The Moderating Role of Psychological Capital on the Impact of Areas of Worklife on Employee Well-being and Turnover Intentions: A Field Study

The Moderating Role of Psychological Capital on the Impact of Areas of Worklife on Employee Well-being and Turnover Intentions: A Field Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Moderating Role of Psychological Capital on the Impact of Areas of Worklife on Employee Well-being and Turnover Intentions: A Field Study Ozge Tayfur Ekmekci, Hilal Demirel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9059165/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Areas of work-life (AoWL) — comprising workload, control, rewards, community, fairness and values — represent key organisational dimensions that shape employee attitudes and behaviours. Despite growing interest in employee wellbeing and retention, the boundary conditions under which AoWL dimensions influence these outcomes remain underexplored. This study examines the effects of AoWL on employee wellbeing and turnover intention, and investigates the moderating role of psychological capital (PsyCap) in these relationships. Methods A cross-sectional survey design was employed with a sample of 369 white-collar employees from Türkiye. Hierarchical regression analysis was used to test the direct effects of AoWL dimensions on employee wellbeing and turnover intention, as well as the moderating role of PsyCap. Demographic variables including number of children and organisational tenure were controlled in the analyses. Results Among the six AoWL dimensions, only rewards and values significantly and positively predicted employee wellbeing. PsyCap demonstrated a significant positive main effect on employee wellbeing; however, its moderating effect on the AoWL–wellbeing relationship was non-significant. Perceived fit on rewards and values negatively predicted turnover intention, while neither the main nor moderating effects of PsyCap on turnover intention reached significance. Number of children and tenure were negatively associated with turnover intention. Conclusions Rewards and values emerge as the most critical AoWL dimensions for fostering employee wellbeing and reducing turnover intention among white-collar employees. PsyCap functions as an independent personal resource rather than a buffer in this context. These findings offer practical guidance for human resource management practitioners seeking to design organizational environments that enhance wellbeing and reduce voluntary turnover. areas of work-life psychological capital employee wellbeing turnover intention Introduction In today’s competitive business world, organizations desire to achieve sustainable competitive advantage, thus expect their employees show higher levels of performance. Pfeffer ( 2018 ) examines the unsustainable employment practices in his book, "Dying for a Paycheck," and advises businesses to consider the human costs associated with their constant pursuit of increased productivity and work hours. More importantly, Pfeffer ( 2018 ) labels work stress one of the biggest causes of death and accuses managers of having unreasonable expectations from employees. Due to fierce competition in corporate world, most employees find themselves under constant pressure, with a workload that is more than they can cope with from time to time, and in a work environment that does not meet their expectations. Employees are forced to make a choice between continuing to work or quitting their current job, which does not meet their expectations adequately and sometimes this negatively affects their psychological and physiological health. To this end, determining the factors that affect the well-being of the employees and bring them to the stage of leaving the job has been a very important research topic in recent years. As Juniper ( 2011 , p.25) notes, the reason for the rising interest to “employee wellbeing” concept is “the established link between health and performance in the workplace and the general hypothesis is that the healthier and happier people tend to be more productive in the workplace.” Previous studies (e.g., Brunetto et al., 2012 ; McCarthy et al., 2011 ) have demonstrated that employee well-being affects employees' attitudes and behaviors towards work and organizational outputs in a positive way. Similarly, Warr ( 2003 , p.392) argues that “employee well-being is significantly and positively associated with better job performance, lower absenteeism, lower turnover intention, and occurrence of more discretionary behaviors in the workplace.” In a recent study, Krekel et al. ( 2019 ) reported high level of positive correlation with wellbeing at work and business-unit level profitability. Based on prior studies, Baptiste ( 2008 ) emphasizes the importance of human resource management practices in boosting employee well-being. The researcher argues that human resources practices increase employee support and trust in management; thereby enhance organizational output and productivity by strengthening long-term relationships. Strong bonds between employees and management and high level of wellbeing, on the other hand, require managers understand employees’ expectations from their organizations and satisfy those expectations. The main aim of this study is to uncover the organizational factors affecting employee well-being and turnover intention. To this end, employees’ assessment of work place conditions, particularly employees’ perceptions regarding fit between their expectations and what the organization provides them would be main variable of interest. Employees’ perceptions will be measured using Leiter and Maslach’s ( 2003 , p.93) Areas of Work-life Model, which focuses on six factors, namely workload, control, rewards, community, fairness and values. The model assumes that inexistence of fit between expectations and perceived organizational practices in relation to these six-factors creates chronic job stress, leading higher level of exhaustion, cynicism and sense of inefficacy. Based on the premises of Rousseau’s ( 1995 ) psychological contract theory, Leiter and Maslach ( 1999 , p.473) argue that employees and employers should reach an acceptable balance through explicit or implicit negotiations over these six areas of worklife. To this end, The Areas of Worklife Model focuses on the difference between the expectations of the employee and the perception of how much of these expectations realized in terms of these six areas of worklife. So far, most studies have focused on the effects of six areas of work-life on either burnout or engagement, yet ignored the plausible role of these areas on employees’ wellbeing and propensity to quit their jobs (i.e., turnover intention). Taking a positive stance, this study assumes that the perceived harmony in areas of work-life will have a positive effect on employee well-being and negative effect on turnover intention. Therefore, the main purpose of the study will be to determine whether better management of employee expectations by focusing on person-organization harmony in areas of work-life results in higher employee well-being and reduced turnover intention. Secondly, this study examines the moderating effects of psychological capital (PsyCap) in the relationship between areas of worklife and employee well-being and also on the linkage between areas of work-life and turnover intention. Laschinger and Fida ( 2014 ) argue that PsyCap, which is an internal resource; could act as a protective factor against burnout, thus has a positive impact on wellbeing. In fact, Kun and Gadanecz ( 2019 ) reported significant correlations between sub-dimensions of PsyCap, namely hope and optimism, and workplace well-being and happiness. Similarly, Vîrgă et al.’s ( 2020 ) research demonstrates how PsyCap protects against burnout and secondary traumatic stress. Based on the premises of “Job Demands and Resources Model”, even when job demands are very high within an organization, employees who have greater access to resources for their jobs, such as PsyCap as a personal resource, could withstand the negative effects of pressure from job demands, reduce their desire to quit their jobs, and still experience higher wellbeing. Therefore, it is expected that the effect of perceptions of fit in areas of worklife on employee well-being and turnover intention will be stronger for employees with higher PsyCap. So far, the antecedents of turnover intention and wellbeing have been analyzed in various aspects. This study intends to contribute to the extant literature by demonstrating the linkages among six areas of work-life, PsyCap and turnover intention, which have not been addressed thus far. Whether or not employees’ expectations from their organizations are met is thought to be a significant predictor of their intention to quit and wellbeing. Moreover, it is believed that enlightening the role of PsyCap in the interaction between individuals and organizations will also be an important contribution to the field, as the studies on psychological capital mostly ignoring the effects of moderation. Contextual Background This study is carried out on white-collar employees in Türkiye, where traditional and modern values seem to coexist. Compared to other countries, coexistence of modernity and traditionalism may differentiate the expectations of Turkish employees from their organizations as well their reactions if these expectations are not met. For instance, the expectations that Turkish people have of organizations can also be influenced by traditional values like contentment and gratitude for little. Due to lower expectations from their employers than those in other countries, employees may not respond as expected, for example by abandoning their positions. Thus, as being conducted in Türkiye, this study might bring a different perspective to the literature. According to the World Happiness Report (2025), Türkiye ranks 94th out of 147 countries with a score of 4.55 out of 10 — the lowest among all OECD member states and a statistical outlier pulling down the OECD average of 6.68 (TheGlobalEconomy.com, 2025). Notably, Türkiye's happiness ranking has declined steadily since 2016 (P.A. Turkey, 2025). OECD's Society at a Glance report (2024), which examines well-being across member countries, further confirms Türkiye's precarious position: among all OECD countries, the composite positive experience index is lowest in Türkiye by a considerable margin, while the negative experience index is highest, indicating that Turkish people report more negative than positive experiences in their daily lives (OECD, 2024). Moreover, life satisfaction in Finland — the top-ranked country — is approximately three scale points higher than in Türkiye, which ranks at the bottom of the OECD (OECD, 2024). Taken together, these findings underscore the persistent and worsening well-being in Türkiye relative to its OECD peers, providing a compelling rationale for investigating the organizational and psychological determinants of employee well-being in the Turkish context. Therefore, it is believed that another practical and theoretical contribution of this study will be the focus on wellbeing of Turkish employees and their perceptions about the areas of worklife, which has not been studied thoroughly in previous researches. Theoretical Background 3.1 The Concept of Employee Wellbeing The definition of well-being is full of misunderstandings and far from being complete (Wright, 2006 ). To this date, researchers have utilized happiness, pleasure, life satisfaction, quality of life, and harmony when defining well-being (Güler and Dönmez, 2011 ). For example, Diener ( 1984 , p. 544) emphasizes life satisfaction while conceptualizing wellbeing as “ the subjective and global assessment of all aspects of a person’s life, including not just the absence of negative factors but also the positive measures ”. On the other hand, Ryff ( 1989 , p.1071) defines well-being along with “ self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life and personal growth.” The elusive and different conceptualizations of well-being reflect two different paradigms, namely hedonism and eudemonism. The hedonic perspective emphasizes “subjective well-being”, which involves “ the subjective evaluations of one's own life and is associated with general satisfaction, positive affectivity, and lack of negative affectivity ” (Diener and Lucas, 1999 ). The eudemonic perspective, on the other hand, involves “ the realization of human development and existential challenges in life ” (Keyes et al., 2002 ), thus emphasizes “ one’s positive psychological functioning and sense of development ” as a reflection of psychological wellbeing. Acknowledging that workplace conditions could be different from life conditions; some researchers point out that employee wellbeing should be distinguished from general wellbeing (Zheng et al. 2015 ) and be treated separately. Therefore, scholars have begun to focus on employee well-being by adapting the premises of the well-being concept to organizational life. “Well-being at work” or simply ‘employee wellbeing’ could be defined as “the physical and mental health of the workforce” (Currie, 2001 ). This study utilizes the construct definition of “employee well-being” proposed by Zheng et al. ( 2015 ). This definition provides a comprehensive perspective by incorporating three sub-dimensions of well-being: subjective well-being (life-wellbeing), psychological well-being and well-being at work (workplace wellbeing) that reflect both hedonism and eudemonism perspectives. 2.2 The Relationship Between Wellbeing and Areas of Worklife Maslach and Leiter ( 1999 ) describe six areas of worklife where person-job harmony or incompatibility can be experienced. Emotional exhaustion, cynicism, and hence "burnout" can occur in each area when the nature of the job and the nature of the individual are not in harmony. On the other hand, "engagement" occurs when a good fit is achieved in these six areas. These six areas are listed as workload, control, rewards, community, fairness and values. Workload is defined as “work demands that exceed human limits” (Maslach and Leiter, 1999 ) and a mismatch in “workload” occurs when work demands consume energy in a way that makes it difficult for a person to recover. In other words, mismatch in workload is inevitable when the job demands exceed the resources of the individual. Another mismatch is confronted in “control” when the employee does not have sufficient control over the resources needed complete the work efficiently. The third mismatch is observed in “rewards” areas when the financial, social and internal rewards offered by the workplace are inconsistent with the expectations of the employees. The fourth mismatch occurs in “community” when employees do not feel positively connected with and supported by the other employees and the supervisors in the workplace. The fifth mismatch arises in “fairness” such that unfairness perception occurs when an employee compares own efforts and achievements with the other employees’ ratio of inputs and outputs and observes inequity. Last incompatibility is observed in values dimension when an employee’s “values” is not consistent with the values of the job; thereby result in conflict of values (Maslach et al., 2001 ; Leiter and Maslach, 2003 ). The areas of work-life model rests on the premises of Psychological Contract Theory and “Person-Job Fit Model”. Rousseau ( 1995 , p.10) defines the psychological contract as “ individual beliefs, shaped by the organization, regarding terms of an exchange agreement between individuals and their organizations ”. According to Leiter and Maslach ( 1999 ), employers and employees should achieve a mutually agreeable balance on areas of work-life through the explicit and implicit negotiations of a psychological contract. The researchers argue that there are a number of unwritten expectations in the relationship between an employee and an organization, and when the employee feels that these expectations are not met, tension, anxiety, mental and physical ailments, as well as unfavorable attitudes and behaviors toward their jobs, may be seen (Maslach & Leiter, 2005 ). Consistent with this argument, researches (e.g., Antonaki & Trivellas, 2014 ; Üçok & Torun, 2014 ; Chin & Hung, 2013 ; Bal et al., 2008 ; Hartmann & Rutherford, 2015 ) demonstrated the adverse effects of psychological contract breach in forms of decreased organizational commitment, trust, and increased turnover intention, organizational cynicism and burnout. Person–organization fit involves either the fulfillment of the expectations of the individual and the organization or the condition that organizations and employees have similar characteristics (Kristof, 1996 ). Thus far, many studies have examined the effects of person-job fit on employee well-being and work-related attitudes and behaviors. For instance, Brandstätter et al., ( 2016 ) investigated the effects of a misfit between motivational needs of employees and the motivation offered by workplaces on burnout and physical symptoms. The researchers found that person-job mismatch was linked to burnout and several physical symptoms such as headache, abdominal pain, runny nose etc., thus adversely affect employees’ wellbeing. Likewise, Brom et al ( 2015 ) revealed that perceived misfit between one’s expectations and the six areas of worklife contributes significantly to diminished well-being and feelings of burnout. Buruck et al. ( 2019 ) found in a recent study that mismatches in workload, control, and social support (community) dimensions have a substantial impact on employees' chronic low back pain. Thus far, studies (e.g., Kausto et al. 2005 ; Çelik et al., 2014 ) have also demonstrated that perception of fairness, which is an important area of worklife positively, affects employee well-being. Relying on the premises of Person-Job Fit Model” and “Psychological Contract Theory”, employees’ perceptions of a fit regarding the areas of worklife (i.e. workload, control, community, rewards, fairness and values) is expected to positively predict the well-being of the employees: Hypothesis 1 Employees’ perceptions of a fit regarding the areas of worklife (i.e. workload (H1a), control (H1b), community (H1c), rewards (H1d), fairness (H1e) and values (H1f)) are positively related to employee wellbeing. 