Impacts of Incentive Factors on Turnover Intention Among Chinese Primary Healthcare Providers: The Mediation Role of Job Burnout

Impacts of Incentive Factors on Turnover Intention Among Chinese Primary Healthcare Providers: The Mediation Role of Job Burnout | 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 Article Impacts of Incentive Factors on Turnover Intention Among Chinese Primary Healthcare Providers: The Mediation Role of Job Burnout Jing Wang, Xiaoting Wang, Lijuan Qiu, Min Li, Ren Chen, Jing Yan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5265617/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 The high turnover rate of primary medical personnel has been a noteworthy issue among primary healthcare services system, which is essential to improving the quality of medical and health service. The impacts of incentive factors such as salary, benefits and promotion opportunities on the turnover intention of primary healthcare providers have been found, but the results were inconsistent. How incentive factors affect turnover intention needs to be further explored to present potential reasons for these inconsistent research results above. Based on Herzberg's two-factor theory of motivation, the relationship between incentive factors including hygiene and motivation factors on turnover intention with the mediation effect of job burnout and the moderating effect of perceived social support were tested to better clarify this potential formation mechanism. A multi-stage cluster random sampling method was applied with a total sample of 1,112 primary healthcare providers from Anhui, China. Finally, the findings indicated that hygiene factors and motivation factors were directly negatively associated with turnover intention. Job burnout mediated the relationship between hygiene factors and turnover intention, whereas does not mediate the relationship between motivation factors and turnover intention was insignificant. It is interesting that the interaction terms of perceived social support and motivation factors negatively affected job burnout. Thus, it is necessary to appropriately utilize incentive factors and social support to alleviate primary healthcare providers' job burnout and reduce their turnover intention to improve the quality of primary healthcare service. Social science/Psychology Social science/Sociology Figures Figure 1 Figure 2 Introduction Primary healthcare can basically handle most people's health needs throughout their whole lifetime, and its pivotal role is becoming more and more evident considering an increasing aging population with more chronic diseases (He et al., 2020). The primary healthcare providers are gatekeepers of the residents' health, who offer basic medical and public health services directly and determine the service capacity and the outcomes of primary healthcare institutions (Espinosa-González & Normand, 2019). To strengthen the role of primary healthcare in China, the Healthy China 2030 Plan released in 2016 further puts forward a new policy of giving priority to prevention and primary care in health development (Li X. et al., 2020). Across the whole world, the shortage of human resources has always been a serious problem in the development of primary healthcare institutions (Goel et al., 2013; Wen et al., 2018). For example, the United States will face a shortage of 7,300 to 43,100 primary healthcare physicians by 2030, according to the Association of American Medical Colleges (AAMC) (Tong, Yan, & Xue, 2018). In Japan, the unbalanced increase in residents' demands and constrained primary healthcare supply potentially exacerbate the existing primary healthcare shortages (Li W. et al., 2020). In China, the phenomenon of primary healthcare labour shortage has led to overwork of medical service providers on duty, increased extra work pressure and duties, less enthusiasm and job satisfaction, less time off, absenteeism and even resignation (Bangdiwala et al., 2010; Gan et al., 2019), which becomes a serious obstacle to develop the quality of healthcare service. Thus, it is necessary to consider how to deal with the shortage of primary healthcare providers and improve the quality of primary healthcare services. Reducing the turnover intention of primary medical staff is important to solve the shortage of human resources (Park & Yu, 2019). The main goal of this research is to alleviate the shortage problem by reducing turnover intention and improving the management of primary healthcare providers in China. Turnover intention reflects a mental response to the specific organizational conditions and jobs, which is regarded as the final cognitive step resulting in actual turnover (Slåtten, Svensson, & Sværi, 2011; Boateng et al., 2022). There is no consensus on the determinants of turnover intention among primary healthcare providers, although relevant studies have been conducted to reduce the intention of primary healthcare providers' turnover in China (He et al. 2020). Some scholars have found that incentive factors can exert an influence on the turnover intention of primary healthcare providers (Ezeh & Olawale, 2017; Ma et al., 2019; Chan & Ao, 2019; Yan et al., 2021; Zhang & Liu, 2020), but the research conclusions were also inconsistent. For example, it has been found that employee satisfaction with financial rewards was negatively associated with turnover intention (Chan & Ao, 2019), although other research has found that there was no significant relationship (Ezeh & Olawale, 2017). Therefore, this study will further explore how incentive factors affect turnover intention in order to provide practical measures to reduce primary healthcare providers' turnover intention. According to Herzberg's two‐factor theory of motivation (motivation-hygiene theory), Herzberg divided the incentive factors that affect the development of organizations into two groups, namely, motivation factors and hygiene factors (Ruthankoon & Olu Ogunlana, 2003). Motivators can make employees feel satisfied with their work and are considered as intrinsic rewards for employees related to the work itself (Alrawahi et al., 2020; Hur, 2018). Hygiene factors cannot increase or decrease satisfaction but can prevent employee dissatisfaction and are regarded as extrinsic rewards connected to working environments and conditions (Hur, 2018; Isaacs et al., 2020). Previous studies suggested that employees' motivation factors and hygiene factors exerted some effects on their turnover intention directly or indirectly (Chiat & Panatik, 2019; Jamison, 2003). It has been found that incentive factors have an impact on job burnout (Tung et al., 2020). Job burnout refers to a psychological response to chronic emotional and interpersonal job stress (Beheshtifar & Omidvar, 2013) and is generally reflected as an emotional exhaustion, cynicism, and diminished personal accomplishment experience (Maslach, 2003; Srivastava, Misra & Madan, 2019). Job burnout is an increasingly significant risk that has been proven to cause higher turnover intention of primary healthcare providers (Lo et al., 2018; Xu et al., 2020). Job burnout occurs in the literature on turnover intention and acts as a mediating variable. For example, it mediates the relationship between work stress and turnover intention (Tziner et al., 2015). The hygiene and motivation factors may affect turnover intention through job burnout. However, in the primary care system whether job burnout mediates the relationship between hygiene or motivation factors and primary healthcare providers' turnover intention has not been studied yet and needs further discussion. In the study of turnover intention, the moderating effect of perceived social support has been emphasized (Kim & Stoner, 2008). Perceived social support refers to an individual's subjective feeling of believing in how much support they potentially can have access to from their social contacts and relationships (Wang et al., 2018). Social support helps create a more employee-friendly work environment by protecting an individual from stress and mitigating stress (Jia & Li, 2022). Both incentives and positive social support can make employees become attached to their work (Didit & Nikmah, 2020). In the meantime, previous studies on turnover intention have found that perceived social support plays a moderating role (Courcy, Morin, & Madore, 2019; Jia & Li, 2022). However, the moderating effect of perceived social support on the relationship between incentive factors and turnover intention has not been paid much attention. Therefore, this study would like to introduce perceived social support as a moderating variable to explore its moderating effect on the relationship between hygiene or motivation factors and turnover intention. Motivation-hygiene theory and turnover intention Herzberg's two-factor theory of motivation first appeared in 1959 and is one of the most important theories in the employee work research area and is suitable for research on employees' job satisfaction initially (Chiat & Panatik, 2019). According to the motivation-hygiene theory, there are two kinds of factors that can affect an employee's job satisfaction, which are satisfiers (motivation factors) and dissatisfiers (hygiene factors). Motivation factors refer to the main factors that can actively drive work and improve job satisfaction (Alrawahi et al., 2020). Hygiene factors refer to elements of work that make employees do not dissatisfy with their job (Isaacs et al., 2020). At present, the motivation-hygiene theory has also been applied to the research of turnover intention, and both hygiene and motivation factors can play important roles in influencing turnover intentions (Chiat & Panatik, 2019). In the research on turnover intention, hygiene factors are external to the task of the job, such as salary, security and other benefits that reflect working conditions (Ajila, & Abiola, 2004). Motivation factors are another form of remuneration for employees in the organization, which stems from rewards that are inherent in the job itself and are different from economic incentives (Armanu & Sudjatno, 2017). The motivation factors include job enrichment, promotion, job training and achievements, which can enhance employees' sense of belonging and satisfaction (Erbasi & Arat, 2012; Ann & Blum, 2020). However, there are limited studies that use Herzberg's two-factor theory framework to investigate these influencing factors and mechanism of primary healthcare providers' turnover intention. Thus, this study is making efforts in this direction. Based on the motivation-hygiene theory, salary, job security and other work benefits, which may cause employees disappointment with their job if they can not satisfy the employees' expectations (Acquah et al., 2021). Hygiene factors were indicated can alleviate turnover intention (A'yuninnisa & Saptoto, 2015; Ann & Blum, 2020). Hence, it can be predicted that when primary medical workers are satisfied with their salary, job security or other benefits, they prefer to say in their current organization. Previous studies have shown that incentives associated with the context can meet the primary healthcare providers' enthusiasm and attitudes towards their work (Rani, Heang, & Rahman, 2018; Sang et al., 2022). According to the motivation-hygiene theory, the motivators generate employees' job commitment to their organization and human needs such as achievements (Katt & Condly, 2009) and self-actualization (Mehrad, 2020). Thus, when primary healthcare providers are satisfied with their incentives related to work, they would stay and intend to work for the organization. Accordingly, the following hypotheses are: H1: Hygiene factors such as salary, job security and other benefits are negatively related to turnover intention. H2: Motivation factors such as job training and opportunities, promotion, and a sense of accomplishment are negatively related to turnover intention. The mediating role of job burnout Job burnout refers to a psychological response to job stress (Beheshtifar & Omidvar, 2013), and its manifestations vary, including emotional depletion, cynical attitudes towards work, and diminished personal accomplishment (Lu & Gursoy, 2016). Many studies have confirmed that turnover intention could occur as a result of job burnout (Abate, Schaefer, & Pavone, 2018). Job burnout can be affected by employees' attitudes towards the motivation factors and hygiene factors. Both motivation and hygiene factors reflect the employees' motivation to work (Sang et al., 2022), which plays an active role in shaping positive attitudes towards work (Chen et al., 2022). And if the employees hold positive enthusiasm and attitudes towards their job, the burnout could be alleviated (Chen et al., 2020). Based on this logic, the primary healthcare providers' motivation and hygiene factors are likely to release the job burnout. Moreover, job burnout may positively predict primary healthcare providers' turnover intention. Job burnout has negative influences on employees in the organization. When employees exhibit job burnout, they are likely to get the sense of too much workload and feel bored, usually accompanied by low productivity and selfishness (Salama et al., 2022). Mental violence was related to turnover intentions (Duan et al., 2019). Furthermore, many studies have found that job burnout can increase absenteeism (Gil-Monte et al., 2014). Hence, it is reasonable to speculate that job burnout positively affects turnover intention. Here, the hypotheses are: H3a: Job burnout plays a mediating role in hygiene factors and turnover intention. H3b: Job burnout plays a mediating role in motivation factors and turnover intention. The moderating role of perceived social support Perceived social support is an individual's subjective feeling of believing in how much support can potentially have access to from their social contacts and relationships (Wang et al., 2018). Previous studies suggested that social support can create a comfortable atmosphere to relieve personal psychological internal friction (Kowalchyk et al., 2023). Perceived social support is likely to moderate the linkage between hygiene factors and job burnout, as well as motivation factors and job burnout. Social support is an external resource of motivation to deal with work stress and helps to mitigate the adverse impact on employees (Kuriakose, Wilson, & MR 2019). Hence, it can be predicted that when primary healthcare workers are satisfied with motivation and hygiene factors in the working environment, which encourages them to handle the work pressure, then the primary healthcare providers will feel less stress. Overall, the negative influences of hygiene factors and motivation factors on job burnout will be strengthened among primary healthcare workers with a high level of social support cognition. Consequently, we posit: H4a: Perceived social support moderates the relationship between hygiene factors and job burnout, such that the negative effect of hygiene factors on job burnout is strengthened when the level of perceived social support is higher. H4b: Perceived social support moderates the relationship between motivation factors and job burnout, such that the negative effect of motivation factors on job burnout is strengthened when the level of perceived social support is higher. Hence, the theoretical framework of this study is depicted in Figure 1. Methods Studying setting The study took place in the eastern province of Anhui, which has an average economic development level. By 2020, Anhui had 2,258 primary healthcare service institutions, which also contain a great number of primary healthcare providers (Chen et al., 2021 ). In this study, the professionally trained investigators conducted this cross-sectional survey using multi-stage cluster random sampling to collect data in three regions of Anhui: Central Anhui, Northern Anhui, and Southern Anhui. A self-administered questionnaire method was employed in this study. Specifically, the investigators distributed questionnaires to potential respondents and the potential respondents completed the questionnaires and returned. Sample and data collection The questionnaire of this survey was divided into three sections. The three sections included survey statements, demographic information and measurement items for the main latent variables. The survey statements contain a description of the purpose of this study and ensure the confidentiality of questionnaire information. The second section is demographic information, such as gender, age, education level, occupation, and work units. The last part is about the main questions of five variables measurement items. After discarding some invalid responses due to significant problems such as missing values and duplicates. Finally, investigators obtained 1112 usable questionnaires. Table 1 shows the demographic characteristics of the collected samples. To be specific, 796 samples were received from the north of Anhui, 208 samples were obtained from the middle of Anhui, and 135 samples were received from the south of Anhui. The number of respondents in the three areas was consistent with the distribution of regional districts and counties (Chen et al., 2021 ). There were 505 men and 607 women among the samples. And 32.014% and 43.525% of respondents were aged between 31–40 years old and 41–50 years old, which is the crucial period of personal career development. In terms of education level, 42.986% of the samples have attended secondary school and below. And about 39.209% and 17.806% of respondents respectively have held associate degrees and bachelor's degrees and above. Also, more than half of the respondents were physicians (51.709%), 6.025% of the samples were pharmacists, nurses accounted for 21.493%, and 20.773% of the respondents were medical managers. Moreover, all the samples of primary medical providers in this study were from community health service centers (11.421%), community health service stations (3.957%), township hospitals (62.050%), village clinics (15.108%), and outpatient departments (7.464%). Table 1 Profile of the samples (n = 1112). Group Frequency Percentage(%) Gender Male 505 45.414 Female 607 54.586 Age under 31 174 15.647 31–40 356 32.014 41–50 484 43.525 51–60 89 8.004 61 and over 9 0.809 Education level Secondary school and below 478 42.986 Associate degree 436 39.209 Bachelor's degree and above 198 17.806 Occupation Physicians 575 51.709 Pharmacists 67 6.025 Nurses 239 21.493 Medical managers 231 20.773 Work units Community health service centers 127 11.421 Community health service stations 44 3.957 Township hospitals 690 62.050 Village clinics 168 15.108 Outpatient department 83 7.464 Regions North of Anhui 769 69.155 Middle of Anhui 208 18.705 South of Anhui 135 12.140 Measurement development In this study, the investigators collected the research data from five latent constructs, and a self-administered questionnaire method was put into use. All the items of the five main latent constructs are referred to in the existing literature. To be specific, based on the study of Michaels & Spector ( 1982 ), three items were developed to reflect the primary healthcare providers' intention to leave their current position. To measure the items of turnover intention, this study used a 4-point Likert method (1-"Never", 2-"Seldom'', 3- "Once in a while" and 4- "Frequently"). Two items of hygiene factors and three items of motivation factors were sourced from Baruffaldi & Landoni ( 2016 ) and were measured on a 5-point Likert scale (1-"Not at all" to 5- "Very satisfied"). The 15-item on job burnout in the research framework also used a 5-point Likert scale (1-"Strongly disagree" to 5- "Strongly agree") which was referenced from the research of Li et al. (2004). Furthermore, 12 items of perceived social support were adapted from the studies of Dahlem et al. (1991) and a 7-point Likert scale (1- "Entirely disagree" to 7- "Entirely agree") was adopted to measure the items of perceived social support. The appendix presents the complete measurement questionnaire of five main latent constructs. According to the previous studies (He et al., 2020 ; Yan et al., 2021 ), gender, age, education level, and occupation were chosen as control variables in the present research framework. Results Reliability and validity To test the reliability and validity of the measurement scale, this study measured the values of Cronbach's alpha, the composite reliability (CR), the standardized loadings, and average variance extracted (AVE), respectively. The Cronbach's alpha and the composite reliability (CR) can be used to prove the reliability of the scale, and the threshold for both is 0.70 (Fornell & Larcker, 1981 ). All the Cronbach's alpha values and CR values were higher than 0.70 in Table 2 , which indicated that the reliability of the constructs was acceptable. AVE and standardized loadings were measured to convergent validity, and the AVE and the values of standardized loadings should be more than 0.50 (Fornell & Larcker, 1981 ; Chen, 2008 ). Table 2 suggested that all the AVE values of the variables exceeded the benchmark value. Furthermore, the results in Table 3 suggested that the square roots of AVE were greater than the correlations of constructs, which evaluated and supported the discriminant validity of the measurement scale (Alhaddad, 2015 ). Thus, the validity was also acceptable. Table 2 Reliability and convergent validity. Construct Cronbach's alpha Standardized loadings CR AVE 1.Hygiene factors (HF) 0.843 0.930–0.930 0.928 0.865 2.Motivation factors (MF) 0.739 0.778–0.857 0.853 0.659 3.Job burnout (JB) 0.738 0.509–0.820 0.938 0.507 4.Perceived social support (PS) 0.938 0.564–0.885 0.934 0.544 