risk environments like HGV driving, social support alone may be insufficient to change on-the-road safety behaviour without the broader systemic support such as financial incentives or safer infrastructure. Additionally, drivers may normalise risk-taking as part of the job, limiting the behavioural influence of supportive relationships. Moreover, environmental challenges like poor road conditions and weak enforcement of traffic regulations could diminish the influence of JR on RDB [17]. The study further confirms that JE partially mediates the relationship between JR and RDB, indicating that while JR may not directly reduce RDB, their influence on engagement plays a significant role. However, it is our view that under the current circumstances, these drivers could not appreciate engagement since they have no control over when to go on a strip. In that case, JE would be a partial mediator. Engaged drivers are more likely to exhibit proactive safety behaviours, but less likely to engage in risky driving practices [34]. This finding resonates well with existing evidence that work engagement enhances energy and focus, reducing the likelihood of errors among workers [33]. Thus, this relationship highlights the importance of fostering engagement in a challenging work environment among these drivers. Drivers who are engaged, despite external pressures, may demonstrate greater adherence to safety practices, although systemic challenges like poor enforcement, inadequate rest facilities and poor working conditions still pose significant barriers to on-the-road safety [17]. Furthermore, this finding highlights the value of investing in JR to enhance psychological engagement, especially in settings where structural limitations persist. Cultivating a sense of purpose and involvement among these drivers could indirectly promote safer driving, even when external conditions cannot be fully controlled [31]. Improving engagement is not just a motivational tool but also a practical strategy for managing risk [42]. Moderation analysis shows that JR significantly buffer the effects of JD on burnout, supporting the buffering hypothesis within the JD-R framework [18,19]. Drivers with high JR experience less burnout even when facing high JD. This finding is consistent with the work of Danudoro et al. [35], who also highlighted the protective role of JR in reducing worker stress, suggesting a similar pattern across different occupational contexts. In Ghana, this buffering effect is critical, as it highlights the potential of social support to mitigate the harmful impacts of excessive demands on these drivers. However, the limited availability and accessibility of social support and the priority given to productivity over well-being in the road transport sector [17] is likely to reduce the effectiveness of this buffering mechanism. The moderation analysis further confirms that JR reduce the impact of burnout on RDB. Drivers with access to JR are less likely to exhibit unsafe driving behaviours even when experiencing burnout. This finding is consistent with studies like that of Xanthopoulou et al. [36], which show that JR enhance resilience, enabling workers to maintain performance under strain conditions. This finding is particularly relevant in Ghana, as drivers often face chronic burnout due to high JD and systemic challenges [17]. While social support from supervisors and colleagues alone cannot eliminate the risks associated with worker burnout, they provide critical support that helps drivers to cope, potentially reducing the frequency and severity of unsafe driving practices. Furthermore, these findings offer theoretical insight by demonstrating the dual moderating role of JR in both the health erosion and behavioural pathways, thereby extending the JD-R model’s applicability in high-risk transport settings. The inclusion of downstream social support as a moderator enriches the model by illustrating how context-specific resources can shape outcomes under extreme job strain, warranting further testing across diverse occupational groups. Implications for practice, policy and research The findings highlight the need for a contextual approach to addressing the psychosocial challenges faced by HGV drivers in Ghana. Reducing JD through realistic driving schedules and mandatory rest periods are essential to mitigate burnout and RDB. Enhancing JR, such as supervisory support, training, and co-worker collaboration, can alleviate worker stress and promote engagement. Though these efforts must be complemented by systemic changes like improved wages, better road infrastructure, integration of modern OHS standards in the transport sector and enforcement of safety regulations. Furthermore, recognising the role of burnout and engagement as mediators explain the importance of mental health support and fostering a positive work environment to improve driving safety outcomes. Finally, the buffering effect of JR demonstrates their potential to shield drivers from the adverse impacts of high JD and burnout, necessitating targeted efforts to strengthen social and organisational support systems within Ghana's road transport sector. In addition to practical relevance, the study contributes to theoretical advancement by extending the JD-R model in the context of professional driving in a low-resource setting. The study identifies JR, specifically social support, as a moderating factor that buffers the effects of JD and burnout on RDB. This highlights the dynamic role of lower-level, downstream JR in both the health erosion and motivational processes within the JD-R framework. Specifically, support from supervisors and co-workers emerged as a critical protective factor, reinforcing the need to priorities such resources in resource-limited settings where structural supports may be lacking. This provides empirical support for the flexibility of the JD-R model and underscores its applicability to high-risk, under-researched occupational groups such as HGV drivers in sub-Saharan Africa. Limitations in this study The cross-sectional design limits the ability to infer causality among JD, JR, burnout, engagement, and RDB; thus, a longitudinal approach would have provided deeper insights into the temporal dynamics of these relationships. Additionally, the assessment of RDB relied on self-reported data, which could introduce social desirability bias. Participants may have underreported unsafe driving practices to present themselves in a favourable light, thereby affecting the accuracy of the findings. Future studies should consider complementing self-reports with objective measures such as GPS tracking data, telematics, or official traffic violation records to improve the validity of RDB assessment and provide a more nuanced understanding of driving behaviours. Moreover, the sample was drawn exclusively from HGV drivers in Tema (Ghana) that may limit the generalisability of the results to other contexts in the countries, particularly those with different socio-economic conditions or regulatory frameworks. To enhance external validity, future studies should consider incorporating a broader and more diverse sample of drivers across multiple geographic locations and transport corridors within Ghana. Such efforts would enable comparative insights and provide a more comprehensive understanding of how contextual variations influence the relationship between psychosocial work factors and RDB. While the study examined psychosocial factors, it did not account for external variables such as road quality, traffic density, or vehicle maintenance, which may also influence RDB. Conclusion The data suggest that psychosocial work factors may significantly influence the well-being and safety behaviours of HGV drivers in Ghana. Also, data from this study suggest that high job demands may directly increase burnout and risky driving behaviours, while job resources via social support from vehicle owners or supervisors and colleague drivers play a protective role by reducing burnout and enhancing engagement, though their direct impact on risky driving behaviours appears limited. Burnout and engagement partially mediate these relationships, highlighting their critical role in linking work conditions to driver safety. Moreover, job resources buffer the negative effects of job demands and burnout, suggesting their potential to mitigate workplace stress. These findings explain the complex interplay among job demands, job resources, and individual outcomes, offering valuable insights into improving driver well-being and road safety. To address these issues, it is recommended that targeted interventions focus on optimising job demands by enforcing mandatory rest periods and creating realistic delivery schedules to reduce burnout and its associated safety risks on the road. Improved wages, better road infrastructure, integration of modern OHS standards in the transport sector and enforcement of safety regulations may be essential. Abbreviations HGV– Heavy Goods Vehicle JD-Job Demands JE-Job engagement JR-Job