2.3 The Relationship Between Turnover Intention and Areas of Work-life Till this date, many studies have been conducted to uncover the antecedents of employee turnover given the higher costs associated with employees’ quitting their jobs. Since intentions often predict behaviors (e.g. Cho & Lewis, 2012 ), researchers have mostly directed their attention to conceptualize the concept of turnover intention. Tett and Meyer ( 1993 , p.262) defined turnover intention as “the conscious and deliberate willfulness to leave the organization” , while Takase ( 2010 , p.3) conceptualized the concept as a voluntary process, which encompassing “a multi-stage process involving the voluntary departure of employees from their current position”. Albeit not the sole reason, the apparent mismatch between what employees anticipate from their organizations and what they receive may raise employees' intention to leave their positions. Although the psychological contract is not a written contract that legally binds the parties, it could still shape the attitudes of the employees by creating unwritten expectations. If the unwritten expectations are not fulfilled by the organization, the employees might perceive psychological contract breach, which could result in negative feelings, attitudes and behaviors towards the job and the organization (Rousseau, 1995 ). In fact, Boamah and Laschinger ( 2016 ) demonstrated that person-job fit in six areas of worklife reduces the feelings of burnout, which in turn reduces the turnover intention. Similarly, Leiter and Maslach ( 2009 ) shows that areas of worklife predicts burnout, which in turn predicts turnover intentions, and that burnout plays a mediating role in the relationship between areas of worklife and the turnover intention. Based on previous research’s findings and the premises of Psychological Contract Theory, it is proposed that fulfilling the employees’ expectations of regarding workload, control, values, rewards, fairness and rewards might decrease the employees’ turnover intentions: Hypothesis 2 Employees’ perceptions of a fit regarding the areas of worklife (i.e. workload (H2a), control (H2b), community (H2c), rewards (H2d), fairness (H2e) and values (H2f)) are negatively related to turnover intention. 2.4 Moderating Role of Psychological Capital Since the last decade, scholars have begun to focus on the strengths of people such as personal traits, abilities, and psychological capital rather than trying to correct what is wrong with them (Luthans & Youssef, 2004 ). To this end, the discipline of positive organizational behavior seeks to understand the concept of "psychological capital" in order to better manage employees and reach organizational outcomes (Özer et al., 2013 ). Luthans et al., ( 2007 , p.3) defines psychological capital (PsyCap) and its dimensions (i.e. self-efficacy, optimism, hope and resiliency) as an individual’s positive psychological state of development and is characterized by having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; making a positive attribution (optimism) about succeeding now and in the future; persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and when beset by problems and adversity, sustaining and bouncing back and even beyond (resiliency) to attain success. Studies reveal that PsyCap is a crucial employee resource that helps combat stress problems, lowers turnover intentions, improves performance, and strengthens employee well-being. Additionally, it provides firms with a competitive advantage, encourages people to adopt good attitudes and behaviors toward their jobs, and guards against burnout syndrome. (e.g. Larson & Luthans, 2006 ; Avey et al., 2010 ; Çetin and Basım, 2011 ; Luthans et al., 2008 ; Tokmak, 2018 ). In their comprehensive studies, Avey et al ( 2011 ) demonstrated that PsyCap provides firms with a competitive advantage, encourages people to adopt good attitudes and behaviors toward their jobs, and guards against burnout syndrome. The direct impact of PsyCap on employee wellbeing and turnover intentions could be explained based on the premises of “Job Demands and Resources Model” (JD–R). The model conceptualizes job demands as “physical, social or organizational aspects of the job that require sustained physical or mental effort”; while it defines job resources as “ physical, psychological, social, or organizational aspects of the job that are either/or: (1) functional in achieving work goals; (2) reduce job demands and the associated physiological and psychological costs; (3) stimulate personal growth and development” (Demerouti et al., 2001 , p. 501). The researchers argue that PsyCap, as a personal resource, could influence the perceptions of job demands and resources in the workplace. Consistent with this, Grover et al ( 2018 ) revealed that employees with high levels of PsyCap tend to have more positive perceptions regarding job demands and resources suggesting that PsyCap might act as a personal resource shaping employees’ attitudes toward their jobs and organizations.As reported by Gu ( 2016 ); PsyCap can improve the well-being of employees, and contribute to employee performance. The past researches also emphasize the significant impact of PsyCap on turnover intention (e.g., Yim et al.; Salam, 2017 ) and well-being (e.g., Avey, Luthans, Smith et al., 2010 ; Rabenu et al., 2017 ). Therefore, PsyCap is expected to have a direct impact on both wellbeing and turnover intention. Hypothesis 3 Psychological capital (PsyCap) is positively related to employee wellbeing. Hypothesis 4 Psychological capital is negatively related to turnover intention. Avey et al., ( 2009 , p.680) refer to PsyCap as “ a positive strength for combating stress" and state that the resource employees need in their struggle with stressful events and situations in the workplace can be psychological capital. Choi and Lee ( 2014 ) argue that PsyCap plays a fundamental role in preventing negative thoughts that cause employee turnover by increasing positive experiences in the workplace and in social relationships, helping to resolve conflicts, and reducing stress in the workplace. Therefore; it will be possible to evaluate PsyCap as a resource that supports and strengthens the employee against all demands and expectations of the job and moderates the negative effects by acting as a kind of buffer. It is expected that employees with high PsyCap can better tolerate negative attitudes and behaviors arising from the misfit they perceive in sub-dimensions of areas of worklife. Based on the aforementioned literature, in this study it is proposed that PsyCap support as a moderating variable between areas of worklife and employee wellbeing, as well as areas of worklife and turnover intention linkages. PsyCap is suggested to have a moderator role in the positive effect of meeting the expectations of the employees regarding the areas of worklife on employee wellbeing. Also, it is expected that PsyCap has a moderator role in the negative effect of meeting the expectations of the employees regarding the areas of worklife on turnover intention. Thus, Hypothesis 5 Psychological capital moderates the relation between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and employee wellbeing such that the effect of the former on the latter will be stronger when employees have higher psychological capital. Hypothesis 6 Psychological capital moderates the relation between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and turnover intention such that the effect of the former on the latter will be stronger when employees have higher psychological capital. Method 3.1 Procedures and Participants This study was approved by the Ethics Committee of Hacettepe University Senate prior to data collection (approval date: January 12, 2021). All procedures were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. Participants were fully informed about the purpose of the study, the voluntary nature of their participation, and the confidentiality of their responses. Written informed consent was obtained from all participants before the data collection. Through online survey, data were collected through convenience sampling technique from 374 white-collar employees working in varied industries from software to aviation in Türkiye. 5 responses were discarded due to randomly distributed, yet substantial amount of missing data. Thus, 369 usable questionnaires were obtained. Of the respondents, 301 were male (81.6%) and 68 females (18.4%), majority at an age between 26 and 35 (n = 140, 37.9%). A significant part of the participants had a bachelor's degree or higher (n = 354, 95.9%) and had an organizational tenure less than 3 years (n = 214, 58%). The majority of the participants were married (n = 224, 60.7%), with at least 1 child (n = 195, 52.8%) and 42.8% (n = 158) of their spouses were non-employed. High percentage (n = 354, 95.9%) of the participants had full-time employment. 3.2 Measures A questionnaire form was designed to examine the four study variables, namely employee wellbeing, turnover intention, areas of work-life and PsyCap. Participants were also requested to answer items pertaining to demographics (i.e., gender, age, education level, marital status, number of children, partner employment) and work-related characteristics (i.e., tenure). Employee Wellbeing : Employee Wellbeing was measured with the 18-item Employee Well-being Scale (EWS) developed by Zheng et al. ( 2015 ). The scale has already been adapted to Turkish by Karapınar et al., ( 2019 ), therefore no translation was made. EWS consists of three sub-dimensions: life-wellbeing (LWB) (subjective well-being), workplace wellbeing (WWB), and psychological well-being (PWB). Each dimension is measured with 6 items. Sample items for LWB, WWB and PWB are “ I am in a good life situation ; “ Work is a meaningful experience for me .”; and “ I am good at making flexible timetables for my work ” respectively. Participants indicate their level of agreement using 7-point Likert-type scale (“1” strongly disagree to “7” strongly agree) and higher scores indicate higher levels well-being. The internal consistencies of subscales and overall scale are found to be satisfactory in Zheng et al.’s study (2015), with Cronbach’s Alpha values of 0.87, 0.87, 0.84 and 0.91 for the LWB, WWB, PWB, and overall Employee Wellbeing scales, respectively. For Turkish adaptation, Karapınar et al., ( 2019 ) reported similar reliability estimates. Since hypothesis of this study were based on overall wellbeing, rather than its sub-dimensions, reliability of overall scale was computed and found to be satisfactory with Cronbach’s Alpha of 0.96. Turnover Intention The 3-item Turnover Intention Scale, created by Cammann et al. ( 1983 ), and translated into Turkish by Mimaroğlu ( 2008 ), is used to gauge participants' intentions to quit the company in the near future. Two items (i.e., “ I will probably look for a new job in the next year ”; “ I rarely think about leaving the job ” are rated with 7-point Likert type scale ranging from 1 (strongly disagree) to 7 (strongly agree); one item (i.e., How likely is it that you could find a job with another employer with about the same pay and benefits you now have ?) is rated with 7-point Likert type scale ranging from 1 (not at all likely) to 7 (extremely likely). Cammann et al. ( 1983 ) reported a satisfactory reliability estimate (Cronbach’s α = 0.85) for the original scale, while Mimaroğlu ( 2008 ) reported a lower reliability estimate for the Turkish translation (α = 0.67). In this study, the reverse-coded item (“I rarely think about leaving the job”) was found to reduce the reliability of the scale. After removal of this item, it was seen that turnover intention could be measured consistently with two remaining items (Cronbach’s α = 0.85). Therefore, reverse-coded item was removed from further analyses. Areas of Worklife : The Areas of Worklife Scale (AWS) of Leiter and Maslach ( 2003 ) is used to measure the perceived congruence or incongruence between the person and the job for each area: workload, control, rewards, community, fairness and values. Along with the permission of the scale, the Turkish translation made by independent researchers was obtained from the Mind Garden Company, which holds the official rights of the scale. The scale consists of six sub-scales and 28 items. There are five items measuring perceptions of workload, four measuring perceptions of control, four measuring perceptions of rewards, five measuring perceptions of community, six measuring perceptions of fairness, and four measuring perceptions of values on a five-point Likert-type scale (ranging from "1" strongly disagree to "5" strongly agree). Cronbach’s Alpha values for the sub-scales are 0.70 for workload, 0.70 for control, 0.82 for rewards, 0.82 for community, 0.82 for fairness and 0.73 for values in Leiter and Maslach’s study (2003). Budak and Sürgevil ( 2005 ) reported Cronbach’s Alpha values ranging from .59 to .90 for Turkish adaptation of the scale. In this study Cronbach’s Alpha values for the sub-scales are found to be 0.72 for workload, 0.83 for control, 0.79 for rewards, 0.93 for community, 0.86 for fairness and 0.88 for values. Psychological Capital : Participants’ positive psychological states were measured with the 24-item Psychological Capital Questionnaire (PCQ) developed by Luthans et al. ( 2007 ). Along with the permission of the scale, the Turkish translation made by independent researchers was obtained from the Mind Garden Company, which has the official rights of the scale. PCQ includes four sub-dimensions: hope, optimism, resilience and self efficacy. Participants are expected to indicate their degree of agreement with the items (i.e., "I always look on the bright side of things regarding my job" ) on a 6-point Likert-type scale (“1: strongly disagree” to “6: strongly agree”). In contrast to Avey et al. ( 2010 ), who reported Cronbach's Alpha values for the original scale ranging from 0.72 to 0.93, Çetin and Basım ( 2012 ) reported lower reliability estimates for Turkish translation, which ranged from 0.67 to 0.91 for the overall scale and its sub-scales. Since hypothesis of this study were based on overall PsyCap, only reliability estimate for overall scale was assessed and determined to be satisfactory (α = 0.94). Results 4.1. Results of Preliminary Analysis The data were initially examined to determine whether univariate and multivariate normality existed. To this end, skewness and kurtosis values were used to evaluate the univariate normality, and Mardia's coefficient of variation was assessed to check for multivariate normality. All of the skewness and kurtosis values were smaller than the crucial value of 3 (Tabachnick & Fidell, 2001), and no violations for multivariate normality were observed. After data screening, Harman's test was conducted to investigate whether common method variance poses a problem in the analysis. An exploratory factor analysis was therefore carried out. The unrotated factor analysis yielded 14 factors, with the first factor accounting for 30.55% of the variance. This variation was below the permissible threshold of 50% (Podsakoff & Organ, 1986), indicating the absence of any overarching/general method factor, thus CMV does not seem to be serious concern for data. Series of Confirmatory Factor Analyses (CFA) were conducted to investigate the factorial structures and construct validity of the study variables. Firstly, the six, five, four, three, two and one-factor measurement models were compared for AWS scale. As seen from Table I, the model fit values and χ 2 values deteriorated when measurement models other than six-factor model was utilized. Besides, the nested comparison of the six and five-factor models yielded Δχ 2 value of 126.57 (p < .01), indicating again the superiority of six-factor model. INSERT TABLE 1 HERE After nested model comparisons, the relationships between items and factors were examined and all items were found to be significantly associated only with hypothesized sub-dimension/factor of AWS. When the error terms of the items measuring the same construct error were allowed, the model fit values were found to improve (χ 2 /df = 1.96; CFI = 0.95, GFI = 0.89; RMSEA = 0.05), indicating that 28-items could be used to measure 6 dimensions of work-life, as presumed. Second CFA was conducted for employee-wellbeing scale. Since hypotheses are based on employee’s general wellbeing, rather than