5.Turnover intention (TI) 0.820 0.835–0.896 0.893 0.735 Descriptive statistics Table 3 also presents the results of the main variables' means, standard deviations, and correlations. As shown in Table 3 , in terms of the control variables, the primary healthcare provider's gender (r = -0.119, P <0.001) and occupation (r = -0.197, P <0.001) were negatively correlated with turnover intention, and work units was positively correlated with turnover intention (r = 0.145, P <0.001), but the other control variables (age and education) were insignificantly correlated with turnover intention. Hygiene factors were significantly related to job burnout (r = -0.170, P <0.001) and turnover intention (r = -0.300, P <0.001). Motivation factors were also significantly related to job burnout (r = -0.151, P <0.001) and turnover intention (r = -0.289, P <0.001). The relationship between job burnout and turnover intention was positive (r = 0.249, P <0.001). The findings above were in line with the initial hypotheses. Table 3 Means, standard deviation, and correlations. Mean SD 1 2 3 4 5 6 7 8 9 10 1.Gender 1.550 0.498 — 2.Age 2.463 0.878 -0.270 *** — 3.Education 2.710 0.811 0.134 *** -0.301 *** — 4.Occupation 2.110 1.246 0.388 *** -0.210 *** 0.067 * — 5.Work units 3.030 0.973 -0.154 *** -0.004 -0.179 *** -0.137 *** — 6.Hygiene factors (HF) 2.514 0.947 0.068 * -0.042 0.086 ** 0.116 *** -0.081 ** 0.930 7.Motivation factors (MF) 2.938 0.794 0.053 -0.049 0.048 0.121 *** 0.032 0.589 *** 0.812 8.Job burnout (JB) 2.386 0.531 -0.228 *** 0.113 *** -0.148 *** -0.186 *** 0.076 * -0.170 *** -0.151 *** 0.712 9.Perceived social support (PS) 5.408 1.088 0.119 *** -0.031 0.062 * 0.104 *** -0.033 0.186 *** 0.241 *** -0.330 *** 0.738 10.Turnover intention (TI) 2.051 0.887 -0.119 *** -0.010 -0.051 -0.197 *** 0.145 *** -0.300 *** -0.289 *** 0.249 *** -0.238 *** 0.857 Note : ES, Economic satisfaction; NES, Non-economic satisfaction; JB, Job burnout; PS, Perceived social support; TI, Turnover intention; SD means standard deviation. The diagonal elements in bold are the square roots of the average variance extracted (AVE), and the off-diagonal elements are the correlation between constructs. Common method variance First, this study used Harman's single-factor test to examine the common method biases of the data (Howard & Henderson, 2023 ). The results showed that the variation explained by the first factor was only 23.860%, less than the critical value of 40%, which indicated there was no serious common method variance problem. Moreover, this study also conducted the results of confirmatory factor analysis(Table 4 )and an unmeasured latent common method factor model (ULMC). The results in Table 4 showed that the multi-factor model was significantly better than the single method model (∆χ 2 = 4908.08, ∆ df = 10, P < 0.05) and was not significantly different from the multi-factor model with ULCMF (∆CFI = 0.001, ∆TLI = 0.001, ∆RMSEA = 0.001). The consistent result showed that common method variance was minimal. Table 4 Fit indices for the measurement models. Model χ 2 df RMSEA CFI TLI IFI Multi-factor model (HF,MF,JB,PS,TI) 2592.858 538 0.059 0.886 0.874 0.882 Single-factor model(HF + MF + JB + PS + TI) 7500.938 548 0.107 0.615 0.582 0.616 Common method factor model(HF,MF,JB,PS,TI,ULCMF) 2572.248 537 0.058 0.887 0.875 0.888 Note : ULCMF, An unmeasured latent common method factor. Hypothesis testing This study conducted hierarchical multiple regression analysis to examine H1, H2, H3a and H3b. To be specific, according to the suggestions of Baron & Kenny ( 1986 ), the control variables, the independent variables, and mediating variables were added to the regression model stepwise. Table 5 showed the results that hygiene factors were negatively correlated with job burnout (β= -0.097, P < 0.01), while the relationship between motivation factors and job burnout was insignificant (β= -0.069, n.s.) in model 2. Model 4 showed that hygiene factors (β= -0.170, P < 0.001) and motivation factors (β= -0.176, P < 0.001) were negatively correlated with turnover intention. The linkage between job burnout and turnover intention revealed by model 5 was positive and significant (β = 0.172, P < 0.001). Moreover, in model 5, the coefficient of the impact of hygiene factors on turnover intention (β= -0.154, P < 0.001) was smaller than the coefficient (β= -0.170, P < 0.001) in model 4. All these results above supported H1, H2, and H3a, while H3b was not supported. The variance inflation factors during hierarchical regression analyses were from 1.104–1.545, which was below 5 (Akinwande, Dikko, & Samson, 2015 ). Thus, there were no serious multicollinearity issues. Table 5 Regression results for mediation effect. Predictors Job burnout Turnover intention Standardized coefficients Standardized coefficients model 1 model 2 model 3 model 4 model 5 Hygiene factors -0.097 ** -0.170 *** -0.154 *** Motivation factors -0.069 -0.176 *** -0.164 *** Job burnout 0.172 *** Gender -0.165 *** -0.163 *** -0.049 -0.046 -0.017 Age 0.012 0.012 -0.071 * -0.071 * -0.073 * Education -0.112 *** -0.102 ** -0.034 -0.014 0.003 Occupation -0.110 ** -0.092 ** -0.176 *** -0.138 *** -0.122 *** Work units 0.016 0.015 0.107 *** 0.108 *** 0.106 *** R 2 0.077 0.099 0.059 0.152 0.178 △R 2 0.077 0.022 0.059 0.093 0.027 F 18.456 *** 17.255 *** 13.766 *** 28.201 *** 29.948 *** Note : *p < 0.05, **p < 0.01, ***p < 0.001. To further test H3a and H3b, a bias-corrected bootstrapping procedure was employed in this study. Table 6 shows that the indirect effect of hygiene factors on turnover intention via job burnout was significant (95% CI= -0.029 to -0.004; excluding 0; indirect effect = -0.016), while the indirect effect of motivation factors on turnover intention was insignificant (95% CI= -0.028 to 0.000; including 0). Hence, the evidence supported H3a. Table 6 Indirect effects of ES and NES on TI. Estimate Bootstrap-indirect effect SE 95%CI HF→JB→TI -0.016 0.006 (-0.029, -0.004) MF→JB→TI -0.013 0.007 (-0.028, 0.000) Note : HF, Hygiene factors; MF, Motivation factors; JB, Job burnout; TI, Turnover intention; SE, Standard error; A 95% bootstrap confidence interval that does not include zero means an effect is significant. To test H4a and H4b, this study also applied the hierarchical moderated regression method, in which the control variables, independent variables, moderator, and the interaction term (Hygiene factors * perceived social support and Motivation factors * perceived social support). The results in Table 7 indicated that the interaction term of motivation factors and perceived social support was positively related to job burnout (model 3, β= -0.074, p < 0.05), which was in line with H4b. However, there was no moderating effect of perceived social support between hygiene factors and job burnout (model 3, β= -0.005, n.s.). The variance inflation factors during hierarchical regression analyses were from 1.104–1.518, which was also below 5 and was good for regression mode l (Akinwande, Dikko, & Samson, 2015 ). Table 7 Regression results for moderation effect. Predictors Job burnout Standardized coefficients model 1 model 2 model 3 Hygiene factors (HF) -0.097 ** -0.074 * Motivation factors (MF) -0.069 -0.005 Perceived social support (PS) -0.296 *** HF*PS -0.005 MF*PS -0.075 * Gender -0.165 *** -0.163 *** -0.139 *** Age 0.012 0.012 0.016 Education -0.112 *** -0.102 ** -0.092 ** Occupation -0.110 ** -0.092 ** -0.081 ** Work units 0.016 0.015 0.019 R 2 0.077 0.099 0.177 △R 2 0.077 0.022 0.078 F 18.456 *** 17.255 *** 23.645 *** Note : *p < 0.05, **p < 0.01, ***p < 0.001. To identify the nature of the interaction effect of perceived social support and motivation factors on job burnout, this study also plotted the slopes which were computed by one standard deviation below and above the mean of perceived social support. Figure 2 shows the results of the interaction impact. To be specific, motivation factors were more negatively correlated with job burnout at the high level of perceived social support in comparison to the low level of perceived social support. Figure 2 . The interaction effect of PS and MF on JB. Discussion Theoretical implications This study applies Herzberg's motivation-hygiene framework to examine the influence of motivation and hygiene factors on primary healthcare providers' turnover intention. The results in this study are consistent with the proposed H1, H2, H3a, and H4b. However, H3b and H4a are not supported according to the results of this study. That is to say, both hygiene factors and motivation factors are positively correlated with turnover intention. Furthermore, though motivation factors are not significantly correlated with job burnout, the interaction term between hygiene factors and perceived social support was significantly related to job burnout. Based on the motivation-hygiene theory, consistent with the expectations of this current study, primary healthcare workers' turnover intention can be affected by hygiene factors and motivation factors. These findings are in line with previous literature, which found that the influence of motivation and hygiene factors can encourage and help sustain employees' dedication and retention, thus reducing their turnover intention (Takawira, Coetzee, & Schreuder, 2014 ). Meanwhile, these results support the existing study that the incentive mechanism can play a positive role in improving the retention rate of primary medical providers (Chen et al., 2021 ). This study enriches the empirical research on the turnover intention of primary healthcare providers through the motivation-hygiene theory. The results also suggested that job burnout mediates the relationship between hygiene factors and turnover intention. However, the mediating effect of job burnout on the relationship between motivation factors and turnover intention does not exist. That is to say, hygiene factors play a positive role in relieving job burnout, thus reducing turnover intention. Existing literature on turnover intention has also found the mediating role of job burnout, such as the research of Leiter & Maslach ( 2009 ) found burnout mediated the relationship between areas of work life and nurse turnover. Moreover, it was also found that job burnout plays a mediating role between perceived organizational justice and turnover intention (Vaamonde, Omar, & Salessi, 2018 ). However, there are few studies considering the mediating role of incentive factors and turnover intention. Thus, this study has extended in this direction, which enriches the internal mechanism of turnover intention of primary healthcare providers. This study has shown that motivation factors can alleviate primary healthcare providers' job burnout when they have a high level of perceived social support. In the study of turnover intention, the moderating effect of employees' perceived social support has also increasingly received