Resources OHS – Occupational Health and Safety RDB-Risky Driving Behaviour RTCs – Road Traffic Crashes SDG– Sustainable Development Goal VIF– Variance Inflation Factor WHO – World Health Organisation Declarations Ethics approval and consent to participate The study adhered to the ethical principles outlined in the Helsinki Declaration and received approval from the Institutional Review Board (IRB) of the University of Cape Coast, Ghana (ID: UCCIRB/CES/2022/82). This approval was obtained before data collection began. Participation in the study was voluntary and anonymous, informed consent obtained from each driver. The privacy of participants was protected, and the intended use of the collected information was clearly communicated and assured. Consent for publication Not Applicable Availability of data and materials We declare that data for this study will be made available upon reasonable request. The data has been uploaded in Open Science Framework (DOI 10.17605/OSF.IO/GF6E3) Competing interest Authors declare no conflict of interest. Funding This research did not receive funding. Author contributions Conceptualization and Methodology: M.A. and EWA; Data collection and analysis: M.A.; Writing of original draft: M.A. and EWA; Review and editing: EWA. All author substantially reviewed the manuscript and approved its current content for publication. 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Statistical Power Analysis for the Behavioral Sciences 2013. https://doi.org/10.4324/9780203771587. Van Den Broeck A, Elst T Vander, Baillien E, Sercu M, Schouteden M, De Witte H, et al. Job Demands, Job Resources, Burnout, Work Engagement, and Their Relationships: An Analysis Across Sectors. J Occup Environ Med 2017;59:369–76. https://doi.org/10.1097/JOM.0000000000000964. Montoro L, Useche S, Alonso F, Cendales B. Work environment, stress, and driving anger: A structural equation model for predicting traffic sanctions of public transport drivers. Int J Environ Res Public Health 2018;15. https://doi.org/10.3390/ijerph15030497. Albrecht SL, Green CR, Marty A. Meaningful Work, Job Resources, and Employee Engagement. Sustainability 2021, Vol 13, Page 4045 2021;13:4045. https://doi.org/10.3390/SU13074045. Tables Table 1: Details of measures Measures Source and example of item No. of items Response set Alpha (α) Job demands Job content questionnaire (JCQ) [31] E.g. My job requires excessive work. 6 items Strongly disagree (SD) to strongly agree (SA). .874 Supervisor support (Job resources) JCQ [31] E.g. My vehicle owner/station master is concerned about the welfare of those who work under him or her. 4 items SD to SA .897 Co-worker support (Job resources) JCQ [31] E.g. My station master/car owner is successful in getting you to work together. 4 items SD to SA .813 Burnout Copenhagen Psychosocial Questionnaire (COPSOQ II) [32] E.g. How often have you beenn physically exhausted. 4 items ‘Not at all’ to ‘All the time’ .931 Job engagement Copenhagen Psychosocial Questionnaire (COPSOQ II) [32] E.g. I am immersed in my work. 3 items ‘Hardly ever’ to ‘Nearly all the time’ .838 Risky driving behaviour Driver behaviour Questionnaire (DBQ) [33]. E.g. Drove above your speed limit. 5 items ‘Hardly ever’ to ‘Nearly all the time’ .839 Table 2: HTMT ratio of correlations Constructs Burnout DBQ JD JE JR Burnout _ DBQ 0.725 _ JD 0.787 0.753 _ JE 0.654 0.888 0.671 _ JR 0.668 0.796 0.689 0.841 _ Table 3: Fornell and Larcker (1981) Criterion Constructs Burnout DBQ JD JE JR Burnout 0.913 DBQ 0.690 0.903 JD 0.759 0.743 0.780 JE -0.617 -0.838 -0.664 0.941 JR -0.633 -0.764 -0.676 0.804 0.840 Table 4: Outer loading, a, CR and AVE of constructs Latent constructs Outer loadings Burnout DBQ JD JE JR Burnout (a =0.933, CR = 0.937, AVE =0.834) Burnout_1 0.945 Burnout_2 0.925 Burnout_3 0.937 Burnout_4 0.844 DBQ (a =0.943, CR = 0.949, AVE =0.816) DBQ_1 0.885 DBQ_2 0.954 DBQ_3 0.950 DBQ_4 0.916 DBQ_5 0.803 JD (a =0.875, CR = 0.907, AVE =0.609) JD_1 0.831 JD_2 0.836 JD_3 0.752 JD_4 0.709 JD_5 0.745 JD_6 0.800 JE (a =0.935, CR = 0.941, AVE =0.885) JE_1 0.932 JE_2 0.958 JE_3 0.932 JR(a =0.917, CR = 0.937, AVE =0.706) JR_1 0.875 JR_2 0.907 JR_3 0.852 JR_4 0.765 JR_6 0.820 JR_7 0.814 Table 5: VIF and f 2 of the path coefficients Paths VIF f 2 SD T statistics P-values Burnout -> JE 2.892 0.059 0.010 6.101 0.000 Burnout -> RDB 1.667 0.056 0.012 4.754 0.000 JD -> Burnout 2.101 0.383 0.032 12.067 0.000 JD -> RDB 3.100 0.113 0.020 5.603 0.000 JE -> RDB 3.106 0.385 0.039 9.783 0.000 JR -> Burnout 1.998 0.090 0.016 5.731 0.000 JR -> JE 3.784 0.862 0.076 11.226 0.000 JR -> RDB 1.667 0.003 0.003 0.722 0.471 JR x JD-> Burnout 1.144 0.031 0.008 3.541 0.000 JR x Burnout -> RDB 1.529 0.046 0.012 3.661 0.000 SD, Standard deviation Table 6: Mediation effects in the path model Paths Path co-efficient SD T statistics P-values Total effects Burnout -> RDB 0.126 0.016 7.820 0.000 JD -> Burnout 0.611 0.017 35.587 0.000 JD -> RDB 0.302 0.017 17.817 0.000 JE -> RDB -0.518 0.022 23.479 0.000 JR -> Burnout -0.219 0.020 10.756 0.000 JR -> JE 0.804 0.010 77.453 0.000 JR -> RDB -0.560 0.017 32.878 0.000 Total indirect effects JD -> RDB 0.077 0.010 7.409 0.000 JR -> RDB -0.444 0.020 22.363 0.000 Specific indirect effects JD -> Burnout -> RDB 0.077 0.010 7.409 0.000 JR -> JE -> RDB -0.416 0.020 21.203 0.000 JR -> Burnout -> RDB -0.028 0.004 6.615 0.000 Additional Declarations No competing interests reported. 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RDB\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5711969/v1/254c6217f6a0571b7eebd57b.png"},{"id":90819537,"identity":"8b489efe-0156-404a-bac2-640930114b3d","added_by":"auto","created_at":"2025-09-08 13:54:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2074290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5711969/v1/a54ee56b-4fd6-4748-a4b4-8ee9871e0569.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modelling Psychosocial Work Factors and Risky Driving in Ghana: Mediating Roles of Burnout and Engagement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeavy Goods Vehicle (HGV) drivers are indispensable to the global supply chain sector, facilitating the movement of goods across countries and regions. However, the occupational challenges these drivers face and associated road safety consequences remain a major public health concern [1,2]. HGV drivers often endure long hours, irregular schedules, minimal rest, poor remuneration, and inadequate access to healthcare services [1]. These harsh working conditions not only endanger their well-being but also increase the risk of road traffic crashes (RTCs), affecting public safety on a larger scale [2,3]. The World Health Organization (WHO) estimates that RTCs claim approximately 1.19 million lives annually globally, with human error often influenced by fatigue, stress, and other occupational factors being a leading cause [4].\u003c/p\u003e\n\u003cp\u003eIn Africa, the RTC crisis is alarming, with approximately 26.6 deaths per 100,000 population [5]. In Ghana, the situation is even more concerning, with road traffic fatalities accounting for 24.9 deaths per 100,000 people, substantially higher than the global average of 18.2 deaths per 100,000 people [6]. HGV drivers disproportionately contribute to these statistics due to the inherent risks associated with their work. Crashes involving HGVs tend to be more severe than those involving smaller vehicles because of the high mass of HGVs [7]. These crashes often result in serious injuries or fatalities, particularly among occupants of smaller vehicles such as cars and motorcycles [6]. Notably, over 80% of fatalities in secondary vehicles are linked to collisions involving HGVs [8]. In Ghana, evidence shows that, on average, whenever a heavy vehicle driver dies in a RTC, three additional road users also lose their lives [9]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn developed countries, significant strides have been made in integrating contemporary occupational health and safety (OHS) practices into the road transport industry [10]. These efforts have facilitated evidence-based interventions that enhance the safety of road users [11]. For example, Europe and other high-income regions have substantially reduced occupational traffic crashes through the implementation of robust regulations, policies, and OHS research tailored to the road transport sector [10,12]. Conversely, developing countries, including Ghana, face worsening conditions in their road transport sector [13]. In Ghana, OHS practices remain underdeveloped, with the framework still in its infancy [14]. Consequently, road transport accidents, injuries, and fatalities are not only common but also increasingly challenging to address [14].