its sub-dimensions, second-order measurement model in which 18-items were assumed to measure 3-subdimension of wellbeing, which in turn supposed to measure employee general wellbeing. Although the hypothesized relations were found to be significant, the model fit values indicated the existence of poor fit. After adding covariances between error terms of the same wellbeing dimension, the model fit values improved, and model provided relatively good fit to the data (χ 2 /df = 2.85; CFI = 0.94, GFI = 0.88; RMSEA = 0.08). Eighteen items were found to measure three-sub-dimensions of wellbeing, and general wellbeing. The last CFA was carried out for PsyCap scale. In the second-order measurement model, 24 items were suggested to assess PsyCap across its four sub-dimensions, which in turn were intended to gauge the degree of PsyCap across workers as a whole. The results revealed all the items except the items measuring resiliency were found to be significantly associated with self-efficacy, optimism, and hope dimensions. As in previous studies (e.g., Shah et al., 2019 ; Abbas et al., 2014 ), problems regarding the factor-structure of the PsyCap scale have been observed in this study. Considering this and the fact that hypotheses are based on employee’s general PsyCap levels, rather than its sub-dimensions, one-dimensional model was specified and tested. Except for two items (resiliency and optimism items), all items were found to be significantly associated with overall PsyCap construct. When these two items were removed from the model, the model adequately fit to the data (χ 2 /df = 2.89; CFI = 0.97, GFI = 0.95; RMSEA = 0.07). Based on CFA results, twenty-two items were decided to be used in further analysis. After factor analysis, reliability analyses have been conducted and all scales were found to be internally consistent. Based on this, we computed the composite scores by taking the mean of the responses to PsyCap, turnover intention, areas of worklife and employee-wellbeing. By using these composite scores, we examined the correlations between study variables and demographics (see Table II). The correlations are consistent with expectations in that all areas of worklife dimensions negatively correlated with turnover intention and positively correlated with employee-wellbeing. The correlation between PsyCap and wellbeing is strong and noteworthy. INSERT TABLE II HERE 4.2 Results of Hypothesis Testing Prior to analyses, independent and moderating variables were centered and interaction terms were calculated by multiplying the centered variables, based on the suggestions of Aiken and West ( 1991 ). Two hierarchical regression analyses were conducted to examine the main effects of areas of work-life and PsyCap as well the moderating effect of PsyCap. In the first regression analysis, employee wellbeing is regressed firstly on control variables, age, marital status and number of children, secondly on six areas of work-life and PsyCap, thirdly on the interaction terms of areas of life dimensions and PsyCap (please see Table III). In the first step, control variables explained only 4% of variance in employee well-being ( ΔF = 4.44, p < 0.01), and only age significantly predicted employee wellbeing ( β = 0.14, p < 0.05). In the second step, regression model was found to be significant ( F (3, 368) = 49.85; p < 0.05; ΔF = 70.05, p < 0.01); six areas of work-life, PsyCap and demographic variables accounted for 56% of the variance in employee wellbeing. PsyCap (β = 0.48, p < 0.01) and only two of six areas of work-life, rewards (β = 0.25, p < 0.01) and values (β = 0.15, p < 0.01), contributed significantly to prediction of employee wellbeing. In the third step, all main and interaction effects explained 57% of the total variance (F (14, 368) = 32.83; p 0.05). Despite data centering, the main effect of community and the interaction effect of fairness and PsyCap were not added to the model by the program due to low tolerance values. As in 2nd step, age (β = 0.14, p < 0.01), PsyCap (β = 0.47, p < 0.01), rewards (β = 0.24, p < 0.01) and values (β = 0.16, p < 0.01) positively and significantly predicted employee wellbeing. All the interaction effects and the main effects of number of children, marital status, workload, control, and fairness were found to be insignificant. While Hypothesis 5 was not verified, the results supported Hypothesis 3 , and partially supported Hypothesis 1 (only supported H1d & H1f). INSERT TABLE III HERE Table IV reports the findings of hierarchical regression analysis regarding turnover intention. In the first step, the control variables (age, marital status, number of children, partner employment and tenure) were included to the model. Regression analysis found that, demographic variables explained % 11 of total variance in turnover intention (ΔF = 8.52, p < 0.01). Among the demographic variables, only number of children ( β = -0.25, p < 0.01) and tenure ( β = 0.11, p < 0.05) significantly predicted turnover intention. In the second step, main effects of PsyCap and areas of work-life on turnover intention were added to model, yet the main effect of community was excluded by the program. Among the six areas of work-life, only rewards (β=-0.14, p < 0.01) and values (β=-0.26, p 0.05). Therefore, Hypothesis 4 was rejected and Hypothesis 2 was partially supported (only H2d & H2f). In the third step, all main and interaction effects explained 26% of the total variance (F (15, 368) = 8.35; p 0.05). So, contrary to the Hypothesis 6 , the moderating effect of PsyCap on the areas of worklife and turnover intention linkage was not supported. As in 2nd step, number of children (β=-0.19, p < 0.01), tenure (β=-0.12, p < 0.05), rewards (β=-0.15, p < 0.01) and values (β=-0.26, p < 0.01) negatively and significantly predicted employee wellbeing. INSERT TABLE IV HERE 4.2 Results of Supplementary Analyses As indicated before, the main effect of community and interaction effect of fairness and PsyCap were excluded in both hierarchical regression analyses. This exclusion was due to multicollinearity problem arising from moderate-to-high correlations among six areas of work-life. It is thought that the multicollinearity problem might have increased some of the variables' regression weights while attenuating others. Therefore, separate regression analyses were carried out for each of the work-life domains to establish whether each domain and its interaction with PsyCap had a substantial impact on employee wellbeing and turnover intention, acknowledging the likelihood of increased Type I error. To this end, Process Macro was used to conduct twelve regression analyses for employee wellbeing and intention to leave (Hayes, 2003). Again, in these analyses, the same control variables were added to the regression models together with centered values of PsyCap, areas of work-life and their interaction terms. As seen from Table V, the main effects of each work-life area on employee wellbeing were significant, when work-life areas were considered alone, not together with others. However, the findings of Process Macro and hierarchical regression analyses were consistent with each other regarding the insignificant effects of interaction terms. Unlike hierarchical regression analyses, six areas of work-life negatively and significantly predicted employee’s turnover intention. The effects of PsyCap and interaction terms, on the other hand, turned out to be insignificant, which supported the results of hierarchical regression analyses. INSERT TABLE V HERE Discussions This study aimed to examine the effects of areas of worklife on employee wellbeing and turnover intention and also analyze the role of psychological capital on these linkages. To this end, perceived fit on areas of worklife and PsyCap were proposed to positively affect employee wellbeing whereas negatively affect turnover intention. Moreover, PsyCap was assumed to moderate the relationship between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and outcome variables (i.e., wellbeing and turnover intention). According to hierarchical regression analyses, two prominent areas of worklife (i.e., rewards and values) predicted both employee wellbeing and turnover intention. The perceived fit between employees’ expectations regarding rewards and the rewards offered by the company seems to enhance employees’ wellbeing and decrease their desire to leave their organizations. Also, perceived congruence between organizations’ values and employees’ values contribute to the wellbeing of the employees and lessen their turnover intentions. However, it is noteworthy to mention that all worklife areas (i.e. workload, control, rewards, community, fairness and values) were able to predict employee wellbeing and turnover intention when the analyses were conducted separately for each area of worklife dimensions. These seemingly conflicting results could be explained with the interactions of the worklife dimensions that might suppress the effects of some dimensions. Similar to the studies conducted in Western and individualistic societies, this study supports the premises of Psychological Contract Theory (Rousseau ( 1995 )) and the discussions in extant literature (e.g., Van Vianen et al., 2007 ) about the importance of reaching person-job fit in different areas of work life. It seems that when Turkish employees observe inconsistencies between their expectations and organizational practices/procedures, they perceive breach of psychological contract and react that breach in forms of increased turnover intention and reduced wellbeing. Just like employees in western cultures, white-collar employees in Türkiye seem to attach importance to the fit in regards to workload, rewards, values, community, fairness and control. When written or unwritten expectations that exist between an employee and an organization are not met, this violates psychological contract and result in lack of person-job fit. As a result, employee well-being appears to decline and their intention to leave the company tends to rise. However, rewards and values are thought to stand out among the other areas of worklife in Turkish setting, considering their strong main effects. This finding could be explained by the characteristics of the white-collar employees in Türkiye and Turkish culture and wage levels. Among the OECD countries, Türkiye has been listed as the last country in terms of low pay criterion, which reflects "the share of workers earning less than two-thirds of median earnings" (OECD, 2023). Additionally, Türkiye's average disposable income per person is substantially lower than the OECD average (USD 19 482 versus USD 30 490 a year). It seems that low levels of wages, thereby perceived deprivation makes Turkish employees more sensitive about rewards and lead them to respond negatively to lack of fit in forms of decreased wellbeing and increased turnover intention. Consistent with prior studies conducted in Türkiye, “values” turned out to be significant predictors of employee wellbeing and turnover intention. Erdoğan et al. ( 2004 ) pointed out the importance of employee-organization work value congruence, which is the degree to which the employee’s values match the values of the organization’s culture for Turkish employees. Likewise, Maden and Kabasakal ( 2014 ) investigated the effects of employees' value fit with their organization and argued that when employees believe they share similar characteristics within the work environment and experience value compatibility, they tend to have more positive reactions toward their job and feel emotionally attached to their organization in Türkiye. Although comparisons have not been made in this study, compared to employees in other countries, Turkish employees seem to have higher expectations regarding value-alignment and this could be explained with characteristics of Turkish culture. As researchers (e.g. House et al., 2004 ; Wasti, 2002 ) point out, Türkiye is a relatively collectivist society and that a sense of being part of a group is important for Turkish people. Schroeder ( 2011 ) underlines that feelings of belonging to a group and making sacrifices for the group are the prominent characteristics of collectivist cultures. People in collectivist countries define their identities with the culture they live in and want to feel a sense of belonging and it is thought that Turkish employees might want to feel in harmony with the values of the organization and to be committed to those values. According to Mengüc (2000), in Turkish companies, practices are generally based on loyalty and when Turkish employees do not feel like family, they become alienated from their organizations. And thus, we could expect that their tendency to leave their jobs might increase as a result of higher expectations. This situation shows that Turkish employees may not want to work in an organization that is not compatible with their value judgments despite the economic factors. Thus employees who do not feel any harmony or commitment to the values of the organization could become alienated from the companies and their tendency to leave their job increases. Likewise, employee wellbeing decreases as a result of negative feelings arising from the perceived misfit in values. Another notable finding is the relatively strong and positive effect of PsyCap on employee wellbeing. It seems that employees who have higher levels of self-efficacy, hope, optimism and resilience are better able to stand for the challenges in life. As Job Demand-Resources Model suggests, psychological capital might act as a resource protecting employees in demanding and stressful situations, thereby increase one’s sense of wellbeing. Despite its considerably strong main effect, the moderating effect of PsyCap on the relationship between areas of worklife and employee wellbeing was not confirmed. This may be explained by the strong direct effect of psychological capital on the employee wellbeing, which could surpass the moderation. Quite unexpectedly, neither the main effect of PsyCap on turnover intention, nor the moderation effect of it on the linkage between areas of worklife and turnover intention was found to be significant. It is thought that this unexpected finding may be due to the fact that the turnover intention is more affected by demographic and economic factors, rather than the personal resources like psychological capital. In fact, when effects of demographic variables on turnover intention were examined, the number of children and tenure turned out to be prominent predictors of the turnover intention. Accordingly, when the number of children or employees’ tenure increases, they are less likely to quit their job, which could be explained with financial necessities and increased sense of continuance commitment. 5.2 Practical Implications The results might have important practical implications for organizations. First, it is advised that HR and department managers strive for Person-Job Fit in different areas of work life to ensure that the assigned jobs match the expectations of the employees. To this end, managers need to take actions to appoint right people to right jobs in order to maximize each employee's potential and produce the desired outcomes for their organizations. Prior to hiring, measuring candidates’ interests, asking their expectations from the organizations, and giving realistic job previews could enable managers to better understand and manage the expectations. After hiring, orientation programs could be designed to inform new employees about organization’s rules, procedures and values so that they could develop more realistic expectations and internalize organization’s culture and value system. Previously believed to be the property of the "gifted" (Garmezy, 1974 ), dimensions of psychological capital, self-efficacy, optimism, hope and resilience, now have empirical support that they may be developed (Masten & Reed, 2002). Given the strong association between PsyCap and employee wellbeing, organizations are advised to provide various training programs to help employees develop their psychological capital. For instance, managers could implement “Positive Psychological Micro Coaching” programs (see Corbu et al., 2021 for details) that aim to raise employees’ self-efficacy and resiliency. Employees could be trained to improve their PsyCap by providing lectures on positive psychology, creating awareness about their personal resources and enabling them to set and reach the goals. Page and Vella-Brodrick ( 2009 ) have argued that employee well-being is an important precursor to organizational well-being, as indicated by its links to employee turnover and performance. Nevertheless, previous studies (e.g. Abdullah et al. 2021 ; DiMaria et al.; 2020 ; Krekel et al., 2019 ) have already demonstrated the positive impact of wellbeing on desirable work-related outcomes such as job satisfaction, productivity and higher performance. Therefore, employees’ wellbeing could also be improved by several primary, secondary, and tertiary interventions. Primary interventions might concentrate on inside of the organization to reduce or eliminate workplace stressors. Secondary interventions might focus on altering an individual's perception of or response to a stressor, while tertiary interventions might rehabilitate people who are experiencing stress in response to stressors (Tetrick and Quick, 2011). To this end, managers could provide employees mindfulness trainings, enable them trying something new or give them challenging tasks that they will enjoy achieving and organize activities that employees can develop sense of community. In conclusion, if managers want to reduce the turnover intention, organizational interventions should be carefully designed to match the values of the employees with the organization, to establish mutually desirable long-term relationships between the organization and the employee, and enhance the wellbeing of the employee. Limitations and Future Research Directions Notwithstanding its contributions to the field, this study has a number of limitations that must be acknowledged. The first limitation arises from the use of self-report measures. Several responses biases such as social desirability, acquiescence or dissent biases might have affected participant’s answers, threatening the validity and reliability of the results. Therefore, in future studies, it is recommended to collect data using different methods such as observation and interview, to prevent the possible confounding effects of self-reporting. Second limitation is related to the cross-sectional research design. Since data were collected at one point in time, it is not possible to make causal inferences and ensure the directionality between study variables. Hence, researchers are advised to use longitudinal research designs to test the hypothesized relationships in the future. The third limitation arises from the nature and size of the sample. This study was conducted with Turkish white-collar employees, working in private sector. The results could be affected by differences resulting from the sector, organization, employee status (white or blue-collar) or even the socio-economic and cultural characteristics of the country. Thus, examining the proposed connections with a different sample in a different industry or even in a different nation may offer more detailed and diverse perspectives on wellbeing, turnover intention, and areas of work-life links. Finally, collecting data from a larger sample may increase the robustness and generalizability of the results. Declarations Ethics Approval and consent to participate "This study was approved by the Ethics Committee of Hacettepe University Senate (approval date: January 12, 2021-numbered E-35853172-300-00001403022). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee. Informed consent was obtained from all participants prior to data collection This study was conducted in accordance with the Declaration of Helsinki. Participants were fully informed about the purpose of the study, their right to withdraw at any time, and the confidentiality of their responses. Written informed consent was obtained from all participants." Consent for publication Not applicable. Availability of data and materials The data will be made available upon the request from the corresponding author. Competing Interests The authors declare that there are no conflicts of interest. Fundings No funding received for this article. Authors’ contributions All authors contributed to the conception and design of the study. 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Nested Model Comparison of Measurement Models for AWS scale Model /Model Comparison Tests χ 2 df χ 2 /df CFI Original Six-Factor Model 998.31 335 2.98 0.91 Five-factor Model 1124.88 340 3.31 0.87 Four-factor model 1196.14 344 3.48 0.86 Three-factor model 1824.01 347 5..26 0.75 Two-factor model 2404.69 349 6.89 0.65 One-factor model 2648.60 350 7.57 0.61 Six-factor vs five-factor model 126.57 5 -- -- Five-factor vs four-factor model 71.26 4 -- -- Four-factor vs three-factor model 627.87 3 -- -- Three-factor vs two-factor model 580.68 2 -- -- Two-factor vs one-factor model 243.91 1 -- -- Note: CFI: Confirmatory Factor Analysis. Table II. Descriptive Statistics and Inter-Correlations among Variables Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. Gender -- -- 2. Age -- -- 0,05 3. Education -- -- -0,04 0,09 4. Marital Status -- -- 0,10* 0,50** -0,06 5. Partner Employment -- -- 0,15** 0,59** -0,05 0,80** 6. # of Children -- -- 0,16** 0,67** -0,09 0,68** 0,70** 7. Tenure -- -- -0,08 0,31** 0,10* 0,19** 0,20** 0,24** 8. Workload 3.23 0.88 -0,01 0,09 -0,01 0,16** 0,10* 0,16** 0,04 9. Control 3.90 0.83 0,16** -0,01 -0,06 0,09 0,05 0,06 0,10 0,16** 10. Rewards 3.92 0.81 -0,02 -0,03 0,01 0,04 -0,01 0,02 -0,00 0,22** 0,61** 11. Community 3.49 1.06 0,05 -0,09 0,04 -0,01 -0,03 0,02 -0,07 0,16** 0,41** 0,54** 12. Fairness 3.06 1.00 0,13* 0,05 -0,06 0,02 0,01 0,12* -0,05 0,17** 0,41** 0,52** 0,62** 13. Values 3.26 1.13 0,08 0,07 -0,03 0,06 0,05 0,10 0,02 0,19** 0.43** 0.51** 0.59** 0.75** 14. Psy. Cap. 4.89 0.77 0,12 0,12 0,01 0,19** 0,12 0,17** 0,02 0,09 0,47** 0,47** 0.28** 0,34** 0.36** 15. Turnover Int. 5.19 1.21 0.05 -0.25** 0,08 -0.16** -0.18** -0.30** -0.16** -0,18** -0,17** -0,27** -0,29** -0,36** -0.38** -0.13* 16. Emp. Wellbeing 2.58 1.22 0.08 0.18** 0,04 0.13* 0.09 0.16** -0.01 0.20** 0,42** 0.56** 0.41** 0.47** 0.49** 0.65** -0.30** -- Note:*p<0.01; **p<0.05 . Tunover Int: Turnover Intention Table III. The Moderating Role of Psychological Capital on the Linkages between AWS Dimensions and Employee Wellbeing Variable Steps 1 2 3 Age 0.14 * 0.15** 0.14** Marital Status 0.05 -0.03 -0.04 Number of Children 0.03 -0.02 -0.01 Workload 0.05 0.06 Control -0.07 -0.08 Rewards 0.25** 0.24** Community -- -- Fairness 0.08 0.08 Values 0.15** 0.16** Psy.Cap 0.48** 0.47** Workload*Psy.Cap -0.07 Control*Psy.Cap -0.07 Rewards*Psy.Cap -0.07 Community*Psy.Cap 0.09 Fairness*Psy.Cap -- Values* Psy.Cap -0.04 R 2 0.04 0.56 0.57 R 2 change 0.04 0.52 0.01 F change 4.44** 70.05** 1.52 Note: In the second step, the main effect of community was excluded from the model. In the third step the main effect of community and interaction effect of fairness and psychological capital were excluded from the model. *p<0.01; **p<0.05 Table IV. The Moderating Role of Psychological Capital on the Linkages between AWS Dimensions and Turnover Intention Variable Steps 1 2 3 Age -0.08 -0.10 -0.10 Marital Status 0.06 0.06 0.05 Number of Children -0.25** -0.20** -0.20** Partner Employment 0.01 -0.01 -0.01 Tenure -0.11* -0.12* -0.12* Workload -0.05 -0.05 Control 0.09 0.08 Rewards -0.14* -0.15* Community -- -- Fairness -0.12 -0.11 Values -0.26** -0.26** Psy.Cap 0.07 0.06 Workload* Psy.Cap -0.07 Control* Psy.Cap -0.04 Rewards* Psy.Cap -0.05 Community* Psy.Cap -0.02 Fairness*Psy.Cap -- Values* Psy.Cap 0.01 R 2 0.11 0.26 0.26 R 2 change 0.11 0.15 0.00 F change 8.52** 12.3** 0.42 Note: In the second step, the main effect of community was excluded from the model. In the third step the main effect of community and interaction effect of fairness and psychological capital were excluded from the model. *p<0.01; **p<0.05 Table V. Results of Supplementary Analyses E. Wellbeing Turnover Intention Variables b T p LLCI ULCI b t p LLCI ULCI Workload 0.19 3.48 0.00** 0.08 0.30 -0.18 -2.65 0.01* -0.32 -0.05 Control 0.24 3.78 0.00** 0.12 0.37 -0.23 -2.77 0.01* -0.39 -0.07 Reward 0.51 8.16 0.00** 0.38 0.63 -0.44 -5.39 .00** -0.60 -0.28 Community 0.29 6.46 0.00** 0.20 0.38 -0.34 -6.01 0.00** -0.46 -0.23 Fairness 0.33 6.47 0.00** 0.23 0.43 -0.40 -6.17 0.00** -0.53 -0.27 Values 0.31 7.17 0.00** 0.23 0.40 -0.39 -7.20 0.00** -0.50 -0.28 Workload*PsyCap -0.07 -1.15 0.25 -0.20 0.05 -0.15 -1.79 0.07 -0.31 0.01 Control *PsyCap 0.04 0.72 0.47 -0.07 0.16 -0.08 -1.08 -0.28 -0.22 0.07 Reward *PsyCap 0.04 0.70 0.48 -0.08 0.17 -0.03 0.39 0.70 -0.20 0.13 Community *PsyCap 0.10 1.91 0.06 -0.01 0.21 -0.06 -0.85 0.40 -0.19 0.08 Fairness *PsyCap 0.03 0.39 0.70 -0.11 0.16 -0.07 -0.77 0.44 -0.23 0.10 Values *PsyCap 0.01 0.03 0.98 -0.09 0.10 -0.03 -0.47 0.64 -0.14 0.09 Note: *: p<0.05; **: p <0.01 1. The unstandardized regression weights of PsyCap on employee wellbeing ranged from 0.77 to 0.99 and were significant. 2. The unstandardized regression weights of PsyCap on employee wellbeing ranged from -0.01 to 0.06 and were insignificant. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9059165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622921641,"identity":"bee9f48c-c2e4-4de2-b1ad-9feeb5564398","order_by":0,"name":"Ozge Tayfur Ekmekci","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYLCCCgYGOQNkAQm8ytmA+AwDgzFYy4EEuBYDgloSNxCthV+++ZnEwbZ76dvZDz98/PGHTR4/A/PB2zwMf/JxaZFsYzMDainO3dmTZmxwICGtWLKBLdmah8HAsgGHFoNjDGbSH9sScjccyGGTOJBwOHHDAR4zaaAWnC4zOMb+DWhLQrrB+TfsPw4k/E/cf4D/GwEtPCCHJSQY3MhhA3r/ADAceNjwapFsyym2OHAuwXDDjWfGEmfSkoslDrMZW84xMMaphZ/5+MYbB8oS5A3OJz/8UGFjl8ff3vzwxpsKOTwRw8CCEtcJDMxgB+PRwMDA/AFVyygYBaNgFIwCNAAAoutWfw15t9EAAAAASUVORK5CYII=","orcid":"","institution":"Hacettepe University","correspondingAuthor":true,"prefix":"","firstName":"Ozge","middleName":"Tayfur","lastName":"Ekmekci","suffix":""},{"id":622921642,"identity":"001fba29-537a-4396-a227-e13bb1263ffb","order_by":1,"name":"Hilal Demirel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hilal","middleName":"","lastName":"Demirel","suffix":""}],"badges":[],"createdAt":"2026-03-07 14:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9059165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9059165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108490695,"identity":"2c09ae03-46fc-45cd-83da-797ffecc0e16","added_by":"auto","created_at":"2026-05-05 09:46:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":689194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9059165/v1/7b38b2e9-8e23-4c7a-91e1-95a456c42250.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Moderating Role of Psychological Capital on the Impact of Areas of Worklife on Employee Well-being and Turnover Intentions: A Field Study\u003c/p\u003e","fulltext":[{"header":" Introduction","content":"\u003cp\u003eIn today\u0026rsquo;s competitive business world, organizations desire to achieve sustainable competitive advantage, thus expect their employees show higher levels of performance. Pfeffer (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examines the unsustainable employment practices in his book, \"Dying for a Paycheck,\" and advises businesses to consider the human costs associated with their constant pursuit of increased productivity and work hours. More importantly, Pfeffer (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) labels work stress one of the biggest causes of death and accuses managers of having unreasonable expectations from employees. Due to fierce competition in corporate world, most employees find themselves under constant pressure, with a workload that is more than they can cope with from time to time, and in a work environment that does not meet their expectations. Employees are forced to make a choice between continuing to work or quitting their current job, which does not meet their expectations adequately and sometimes this negatively affects their psychological and physiological health. To this end, determining the factors that affect the well-being of the employees and bring them to the stage of leaving the job has been a very important research topic in recent years. As Juniper (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, p.25) notes, the reason for the rising interest to \u0026ldquo;employee wellbeing\u0026rdquo; concept is \u0026ldquo;the established link between health and performance in the workplace and the general hypothesis is that the healthier and happier people tend to be more productive in the workplace.\u0026rdquo; Previous studies (e.g., Brunetto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McCarthy et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have demonstrated that employee well-being affects employees' attitudes and behaviors towards work and organizational outputs in a positive way. Similarly, Warr (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, p.392) argues that \u003cem\u003e\u0026ldquo;employee well-being is significantly and positively associated with better job performance, lower absenteeism, lower turnover intention, and occurrence of more discretionary behaviors in the workplace.\u0026rdquo;\u003c/em\u003e In a recent study, Krekel et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported high level of positive correlation with wellbeing at work and business-unit level profitability. Based on prior studies, Baptiste (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) emphasizes the importance of human resource management practices in boosting employee well-being. The researcher argues that human resources practices increase employee support and trust in management; thereby enhance organizational output and productivity by strengthening long-term relationships. Strong bonds between employees and management and high level of wellbeing, on the other hand, require managers understand employees\u0026rsquo; expectations from their organizations and satisfy those expectations.\u003c/p\u003e \u003cp\u003eThe main aim of this study is to uncover the organizational factors affecting employee well-being and turnover intention. To this end, employees\u0026rsquo; assessment of work place conditions, particularly employees\u0026rsquo; perceptions regarding fit between their expectations and what the organization provides them would be main variable of interest. Employees\u0026rsquo; perceptions will be measured using Leiter and Maslach\u0026rsquo;s (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, p.93) Areas of Work-life Model, which focuses on six factors, namely workload, control, rewards, community, fairness and values. The model assumes that inexistence of fit between expectations and perceived organizational practices in relation to these six-factors creates chronic job stress, leading higher level of exhaustion, cynicism and sense of inefficacy. Based on the premises of Rousseau\u0026rsquo;s (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) psychological contract theory, Leiter and Maslach (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, p.473) argue that employees and employers should reach an acceptable balance through explicit or implicit negotiations over these six areas of worklife. To this end, The Areas of Worklife Model focuses on the difference between the expectations of the employee and the perception of how much of these expectations realized in terms of these six areas of worklife.\u003c/p\u003e \u003cp\u003eSo far, most studies have focused on the effects of six areas of work-life on either burnout or engagement, yet ignored the plausible role of these areas on employees\u0026rsquo; wellbeing and propensity to quit their jobs (i.e., turnover intention). Taking a positive stance, this study assumes that the perceived harmony in areas of work-life will have a positive effect on employee well-being and negative effect on turnover intention. Therefore, the main purpose of the study will be to determine whether better management of employee expectations by focusing on person-organization harmony in areas of work-life results in higher employee well-being and reduced turnover intention.\u003c/p\u003e \u003cp\u003eSecondly, this study examines the moderating effects of psychological capital (PsyCap) in the relationship between areas of worklife and employee well-being and also on the linkage between areas of work-life and turnover intention. Laschinger and Fida (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) argue that PsyCap, which is an internal resource; could act as a protective factor against burnout, thus has a positive impact on wellbeing. In fact, Kun and Gadanecz (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported significant correlations between sub-dimensions of PsyCap, namely hope and optimism, and workplace well-being and happiness. Similarly, V\u0026icirc;rgă et al.\u0026rsquo;s (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) research demonstrates how PsyCap protects against burnout and secondary traumatic stress. Based on the premises of \u0026ldquo;Job Demands and Resources Model\u0026rdquo;, even when job demands are very high within an organization, employees who have greater access to resources for their jobs, such as PsyCap as a personal resource, could withstand the negative effects of pressure from job demands, reduce their desire to quit their jobs, and still experience higher wellbeing. Therefore, it is expected that the effect of perceptions of fit in areas of worklife on employee well-being and turnover intention will be stronger for employees with higher PsyCap.\u003c/p\u003e \u003cp\u003eSo far, the antecedents of turnover intention and wellbeing have been analyzed in various aspects. This study intends to contribute to the extant literature by demonstrating the linkages among six areas of work-life, PsyCap and turnover intention, which have not been addressed thus far. Whether or not employees\u0026rsquo; expectations from their organizations are met is thought to be a significant predictor of their intention to quit and wellbeing. Moreover, it is believed that enlightening the role of PsyCap in the interaction between individuals and organizations will also be an important contribution to the field, as the studies on psychological capital mostly ignoring the effects of moderation.