attention (Courcy, Morin, & Madore, 2019 ). For example, previous research has found that perceived social support can moderate the relationship between job burnout and subjective well-being (Wang et al., 2020 ). However, little attention has been paid to its moderating role in the relationship between motivation factors and turnover intention. The result of this study has made this effort to bridge this gap, which also extends the application of perceived social support as a boundary condition in the mechanism of turnover intention. Practical implications From a practical perspective, there are several managing enlightenments for the government, primary healthcare institutions, and people from all walks of life associated with primary medical providers to increase their enthusiasm and thus reduce turnover intention. First, the current study has found that hygiene factors are essential to turnover intention. The more satisfied primary healthcare providers are with their salary, welfare, and other benefits, the less willing they are to leave their primary healthcare institutions. Thus, the government must continue to increase the proportion of financial subsidies for the regular expenditure of primary healthcare institutions and the salary of primary medical providers, as well as consider personalized benefit and welfare guarantees, to meet the needs of health personnel and protect the interests of primary medical providers. Moreover, motivation factors, such as opportunities for advancement and achievement, are important determinants of turnover intention. Hence, primary healthcare institutions should take responsibility to increase the training of primary healthcare workers and improve their professional skills to improve primary healthcare providers' satisfaction and provide a guarantee for a safe and effective primary health service. Meanwhile, managers in primary healthcare institutions also need to focus on providers' career development and advancement to enhance their professional identity. Additionally, the finding in this study reveals that the interaction between motivation factors and perceived social support can alleviate job burnout and thus reduce turnover intention. Thus, the supports from organizations, family and friends are also essential to the job stability of primary healthcare providers, especially the primary healthcare institutions that make use of motivation factors to boost employee enthusiasm and mitigate employee's burnout. Limitations and future prospects There are also some limitations in the present studies. On the one hand, a cross-sectional survey was used to conduct this study, so it is difficult to test the alternative causal sequences and observe the dynamic changes of main variables in the present model framework. Thus, a longitudinal design can be considered in future research. On the other hand, this study considers job burnout as a mediating variable between hygiene factors and turnover intention, while the mediating effect of the relationship between motivation factors and turnover intention is not valid. Thus, future research could consider other potential mediating variables in the relationship between hygiene factors and turnover intention. Last but not least, this study measured the level of health factors by satisfaction with hygiene factors such as salary and benefits, future studies could consider quantifying specific values of salary and benefits. Conclusion Based on motivation-hygiene theory this study builds an integral model framework for influencing the mechanism of turnover intention and provides an effective mechanism to reduce the turnover intention of primary healthcare providers. Job burnout exerts a mediating effect on the association between motivation factors and turnover intention. Meanwhile, the interaction term of perceived social support and motivation factors is negatively related to job burnout. This study may also offer some enlightenment on practice management to reduce primary healthcare providers' turnover intention. Hygiene factors, such as salary, welfare, and other benefits should be made full use of to alleviate employees' burnout and reduce their turnover intention. Social support should be valued when the primary healthcare institution aims to give full play to the role of motivation factors (career advancement and training in reducing job burnout). Thus, the comprehensive practical measures need to be considered. Declarations Ethics declarations Conflict interests The authors declare no competing interests. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Anhui Medical University (No. AMUREC: 20170260) Informed consent Each participant is informed of the purpose and content of the survey prior to the formal survey, and its anonymity and privacy are guaranteed. Informed consent was obtained from all individual participants included in the study. Data availability statement The dataset generated and analyzed during this study is available from the corresponding authors upon reasonable request. Funding This study was funded by research funds from Key scientific research projects of colleges and universities in Anhui Province (natural science) (grant no. 2022AH050692) and the Excellent research and innovation team project of Anhui Province (grant no. 2023AH010036) Author contributions GC, JW, and XW contributed to the conception and design of the study. LJQ, ML, RC, JY, JC, LW, YZ, and HD collected the data and organized the database. XW performed the statistical analysis. JW wrote the first draft of the manuscript. GC revised the manuscript for important intellectual content. All authors contributed to the manuscript revision, and read, and approved the submitted version. References Abate, J., Schaefer, T., & Pavone, T. (2018). 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06:54:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1390386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5265617/v1/e5857325-7db2-4851-beba-85dc139e0c70.pdf"},{"id":69280999,"identity":"a9265056-5145-4d28-b5f2-c8d54e1613e9","added_by":"auto","created_at":"2024-11-18 18:38:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20274,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixtabLeHSSC.docx","url":"https://assets-eu.researchsquare.com/files/rs-5265617/v1/10a40946afa8381d660a1c93.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of Incentive Factors on Turnover Intention Among Chinese Primary Healthcare Providers: The Mediation Role of Job Burnout","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary healthcare can basically handle most people\u0026apos;s health needs throughout their whole lifetime, and its pivotal role is becoming more and more evident considering an increasing aging population with more chronic diseases (He\u0026nbsp;et al., 2020). The primary healthcare providers are gatekeepers of the residents\u0026apos; health, who offer basic medical and public health services directly and determine the service capacity and the outcomes of primary healthcare institutions (Espinosa-Gonz\u0026aacute;lez\u0026nbsp;\u0026amp; Normand, 2019). To strengthen the role of primary healthcare in China, the Healthy China 2030 Plan released in 2016 further puts forward a new policy of giving priority to prevention and primary care in health development (Li\u0026nbsp;X.\u0026nbsp;et al., 2020). Across the whole world, the shortage of human resources has always been a serious problem in the development of primary healthcare institutions (Goel\u0026nbsp;et al., 2013;\u0026nbsp;Wen\u0026nbsp;et al., 2018). For example, the United States will face a shortage of 7,300 to 43,100 primary healthcare physicians by 2030, according to the Association of American Medical Colleges (AAMC) (Tong, Yan, \u0026amp;\u0026nbsp;Xue, 2018). In Japan, the unbalanced increase in residents\u0026apos; demands and constrained primary healthcare supply potentially exacerbate the existing primary healthcare shortages (Li\u0026nbsp;W. et al., 2020). In China, the phenomenon of primary healthcare labour shortage has led to overwork of medical service providers on duty, increased extra work pressure and duties, less enthusiasm and job satisfaction, less time off, absenteeism and even resignation (Bangdiwala\u0026nbsp;et al.,\u0026nbsp;2010;\u0026nbsp;Gan\u0026nbsp;et al., 2019), which becomes a serious obstacle to develop the quality of healthcare service. Thus, it is necessary to consider how to deal with the shortage of primary healthcare providers and improve the quality of primary healthcare services. Reducing the turnover intention of primary medical staff is important to solve the shortage of human resources (Park\u0026nbsp;\u0026amp; Yu, 2019). The main goal of this research is to alleviate the shortage problem by reducing turnover intention and improving the management of primary healthcare providers in China.\u003c/p\u003e\n\u003cp\u003eTurnover intention reflects a mental response to the specific organizational conditions and jobs, which is regarded as the final cognitive step resulting in actual turnover (Sl\u0026aring;tten,\u0026nbsp;Svensson,\u0026nbsp;\u0026amp; Sv\u0026aelig;ri,\u0026nbsp;2011; Boateng\u0026nbsp;et al., 2022). There is no consensus on the determinants of turnover intention among primary healthcare providers, although relevant studies have been conducted to reduce the intention of primary healthcare providers\u0026apos; turnover in China (He\u0026nbsp;et al.\u0026nbsp;2020). Some scholars have found that incentive factors can exert an influence on the turnover intention of primary healthcare providers (Ezeh \u0026amp; Olawale, 2017; Ma\u0026nbsp;et al.,\u0026nbsp;2019; Chan\u0026nbsp;\u0026amp; Ao, 2019; Yan et al., 2021; Zhang\u0026nbsp;\u0026amp; Liu, 2020), but the research conclusions were also inconsistent. For example, it has been found that employee satisfaction with financial rewards was negatively associated with turnover intention (Chan\u0026nbsp;\u0026amp; Ao, 2019), although other research has found that there was no significant relationship (Ezeh\u0026nbsp;\u0026amp;\u0026nbsp;Olawale, 2017). Therefore, this study will further explore how incentive factors affect turnover intention in order to provide practical measures to reduce primary healthcare providers\u0026apos; turnover intention.\u003c/p\u003e\n\u003cp\u003eAccording to Herzberg\u0026apos;s two‐factor theory\u0026nbsp;of motivation (motivation-hygiene theory), Herzberg divided the incentive factors that affect the development of organizations into two groups, namely, motivation factors and hygiene factors (Ruthankoon\u0026nbsp;\u0026amp; Olu Ogunlana, 2003). Motivators can make employees feel satisfied with their work and are considered as intrinsic rewards for employees related to the work itself (Alrawahi\u0026nbsp;et al., 2020; Hur, 2018). Hygiene factors cannot increase or decrease satisfaction but can prevent employee dissatisfaction and are regarded as extrinsic rewards connected to working environments and conditions (Hur,\u0026nbsp;2018; Isaacs\u0026nbsp;et al., 2020). Previous studies suggested that employees\u0026apos; motivation factors and hygiene factors exerted some effects on their turnover intention directly or indirectly (Chiat\u0026nbsp;\u0026amp; Panatik, 2019;\u0026nbsp;Jamison, 2003).