# Managing job performance, social support and work-life conflict to reduce workplace stress

Tommy Foy (University of Limerick, Limerick, Ireland)
Rocky J. Dwyer (College of Management and Technology, Walden University, Minneapolis, Minnesota, USA)
Roy Nafarrete (College of Management and Technology, Walden University, Minneapolis, Minnesota, USA)
Mohamad Saleh Saleh Hammoud (College of Management and Technology, Walden University, Minneapolis, Minnesota, USA)
Pat Rockett (University of Limerick, Limerick, Ireland)

ISSN: 1741-0401

Publication date: 8 July 2019

## Abstract

### Purpose

Workplace stress costs £3.7bn per annum in the UK and in excess of $300bn per annum in the USA. The purpose of this paper is to examine the existence, strength and direction of relationships between perceptions of social support, work–life conflict, job performance and workplace stress in an Irish higher education institution. ### Design/methodology/approach The selected theoretical framework consisted of a combination of reward imbalance theory, expectancy theory and equity theory. An organizational stress screening survey instrument was used to survey the staff (n = 1,420) of an academic institution. Multiple linear regression analysis was used to evaluate the relationships between the independent variables (social support, work–life conflict, job performance), the covariates (staff category, direct reports, age, gender), and the dependent variable (workplace stress). ### Findings The results showed a negative correlation between social support and workplace stress, a positive correlation between work–life conflict and workplace stress, and a negative correlation between job performance and workplace stress (p < 0.05). The results also revealed significant relationships between the covariates direct reports and gender and the dependent variable workplace stress. ### Practical implications The findings from this research can trigger an organizational approach where educational leaders can enable workplace change by developing and implementing social support and work–life strategies, and potential pathways to reduce levels of workplace stress and improve quality of life for employees and enhance performance. ### Originality/value The examination and establishment of particular relationships between social support, work–life conflict and job performance with workplace stress is significant for managers. ## Keywords #### Citation Foy, T., Dwyer, R.J., Nafarrete, R., Hammoud, M.S.S. and Rockett, P. (2019), "Managing job performance, social support and work-life conflict to reduce workplace stress", International Journal of Productivity and Performance Management, Vol. 68 No. 6, pp. 1018-1041. https://doi.org/10.1108/IJPPM-03-2017-0061 Download as .RIS ### Publisher : Emerald Publishing Limited Copyright © 2019, Emerald Publishing Limited ## Introduction Collectively, business leaders are spending billions of dollars annually related to employee lost time claims, increased health costs and decreased employee productivity due to work-related stress (Burton et al., 2012; Nasr, 2012; Spurgeon et al., 2012). Stressful workplaces result in: “employee tardiness; absenteeism; low productivity; high employee turnover; wasted investment in training; increased costs due to training replacements for sick leave; depression; aggression; and violence” (Foy, 2015, p. 23; Khoury et al., 2010, p. 487). Dealing with workplace stress makes good business sense because lowering stress levels can: reduce absenteeism; improve job satisfaction; increase productivity; enhance the organization’s image; and improve performance outcome satisfaction (Kobussen et al., 2014; Swayze and Burke, 2013). Workplace stress is one of the most significant problems facing leaders of organizations across the European Union (Kelloway et al., 2012). Between 1970 and 2010, researchers found that workplace stress had detrimental consequences for productivity and employee well-being (Billing et al., 2014). Compared to employees with normal levels of stress, employees with high levels of stress cost organizations more, are less productive, and are more likely to suffer from conditions such as: cardiovascular disease; obesity; cancer; diabetes; depression and anxiety; and musculoskeletal disorder (Wolever et al., 2012). Furthermore, long-term workplace stressors cause more acute mental and physical health problems than short-term workplace stressors (Dhabhar, 2014). Workplace stress can incur a significant emotional cost to employee well-being and a substantial economic cost to organizational performance (Kelloway et al., 2012). The Higher Education Institute (HEI) which is the subject of this study has seen an increase in absenteeism due to workplace stress. The reported average absenteeism rate for the HEI is 5 percent which is above the national average of 4 percent (Department of Public Expenditure and Reform, 2017). The pay budget for the HEI is €125m, therefore, the costs to the institution which includes replacement costs for staff on sick leave is close to €10m. These costs do not reflect lower performance due to the disruption caused by absenteeism, lower performance due to workplace stress, training costs for staff replacements and disruptive behavior caused by workplace stress. The HEI is dependent on people to deliver its services as an education provider, therefore, absenteeism due to workplace stress is a major issue for the HEI. In total, 70 percent of the overall budget for the HEI is consumed in payroll costs. The specific business problem is that some educational leaders do not have sufficient information about the relationships between social support, work–life conflict, job performance, and workplace stress to address the potential consequences for productivity, costs, and profits (Ipsen and Jensen, 2012; Wang et al., 2013). Since 1980, human resources practitioners, occupational health physicians, professionals and managers in many types of organizations have placed a significant focus on workplace stress because of the effects it has on productivity (Biron and Karanika-Murray, 2014; Gachter et al., 2011; Kossek et al., 2011; Pridgeon and Whitehead, 2013). Globalization, innovation in technology, increased competition, work intensification, and workforce diversification have all led to increased pressure and stress in the workplace (Kalliath and Kalliath, 2012). Workplace stress has increased continually since the mid-1980s and creates a significant burden for organizations through direct and indirect costs such as: lost workdays; lower productivity; high turnover rates; increased staffing; and health benefit costs (Walinga and Rowe, 2013). Workplace stress in the UK costs employers £3.7bn per annum; in the USA, the cost exceeds$300bn per annum (Spurgeon et al., 2012). Organizational leaders must intervene to: ensure a healthy workforce; increase productivity; remove inefficiencies; lower costs; and encourage behaviors that will contribute positively to the social-psychological environment of the workplace (Karam, 2011). Although researchers have examined a number of issues that give rise to workplace stress, social support and work–life conflict and their impact on workplace stress have remained an underdeveloped topic (Fernandes and Tewari, 2012; Jain et al., 2013; Kossek et al., 2011; Pridgeon and Whitehead, 2013).