\u003c/p\u003e"},{"header":"Contextual Background","content":"\u003cp\u003eThis study is carried out on white-collar employees in T\u0026uuml;rkiye, where traditional and modern values seem to coexist. Compared to other countries, coexistence of modernity and traditionalism may differentiate the expectations of Turkish employees from their organizations as well their reactions if these expectations are not met. For instance, the expectations that Turkish people have of organizations can also be influenced by traditional values like contentment and gratitude for little. Due to lower expectations from their employers than those in other countries, employees may not respond as expected, for example by abandoning their positions. Thus, as being conducted in T\u0026uuml;rkiye, this study might bring a different perspective to the literature.\u003c/p\u003e \u003cp\u003eAccording to the World Happiness Report (2025), T\u0026uuml;rkiye ranks 94th out of 147 countries with a score of 4.55 out of 10 \u0026mdash; the lowest among all OECD member states and a statistical outlier pulling down the OECD average of 6.68 (TheGlobalEconomy.com, 2025). Notably, T\u0026uuml;rkiye's happiness ranking has declined steadily since 2016 (P.A. Turkey, 2025). OECD's Society at a Glance report (2024), which examines well-being across member countries, further confirms T\u0026uuml;rkiye's precarious position: among all OECD countries, the composite positive experience index is lowest in T\u0026uuml;rkiye by a considerable margin, while the negative experience index is highest, indicating that Turkish people report more negative than positive experiences in their daily lives (OECD, 2024). Moreover, life satisfaction in Finland \u0026mdash; the top-ranked country \u0026mdash; is approximately three scale points higher than in T\u0026uuml;rkiye, which ranks at the bottom of the OECD (OECD, 2024). Taken together, these findings underscore the persistent and worsening well-being in T\u0026uuml;rkiye relative to its OECD peers, providing a compelling rationale for investigating the organizational and psychological determinants of employee well-being in the Turkish context. Therefore, it is believed that another practical and theoretical contribution of this study will be the focus on wellbeing of Turkish employees and their perceptions about the areas of worklife, which has not been studied thoroughly in previous researches.\u003c/p\u003e"},{"header":"Theoretical Background","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The Concept of Employee Wellbeing\u003c/h2\u003e \u003cp\u003eThe definition of well-being is full of misunderstandings and far from being complete (Wright, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). To this date, researchers have utilized happiness, pleasure, life satisfaction, quality of life, and harmony when defining well-being (G\u0026uuml;ler and D\u0026ouml;nmez, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For example, Diener (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1984\u003c/span\u003e, p. 544) emphasizes life satisfaction while conceptualizing wellbeing as \u0026ldquo;\u003cem\u003ethe subjective and global assessment of all aspects of a person\u0026rsquo;s life, including not just the absence of negative factors but also the positive measures\u003c/em\u003e\u0026rdquo;. On the other hand, Ryff (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, p.1071) defines well-being along with \u0026ldquo;\u003cem\u003eself-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life and personal growth.\u0026rdquo;\u003c/em\u003e The elusive and different conceptualizations of well-being reflect two different paradigms, namely hedonism and eudemonism. The hedonic perspective emphasizes \u0026ldquo;subjective well-being\u0026rdquo;, which involves \u0026ldquo;\u003cem\u003ethe subjective evaluations of one's own life and is associated with general satisfaction, positive affectivity, and lack of negative affectivity\u003c/em\u003e\u0026rdquo; (Diener and Lucas, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The eudemonic perspective, on the other hand, involves \u0026ldquo;\u003cem\u003ethe realization of human development and existential challenges in life\u003c/em\u003e\u0026rdquo; (Keyes et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), thus emphasizes \u0026ldquo;\u003cem\u003eone\u0026rsquo;s positive psychological functioning and sense of development\u003c/em\u003e\u0026rdquo; as a reflection of psychological wellbeing.\u003c/p\u003e \u003cp\u003eAcknowledging that workplace conditions could be different from life conditions; some researchers point out that employee wellbeing should be distinguished from general wellbeing (Zheng et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and be treated separately. Therefore, scholars have begun to focus on employee well-being by adapting the premises of the well-being concept to organizational life. \u0026ldquo;Well-being at work\u0026rdquo; or simply \u0026lsquo;employee wellbeing\u0026rsquo; could be defined as \u0026ldquo;the physical and mental health of the workforce\u0026rdquo; (Currie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This study utilizes the construct definition of \u0026ldquo;employee well-being\u0026rdquo; proposed by Zheng et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This definition provides a comprehensive perspective by incorporating three sub-dimensions of well-being: subjective well-being (life-wellbeing), psychological well-being and well-being at work (workplace wellbeing) that reflect both hedonism and eudemonism perspectives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Relationship Between Wellbeing and Areas of Worklife\u003c/h2\u003e \u003cp\u003eMaslach and Leiter (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) describe six areas of worklife where person-job harmony or incompatibility can be experienced. Emotional exhaustion, cynicism, and hence \"burnout\" can occur in each area when the nature of the job and the nature of the individual are not in harmony. On the other hand, \"engagement\" occurs when a good fit is achieved in these six areas. These six areas are listed as workload, control, rewards, community, fairness and values. Workload is defined as \u0026ldquo;work demands that exceed human limits\u0026rdquo; (Maslach and Leiter, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and a mismatch in \u0026ldquo;workload\u0026rdquo; occurs when work demands consume energy in a way that makes it difficult for a person to recover. In other words, mismatch in workload is inevitable when the job demands exceed the resources of the individual. Another mismatch is confronted in \u0026ldquo;control\u0026rdquo; when the employee does not have sufficient control over the resources needed complete the work efficiently. The third mismatch is observed in \u0026ldquo;rewards\u0026rdquo; areas when the financial, social and internal rewards offered by the workplace are inconsistent with the expectations of the employees. The fourth mismatch occurs in \u0026ldquo;community\u0026rdquo; when employees do not feel positively connected with and supported by the other employees and the supervisors in the workplace. The fifth mismatch arises in \u0026ldquo;fairness\u0026rdquo; such that unfairness perception occurs when an employee compares own efforts and achievements with the other employees\u0026rsquo; ratio of inputs and outputs and observes inequity. Last incompatibility is observed in values dimension when an employee\u0026rsquo;s \u0026ldquo;values\u0026rdquo; is not consistent with the values of the job; thereby result in conflict of values (Maslach et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Leiter and Maslach, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe areas of work-life model rests on the premises of Psychological Contract Theory and \u0026ldquo;Person-Job Fit Model\u0026rdquo;. Rousseau (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, p.10) defines the psychological contract as \u0026ldquo;\u003cem\u003eindividual beliefs, shaped by the organization, regarding terms of an exchange agreement between individuals and their organizations\u003c/em\u003e\u0026rdquo;. According to Leiter and Maslach (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), employers and employees should achieve a mutually agreeable balance on areas of work-life through the explicit and implicit negotiations of a psychological contract. The researchers argue that there are a number of unwritten expectations in the relationship between an employee and an organization, and when the employee feels that these expectations are not met, tension, anxiety, mental and physical ailments, as well as unfavorable attitudes and behaviors toward their jobs, may be seen (Maslach \u0026amp; Leiter, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Consistent with this argument, researches (e.g., Antonaki \u0026amp; Trivellas, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u0026Uuml;\u0026ccedil;ok \u0026amp; Torun, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chin \u0026amp; Hung, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bal et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hartmann \u0026amp; Rutherford, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) demonstrated the adverse effects of psychological contract breach in forms of decreased organizational commitment, trust, and increased turnover intention, organizational cynicism and burnout.\u003c/p\u003e \u003cp\u003ePerson\u0026ndash;organization fit involves either the fulfillment of the expectations of the individual and the organization or the condition that organizations and employees have similar characteristics (Kristof, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus far, many studies have examined the effects of person-job fit on employee well-being and work-related attitudes and behaviors. For instance, Brandst\u0026auml;tter et al., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) investigated the effects of a misfit between motivational needs of employees and the motivation offered by workplaces on burnout and physical symptoms. The researchers found that person-job mismatch was linked to burnout and several physical symptoms such as headache, abdominal pain, runny nose etc., thus adversely affect employees\u0026rsquo; wellbeing. Likewise, Brom et al (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) revealed that perceived misfit between one\u0026rsquo;s expectations and the six areas of worklife contributes significantly to diminished well-being and feelings of burnout. Buruck et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found in a recent study that mismatches in workload, control, and social support (community) dimensions have a substantial impact on employees' chronic low back pain. Thus far, studies (e.g., Kausto et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; \u0026Ccedil;elik et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) have also demonstrated that perception of fairness, which is an important area of worklife positively, affects employee well-being.\u003c/p\u003e \u003cp\u003eRelying on the premises of Person-Job Fit Model\u0026rdquo; and \u0026ldquo;Psychological Contract Theory\u0026rdquo;, employees\u0026rsquo; perceptions of a fit regarding the areas of worklife (i.e. workload, control, community, rewards, fairness and values) is expected to positively predict the well-being of the employees:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEmployees\u0026rsquo; perceptions of a fit regarding the areas of worklife (i.e. workload (H1a), control (H1b), community (H1c), rewards (H1d), fairness (H1e) and values (H1f)) are positively related to employee wellbeing.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The Relationship Between Turnover Intention and Areas of Work-life\u003c/h2\u003e \u003cp\u003eTill this date, many studies have been conducted to uncover the antecedents of employee turnover given the higher costs associated with employees\u0026rsquo; quitting their jobs. Since intentions often predict behaviors (e.g. Cho \u0026amp; Lewis, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), researchers have mostly directed their attention to conceptualize the concept of turnover intention. Tett and Meyer (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, p.262) defined turnover intention as \u003cem\u003e\u0026ldquo;the conscious and deliberate willfulness to leave the organization\u0026rdquo;\u003c/em\u003e, while Takase (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, p.3) conceptualized the concept as a voluntary process, which encompassing \u003cem\u003e\u0026ldquo;a multi-stage process involving the voluntary departure of employees from their current position\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAlbeit not the sole reason, the apparent mismatch between what employees anticipate from their organizations and what they receive may raise employees' intention to leave their positions. Although the psychological contract is not a written contract that legally binds the parties, it could still shape the attitudes of the employees by creating unwritten expectations. If the unwritten expectations are not fulfilled by the organization, the employees might perceive psychological contract breach, which could result in negative feelings, attitudes and behaviors towards the job and the organization (Rousseau, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In fact, Boamah and Laschinger (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that person-job fit in six areas of worklife reduces the feelings of burnout, which in turn reduces the turnover intention. Similarly, Leiter and Maslach (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) shows that areas of worklife predicts burnout, which in turn predicts turnover intentions, and that burnout plays a mediating role in the relationship between areas of worklife and the turnover intention. Based on previous research\u0026rsquo;s findings and the premises of Psychological Contract Theory, it is proposed that fulfilling the employees\u0026rsquo; expectations of regarding workload, control, values, rewards, fairness and rewards might decrease the employees\u0026rsquo; turnover intentions:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEmployees\u0026rsquo; perceptions of a fit regarding the areas of worklife (i.e. workload (H2a), control (H2b), community (H2c), rewards (H2d), fairness (H2e) and values (H2f)) are negatively related to turnover intention.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Moderating Role of Psychological Capital\u003c/h2\u003e \u003cp\u003eSince the last decade, scholars have begun to focus on the strengths of people such as personal traits, abilities, and psychological capital rather than trying to correct what is wrong with them (Luthans \u0026amp; Youssef, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). To this end, the discipline of positive organizational behavior seeks to understand the concept of \"psychological capital\" in order to better manage employees and reach organizational outcomes (\u0026Ouml;zer et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Luthans et al., (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, p.3) defines psychological capital (PsyCap) and its dimensions (i.e. self-efficacy, optimism, hope and resiliency) as\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ean individual\u0026rsquo;s positive psychological state of development and is characterized by having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; making a positive attribution (optimism) about succeeding now and in the future; persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and when beset by problems and adversity, sustaining and bouncing back and even beyond (resiliency) to attain success.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStudies reveal that PsyCap is a crucial employee resource that helps combat stress problems, lowers turnover intentions, improves performance, and strengthens employee well-being. Additionally, it provides firms with a competitive advantage, encourages people to adopt good attitudes and behaviors toward their jobs, and guards against burnout syndrome. (e.g. Larson \u0026amp; Luthans, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Avey et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u0026Ccedil;etin and Basım, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Luthans et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tokmak, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In their comprehensive studies, Avey et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) demonstrated that PsyCap provides firms with a competitive advantage, encourages people to adopt good attitudes and behaviors toward their jobs, and guards against burnout syndrome.