\u003c/p\u003e\n\u003cp\u003eWhile efforts to reduce risky driving behaviours, such as over speeding or substance use, are important, they often overlook the broader work environment in which HGV drivers operate [14]. In Ghana, long driving hours, lack of job security, inadequate support from vehicle owners, poor remuneration, and limited rest stop facilities contribute to high stress among drivers [13]. These challenges not only harm drivers\u0026rsquo; well-being but also increase the risk of errors and the resultant RTCs [12]. Additionally, the poor work environment hinders progress towards Sustainable Development Goals (SDGs), particularly Goal 8.5, which promotes decent, safe, and healthy workplaces, and Goal 3.6, aimed at halving RTCs by 2030.\u003c/p\u003e\n\u003cp\u003eA recent review identified limited evidence from the African region on psychosocial work factors such as psychological demands and social support on risky driving and RTCs[3]. The review further reported that psychosocial work factors among hazardous transport drivers in low-and middle-income countries (LMICs) remain underexplored, with few studies [15,16] addressing this critical occupational group. This lack of research attention has contributed to a narrow understanding of the underlying causes of risky driving in LMICs. Without sufficient evidence on work-related stressors, interventions often fail to capture the organisational psychosocial dimensions that shape driver behaviour. Despite the critical role of HGV drivers in national and regional economies, there is limited research on their psychosocial work environment in Ghana. Consequently, current road safety interventions tend to focus predominantly on individual risky driving behaviours, while overlooking the broader systemic and workplace factors that contribute to these on-the-road behaviours \u0026nbsp;[17]. This gap persists partly due to the dominant behavioural framing of road safety in Ghana, which tends to individualise responsibilities for occurrence of accidents [13]. As a result, systemic work-related stressors remain underexplored, limiting the development of holistic interventions that address the root causes of risky driving. Bridging this gap is essential for advancing theory and practice in road safety within the sub-Saharan African context [13,17].\u003c/p\u003e\n\u003cp\u003eThis study aims to model the relationships between psychosocial work factors, burnout, work engagement, and driving behaviour among HGV drivers in Ghana. In this study, psychological demands were selected as the key job demands because HGV drivers in Ghana often face long working hours, tight schedules, creating mental fatigue, which can increase stress and risky behaviour [13]. Social support from supervisors and co-workers was chosen as a job resource because it is a practical and accessible form of support in the sector. These variables reflect common and relevant aspects of the drivers\u0026rsquo; daily work experiences that influence their well-being and road safety. The findings provide knowledge useful in designing interventions that address root causes of risky driving, improving driver well-being and road safety. Thus, these findings have the potential to inform occupational health and transport policies by demonstrating the need to integrate psychosocial risk management into road safety strategies. They can guide employers, regulatory bodies such as the National Road Safety Authority, and other policymakers in developing context-specific interventions that prioritise supportive work environments alongside behavioural road safety measures, the aim is to protect the health, safety and well-being of the drivers and promote safety on the road.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical model \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Job Demand-Resource (JD-R) Model [18] underpins this study. The main argument of the JD-R theory is that JD lead to strain and negative outcomes (health erosion), while JR promote motivation and positive outcomes (motivation), with both processes influencing overall job performance and well-being of the worker. The model highlights two pathways: the\u0026nbsp;health erosion hypothesis, where excessive JD without adequate JR lead to burnout, compromising health, the affect is likely increases in RDB. The other is the\u0026nbsp;motivational hypothesis, where JR (in this study support from supervisors and co-workers) enhance job engagement (JE), fostering motivation and reducing unsafe driving behaviours. In this study, JD and JR serve as predictors, with burnout and JE acting as mediators, explaining the mechanisms through which these factors impact risky driving or driving behaviours. The JD-R model provides a practical framework for understanding the dual influence of stress-inducing demands and protective resources from the organisational and worker perspectives.\u003c/p\u003e\n\u003cp\u003eThis study extends the JD-R model by introducing job resources, specifically, social support from supervisors and co-workers as moderating variables in two key pathways: the relationship between job demands and burnout, and between burnout and RDB. Unlike traditional JD-R applications that primarily position resources as direct predictors or mediators of engagement [18], this study conceptualises downstream job resources (e.g., interpersonal support) as buffering mechanisms that mitigate the adverse effects of JD and burnout. Authors argue that by applying this extended framework in the understudied context of HGV drivers in Ghana, the study provides novel insights into how interpersonal support can dampen the health erosion process and reduce the likelihood of unsafe driving behaviours in these high-risk occupational settings. In a resource-limited setting and within a largely informal occupational group like long-distance HGV drivers in Ghana, downstream job resources such as supervisors and co-drivers supports are particularly crucial. These interpersonal forms of support often substitute for more structured organisational resources such as formal employee assistance programmes, occupational health services, and robust enforcement of labour protections, which are typically absent or poorly implemented in such contexts. The extension of the JD-R model to include social support as a moderator may help explain why these lower-level resources are effective in reducing the impact of job stress on risky driving behaviour, offering a more context-sensitive understanding of how drivers cope with high job demands and burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth erosion hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the JD-R framework, JD are theorised to trigger a health impairment process, whereby sustaining the exposure to excessive workload, time pressures, and psychological strain that progressively deplete energy reserves [19]. This depletion compromises cognitive functioning, induces emotional exhaustion, and undermines drivers\u0026apos; capacity to make sound decisions, ultimately heightening the risk of unsafe driving behaviours [20\u0026ndash;22]. JD, such as excessive working hours, high workload, and time pressures, drain physical and mental resources of these drivers, leading to burnout [18,23]. Prolonged exposure to these demands exacerbates stress and exhaustion, making individuals (drivers) vulnerable to burnout [24]. Among commercial drivers, studies reveal that high JD significantly elevate burnout levels [25,26]. Additionally, high JD contribute to RDB by inducing fatigue and stress, which compromise decision-making and reduce adherence to safety protocols while on the road [27,28]. Professional drivers facing time pressures or heavy workloads are more likely to engage in unsafe practices, such as over speeding and or ignoring traffic rules [26]. In contrast, high JR mitigate burnout by fostering resilience and providing support for workers [26]. Resources such as supportive supervision and flexible schedules reduce burnout and help drivers cope with JD effectively [23,25]. For drivers, these resources strengthen physical and psychological resilience, thereby lowering burnout [26]. The Challenge is that burnout impairs cognitive functioning and decision-making, which further increase RDB [28]. Research among drivers has shown that burnout is strongly associated with unsafe driving practices [21]. Evidence further shows that JD\u0026nbsp;and JR affect driving performance through burnout and work engagement [10]. Based on this evidence it is hypothesised that:\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: JD have positive and significant relationship with burnout.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Burnout have positive and significant relationship with RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e: JR have negative and significant relationship with burnout.