In 2012, 95 million Americans acquired antistress medications (Nasr, 2012). In the UK, employers lose 9.1 million workdays each year, at a cost of £3.7bn, because of workplace stress, and in the USA, the cost of workplace stress exceeds \$300bn per annum (Spurgeon et al., 2012). The general business problem is that excessive workplace stress results in: lower productivity; increased costs; and lower profits (Avey et al., 2012; Bucurean and Costin, 2011; Burton et al., 2012; Leung et al., 2011; Sinha and Subramanian, 2012).

The structure, hierarchy, resource and work allocations in organizations determine the nature of relationships in the workplace (Rockett et al., 2017). Workplace stress can derive from specific aspects of work, such as job demands, excessive workload, and role ambiguity, or from social factors, such as poor leadership and feeling unappreciated or undervalued (Spurgeon et al., 2012). In effort–reward imbalance (ERI) theory, social reciprocity and social exchange reflect the norm of return expectancy in which separate rewards reciprocate efforts (Ganster and Perrewe, 2011). Reciprocity leads to positive emotions that promote positive health and well-being (Parker, 2014), whereas failure to reciprocate leads to negative emotions and sustained stress (Siegrist, 2001). The central concept of ERI theory relates to the existence of an imbalance between perceived effort (job demands) and reward (Ganster and Perrewe, 2011; Siegrist, 2001). Foy (2015, p. 24) noted “Researchers using the ERI model have found that employees who demonstrate unreciprocated high effort over a prolonged period can become ill” a point also noted by Hyvonen et al. (2011). High ERI can involve low heart rate variability, which may lead to a higher risk of heart disease (Uusitalo et al., 2011). Foy (2015) reinforced van Scheppingen et al.’s (2013) perspective that workplace stress can have a negative impact on an individual’s physical and mental well-being. When leaders function in a knowledge economy, they should view health as a strategic asset since having a good mental health can be a source of innovation and creativity.

## Job performance

Workplace stress is a major issue for organizational leaders because of its significant economic implications and impact on productivity, organizational performance, and the health and well-being of employees (Bucurean and Costin, 2011; Leung et al., 2011). Unrealistic demands, lack of resources and constraints on employees lead to stressful workplaces and can negatively affect performance (Sinha and Subramanian, 2012). Prolonged exposure to workplace stress will negatively affect job performance by reducing interest in work activities and initiatives and can lead to physical ill health and psychological symptoms of distress (Spurgeon et al., 2012). Conversely, regular interactions between managers and employees have a direct positive effect on employee work output (Evers et al., 2014). Leaders of high-performing organizations foster and nurture a climate of social interaction where managers and team members embrace meaningful engagement and team members participate in organizational activities and decision-making processes (Abugre, 2012).