\u003c/p\u003e \u003cp\u003eThe direct impact of PsyCap on employee wellbeing and turnover intentions could be explained based on the premises of \u0026ldquo;Job Demands and Resources Model\u0026rdquo; (JD\u0026ndash;R). The model conceptualizes job demands as \u003cem\u003e\u0026ldquo;physical, social or organizational aspects of the job that require sustained physical or mental effort\u0026rdquo;;\u003c/em\u003e while it defines job resources as \u0026ldquo;\u003cem\u003ephysical, psychological, social, or organizational aspects of the job that are either/or: (1) functional in achieving work goals; (2) reduce job demands and the associated physiological and psychological costs; (3) stimulate personal growth and development\u0026rdquo;\u003c/em\u003e (Demerouti et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, p. 501). The researchers argue that PsyCap, as a personal resource, could influence the perceptions of job demands and resources in the workplace. Consistent with this, Grover et al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) revealed that employees with high levels of PsyCap tend to have more positive perceptions regarding job demands and resources suggesting that PsyCap might act as a personal resource shaping employees\u0026rsquo; attitudes toward their jobs and organizations.As reported by Gu (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); PsyCap can improve the well-being of employees, and contribute to employee performance. The past researches also emphasize the significant impact of PsyCap on turnover intention (e.g., Yim et al.; Salam, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and well-being (e.g., Avey, Luthans, Smith et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rabenu et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, PsyCap is expected to have a direct impact on both wellbeing and turnover intention.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological capital (PsyCap) is positively related to employee wellbeing.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological capital is negatively related to turnover intention.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eAvey et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, p.680) refer to PsyCap as \u0026ldquo;\u003cem\u003ea positive strength for combating stress\"\u003c/em\u003e and state that the resource employees need in their struggle with stressful events and situations in the workplace can be psychological capital. Choi and Lee (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) argue that PsyCap plays a fundamental role in preventing negative thoughts that cause employee turnover by increasing positive experiences in the workplace and in social relationships, helping to resolve conflicts, and reducing stress in the workplace. Therefore; it will be possible to evaluate PsyCap as a resource that supports and strengthens the employee against all demands and expectations of the job and moderates the negative effects by acting as a kind of buffer. It is expected that employees with high PsyCap can better tolerate negative attitudes and behaviors arising from the misfit they perceive in sub-dimensions of areas of worklife. Based on the aforementioned literature, in this study it is proposed that PsyCap support as a moderating variable between areas of worklife and employee wellbeing, as well as areas of worklife and turnover intention linkages. PsyCap is suggested to have a moderator role in the positive effect of meeting the expectations of the employees regarding the areas of worklife on employee wellbeing. Also, it is expected that PsyCap has a moderator role in the negative effect of meeting the expectations of the employees regarding the areas of worklife on turnover intention. Thus,\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 5\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological capital moderates the relation between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and employee wellbeing such that the effect of the former on the latter will be stronger when employees have higher psychological capital.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 6\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological capital moderates the relation between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and turnover intention such that the effect of the former on the latter will be stronger when employees have higher psychological capital.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Procedures and Participants\u003c/h2\u003e \u003cp\u003eThis study was approved by the Ethics Committee of Hacettepe University Senate prior to data collection (approval date: January 12, 2021). All procedures were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. Participants were fully informed about the purpose of the study, the voluntary nature of their participation, and the confidentiality of their responses. Written informed consent was obtained from all participants before the data collection.\u003c/p\u003e \u003cp\u003eThrough online survey, data were collected through convenience sampling technique from 374 white-collar employees working in varied industries from software to aviation in T\u0026uuml;rkiye. 5 responses were discarded due to randomly distributed, yet substantial amount of missing data. Thus, 369 usable questionnaires were obtained. Of the respondents, 301 were male (81.6%) and 68 females (18.4%), majority at an age between 26 and 35 (n\u0026thinsp;=\u0026thinsp;140, 37.9%). A significant part of the participants had a bachelor's degree or higher (n\u0026thinsp;=\u0026thinsp;354, 95.9%) and had an organizational tenure less than 3 years (n\u0026thinsp;=\u0026thinsp;214, 58%). The majority of the participants were married (n\u0026thinsp;=\u0026thinsp;224, 60.7%), with at least 1 child (n\u0026thinsp;=\u0026thinsp;195, 52.8%) and 42.8% (n\u0026thinsp;=\u0026thinsp;158) of their spouses were non-employed. High percentage (n\u0026thinsp;=\u0026thinsp;354, 95.9%) of the participants had full-time employment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measures\u003c/h2\u003e \u003cp\u003eA questionnaire form was designed to examine the four study variables, namely employee wellbeing, turnover intention, areas of work-life and PsyCap. Participants were also requested to answer items pertaining to demographics (i.e., gender, age, education level, marital status, number of children, partner employment) and work-related characteristics (i.e., tenure).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEmployee Wellbeing\u003c/em\u003e: Employee Wellbeing was measured with the 18-item Employee Well-being Scale (EWS) developed by Zheng et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The scale has already been adapted to Turkish by Karapınar et al., (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), therefore no translation was made. EWS consists of three sub-dimensions: life-wellbeing (LWB) (subjective well-being), workplace wellbeing (WWB), and psychological well-being (PWB). Each dimension is measured with 6 items. Sample items for LWB, WWB and PWB are \u0026ldquo;\u003cem\u003eI am in a good life situation\u003c/em\u003e; \u0026ldquo;\u003cem\u003eWork is a meaningful experience for me\u003c/em\u003e.\u0026rdquo;; and \u0026ldquo;\u003cem\u003eI am good at making flexible timetables for my work\u003c/em\u003e\u0026rdquo; respectively. Participants indicate their level of agreement using 7-point Likert-type scale (\u0026ldquo;1\u0026rdquo; strongly disagree to \u0026ldquo;7\u0026rdquo; strongly agree) and higher scores indicate higher levels well-being. The internal consistencies of subscales and overall scale are found to be satisfactory in Zheng et al.\u0026rsquo;s study (2015), with Cronbach\u0026rsquo;s Alpha values of 0.87, 0.87, 0.84 and 0.91 for the LWB, WWB, PWB, and overall Employee Wellbeing scales, respectively. For Turkish adaptation, Karapınar et al., (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported similar reliability estimates. Since hypothesis of this study were based on overall wellbeing, rather than its sub-dimensions, reliability of overall scale was computed and found to be satisfactory with Cronbach\u0026rsquo;s Alpha of 0.96.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTurnover Intention\u003c/strong\u003e \u003cp\u003eThe 3-item Turnover Intention Scale, created by Cammann et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), and translated into Turkish by Mimaroğlu (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), is used to gauge participants' intentions to quit the company in the near future. Two items (i.e., \u0026ldquo;\u003cem\u003eI will probably look for a new job in the next year\u003c/em\u003e\u0026rdquo;; \u0026ldquo;\u003cem\u003eI rarely think about leaving the job\u003c/em\u003e\u0026rdquo; are rated with 7-point Likert type scale ranging from 1 (strongly disagree) to 7 (strongly agree); one item (i.e., \u003cem\u003eHow likely is it that you could find a job with another employer with about the same pay and benefits you now have\u003c/em\u003e?) is rated with 7-point Likert type scale ranging from 1 (not at all likely) to 7 (extremely likely). Cammann et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) reported a satisfactory reliability estimate (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.85) for the original scale, while Mimaroğlu (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported a lower reliability estimate for the Turkish translation (α\u0026thinsp;=\u0026thinsp;0.67). In this study, the reverse-coded item (\u0026ldquo;I rarely think about leaving the job\u0026rdquo;) was found to reduce the reliability of the scale. After removal of this item, it was seen that turnover intention could be measured consistently with two remaining items (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.85). Therefore, reverse-coded item was removed from further analyses.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAreas of Worklife\u003c/em\u003e: The Areas of Worklife Scale (AWS) of Leiter and Maslach (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) is used to measure the perceived congruence or incongruence between the person and the job for each area: workload, control, rewards, community, fairness and values. Along with the permission of the scale, the Turkish translation made by independent researchers was obtained from the Mind Garden Company, which holds the official rights of the scale. The scale consists of six sub-scales and 28 items. There are five items measuring perceptions of workload, four measuring perceptions of control, four measuring perceptions of rewards, five measuring perceptions of community, six measuring perceptions of fairness, and four measuring perceptions of values on a five-point Likert-type scale (ranging from \"1\" strongly disagree to \"5\" strongly agree). Cronbach\u0026rsquo;s Alpha values for the sub-scales are 0.70 for workload, 0.70 for control, 0.82 for rewards, 0.82 for community, 0.82 for fairness and 0.73 for values in Leiter and Maslach\u0026rsquo;s study (2003). Budak and S\u0026uuml;rgevil (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) reported Cronbach\u0026rsquo;s Alpha values ranging from .59 to .90 for Turkish adaptation of the scale. In this study Cronbach\u0026rsquo;s Alpha values for the sub-scales are found to be 0.72 for workload, 0.83 for control, 0.79 for rewards, 0.93 for community, 0.86 for fairness and 0.88 for values.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePsychological Capital\u003c/em\u003e: Participants\u0026rsquo; positive psychological states were measured with the 24-item Psychological Capital Questionnaire (PCQ) developed by Luthans et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Along with the permission of the scale, the Turkish translation made by independent researchers was obtained from the Mind Garden Company, which has the official rights of the scale. PCQ includes four sub-dimensions: hope, optimism, resilience and self efficacy. Participants are expected to indicate their degree of agreement with the items (i.e., \u003cem\u003e\"I always look on the bright side of things regarding my job\"\u003c/em\u003e) on a 6-point Likert-type scale (\u0026ldquo;1: strongly disagree\u0026rdquo; to \u0026ldquo;6: strongly agree\u0026rdquo;). In contrast to Avey et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who reported Cronbach's Alpha values for the original scale ranging from 0.72 to 0.93, \u0026Ccedil;etin and Basım (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported lower reliability estimates for Turkish translation, which ranged from 0.67 to 0.91 for the overall scale and its sub-scales. Since hypothesis of this study were based on overall PsyCap, only reliability estimate for overall scale was assessed and determined to be satisfactory (α\u0026thinsp;=\u0026thinsp;0.94).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Results of Preliminary Analysis\u003c/h2\u003e \u003cp\u003eThe data were initially examined to determine whether univariate and multivariate normality existed. To this end, skewness and kurtosis values were used to evaluate the univariate normality, and Mardia's coefficient of variation was assessed to check for multivariate normality. All of the skewness and kurtosis values were smaller than the crucial value of 3 (Tabachnick \u0026amp; Fidell, 2001), and no violations for multivariate normality were observed. After data screening, Harman's test was conducted to investigate whether common method variance poses a problem in the analysis. An exploratory factor analysis was therefore carried out. The unrotated factor analysis yielded 14 factors, with the first factor accounting for 30.55% of the variance. This variation was below the permissible threshold of 50% (Podsakoff \u0026amp; Organ, 1986), indicating the absence of any overarching/general method factor, thus CMV does not seem to be serious concern for data.\u003c/p\u003e \u003cp\u003eSeries of Confirmatory Factor Analyses (CFA) were conducted to investigate the factorial structures and construct validity of the study variables. Firstly, the six, five, four, three, two and one-factor measurement models were compared for AWS scale. As seen from Table I, the model fit values and χ\u003csup\u003e2\u003c/sup\u003e values deteriorated when measurement models other than six-factor model was utilized. Besides, the nested comparison of the six and five-factor models yielded Δχ\u003csup\u003e2\u003c/sup\u003e value of 126.57 (p \u0026lt;\u0026thinsp;.01), indicating again the superiority of six-factor model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT TABLE 1 HERE\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter nested model comparisons, the relationships between items and factors were examined and all items were found to be significantly associated only with hypothesized sub-dimension/factor of AWS. When the error terms of the items measuring the same construct error were allowed, the model fit values were found to improve (χ\u003csup\u003e2\u003c/sup\u003e /df\u0026thinsp;=\u0026thinsp;1.96; CFI\u0026thinsp;=\u0026thinsp;0.95, GFI\u0026thinsp;=\u0026thinsp;0.89; RMSEA\u0026thinsp;=\u0026thinsp;0.05), indicating that 28-items could be used to measure 6 dimensions of work-life, as presumed.\u003c/p\u003e \u003cp\u003eSecond CFA was conducted for employee-wellbeing scale. Since hypotheses are based on employee\u0026rsquo;s general wellbeing, rather than its sub-dimensions, second-order measurement model in which 18-items were assumed to measure 3-subdimension of wellbeing, which in turn supposed to measure employee general wellbeing. Although the hypothesized relations were found to be significant, the model fit values indicated the existence of poor fit. After adding covariances between error terms of the same wellbeing dimension, the model fit values improved, and model provided relatively good fit to the data (χ\u003csup\u003e2\u003c/sup\u003e /df\u0026thinsp;=\u0026thinsp;2.85; CFI\u0026thinsp;=\u0026thinsp;0.94, GFI\u0026thinsp;=\u0026thinsp;0.88; RMSEA\u0026thinsp;=\u0026thinsp;0.08). Eighteen items were found to measure three-sub-dimensions of wellbeing, and general wellbeing.\u003c/p\u003e \u003cp\u003eThe last CFA was carried out for PsyCap scale. In the second-order measurement model, 24 items were suggested to assess PsyCap across its four sub-dimensions, which in turn were intended to gauge the degree of PsyCap across workers as a whole. The results revealed all the items except the items measuring resiliency were found to be significantly associated with self-efficacy, optimism, and hope dimensions. As in previous studies (e.g., Shah et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), problems regarding the factor-structure of the PsyCap scale have been observed in this study. Considering this and the fact that hypotheses are based on employee\u0026rsquo;s general PsyCap levels, rather than its sub-dimensions, one-dimensional model was specified and tested. Except for two items (resiliency and optimism items), all items were found to be significantly associated with overall PsyCap construct. When these two items were removed from the model, the model adequately fit to the data (χ\u003csup\u003e2\u003c/sup\u003e /df\u0026thinsp;=\u0026thinsp;2.89; CFI\u0026thinsp;=\u0026thinsp;0.97, GFI\u0026thinsp;=\u0026thinsp;0.95; RMSEA\u0026thinsp;=\u0026thinsp;0.07). Based on CFA results, twenty-two items were decided to be used in further analysis.