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e: JD have positive and significant association RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e5\u003c/sub\u003e: Burnout significantly mediates the association between JD and RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e6\u003c/sub\u003e: Burnout significantly mediates the association between JR and RDB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotivation hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the JD-R model, JR activate the motivational process by enhancing individuals\u0026rsquo; capacity to remain committed, focused, and psychologically resilient in the face of occupational challenges [19]. In the transport sector, supportive interactions with supervisors and peers serve not only to bolster engagement, but also to buffer the negative effects of high demands, thereby promoting adherence to safe driving behaviours and reducing the propensity for risk-taking\u0026nbsp;[25,28,29]. JR, such as social support from supervisors and colleagues enhance engagement by fostering motivation and resilience among workers [18,19]. Dollard et al. [30] found that access to JR positively influence energy and dedication at work. While the link between JR and JE among drivers is underexplored, existing studies indicate a positive relationship between these variables [31,32]. JR serve as protective factors against unsafe driving behaviours by providing drivers with motivation and support to follow safety standards while on the road [21]. The connection between JR and driving performance is well-documented [12,21]. Burnout diminishes engagement by draining the emotional and physical resources [33], leading to disengagement and decreased motivation among workers [19,33]. However, this relationship is extensively examined among professional drivers. Furthermore, JR influence organisational and employee outcomes through work engagement [19,34]. Hence, these hypothesises:\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e7\u003c/sub\u003e: JR have positive and significant association with JE.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e8\u003c/sub\u003e: JE have negative and significant association with RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e9\u003c/sub\u003e: JR have negative and significant association with RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e10\u003c/sub\u003e: JE significantly mediates the association between JR and RDB.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e11\u003c/sub\u003e: Burnout has a negative and significant association with JE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBuffering effect of job resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemerouti et al. [18] demonstrated that JR mitigate the adverse effects of high JD on employee well-being. This buffering effect protects employees from experiencing excessive burnout by providing the necessary resources to cope with high JD effectively [35]. The buffering effect of JR means that in a high JR work setting, the effect of high JD on burnout or the effect of burnout on job performance could be reduced significantly. The moderating role of JR in the health erosion pathway has been explored in the general working population [35,36] but isamong HGV drivers in Africa. A review shows that previous studies have consistently applied the JD-R model in a more conventional manner [19], often without exploring alternative roles of job resources in settings with limited infrastructure and institutional support. This consistency, while valuable, points to an opportunity to expand and test the model\u0026rsquo;s boundaries. Hence, we hypothesised that:\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e12\u003c/sub\u003e: JR significantly moderate the association between JD and burnout.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e13\u003c/sub\u003e: JR significantly moderate the relationship between burnout and RDB.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesigns and population \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional survey involved 1,575 HGV drivers in Ghana, consisting of 910 truck (haulage) drivers and 665 tanker drivers. The sample size represents 26.6% of the estimated target population of 5,312 HGV drivers. This population includes 3,240 haulage drivers operating at the Tema Port truck terminal and 2,072 tanker drivers working at seven bulk storage terminals in the same area. In this study, long-distance driving refers to drivers who travel at least 140 km per trip to their destinations. The truck drivers are registered members of the Ghana Haulage Truck Drivers Association, they operate from the Tema Port truck terminal. Similarly, the tanker drivers are members of the National Petroleum Tanker Drivers Union and the Liquified Petroleum Gas Tanker Drivers Union. All the drivers included in the study were full-time workers and held professional licenses issued by the Driver and Vehicle Licensing Authority (DVLA) of Ghana. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe drivers provided information about their age, highest educational attainment, marital status, years of work experience as a professional truck/tanker driver, daily driving hours and weekly working days. The summary are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 1: Details of measures\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures and Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study recruited drivers with the assistance of their union executives and company administrators. Drivers waiting for their next load were conveniently sampled for the study. The questionnaire was translated into Twi, a widely spoken local language, and back-translated into English to ensure accuracy by a language expert from the University of Cape Coast. Data collection was conducted with the help of eight trained field assistants through survey interviews lasting 25 to 45 minutes. The survey interviews took place over three months, from June to August 2023. Drivers were informed of their right to withdraw from the study at any time and were assured of confidentiality. For drivers who could not read or understand the English language, field assistants explained the consent form before obtaining their written consent. No financial or material incentives were provided for participation in this study.\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Institutional Review Board of the University of Cape Coast, Ghana (ID: UCCIRB/CES/2022/82). Prior to data collection, a pre-test involving 91 heavy goods vehicle (HGV) drivers in the Takoradi Metropolis was conducted to evaluate the psychometric properties of the survey instruments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypotheses (paths) in the proposed model were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM) using Smart PLS version 4.1.0.9. The procedures proposed by Hair et al. [37] in analysing path models were followed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel specification and assessment of outer model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictors (JD and JR), mediators (Burnout and JE), and the outcome variable (RDB) were quantitative latent variables measured with quantitative indicators and reflectively modelled (see Figure 1). Composite reliability (CR) with acceptable values \u0026ge; 0.70 [37] was used to establish the internal consistency of constructs in the path models. Average variance extracted (AVE) values \u0026ge; 0.50 [37] were used to assess convergent validity. To assess the discriminant validity of the constructs, the criteria by Fornell and Larcker [38] and Heterotrait-Monotrait ratio of correlation (HTMT) values (\u0026lt; 0.90) were applied (See Table 2 and 3). Indicators with outer loadings \u0026lt; 0.70 were deleted, and the analysis continued until the desired outer loading (\u0026ge; 0.70) was achieved. This led to the deletion of two items measuring social support (JR5 and JR8) due to outer loadings \u0026lt; 0.70. The outer loadings, CR, Cronbach\u0026rsquo;s alpha (a), and AVE of constructs in the path model are presented in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 1: Structural model based on the JD-R model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 2: HTMT ratio of correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 3: Fornell and Larcker (1981) Criterion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 4: Outer loading,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea, CR and AVE of constructs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of inner model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of the inner model started with the assessment of the multiconllinearity. The Fornell and Larcker\u0026rsquo;s criterion was used, we found that issues of multicollinearity did not exist in the path model (See Table 3). \u0026nbsp;This was further confirmed using the variance inflation factor (VIF) (See Table 5), which were between the acceptable range (VIF \u0026gt;0.10 and \u0026lt; 5) [37,38]. Satndardised root mean square residual (SRMR) was then used to assess the model fit with a criterion of \u0026pound; 0.10 (Hairt et al., 2021). The SRMR of 0.10 of the inner model was acceptable.