\u003c/p\u003e\n\u003cp\u003eIt has been found that incentive factors have an impact on job burnout (Tung\u0026nbsp;et al., 2020). Job burnout refers to a psychological response to chronic emotional and interpersonal job stress (Beheshtifar\u0026nbsp;\u0026amp; Omidvar, 2013) and is generally reflected as an emotional exhaustion, cynicism, and diminished personal accomplishment experience (Maslach,\u0026nbsp;2003; Srivastava,\u0026nbsp;Misra \u0026amp; Madan, 2019). Job burnout is an increasingly significant risk that has been proven to cause higher turnover intention of primary healthcare providers (Lo\u0026nbsp;et al., 2018; Xu\u0026nbsp;et al., 2020). Job burnout occurs in the literature on turnover intention and acts as a mediating variable. For example, it mediates the relationship between work stress and turnover intention (Tziner\u0026nbsp;et al., 2015). The hygiene and motivation factors may affect turnover intention through job burnout. However, in the primary care system whether job burnout mediates the relationship between hygiene or motivation factors and primary healthcare providers\u0026apos; turnover intention has not been studied yet and needs further discussion.\u003c/p\u003e\n\u003cp\u003eIn the study of turnover intention, the moderating effect of perceived social support has been emphasized (Kim\u0026nbsp;\u0026amp; Stoner, 2008). Perceived social support refers to an individual\u0026apos;s subjective feeling of believing in how much support they potentially can have access to from their social contacts and relationships (Wang\u0026nbsp;et al., 2018). Social support helps create a more employee-friendly work environment by protecting an individual from stress and mitigating stress (Jia \u0026amp; Li, 2022). Both incentives and positive social support can make employees become attached to their work (Didit\u0026nbsp;\u0026amp; Nikmah, 2020). In the meantime, previous studies on turnover intention have found that perceived social support plays a moderating role (Courcy, Morin, \u0026amp; Madore, 2019; Jia\u0026nbsp;\u0026amp; Li, 2022). However, the moderating effect of perceived social support on the relationship between incentive factors and turnover intention has not been paid much attention. Therefore, this study would like to introduce perceived social support as a moderating variable to explore its moderating effect on the relationship between hygiene or motivation factors and turnover intention.\u003c/p\u003e\n\u003ch3\u003eMotivation-hygiene theory and turnover intention\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eHerzberg\u0026apos;s two-factor theory of motivation first appeared in 1959 and is one of the most important theories in the employee work research area and is suitable for research on employees\u0026apos; job satisfaction initially (Chiat \u0026amp; Panatik, 2019). According to the motivation-hygiene theory, there are two kinds of factors that can affect an employee\u0026apos;s job satisfaction, which are satisfiers (motivation factors) and dissatisfiers (hygiene factors). Motivation factors refer to the main factors that can actively drive work and improve job satisfaction (Alrawahi et al., 2020). Hygiene factors refer to elements of work that make employees do not dissatisfy with their job (Isaacs et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt present, the motivation-hygiene theory has also been applied to the research of turnover intention, and both hygiene and motivation factors can play important roles in influencing turnover intentions (Chiat\u0026nbsp;\u0026amp; Panatik, 2019). In the research on turnover intention, hygiene factors are external to the task of the job, such as salary, security and other benefits that reflect working conditions (Ajila,\u0026nbsp;\u0026amp; Abiola, 2004). Motivation factors\u0026nbsp;are another form of remuneration for employees in the organization, which stems from rewards that are inherent in the job itself and are different from economic incentives (Armanu\u0026nbsp;\u0026amp; Sudjatno, 2017). The\u0026nbsp;motivation factors\u0026nbsp;include job enrichment, promotion, job training and achievements, which can enhance employees\u0026apos; sense of belonging and satisfaction (Erbasi\u0026nbsp;\u0026amp; Arat, 2012; Ann\u0026nbsp;\u0026amp; Blum, 2020).\u0026nbsp;However, there are limited studies that use Herzberg\u0026apos;s two-factor theory framework to investigate these influencing factors and mechanism of primary healthcare providers\u0026apos; turnover intention. Thus, this study is making efforts in this direction.\u003c/p\u003e\n\u003cp\u003eBased on the motivation-hygiene theory, salary, job security and other work benefits, which may cause employees disappointment with their job if they can not satisfy the employees\u0026apos; expectations (Acquah\u0026nbsp;et al., 2021). Hygiene factors were indicated can alleviate turnover intention (A\u0026apos;yuninnisa\u0026nbsp;\u0026amp; Saptoto, 2015;\u0026nbsp;Ann\u0026nbsp;\u0026amp; Blum, 2020). Hence, it can be predicted that when primary medical workers are satisfied with their salary, job security or other benefits, they prefer to say in their current organization.\u0026nbsp;Previous studies have shown that incentives associated with the context can meet the primary healthcare providers\u0026apos; enthusiasm and attitudes towards their work (Rani, Heang, \u0026amp; Rahman, 2018; Sang\u0026nbsp;et al., 2022). According to the motivation-hygiene theory, the motivators generate employees\u0026apos; job commitment to their organization and human needs such as\u0026nbsp;achievements (Katt \u0026amp; Condly, 2009) and\u0026nbsp;self-actualization (Mehrad, 2020). Thus, when primary healthcare providers are satisfied with their incentives related to work, they would stay and intend to work for the organization.\u003c/p\u003e\n\u003cp\u003eAccordingly, the following hypotheses are:\u003c/p\u003e\n\u003cp\u003eH1: Hygiene factors such as salary, job security and other benefits are negatively related to turnover intention.\u003c/p\u003e\n\u003cp\u003eH2:\u0026nbsp;Motivation factors such as\u0026nbsp;job training and opportunities, promotion, and a sense of accomplishment\u0026nbsp;are negatively related to\u0026nbsp;turnover intention.\u003c/p\u003e\n\u003ch3\u003eThe mediating role of job burnout\u003c/h3\u003e\n\u003cp\u003eJob burnout refers to a psychological response to job stress (Beheshtifar\u0026nbsp;\u0026amp; Omidvar, 2013), and its manifestations vary, including emotional depletion, cynical attitudes towards work, and diminished personal accomplishment (Lu\u0026nbsp;\u0026amp; Gursoy, 2016). Many studies have confirmed that turnover intention could occur as a result of job burnout (Abate, Schaefer,\u0026nbsp;\u0026amp; Pavone, 2018).\u003c/p\u003e\n\u003cp\u003eJob burnout can be affected by employees\u0026apos; attitudes towards the motivation factors and hygiene factors. Both motivation and hygiene factors reflect the employees\u0026apos; motivation to work (Sang et al., 2022), which plays an active role in shaping positive attitudes towards work (Chen et al., 2022). And if the employees hold positive enthusiasm and attitudes towards their job, the burnout could be alleviated (Chen et al., 2020). Based on this logic, the primary healthcare providers\u0026apos; motivation and hygiene factors are likely to release the job burnout.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, job burnout may positively predict primary healthcare providers\u0026apos; turnover intention. Job burnout has negative influences on employees in the organization. When employees exhibit job burnout, they are likely to get the sense of too much workload and feel bored, usually accompanied by low productivity and selfishness (Salama\u0026nbsp;et al., 2022). Mental violence was related to turnover intentions (Duan\u0026nbsp;et al., 2019). Furthermore, many studies have found that job burnout can increase absenteeism (Gil-Monte\u0026nbsp;et al., 2014). Hence, it is reasonable to speculate that job burnout positively affects turnover intention.\u003c/p\u003e\n\u003cp\u003eHere, the hypotheses are:\u003c/p\u003e\n\u003cp\u003eH3a: Job burnout plays a mediating role in\u0026nbsp;hygiene factors\u0026nbsp;and turnover intention.\u003c/p\u003e\n\u003cp\u003eH3b: Job burnout plays a mediating role in\u0026nbsp;motivation\u0026nbsp;factors and turnover intention.\u003c/p\u003e\n\u003ch3\u003eThe moderating role of perceived social support\u003c/h3\u003e\n\u003cp\u003ePerceived social support is an individual\u0026apos;s subjective feeling of believing in how much support can potentially have access to from their social contacts and relationships (Wang et al., 2018). Previous studies suggested that social support can create a comfortable atmosphere to relieve personal psychological internal friction (Kowalchyk et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePerceived social support is likely to moderate the linkage between\u0026nbsp;hygiene\u0026nbsp;factors and job burnout, as well as\u0026nbsp;motivation\u0026nbsp;factors and job burnout. Social support is an external resource of motivation to deal with work stress and helps to mitigate the adverse impact on employees (Kuriakose, Wilson,\u0026nbsp;\u0026amp; MR 2019). Hence, it can be predicted that when primary healthcare workers are satisfied with\u0026nbsp;motivation\u0026nbsp;and\u0026nbsp;hygiene\u0026nbsp;factors in the working environment, which encourages them to handle the work pressure, then the primary healthcare providers will feel less stress. Overall, the negative influences of\u0026nbsp;hygiene\u0026nbsp;factors and\u0026nbsp;motivation\u0026nbsp;factors on job burnout will be strengthened among primary healthcare workers with a high level of social support cognition.\u003c/p\u003e\n\u003cp\u003eConsequently, we posit:\u003c/p\u003e\n\u003cp\u003eH4a: Perceived social support moderates the relationship between\u0026nbsp;hygiene\u0026nbsp;factors and job burnout, such that the negative effect of\u0026nbsp;hygiene\u0026nbsp;factors on job burnout is strengthened when the level of perceived social support is higher.\u003c/p\u003e\n\u003cp\u003eH4b: Perceived social support moderates the relationship between\u0026nbsp;motivation\u0026nbsp;factors and job burnout, such that the negative effect of\u0026nbsp;motivation\u0026nbsp;factors on job burnout is strengthened when the level of perceived social support is higher.