\u003c/p\u003e \u003cp\u003eAfter factor analysis, reliability analyses have been conducted and all scales were found to be internally consistent. Based on this, we computed the composite scores by taking the mean of the responses to PsyCap, turnover intention, areas of worklife and employee-wellbeing. By using these composite scores, we examined the correlations between study variables and demographics (see Table II). The correlations are consistent with expectations in that all areas of worklife dimensions negatively correlated with turnover intention and positively correlated with employee-wellbeing. The correlation between PsyCap and wellbeing is strong and noteworthy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT TABLE II HERE\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of Hypothesis Testing\u003c/h2\u003e \u003cp\u003ePrior to analyses, independent and moderating variables were centered and interaction terms were calculated by multiplying the centered variables, based on the suggestions of Aiken and West (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Two hierarchical regression analyses were conducted to examine the main effects of areas of work-life and PsyCap as well the moderating effect of PsyCap. In the first regression analysis, employee wellbeing is regressed firstly on control variables, age, marital status and number of children, secondly on six areas of work-life and PsyCap, thirdly on the interaction terms of areas of life dimensions and PsyCap (please see Table III). In the first step, control variables explained only 4% of variance in employee well-being (\u003cem\u003eΔF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and only age significantly predicted employee wellbeing (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the second step, regression model was found to be significant (\u003cem\u003eF\u003c/em\u003e(3, 368)\u0026thinsp;=\u0026thinsp;49.85; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cem\u003eΔF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); six areas of work-life, PsyCap and demographic variables accounted for 56% of the variance in employee wellbeing. PsyCap (β\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and only two of six areas of work-life, rewards (β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and values (β\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), contributed significantly to prediction of employee wellbeing. In the third step, all main and interaction effects explained 57% of the total variance (F (14, 368)\u0026thinsp;=\u0026thinsp;32.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however the addition of the interaction effects did not significantly improve the model (ΔR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01; F change\u0026thinsp;=\u0026thinsp;1.52, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Despite data centering, the main effect of community and the interaction effect of fairness and PsyCap were not added to the model by the program due to low tolerance values. As in 2nd step, age (β\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), PsyCap (β\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), rewards (β\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and values (β\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) positively and significantly predicted employee wellbeing. All the interaction effects and the main effects of number of children, marital status, workload, control, and fairness were found to be insignificant. While Hypothesis \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e5\u003c/span\u003e was not verified, the results supported Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and partially supported Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (only supported H1d \u0026amp; H1f).\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT TABLE III HERE\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable IV reports the findings of hierarchical regression analysis regarding turnover intention. In the first step, the control variables (age, marital status, number of children, partner employment and tenure) were included to the model. Regression analysis found that, demographic variables explained % 11 of total variance in turnover intention (ΔF\u0026thinsp;=\u0026thinsp;8.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among the demographic variables, only number of children (\u003cem\u003eβ\u003c/em\u003e= -0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and tenure (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) significantly predicted turnover intention. In the second step, main effects of PsyCap and areas of work-life on turnover intention were added to model, yet the main effect of community was excluded by the program. Among the six areas of work-life, only rewards (β=-0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and values (β=-0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) significantly predicted turnover intention, yet the main effect of PsyCap on the turnover intention was not significant (β\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e was rejected and Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was partially supported (only H2d \u0026amp; H2f).\u003c/p\u003e \u003cp\u003eIn the third step, all main and interaction effects explained 26% of the total variance (F (15, 368)\u0026thinsp;=\u0026thinsp;8.35; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however the addition of the interaction effects did not significantly improve the model (Δχ2\u0026thinsp;=\u0026thinsp;0.01; ΔF\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). So, contrary to the Hypothesis \u003cspan refid=\"FPar6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the moderating effect of PsyCap on the areas of worklife and turnover intention linkage was not supported. As in 2nd step, number of children (β=-0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), tenure (β=-0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), rewards (β=-0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and values (β=-0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) negatively and significantly predicted employee wellbeing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT TABLE IV HERE\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of Supplementary Analyses\u003c/h2\u003e \u003cp\u003eAs indicated before, the main effect of community and interaction effect of fairness and PsyCap were excluded in both hierarchical regression analyses. This exclusion was due to multicollinearity problem arising from moderate-to-high correlations among six areas of work-life. It is thought that the multicollinearity problem might have increased some of the variables' regression weights while attenuating others. Therefore, separate regression analyses were carried out for each of the work-life domains to establish whether each domain and its interaction with PsyCap had a substantial impact on employee wellbeing and turnover intention, acknowledging the likelihood of increased Type I error. To this end, Process Macro was used to conduct twelve regression analyses for employee wellbeing and intention to leave (Hayes, 2003). Again, in these analyses, the same control variables were added to the regression models together with centered values of PsyCap, areas of work-life and their interaction terms. As seen from Table V, the main effects of each work-life area on employee wellbeing were significant, when work-life areas were considered alone, not together with others. However, the findings of Process Macro and hierarchical regression analyses were consistent with each other regarding the insignificant effects of interaction terms. Unlike hierarchical regression analyses, six areas of work-life negatively and significantly predicted employee\u0026rsquo;s turnover intention. The effects of PsyCap and interaction terms, on the other hand, turned out to be insignificant, which supported the results of hierarchical regression analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINSERT TABLE V HERE\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study aimed to examine the effects of areas of worklife on employee wellbeing and turnover intention and also analyze the role of psychological capital on these linkages. To this end, perceived fit on areas of worklife and PsyCap were proposed to positively affect employee wellbeing whereas negatively affect turnover intention. Moreover, PsyCap was assumed to moderate the relationship between perceived fit on the areas of worklife (i.e. workload, control, community, rewards, fairness and values) and outcome variables (i.e., wellbeing and turnover intention).\u003c/p\u003e \u003cp\u003eAccording to hierarchical regression analyses, two prominent areas of worklife (i.e., rewards and values) predicted both employee wellbeing and turnover intention. The perceived fit between employees\u0026rsquo; expectations regarding rewards and the rewards offered by the company seems to enhance employees\u0026rsquo; wellbeing and decrease their desire to leave their organizations. Also, perceived congruence between organizations\u0026rsquo; values and employees\u0026rsquo; values contribute to the wellbeing of the employees and lessen their turnover intentions. However, it is noteworthy to mention that all worklife areas (i.e. workload, control, rewards, community, fairness and values) were able to predict employee wellbeing and turnover intention when the analyses were conducted separately for each area of worklife dimensions. These seemingly conflicting results could be explained with the interactions of the worklife dimensions that might suppress the effects of some dimensions.\u003c/p\u003e \u003cp\u003eSimilar to the studies conducted in Western and individualistic societies, this study supports the premises of Psychological Contract Theory (Rousseau (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1995\u003c/span\u003e)) and the discussions in extant literature (e.g., Van Vianen et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) about the importance of reaching person-job fit in different areas of work life. It seems that when Turkish employees observe inconsistencies between their expectations and organizational practices/procedures, they perceive breach of psychological contract and react that breach in forms of increased turnover intention and reduced wellbeing. Just like employees in western cultures, white-collar employees in T\u0026uuml;rkiye seem to attach importance to the fit in regards to workload, rewards, values, community, fairness and control. When written or unwritten expectations that exist between an employee and an organization are not met, this violates psychological contract and result in lack of person-job fit. As a result, employee well-being appears to decline and their intention to leave the company tends to rise.\u003c/p\u003e \u003cp\u003eHowever, rewards and values are thought to stand out among the other areas of worklife in Turkish setting, considering their strong main effects. This finding could be explained by the characteristics of the white-collar employees in T\u0026uuml;rkiye and Turkish culture and wage levels. Among the OECD countries, T\u0026uuml;rkiye has been listed as the last country in terms of low pay criterion, which reflects \"the share of workers earning less than two-thirds of median earnings\" (OECD, 2023). Additionally, T\u0026uuml;rkiye's average disposable income per person is substantially lower than the OECD average (USD 19 482 versus USD 30 490 a year). It seems that low levels of wages, thereby perceived deprivation makes Turkish employees more sensitive about rewards and lead them to respond negatively to lack of fit in forms of decreased wellbeing and increased turnover intention.\u003c/p\u003e \u003cp\u003eConsistent with prior studies conducted in T\u0026uuml;rkiye, \u0026ldquo;values\u0026rdquo; turned out to be significant predictors of employee wellbeing and turnover intention. Erdoğan et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) pointed out the importance of employee-organization work value congruence, which is the degree to which the employee\u0026rsquo;s values match the values of the organization\u0026rsquo;s culture for Turkish employees. Likewise, Maden and Kabasakal (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) investigated the effects of employees' value fit with their organization and argued that when employees believe they share similar characteristics within the work environment and experience value compatibility, they tend to have more positive reactions toward their job and feel emotionally attached to their organization in T\u0026uuml;rkiye. Although comparisons have not been made in this study, compared to employees in other countries, Turkish employees seem to have higher expectations regarding value-alignment and this could be explained with characteristics of Turkish culture. As researchers (e.g. House et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wasti, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) point out, T\u0026uuml;rkiye is a relatively collectivist society and that a sense of being part of a group is important for Turkish people. Schroeder (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) underlines that feelings of belonging to a group and making sacrifices for the group are the prominent characteristics of collectivist cultures. People in collectivist countries define their identities with the culture they live in and want to feel a sense of belonging and it is thought that Turkish employees might want to feel in harmony with the values of the organization and to be committed to those values. According to Meng\u0026uuml;c (2000), in Turkish companies, practices are generally based on loyalty and when Turkish employees do not feel like family, they become alienated from their organizations. And thus, we could expect that their tendency to leave their jobs might increase as a result of higher expectations.\u003c/p\u003e \u003cp\u003eThis situation shows that Turkish employees may not want to work in an organization that is not compatible with their value judgments despite the economic factors. Thus employees who do not feel any harmony or commitment to the values of the organization could become alienated from the companies and their tendency to leave their job increases. Likewise, employee wellbeing decreases as a result of negative feelings arising from the perceived misfit in values.\u003c/p\u003e \u003cp\u003eAnother notable finding is the relatively strong and positive effect of PsyCap on employee wellbeing. It seems that employees who have higher levels of self-efficacy, hope, optimism and resilience are better able to stand for the challenges in life. As Job Demand-Resources Model suggests, psychological capital might act as a resource protecting employees in demanding and stressful situations, thereby increase one\u0026rsquo;s sense of wellbeing. Despite its considerably strong main effect, the moderating effect of PsyCap on the relationship between areas of worklife and employee wellbeing was not confirmed. This may be explained by the strong direct effect of psychological capital on the employee wellbeing, which could surpass the moderation.\u003c/p\u003e \u003cp\u003eQuite unexpectedly, neither the main effect of PsyCap on turnover intention, nor the moderation effect of it on the linkage between areas of worklife and turnover intention was found to be significant. It is thought that this unexpected finding may be due to the fact that the turnover intention is more affected by demographic and economic factors, rather than the personal resources like psychological capital. In fact, when effects of demographic variables on turnover intention were examined, the number of children and tenure turned out to be prominent predictors of the turnover intention. Accordingly, when the number of children or employees\u0026rsquo; tenure increases, they are less likely to quit their job, which could be explained with financial necessities and increased sense of continuance commitment.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThe results might have important practical implications for organizations. First, it is advised that HR and department managers strive for Person-Job Fit in different areas of work life to ensure that the assigned jobs match the expectations of the employees. To this end, managers need to take actions to appoint right people to right jobs in order to maximize each employee's potential and produce the desired outcomes for their organizations. Prior to hiring, measuring candidates\u0026rsquo; interests, asking their expectations from the organizations, and giving realistic job previews could enable managers to better understand and manage the expectations. After hiring, orientation programs could be designed to inform new employees about organization\u0026rsquo;s rules, procedures and values so that they could develop more realistic expectations and internalize organization\u0026rsquo;s culture and value system.\u003c/p\u003e \u003cp\u003ePreviously believed to be the property of the \"gifted\" (Garmezy, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), dimensions of psychological capital, self-efficacy, optimism, hope and resilience, now have empirical support that they may be developed (Masten \u0026amp; Reed, 2002). Given the strong association between PsyCap and employee wellbeing, organizations are advised to provide various training programs to help employees develop their psychological capital. For instance, managers could implement \u0026ldquo;Positive Psychological Micro Coaching\u0026rdquo; programs (see Corbu et al., 2021 for details) that aim to raise employees\u0026rsquo; self-efficacy and resiliency. Employees could be trained to improve their PsyCap by providing lectures on positive psychology, creating awareness about their personal resources and enabling them to set and reach the goals.