\u003c/p\u003e\n\u003cp\u003eThe adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e) was used to determine predictive ability of the model. JD, JR, burnout and JE explained 78.5% of variance in RDB (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e = 0.785). JD and JR explained 61.4% of variance in burnout (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e = 0.614) and JR and burnout explained 66.6% of variance in JE (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e = 0.666). See the details in Figure 2. Cross-Validated Redundancy (\u003cem\u003eQ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was used to assess the predictive relevance of the inner model using the Stone-Geisser criterion of \u003cem\u003eQ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026gt;0 [37]. The three endogenous constructs, burnout, JE and RDB had \u003cem\u003eQ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values of 0.608, 0.664 and 0.691 respectively, the higher it is from zero, the better the predictive relevance of the construct. Finally, the model\u0026rsquo;s path coefficients and its significance (See Figure 3) and effect size (See Table 5) using Cohen \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e were assessed using the bootstrapping process (Table 5). A path is significant if t-value is \u0026gt;1.96 at p\u0026lt;0.05. Cohen \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of 0.02, 0.15 and 0.35 imply small, moderate and large practical, respectively [39].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of mediation and moderation models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the mediation role of burnout and JE, the direct paths need to be significant to establish the basis for assessing the mediation roles to be partial or full mediation (Hair et al., 2021). In full mediation, direct paths is no more significant in the presence of the mediators. In a partial mediation, the direct paths are still significant but reduced when the mediators are present in the model (Hair et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe moderating roles of JR was examined using the two-stage approach [37]. The significance of the interaction effects (H\u003csub\u003e12\u003c/sub\u003e and H\u003csub\u003e13\u003c/sub\u003e) were assessed using the bootstrapping process, which is supported when the t-value is \u0026gt; 1.96 at p\u0026lt; 0.05. The \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of the interaction effect were evaluated using criterion .005. 0.01 and 0.25 for small, moderate and large effects respectively [37]. Slop plots were then presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 5: VIF and \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of the path coefficients\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 2: Path coefficient, outer loading and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e of the path model\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 3: Path coefficients, p-values and outer weights of the path model\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic characteristics of the HGV drivers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 1,575 drivers, predominantly males (94.7%), with a mean age of 39.2 years and an average HGV driving experience of 13.2 years. Educational levels varied, with 39.7% having basic education and 11.4% attained tertiary education. Half of these drivers were single (50.2%), and the average monthly salary was $92.13, about 1,500 Ghana cedis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 1 suggests that JD have a direct influence on burnout (r = 0.556, t = 29.609, p \u0026lt; 0.001), which is supported. Hypothesis 2 proposes that burnout has a direct impact on RDB, and this is also confirmed (r = 0.185, t = 10.333, p \u0026lt; 0.001). Hypothesis 3 states that JR have a negative association with burnout, it is supported (r = -0.263, t = 13.018, p \u0026lt; 0.001). Hypothesis 4 posits that JD are directly associated with RDB, and this is confirmed (r = 0.272, t = 12.093, p \u0026lt; 0.001). Hypothesis 5 proposed that burnout mediates the association between JD and RDB. This hypothesis is confirmed as the specific indirect effect (JD -\u0026gt; Burnout -\u0026gt; RDB) is significant (t = 7.409, p \u0026lt; 0.001), suggesting that JD increase RDB (r = 0.077) indirectly through increased burnout.\u003c/p\u003e\n\u003cp\u003eHypothesis 6 predicts that JR mediate the association between burnout and RDB. This is confirmed, with the specific indirect effect (JR -\u0026gt; Burnout -\u0026gt; RDB) being significant (t = 6.615, p \u0026lt; 0.001), meaning JR reduce RDB (r = -0.028) by decreasing burnout. Hypothesis 7 is that JR have a positive and significant association with JE, which is supported (r = 0.690, t = 47.134, p \u0026lt; 0.001). Hypothesis 8 proposed a negative association between JE and RDB, and this is also supported (r = -0.504, t = 22.310, p \u0026lt; 0.001). Hypothesis 9 posited a negative association between JR and RDB, but this is not supported (r = -0.042, t = 1.557, p = 0.120). Hypothesis 10 proposed that JR mediate the association between JE and RDB, and this is confirmed (t = 21, p \u0026lt; 0.001), with JR reducing RDB (r = -0.416) through increased in JE. Hypothesis 11 suggested that burnout has a negative association with JE, and this is supported (r = -0.180, t = 13.068, p \u0026lt; 0.001). All mediation effects were partial, as both the direct and indirect paths remained significant in the presence of the mediators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypotheses 12 and 13 proposed that JR play significant moderating role in the association between JD and burnout and between burnout and RDB. These hypotheses were confirmed indicating a significant moderating role of JR in these paths (See Figure 3). The \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e for the moderating effects JR x JD-\u0026gt; Burnout and JR x Burnout -\u0026gt; RDB were 0.031 and 0.046 respectively, indicating moderate effect sizes [37] (see Table 5). Slope plots for the hypothesis 12 (JR x JD-\u0026gt; Burnout) is presented in Figure 4, which shows that at low levels of JR, JD and burnout rise significantly (red line). At high levels of JR, the slope is weak (green line). Thus, increased JR reduces the impact JD has on burnout, reducing the likelihood of burnout even as JD rise. The slope plot for Hypothesis 13 (JR x Burnout -\u0026gt; RDB) is presented in Figure 5. This slope plot shows that at low levels of JR, burnout and RDB increase significantly but as JR increase the slope becomes flat (green line) indicating that higher JR reduces the impact burnout has on RDB. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 6: Mediation effects in the path model\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 4: Slope plots for the moderating role of JR on the path JD-\u0026gt;burnout\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 5: Slope plots for the moderating role of JR on the path burnout -\u0026gt; RDB\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings confirm that high JD directly increase the level of burnout and RDB among HGV drivers in Ghana, while burnout increases the occurrence of RDB and negatively affects JE. High JR reduce burnout and increase JE among the drivers. However, high JR did not significantly reduce RDB among the drivers. Mediation analyses reveal that burnout partially reduces the effect of JD on RDB, and JR on RDB, while JE partially mediates the effect of JR on RDB. Moderation analyses show that JR significantly buffer the effects of JD on burnout, and burnout on RDB, reducing their impacts.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion of findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study confirms that JD significantly increase both burnout and RDB among HGV drivers in Ghana. This is consistent with the health erosion hypothesis of the JD-R model. High JD, such as long driving hours, tight delivery deadlines, and limited rest, are prevalent among Ghanaian HGV drivers, as found in the current study and also in the previous ones [17]. Burnout among drivers in this context arises from chronic exposure to these driving demands. The influence of JD on burnout and RDB are reported in studies in China [26] and the United States of America [25]. These studies reported that JD elevate stress and worker burnout, thereby impairing decision-making, which increases risky behaviours among drivers [25,26]. Ghana\u0026apos;s unique road transport challenges, such as poor road infrastructure, long distances between rest stops, and inadequate enforcement of rest breaks [17], likely amplify these effects. The significant link between JD and RDB reemphasises that Ghanaian HGV drivers who drive under high pressure may engage in unsafe practices like over speeding or driving under high level of fatigue [26], or even under the influence of drugs. These behaviours are not merely individual lapses but stem from systemic issues in the work environment that demand immediate attention, unfortunately, they lead to crashes with attendant injuries, fatalities and their economic costs, both to the individual and the nation.