\u003c/p\u003e\n\u003cp\u003eHence, the theoretical framework of this study is depicted in Figure 1.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudying setting\u003c/h2\u003e \u003cp\u003eThe study took place in the eastern province of Anhui, which has an average economic development level. By 2020, Anhui had 2,258 primary healthcare service institutions, which also contain a great number of primary healthcare providers (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the professionally trained investigators conducted this cross-sectional survey using multi-stage cluster random sampling to collect data in three regions of Anhui: Central Anhui, Northern Anhui, and Southern Anhui. A self-administered questionnaire method was employed in this study. Specifically, the investigators distributed questionnaires to potential respondents and the potential respondents completed the questionnaires and returned.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample and data collection\u003c/h3\u003e\n\u003cp\u003eThe questionnaire of this survey was divided into three sections. The three sections included survey statements, demographic information and measurement items for the main latent variables. The survey statements contain a description of the purpose of this study and ensure the confidentiality of questionnaire information. The second section is demographic information, such as gender, age, education level, occupation, and work units. The last part is about the main questions of five variables measurement items. After discarding some invalid responses due to significant problems such as missing values and duplicates. Finally, investigators obtained 1112 usable questionnaires.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of the collected samples. To be specific, 796 samples were received from the north of Anhui, 208 samples were obtained from the middle of Anhui, and 135 samples were received from the south of Anhui. The number of respondents in the three areas was consistent with the distribution of regional districts and counties (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There were 505 men and 607 women among the samples. And 32.014% and 43.525% of respondents were aged between 31\u0026ndash;40 years old and 41\u0026ndash;50 years old, which is the crucial period of personal career development. In terms of education level, 42.986% of the samples have attended secondary school and below. And about 39.209% and 17.806% of respondents respectively have held associate degrees and bachelor's degrees and above. Also, more than half of the respondents were physicians (51.709%), 6.025% of the samples were pharmacists, nurses accounted for 21.493%, and 20.773% of the respondents were medical managers. Moreover, all the samples of primary medical providers in this study were from community health service centers (11.421%), community health service stations (3.957%), township hospitals (62.050%), village clinics (15.108%), and outpatient departments (7.464%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProfile of the samples (n\u0026thinsp;=\u0026thinsp;1112).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunder 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61 and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical managers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork units\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity health service centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity health service stations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownship hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillage clinics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth of Anhui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle of Anhui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth of Anhui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement development\u003c/h2\u003e \u003cp\u003eIn this study, the investigators collected the research data from five latent constructs, and a self-administered questionnaire method was put into use. All the items of the five main latent constructs are referred to in the existing literature. To be specific, based on the study of Michaels \u0026amp; Spector (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), three items were developed to reflect the primary healthcare providers' intention to leave their current position. To measure the items of turnover intention, this study used a 4-point Likert method (1-\"Never\", 2-\"Seldom'', 3- \"Once in a while\" and 4- \"Frequently\"). Two items of hygiene factors and three items of motivation factors were sourced from Baruffaldi \u0026amp; Landoni (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and were measured on a 5-point Likert scale (1-\"Not at all\" to 5- \"Very satisfied\"). The 15-item on job burnout in the research framework also used a 5-point Likert scale (1-\"Strongly disagree\" to 5- \"Strongly agree\") which was referenced from the research of Li et al. (2004). Furthermore, 12 items of perceived social support were adapted from the studies of Dahlem et al. (1991) and a 7-point Likert scale (1- \"Entirely disagree\" to 7- \"Entirely agree\") was adopted to measure the items of perceived social support. The appendix presents the complete measurement questionnaire of five main latent constructs.\u003c/p\u003e \u003cp\u003eAccording to the previous studies (He et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), gender, age, education level, and occupation were chosen as control variables in the present research framework.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eReliability and validity\u003c/h2\u003e \u003cp\u003eTo test the reliability and validity of the measurement scale, this study measured the values of Cronbach's alpha, the composite reliability (CR), the standardized loadings, and average variance extracted (AVE), respectively. The Cronbach's alpha and the composite reliability (CR) can be used to prove the reliability of the scale, and the threshold for both is 0.70 (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). All the Cronbach's alpha values and CR values were higher than 0.70 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which indicated that the reliability of the constructs was acceptable. AVE and standardized loadings were measured to convergent validity, and the AVE and the values of standardized loadings should be more than 0.50 (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Chen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e suggested that all the AVE values of the variables exceeded the benchmark value. Furthermore, the results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggested that the square roots of AVE were greater than the correlations of constructs, which evaluated and supported the discriminant validity of the measurement scale (Alhaddad, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, the validity was also acceptable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and convergent validity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Hygiene factors (HF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.930\u0026ndash;0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.Motivation factors (MF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.778\u0026ndash;0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.Job burnout (JB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.509\u0026ndash;0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.Perceived social support (PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u0026ndash;0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.Turnover intention (TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u0026ndash;0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also presents the results of the main variables' means, standard deviations, and correlations. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in terms of the control variables, the primary healthcare provider's gender (r = -0.119, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and occupation (r = -0.197, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) were negatively correlated with turnover intention, and work units was positively correlated with turnover intention (r\u0026thinsp;=\u0026thinsp;0.145, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), but the other control variables (age and education) were insignificantly correlated with turnover intention. Hygiene factors were significantly related to job burnout (r = -0.170, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and turnover intention (r = -0.300, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Motivation factors were also significantly related to job burnout (r = -0.151, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and turnover intention (r = -0.289, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The relationship between job burnout and turnover intention was positive (r\u0026thinsp;=\u0026thinsp;0.249, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The findings above were in line with the initial hypotheses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans, standard deviation, and correlations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.301\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.210\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.Work units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.154\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.137\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.Hygiene factors (HF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.081\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.930\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.Motivation factors (MF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.589\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.812\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.Job burnout (JB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.228\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.148\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.170\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.151\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.712\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.Perceived social support (PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.241\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.330\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.738\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.Turnover intention (TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.197\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.289\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.249\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.238\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.857\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cb\u003eNote\u003c/b\u003e: ES, Economic satisfaction; NES, Non-economic satisfaction; JB, Job burnout; PS, Perceived social support; TI, Turnover intention; SD means standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagonal elements in bold are the square roots of the average variance extracted (AVE), and the off-diagonal elements are the correlation between constructs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCommon method variance\u003c/h2\u003e \u003cp\u003eFirst, this study used Harman's single-factor test to examine the common method biases of the data (Howard \u0026amp; Henderson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results showed that the variation explained by the first factor was only 23.860%, less than the critical value of 40%, which indicated there was no serious common method variance problem. Moreover, this study also conducted the results of confirmatory factor analysis(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)and an unmeasured latent common method factor model (ULMC). The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that the multi-factor model was significantly better than the single method model (∆χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4908.08, ∆\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and was not significantly different from the multi-factor model with ULCMF (∆CFI\u0026thinsp;=\u0026thinsp;0.001, ∆TLI\u0026thinsp;=\u0026thinsp;0.001, ∆RMSEA\u0026thinsp;=\u0026thinsp;0.001). The consistent result showed that common method variance was minimal.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit indices for the measurement models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-factor model (HF,MF,JB,PS,TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2592.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-factor model(HF\u0026thinsp;+\u0026thinsp;MF\u0026thinsp;+\u0026thinsp;JB\u0026thinsp;+\u0026thinsp;PS\u0026thinsp;+\u0026thinsp;TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7500.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon method factor model(HF,MF,JB,PS,TI,ULCMF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2572.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: ULCMF, An unmeasured latent common method factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis testing\u003c/h2\u003e \u003cp\u003eThis study conducted hierarchical multiple regression analysis to examine H1, H2, H3a and H3b. To be specific, according to the suggestions of Baron \u0026amp; Kenny (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), the control variables, the independent variables, and mediating variables were added to the regression model stepwise. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed the results that hygiene factors were negatively correlated with job burnout (β= -0.097, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the relationship between motivation factors and job burnout was insignificant (β= -0.069, n.s.) in model 2. Model 4 showed that hygiene factors (β= -0.170, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and motivation factors (β= -0.176, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were negatively correlated with turnover intention. The linkage between job burnout and turnover intention revealed by model 5 was positive and significant (β\u0026thinsp;=\u0026thinsp;0.172, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, in model 5, the coefficient of the impact of hygiene factors on turnover intention (β= -0.154, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was smaller than the coefficient (β= -0.170, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in model 4. All these results above supported H1, H2, and H3a, while H3b was not supported. The variance inflation factors during hierarchical regression analyses were from 1.104\u0026ndash;1.545, which was below 5 (Akinwande, Dikko, \u0026amp; Samson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, there were no serious multicollinearity issues.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for mediation effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eJob burnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eStandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emodel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emodel 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHygiene factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.097\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.170\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.154\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMotivation factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.176\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.164\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob burnout\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.073\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.110\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.092\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.176\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e△R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.456\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.255\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.766\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.948\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further test H3a and H3b, a bias-corrected bootstrapping procedure was employed in this study. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the indirect effect of hygiene factors on turnover intention via job burnout was significant (95% CI= -0.029 to -0.004; excluding 0; indirect effect = -0.016), while the indirect effect of motivation factors on turnover intention was insignificant (95% CI= -0.028 to 0.000; including 0). Hence, the evidence supported H3a.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndirect effects of ES and NES on TI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBootstrap-indirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHF\u0026rarr;JB\u0026rarr;TI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.029, -0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMF\u0026rarr;JB\u0026rarr;TI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.028, 0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: HF, Hygiene factors; MF, Motivation factors; JB, Job burnout; TI, Turnover intention; SE, Standard error; A 95% bootstrap confidence interval that does not include zero means an effect is significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo test H4a and H4b, this study also applied the hierarchical moderated regression method, in which the control variables, independent variables, moderator, and the interaction term (Hygiene factors \u003csub\u003e*\u003c/sub\u003e perceived social support and Motivation factors \u003csub\u003e*\u003c/sub\u003e perceived social support). The results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicated that the interaction term of motivation factors and perceived social support was positively related to job burnout (model 3, β= -0.074, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which was in line with H4b. However, there was no moderating effect of perceived social support between hygiene factors and job burnout (model 3, β= -0.005, n.s.). The variance inflation factors during hierarchical regression analyses were from 1.104\u0026ndash;1.518, which was also below 5 and was good for regression mode l (Akinwande, Dikko, \u0026amp; Samson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for moderation effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eJob burnout\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHygiene factors (HF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.097\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.074\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMotivation factors (MF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived social support (PS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.296\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHF*PS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMF*PS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.092\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.110\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.092\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e△R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.456\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.255\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.645\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo identify the nature of the interaction effect of perceived social support and motivation factors on job burnout, this study also plotted the slopes which were computed by one standard deviation below and above the mean of perceived social support. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the interaction impact. To be specific, motivation factors were more negatively correlated with job burnout at the high level of perceived social support in comparison to the low level of perceived social support.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The interaction effect of PS and MF on JB.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical implications\u003c/h2\u003e \u003cp\u003eThis study applies Herzberg's motivation-hygiene framework to examine the influence of motivation and hygiene factors on primary healthcare providers' turnover intention. The results in this study are consistent with the proposed H1, H2, H3a, and H4b. However, H3b and H4a are not supported according to the results of this study. That is to say, both hygiene factors and motivation factors are positively correlated with turnover intention. Furthermore, though motivation factors are not significantly correlated with job burnout, the interaction term between hygiene factors and perceived social support was significantly related to job burnout.\u003c/p\u003e \u003cp\u003eBased on the motivation-hygiene theory, consistent with the expectations of this current study, primary healthcare workers' turnover intention can be affected by hygiene factors and motivation factors. These findings are in line with previous literature, which found that the influence of motivation and hygiene factors can encourage and help sustain employees' dedication and retention, thus reducing their turnover intention (Takawira, Coetzee, \u0026amp; Schreuder, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Meanwhile, these results support the existing study that the incentive mechanism can play a positive role in improving the retention rate of primary medical providers (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study enriches the empirical research on the turnover intention of primary healthcare providers through the motivation-hygiene theory.