\u003c/p\u003e \u003cp\u003ePage and Vella-Brodrick (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) have argued that employee well-being is an important precursor to organizational well-being, as indicated by its links to employee turnover and performance. Nevertheless, previous studies (e.g. Abdullah et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; DiMaria et al.; \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Krekel et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have already demonstrated the positive impact of wellbeing on desirable work-related outcomes such as job satisfaction, productivity and higher performance. Therefore, employees\u0026rsquo; wellbeing could also be improved by several primary, secondary, and tertiary interventions. Primary interventions might concentrate on inside of the organization to reduce or eliminate workplace stressors. Secondary interventions might focus on altering an individual's perception of or response to a stressor, while tertiary interventions might rehabilitate people who are experiencing stress in response to stressors (Tetrick and Quick, 2011). To this end, managers could provide employees mindfulness trainings, enable them trying something new or give them challenging tasks that they will enjoy achieving and organize activities that employees can develop sense of community. In conclusion, if managers want to reduce the turnover intention, organizational interventions should be carefully designed to match the values of the employees with the organization, to establish mutually desirable long-term relationships between the organization and the employee, and enhance the wellbeing of the employee.\u003c/p\u003e \u003c/div\u003e"},{"header":"Limitations and Future Research Directions","content":"\u003cp\u003eNotwithstanding its contributions to the field, this study has a number of limitations that must be acknowledged. The first limitation arises from the use of self-report measures. Several responses biases such as social desirability, acquiescence or dissent biases might have affected participant\u0026rsquo;s answers, threatening the validity and reliability of the results. Therefore, in future studies, it is recommended to collect data using different methods such as observation and interview, to prevent the possible confounding effects of self-reporting.\u003c/p\u003e \u003cp\u003eSecond limitation is related to the cross-sectional research design. Since data were collected at one point in time, it is not possible to make causal inferences and ensure the directionality between study variables. Hence, researchers are advised to use longitudinal research designs to test the hypothesized relationships in the future.\u003c/p\u003e \u003cp\u003eThe third limitation arises from the nature and size of the sample. This study was conducted with Turkish white-collar employees, working in private sector. The results could be affected by differences resulting from the sector, organization, employee status (white or blue-collar) or even the socio-economic and cultural characteristics of the country. Thus, examining the proposed connections with a different sample in a different industry or even in a different nation may offer more detailed and diverse perspectives on wellbeing, turnover intention, and areas of work-life links. Finally, collecting data from a larger sample may increase the robustness and generalizability of the results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;This study was approved by the Ethics Committee of Hacettepe University Senate (approval date: January 12, 2021-numbered E-35853172-300-00001403022). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee. Informed consent was obtained from all participants prior to data collection\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Participants were fully informed about the purpose of the study, their right to withdraw at any time, and the confidentiality of their responses. Written informed consent was obtained from all participants.\u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available upon the request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding received for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conception and design of the study. HD and OTE were responsible for writing the original draft and methodology, HD conducted the investigation, OTE took charge of data curation, and OTE worked on writing review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is derived from the doctoral dissertation of Hilal Demirel, completed at Hacettepe University, Department of Business Administration, under the supervision of Ozge Tayfur Ekmekci.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas M, Raja U, Darr W, Bouckenooghe D. Combined effects of perceived politics and psychological capital on job satisfaction, turnover intentions, and performance. J Manage. 2014;40(7):1813\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah MI, Huang D, Sarfraz M, Ivascu L, Riaz A. 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Int J Intercult Relat. 2002;26(5):525\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellbeing Research Centre. University of Oxford, Gallup \u0026amp; UN SDSN. World Happiness Report 2025 [Internet]. 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldhappiness.report/\u003c/span\u003e\u003cspan address=\"https://worldhappiness.report/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright TA. To be or not to be [happy]: the role of employee wellbeing. Acad Manage Perspect. 2006;20(3):118\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYim HY, Seo HJ, Cho Y, Kim J. Mediating role of psychological capital in relationship between occupational stress and turnover intention among nurses at veterans administration hospitals in Korea. Asian Nurs Res. 2017;11(1):6\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X, Zhu W, Zhao H, Zhang C. Employee well-being in organizations: theoretical model, scale development, and cross-cultural validation. J Organ Behav. 2015;36(5):621\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable I. Nested Model Comparison of Measurement Models for AWS scale\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eModel /Model Comparison Tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e /df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eOriginal Six-Factor Model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e998.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eFive-factor Model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e1124.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eFour-factor model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e1196.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eThree-factor model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e1824.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e5..26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eTwo-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e2404.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eOne-factor model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e2648.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eSix-factor vs five-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e126.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eFive-factor vs four-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e71.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eFour-factor vs three-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e627.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eThree-factor vs two-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e580.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8163%;\"\u003e\n \u003cp\u003eTwo-factor vs one-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e243.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: CFI: Confirmatory Factor Analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable II.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDescriptive Statistics and Inter-Correlations among Variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1024\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1. Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e2. Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e3. Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e4. Marital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5. Partner Employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6. # of Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,70**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e7. Tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8. Workload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e9. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e10. \u0026nbsp; \u0026nbsp; Rewards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e11. Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e12. Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,62**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e13. Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e-0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e14. Psy. Cap.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e15. Turnover Int.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0,18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0,36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e16. Emp. Wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0,42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"19\" valign=\"top\" style=\"width: 1024px;\"\u003e\n \u003cp\u003eNote:*p\u0026lt;0.01; **p\u0026lt;0.05\u003cins cite=\"mailto:MSI\" datetime=\"2023-08-04T10:15\"\u003e.\u0026nbsp;\u003c/ins\u003eTunover Int: Turnover Intention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable III. The Moderating Role of Psychological Capital on the Linkages between AWS Dimensions and Employee Wellbeing\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eSteps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.14\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 0.14**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eNumber of Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eWorkload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eRewards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.24**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eFairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 0.16**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003ePsy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.47**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eWorkload*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eControl*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eRewards*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eCommunity*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eFairness*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eValues* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003eF change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;4.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;70.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn the second step, the main effect of community was excluded from the model. In the third step the main effect of community and interaction effect of fairness and psychological capital were excluded from the model.\u003c/p\u003e\n\u003cp\u003e*p\u0026lt;0.01; **p\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable IV. The Moderating Role of Psychological Capital on the Linkages between AWS Dimensions and Turnover Intention\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eSteps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eNumber of Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;-0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.20**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePartner Employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eTenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.12*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eWorkload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eRewards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.15*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eFairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.26**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; -0.26**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePsy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eWorkload* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eControl* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eRewards* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCommunity* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eFairness*Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eValues* Psy.Cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 8.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 12.3**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: In the second step, the main effect of community was excluded from the model. In the third step the main effect of community and interaction effect of fairness and psychological capital were excluded from the model.\u003c/p\u003e\n\u003cp\u003e*p\u0026lt;0.01; **p\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable V. Results of Supplementary Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eE. Wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTurnover Intention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorkload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorkload*PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eControl *PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReward *PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity *PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFairness *PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValues *PsyCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: *: p\u0026lt;0.05; **: p \u0026lt;0.01\u003c/p\u003e\n\u003cp\u003e1. The unstandardized regression weights of PsyCap on employee wellbeing ranged from 0.77 to 0.99 and were significant. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. The unstandardized regression weights of PsyCap on employee wellbeing ranged from -0.01 to 0.06 and were insignificant. \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"areas of work-life, psychological capital, employee wellbeing, turnover intention","lastPublishedDoi":"10.21203/rs.3.rs-9059165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9059165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAreas of work-life (AoWL) \u0026mdash; comprising workload, control, rewards, community, fairness and values \u0026mdash; represent key organisational dimensions that shape employee attitudes and behaviours. Despite growing interest in employee wellbeing and retention, the boundary conditions under which AoWL dimensions influence these outcomes remain underexplored. This study examines the effects of AoWL on employee wellbeing and turnover intention, and investigates the moderating role of psychological capital (PsyCap) in these relationships.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey design was employed with a sample of 369 white-collar employees from T\u0026uuml;rkiye. Hierarchical regression analysis was used to test the direct effects of AoWL dimensions on employee wellbeing and turnover intention, as well as the moderating role of PsyCap. Demographic variables including number of children and organisational tenure were controlled in the analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the six AoWL dimensions, only rewards and values significantly and positively predicted employee wellbeing. PsyCap demonstrated a significant positive main effect on employee wellbeing; however, its moderating effect on the AoWL\u0026ndash;wellbeing relationship was non-significant. Perceived fit on rewards and values negatively predicted turnover intention, while neither the main nor moderating effects of PsyCap on turnover intention reached significance. Number of children and tenure were negatively associated with turnover intention.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRewards and values emerge as the most critical AoWL dimensions for fostering employee wellbeing and reducing turnover intention among white-collar employees. PsyCap functions as an independent personal resource rather than a buffer in this context. These findings offer practical guidance for human resource management practitioners seeking to design organizational environments that enhance wellbeing and reduce voluntary turnover.\u003c/p\u003e","manuscriptTitle":"The Moderating Role of Psychological Capital on the Impact of Areas of Worklife on Employee Well-being and Turnover Intentions: A Field Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 08:17:16","doi":"10.21203/rs.3.rs-9059165/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":"570508ea-43f8-4eb7-ad0f-c9f2186db66b","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T07:57:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 08:17:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9059165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9059165","identity":"rs-9059165","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}