\u003c/p\u003e\n\u003cp\u003eThe mediation analysis reveals that burnout partially mediates the relationship between JD and RDB. This means that while JD directly increase RDB, a significant portion of this effect operates through burnout. Burnout impairs cognitive, including reduce concentration and judgement, and physical functioning ability, which are critical for safe driving [21]. Previous studies conducted among professional drivers in China reported similar findings [27,28]. These studies highlight the role of burnout in fostering unsafe driving practices. In Ghanaian setting, the compounding effect of burnout on RDB may be particularly severe due to the absence of appropriate occupational health support systems for these drivers [14]. Unlike drivers who benefit from mental health programmes and stress management resources like those from Spain [10], Ghanaian drivers often lack these safeguards, creating a cycle where burnout reinforces risky behaviours, which further endanger drivers and other road users.\u0026nbsp;Also, in the Ghanaian context, peer support and informal check-ins could serve as a culturally relevant and cost-effective approach to alleviating driver burnout, especially where resource-constrains limit formal mental health infrastructure and provision [14]. Such grassroots-level strategies may help reinforce coping mechanisms and build resilience, ultimately reducing the risk of unsafe driving behaviours.\u003c/p\u003e\n\u003cp\u003eThe study demonstrates that high JR, such as supervisor and co-worker supports significantly reduce burnout and enhance JE among the drivers. This supports the motivational hypothesis of the JD-R model, which argues that JR buffer the effects of JD by fostering motivation and resilience of workers [18,19]. Previous studies conducted in China [23] and Colombia [21] among professional drivers corroborate these findings, showing that supportive supervisors and colleagues mitigate stress and promote positive workplace attitudes. Also, among cross-sectoral workers in Belgium, JR was found to reduce burnout while improving JE [40]. In the transport sector in Ghana, however, the effectiveness of JR may be constrained by systemic limitations. For instance, while support from supervisors can alleviate some stress, structural issues such as inadequate wages and lack of proper training might limit the overall impact of these resources [17]. The enhancement of JE through JR also highlights that engaged drivers are less likely to succumb to burnout and its associated risks [23]. In developing countries like Ghana, where formal mental health services are often lacking in the transport sector [14], these informal yet contextually relevant support mechanisms could serve as a cost-effective means to bolster engagement and prevent burnout, especially when complemented with clear policies that reinforce supportive leadership practices.\u003c/p\u003e\n\u003cp\u003eInterestingly, the findings reveal that JR did not directly and significantly reduce RDB among the drivers. This is contrary to evidence reported in previous studies [26,41]. This finding also diverges from a prior study [21] that suggests that JR can directly improve safety outcomes. While JR improve engagement and reduce burnout among the drivers, these effects may not translate into safer driving behaviours. [21]The lack of direct impact of this relation among HGV drivers in Ghana may stem from contextual factors such as external pressures on drivers, including unrealistic delivery schedules and financial insecurities. These pressures may overshadow the protective benefits of JR, forcing drivers to prioritise how to meet the requirements of the job over safety despite supportive environments, from immediate supervisors and co-workers [17]. Also, the absence of a direct impact of JR on risky driving may reflect a mismatch between the type of support provided and the practical needs of the drivers. In high-risk environments like HGV driving, social support alone may be insufficient to change on-the-road safety behaviour without the broader systemic support such as financial incentives or safer infrastructure. Additionally, drivers may normalise risk-taking as part of the job, limiting the behavioural influence of supportive relationships. Moreover, environmental challenges like poor road conditions and weak enforcement of traffic regulations could diminish the influence of JR on RDB [17].\u003c/p\u003e\n\u003cp\u003eThe study further confirms that JE partially mediates the relationship between JR and RDB, indicating that while JR may not directly reduce RDB, their influence on engagement plays a significant role. However, it is our view that under the current circumstances, these drivers could not appreciate engagement since they have no control over when to go on a strip. In that case, JE would be a partial mediator. Engaged drivers are more likely to exhibit proactive safety behaviours, but less likely to engage in risky driving practices [34]. This finding resonates well with existing evidence that work engagement enhances energy and focus, reducing the likelihood of errors among workers [33]. Thus, this relationship highlights the importance of fostering engagement in a challenging work environment among these drivers. Drivers who are engaged, despite external pressures, may demonstrate greater adherence to safety practices, although systemic challenges like poor enforcement, inadequate rest facilities and poor working conditions still pose significant barriers to on-the-road safety [17]. Furthermore, this finding highlights the value of investing in JR to enhance psychological engagement, especially in settings where structural limitations persist. Cultivating a sense of purpose and involvement among these drivers could indirectly promote safer driving, even when external conditions cannot be fully controlled [31]. Improving engagement is not just a motivational tool but also a practical strategy for managing risk [42].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModeration analysis shows that JR significantly buffer the effects of JD on burnout, supporting the buffering hypothesis within the JD-R framework [18,19]. Drivers with high JR experience less burnout even when facing high JD. This finding is consistent with the work of Danudoro et al. [35], who also highlighted the protective role of JR in reducing worker stress, suggesting a similar pattern across different occupational contexts. In Ghana, this buffering effect is critical, as it highlights the potential of social support to mitigate the harmful impacts of excessive demands on these drivers. However, the limited availability and accessibility of social support and the priority given to productivity over well-being in the road transport sector [17] is likely to reduce the effectiveness of this buffering mechanism. The moderation analysis further confirms that JR reduce the impact of burnout on RDB. Drivers with access to JR are less likely to exhibit unsafe driving behaviours even when experiencing burnout. This finding is consistent with studies like that of Xanthopoulou et al. [36], which show that JR enhance resilience, enabling workers to maintain performance under strain conditions. This finding is particularly relevant in Ghana, as drivers often face chronic burnout due to high JD and systemic challenges [17]. While social support from supervisors and colleagues alone cannot eliminate the risks associated with worker burnout, they provide critical support that helps drivers to cope, potentially reducing the frequency and severity of unsafe driving practices. Furthermore, these findings offer theoretical insight by demonstrating the dual moderating role of JR in both the health erosion and behavioural pathways, thereby extending the JD-R model\u0026rsquo;s applicability in high-risk transport settings. The inclusion of downstream social support as a moderator enriches the model by illustrating how context-specific resources can shape outcomes under extreme job strain, warranting further testing across diverse occupational groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for practice, policy and research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings highlight the need for a contextual approach to addressing the psychosocial challenges faced by HGV drivers in Ghana. Reducing JD through realistic driving schedules and mandatory rest periods are essential to mitigate burnout and RDB. Enhancing JR, such as supervisory support, training, and co-worker collaboration, can alleviate worker stress and promote engagement. Though these efforts must be complemented by systemic changes like improved wages, better road infrastructure, integration of modern OHS standards in the transport sector and enforcement of safety regulations. Furthermore, recognising the role of burnout and engagement as mediators explain the importance of mental health support and fostering a positive work environment to improve driving safety outcomes. Finally, the buffering effect of JR demonstrates their potential to shield drivers from the adverse impacts of high JD and burnout, necessitating targeted efforts to strengthen social and organisational support systems within Ghana\u0026apos;s road transport sector.