\u003c/p\u003e \u003cp\u003eThe results also suggested that job burnout mediates the relationship between hygiene factors and turnover intention. However, the mediating effect of job burnout on the relationship between motivation factors and turnover intention does not exist. That is to say, hygiene factors play a positive role in relieving job burnout, thus reducing turnover intention. Existing literature on turnover intention has also found the mediating role of job burnout, such as the research of Leiter \u0026amp; Maslach (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found burnout mediated the relationship between areas of work life and nurse turnover. Moreover, it was also found that job burnout plays a mediating role between perceived organizational justice and turnover intention (Vaamonde, Omar, \u0026amp; Salessi, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, there are few studies considering the mediating role of incentive factors and turnover intention. Thus, this study has extended in this direction, which enriches the internal mechanism of turnover intention of primary healthcare providers.\u003c/p\u003e \u003cp\u003eThis study has shown that motivation factors can alleviate primary healthcare providers' job burnout when they have a high level of perceived social support. In the study of turnover intention, the moderating effect of employees' perceived social support has also increasingly received attention (Courcy, Morin, \u0026amp; Madore, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, previous research has found that perceived social support can moderate the relationship between job burnout and subjective well-being (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, little attention has been paid to its moderating role in the relationship between motivation factors and turnover intention. The result of this study has made this effort to bridge this gap, which also extends the application of perceived social support as a boundary condition in the mechanism of turnover intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eFrom a practical perspective, there are several managing enlightenments for the government, primary healthcare institutions, and people from all walks of life associated with primary medical providers to increase their enthusiasm and thus reduce turnover intention. First, the current study has found that hygiene factors are essential to turnover intention. The more satisfied primary healthcare providers are with their salary, welfare, and other benefits, the less willing they are to leave their primary healthcare institutions. Thus, the government must continue to increase the proportion of financial subsidies for the regular expenditure of primary healthcare institutions and the salary of primary medical providers, as well as consider personalized benefit and welfare guarantees, to meet the needs of health personnel and protect the interests of primary medical providers.\u003c/p\u003e \u003cp\u003eMoreover, motivation factors, such as opportunities for advancement and achievement, are important determinants of turnover intention. Hence, primary healthcare institutions should take responsibility to increase the training of primary healthcare workers and improve their professional skills to improve primary healthcare providers' satisfaction and provide a guarantee for a safe and effective primary health service. Meanwhile, managers in primary healthcare institutions also need to focus on providers' career development and advancement to enhance their professional identity. Additionally, the finding in this study reveals that the interaction between motivation factors and perceived social support can alleviate job burnout and thus reduce turnover intention. Thus, the supports from organizations, family and friends are also essential to the job stability of primary healthcare providers, especially the primary healthcare institutions that make use of motivation factors to boost employee enthusiasm and mitigate employee's burnout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future prospects\u003c/h2\u003e \u003cp\u003eThere are also some limitations in the present studies. On the one hand, a cross-sectional survey was used to conduct this study, so it is difficult to test the alternative causal sequences and observe the dynamic changes of main variables in the present model framework. Thus, a longitudinal design can be considered in future research. On the other hand, this study considers job burnout as a mediating variable between hygiene factors and turnover intention, while the mediating effect of the relationship between motivation factors and turnover intention is not valid. Thus, future research could consider other potential mediating variables in the relationship between hygiene factors and turnover intention. Last but not least, this study measured the level of health factors by satisfaction with hygiene factors such as salary and benefits, future studies could consider quantifying specific values of salary and benefits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on motivation-hygiene theory this study builds an integral model framework for influencing the mechanism of turnover intention and provides an effective mechanism to reduce the turnover intention of primary healthcare providers. Job burnout exerts a mediating effect on the association between motivation factors and turnover intention. Meanwhile, the interaction term of perceived social support and motivation factors is negatively related to job burnout. This study may also offer some enlightenment on practice management to reduce primary healthcare providers' turnover intention. Hygiene factors, such as salary, welfare, and other benefits should be made full use of to alleviate employees' burnout and reduce their turnover intention. Social support should be valued when the primary healthcare institution aims to give full play to the role of motivation factors (career advancement and training in reducing job burnout). Thus, the comprehensive practical measures need to be considered.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Anhui Medical University (No. AMUREC: 20170260)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant is informed of the purpose and content of the survey prior to the formal survey, and its anonymity and privacy are guaranteed. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated and analyzed during this study is available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by research funds from Key scientific research projects of colleges and universities in Anhui Province (natural science) (grant no. 2022AH050692) and the Excellent research and innovation team project of Anhui Province (grant no. 2023AH010036)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGC, JW, and XW contributed to the conception and design of the study. LJQ, ML, RC, JY, JC, LW, YZ, and HD collected the data and organized the database. XW performed the statistical analysis. JW wrote the first draft of the manuscript. GC revised the manuscript for important intellectual content. All authors contributed to the manuscript revision, and read, and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbate, J., Schaefer, T., \u0026amp; Pavone, T. (2018). Understanding generational identity, job burnout, job satisfaction, job tenure and turnover intention. Journal of Organizational Culture, Communications and Conflict, 22(1), 1-12. \u003c/li\u003e\n\u003cli\u003eAcquah, A., Nsiah, T. K., Antie, E. N. A., \u0026amp; Otoo, B. (2021). Literature review on theories of motivation. EPRA International Journal of Economic and Business Review, 9(5), 25-29. http://dx.doi.org/10.36713/epra6848.\u003c/li\u003e\n\u003cli\u003eAjila, C., \u0026amp; Abiola, A. (2004). Influence of rewards on workers performance in an organization. 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Chinese Rural Health Service Administration, 40(9).\u003c/li\u003e\n\u003c/ol\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":"","lastPublishedDoi":"10.21203/rs.3.rs-5265617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5265617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe high turnover rate of primary medical personnel has been a noteworthy issue among primary healthcare services system, which is essential to improving the quality of medical and health service. The impacts of incentive factors such as salary, benefits and promotion opportunities on the turnover intention of primary healthcare providers have been found, but the results were inconsistent. How incentive factors affect turnover intention needs to be further explored to present potential reasons for these inconsistent research results above. Based on Herzberg's two-factor theory of motivation, the relationship between incentive factors including hygiene and motivation factors on turnover intention with the mediation effect of job burnout and the moderating effect of perceived social support were tested to better clarify this potential formation mechanism. A multi-stage cluster random sampling method was applied with a total sample of 1,112 primary healthcare providers from Anhui, China. Finally, the findings indicated that hygiene factors and motivation factors were directly negatively associated with turnover intention. Job burnout mediated the relationship between hygiene factors and turnover intention, whereas does not mediate the relationship between motivation factors and turnover intention was insignificant. It is interesting that the interaction terms of perceived social support and motivation factors negatively affected job burnout. Thus, it is necessary to appropriately utilize incentive factors and social support to alleviate primary healthcare providers' job burnout and reduce their turnover intention to improve the quality of primary healthcare service.\u003c/p\u003e","manuscriptTitle":"Impacts of Incentive Factors on Turnover Intention Among Chinese Primary Healthcare Providers: The Mediation Role of Job Burnout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-18 17:16:33","doi":"10.21203/rs.3.rs-5265617/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":"095470bd-3144-48d4-bf97-4f4a2445fb1e","owner":[],"postedDate":"November 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39849417,"name":"Social science/Psychology"},{"id":39849418,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-02-10T06:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-18 17:16:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5265617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5265617","identity":"rs-5265617","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}