\u003c/p\u003e\n\u003cp\u003eIn addition to practical relevance, the study contributes to theoretical advancement by extending the JD-R model in the context of professional driving in a low-resource setting. The study identifies JR, specifically social support, as a moderating factor that buffers the effects of JD and burnout on RDB. This highlights the dynamic role of lower-level, downstream JR in both the health erosion and motivational processes within the JD-R framework. Specifically, support from supervisors and co-workers emerged as a critical protective factor, reinforcing the need to priorities such resources in resource-limited settings where structural supports may be lacking. This provides empirical support for the flexibility of the JD-R model and underscores its applicability to high-risk, under-researched occupational groups such as HGV drivers in sub-Saharan Africa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations in this study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cross-sectional design limits the ability to infer causality among JD, JR, burnout, engagement, and RDB; thus, a longitudinal approach would have provided deeper insights into the temporal dynamics of these relationships. Additionally, the assessment of RDB relied on self-reported data, which could introduce social desirability bias. Participants may have underreported unsafe driving practices to present themselves in a favourable light, thereby affecting the accuracy of the findings. Future studies should consider complementing self-reports with objective measures such as GPS tracking data, telematics, or official traffic violation records to improve the validity of RDB assessment and provide a more nuanced understanding of driving behaviours. Moreover, the sample was drawn exclusively from HGV drivers in Tema (Ghana) that may limit the generalisability of the results to other contexts in the countries, particularly those with different socio-economic conditions or regulatory frameworks. To enhance external validity, future studies should consider incorporating a broader and more diverse sample of drivers across multiple geographic locations and transport corridors within Ghana. Such efforts would enable comparative insights and provide a more comprehensive understanding of how contextual variations influence the relationship between psychosocial work factors and RDB. While the study examined psychosocial factors, it did not account for external variables such as road quality, traffic density, or vehicle maintenance, which may also influence RDB.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe data suggest that psychosocial work factors may significantly influence the well-being and safety behaviours of HGV drivers in Ghana. Also, data from this study suggest that high job demands may directly increase burnout and risky driving behaviours, while job resources via social support from vehicle owners or supervisors and colleague drivers play a protective role by reducing burnout and enhancing engagement, though their direct impact on risky driving behaviours appears limited. Burnout and engagement partially mediate these relationships, highlighting their critical role in linking work conditions to driver safety. Moreover, job resources buffer the negative effects of job demands and burnout, suggesting their potential to mitigate workplace stress. These findings explain the complex interplay among job demands, job resources, and individual outcomes, offering valuable insights into improving driver well-being and road safety. To address these issues, it is recommended that targeted interventions focus on optimising job demands by enforcing mandatory rest periods and creating realistic delivery schedules to reduce burnout and its associated safety risks on the road. Improved wages, better road infrastructure, integration of modern OHS standards in the transport sector and enforcement of safety regulations may be essential.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHGV\u0026ndash; Heavy Goods Vehicle\u003c/p\u003e\n\u003cp\u003eJD-Job Demands\u003c/p\u003e\n\u003cp\u003eJE-Job engagement\u003c/p\u003e\n\u003cp\u003eJR-Job Resources\u003c/p\u003e\n\u003cp\u003eOHS \u0026ndash; Occupational Health and Safety\u003c/p\u003e\n\u003cp\u003eRDB-Risky Driving Behaviour\u003c/p\u003e\n\u003cp\u003eRTCs \u0026ndash; Road Traffic Crashes\u003c/p\u003e\n\u003cp\u003eSDG\u0026ndash; Sustainable Development Goal\u003c/p\u003e\n\u003cp\u003eVIF\u0026ndash; Variance Inflation Factor\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World Health Organisation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adhered to the ethical principles outlined in the Helsinki Declaration and received approval from the Institutional Review Board (IRB) of the University of Cape Coast, Ghana (ID: UCCIRB/CES/2022/82). This approval was obtained before data collection began. Participation in the study was voluntary and anonymous, informed consent obtained from each driver. The privacy of participants was protected, and the intended use of the collected information was clearly communicated and assured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that data for this study will be made available upon reasonable request. The data has been uploaded in Open Science Framework (DOI 10.17605/OSF.IO/GF6E3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and Methodology: M.A. and EWA; Data collection and analysis: M.A.; Writing of original draft: M.A. and EWA; Review and editing: EWA. All author substantially reviewed the manuscript and approved its current content for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our heartfelt gratitude to the station masters in Accra and Tema, as well as the driver unions, for their invaluable support during the data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBatson A, Newnam S, Koppel S. A preliminary study on the barriers and facilitators to improving the health, safety, and well-being of aging heavy vehicle drivers. J Safety Res 2023;86:262\u0026ndash;73. https://doi.org/10.1016/J.JSR.2023.07.005.\u003c/li\u003e\n\u003cli\u003eKhadka A, Gautam P, Joshi E, Pilkington P, Parkin J, Joshi SK, et al. Road safety and heavy goods vehicle driving in LMICs: Qualitative evidence from Nepal. 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Organ Dyn 2017;46:120\u0026ndash;32. https://doi.org/10.1016/j.orgdyn.2017.04.008.\u003c/li\u003e\n\u003cli\u003eDanudoro K, Zamralita Z, Lie D. The Effect of Job Demands on Burnout with Job Resources as A Moderator Among External Auditors. Proceedings of the International Conference on Economics, Business, Social, and Humanities (ICEBSH 2021) 2021;570:1138\u0026ndash;43. https://doi.org/10.2991/ASSEHR.K.210805.179.\u003c/li\u003e\n\u003cli\u003eXanthopoulou D, Bakker AB, Dollard MF, Demerouti E, Schaufeli WB, Taris TW, et al. When do job demands particularly predict burnout? The moderating role of job resources. Journal of Managerial Psychology 2007;22:766\u0026ndash;86. https://doi.org/10.1108/02683940710837714/FULL/PDF.\u003c/li\u003e\n\u003cli\u003eHair Jr. JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Cham: Springer Nature; 2021. https://doi.org/10.1007/978-3-030-80519-7.\u003c/li\u003e\n\u003cli\u003eFornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research 1981;18:39. https://doi.org/10.2307/3151312.\u003c/li\u003e\n\u003cli\u003eCohen J. Statistical Power Analysis for the Behavioral Sciences. Statistical Power Analysis for the Behavioral Sciences 2013. https://doi.org/10.4324/9780203771587.\u003c/li\u003e\n\u003cli\u003eVan Den Broeck A, Elst T Vander, Baillien E, Sercu M, Schouteden M, De Witte H, et al. Job Demands, Job Resources, Burnout, Work Engagement, and Their Relationships: An Analysis Across Sectors. J Occup Environ Med 2017;59:369\u0026ndash;76. https://doi.org/10.1097/JOM.0000000000000964.\u003c/li\u003e\n\u003cli\u003eMontoro L, Useche S, Alonso F, Cendales B. Work environment, stress, and driving anger: A structural equation model for predicting traffic sanctions of public transport drivers. Int J Environ Res Public Health 2018;15. https://doi.org/10.3390/ijerph15030497.\u003c/li\u003e\n\u003cli\u003eAlbrecht SL, Green CR, Marty A. Meaningful Work, Job Resources, and Employee Engagement. Sustainability 2021, Vol 13, Page 4045 2021;13:4045. https://doi.org/10.3390/SU13074045.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Details of measures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource and example of item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha (\u0026alpha;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eJob demands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eJob content questionnaire (JCQ) [31]\u003c/p\u003e\n \u003cp\u003eE.g. My job requires excessive work.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eStrongly disagree (SD) to strongly agree (SA).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSupervisor support (Job resources)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eJCQ [31]\u003c/p\u003e\n \u003cp\u003eE.g. My vehicle owner/station master is concerned about the welfare of those who work under him or her.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSD to SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCo-worker support (Job resources)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eJCQ [31]\u003c/p\u003e\n \u003cp\u003eE.g.\u0026nbsp;My station master/car owner is successful in getting you to work together.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSD to SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCopenhagen Psychosocial Questionnaire (COPSOQ II) [32]\u003c/p\u003e\n \u003cp\u003eE.g. How often have you beenn physically exhausted.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lsquo;Not at all\u0026rsquo; to \u0026lsquo;All the time\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eJob engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCopenhagen Psychosocial Questionnaire (COPSOQ II) [32]\u003c/p\u003e\n \u003cp\u003eE.g.\u0026nbsp;I am immersed in my work.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lsquo;Hardly ever\u0026rsquo; to \u0026lsquo;Nearly all the time\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRisky driving behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDriver behaviour Questionnaire (DBQ) [33].\u003c/p\u003e\n \u003cp\u003eE.g. Drove above your speed limit.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5 items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lsquo;Hardly ever\u0026rsquo; to \u0026lsquo;Nearly all the time\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: HTMT ratio of correlations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBurnout\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBurnout\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e_\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Fornell and Larcker (1981) Criterion\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBurnout\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBurnout\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.903\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.780\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.941\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.840\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Outer loading,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea, CR and AVE of constructs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatent constructs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOuter loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBurnout\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eBurnout (a =0.933, CR = 0.937, AVE =0.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eBurnout_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eBurnout_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eBurnout_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eBurnout_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ (a =0.943, CR = 0.949, AVE =0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eDBQ_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD (a =0.875, CR = 0.907, AVE =0.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJD_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJE (a =0.935, CR = 0.941, AVE =0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJE_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR(a =0.917, CR = 0.937, AVE =0.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJR_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: VIF and \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of the path coefficients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eT statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP-values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eBurnout -\u0026gt; JE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e6.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eBurnout -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e4.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJD -\u0026gt; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e12.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJD -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e5.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJE -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e9.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJR -\u0026gt; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e5.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJR -\u0026gt; JE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e11.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJR -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJR x JD-\u0026gt; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eJR x Burnout -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD, Standard deviation\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Mediation effects in the path model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ePath co-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eT statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eP-values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eBurnout -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJD -\u0026gt; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e35.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJD -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJE -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e10.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; JE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e77.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e32.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal indirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJD -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e22.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific indirect effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJD -\u0026gt; Burnout -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; JE -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e21.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJR -\u0026gt; Burnout -\u0026gt; RDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Job demands, burnout, risky driving behaviours, Ghanaian HGV drivers","lastPublishedDoi":"10.21203/rs.3.rs-5711969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5711969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Heavy goods vehicle (HGV) drivers face significant psychosocial work challenges that increase burnout and risky driving behaviours (RDB), affecting drivers’ well-being and road safety. However, these psychosocial work challenges have received limited research attention in the developing countries. This study explored the relationships between job demands (JD), job resources (JR), burnout, job engagement (JE), and RDB in Ghanaian HGV drivers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: This cross-sectional survey collected data from 1,575 HGV drivers (truck and tanker drivers) in Tema, Ghana. Data were collected through validated questionnaires and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) using Smart PLS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: High JD directly increases burnout and RDB among HGV drivers in Ghana, while burnout increases the occurrence of RDB and negatively affects JE. High JR reduces burnout and increases JE among the drivers. However, high JR did not significantly reduce RDB among the drivers. Mediation analyses revealed that burnout partially mediates the relationship between JD and RDB, and JR and RDB. JE partially mediates the relationships between JR and RDB. Moderation analyses show that JR significantly buffers the effects of JD on burnout and burnout on RDB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Data suggest that psychosocial factors strongly influence burnout and RDB among Ghanaian HGV drivers. Targeted efforts to balance job demands and enhance support systems are critical to improving the health, safety and well-being of these drivers, and ultimately reducing the on-the-road accidents.\u003c/p\u003e","manuscriptTitle":"Modelling Psychosocial Work Factors and Risky Driving in Ghana: Mediating Roles of Burnout and Engagement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 08:37:27","doi":"10.21203/rs.3.rs-5711969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"685f611e-8dd6-46a6-9733-59e8616a19cf","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T13:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 08:37:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5711969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5711969","identity":"rs-5711969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}