Foy (2015, p. 34) noted that “organizations whose leaders embrace, and value employee engagement perform much better than organizations whose leaders do not.” Engaged leadership also leads to better performance (Fearon et al., 2013). Research indicated that a lack of management recognition for employee effort leads to high ERI (Olejniczak and Salmon, 2014). Workplace stressors have a negative impact on staff motivation and job performance (Adaramola, 2012; Avey et al., 2012; Hancock and Page 2013; Solanki, 2013). Increased workplace stress leads to reduced productivity and performance, and increased job satisfaction leads to increased productivity and performance (Evers et al., 2014; Kobussen et al., 2014). However, stress has both negative and positive effects on performance. Too little stress can lead to boredom and lack of concentration, initiative and motivation (Leung et al., 2011), whereas positive stress, or eustress, can lead to higher levels of performance and productivity (Adaramola, 2012; Avey et al., 2012). The presence of eustress can help employees to maintain: attentiveness; focus; stimulation; and enthusiasm up to a certain point (Avey et al., 2012). Savage and Torgler (2012) found that negative stress has a more significant impact on performance than eustress.

Stressful working conditions have a connection with job performance, and the psychological, physiological, and behavioral outcomes associated with stressful workplace environments may elucidate such conditions (Noblet et al., 2012). Employees in organizations that struggle to survive often experience stressful work environments and potential job insecurity in such organizations can negatively affect employee well-being and job performance (Schreurs et al., 2012). Employees who perceive the workplace to be stressful if the demands for performance are greater than the tools, resources and skills available to them to do the job will feel unrewarded for their efforts, which can lead to perceptions of high ERI (Noblet et al., 2012; Olejniczak and Salmon, 2014; Sinha and Subramanian, 2012). Employees whose jobs are not secure or who find themselves in other stressful workplace situations where performance cannot meet demands can experience symptoms such as: anxiety; hostility; depression; negative attitudes; increased blood pressure; and respiratory problems, which can lead to significantly lower levels of performance (Noblet et al., 2012; Schreurs et al., 2012). Developing new: products; practices; services; processes; and procedures requires creativity and innovation. These qualities are essential for the survival and sustainability of organizations in a rapidly changing global environment (Domínguez, 2013).

From a pragmatic view, when job performance is impacted; and the situation is not managed, it has the potential to create or lead to workplace stress (Dwamena, 2012). In fact, there is ample research to support that job performance impacts workplace stress (Gharib et al., 2016; Goswami, 2015; Mansour and Elmorsey, 2016). In addition, these authors suggest that workplace stress can be a predictor of job performance; and alternately, job performance can be a predictor of workplace stress, which is the premise of what this paper examines.

The following research question and the corresponding hypotheses guided this research:

RQ1.

What is the relationship between employees’ perceptions of job performance and workplace stress while controlling for staff category, direct reports, age and gender in an Irish HEI?

H10.

There is no relationship between the independent variable of job performance, and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

H1a.

There is a relationship between the independent variable of job performance, and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

## Social support

The exploration of social support and its relationship to workplace stress remains an underdeveloped topic (Gachter et al., 2011; Kossek et al., 2011; Pridgeon and Whitehead, 2013). Social support is a critical feature of the workplace because good relationships are necessary between employees and between employees and leadership (Chandra, 2012). Social support refers to an individual’s belief that he or she is: valued; informed; communicated with; emotionally cared for; and part of a relationship group or network (Fernandes and Tewari, 2012). Social support is critical in most contexts in organizational life. In particular, support from leadership and coworkers has a positive impact on well-being; employees who feel supported feel less stressed and believe themselves fairly rewarded for their efforts (Demerouti et al., 2014; Fischer and Martinez, 2013; Thi Giang et al., 2013).

The provision of social support can be one of the most important ways of promoting psychological well-being and buffering the negative impact of workplace stress (Fernandes and Tewari, 2012; Jamal, 2013). Social support represents the robust social networks available to staff through: colleagues; managers; friends; and employee assistance programs to help staff cope with workplace stressors (Nair and Xavier, 2012; Walinga and Rowe, 2013). Employees with robust social support at work are better able to cope with stressful workplaces and are more effective at coping with stress (Ladegård, 2011). Coworkers who have a positive disposition and are emotionally supportive have a positive impact on performance and act as an effective buffer for stress (Smith et al., 2012). An employee has a greater chance of coping with very stressful situations if family and coworkers are well-disposed to supporting the individual (Lopez, 2011). In fact, social support from coworkers can be an effective mechanism for shielding employees from the negative effects of work stressors (Schreurs et al., 2012). When strong networks of coworkers support employees, greater dynamism, bonds and flourishing within the networks or groups in which they operate will ensue (Smith et al., 2012).

Workplace stress can be a by-product of work-related activities but can also be a symptom of the absence of social support (Boscolo et al., 2012). Employees with high psychological demands, limited job control and minimal leadership or coworker support are at risk of developing poor health (DeTienne et al., 2012). Employees with supportive coworkers with whom they have positive relationships run a 5 percent lower risk of misusing alcohol, which can be a consequence of workplace stress (Saade and Marchand, 2013). Stress arises from a misalignment between the individual and the work environment and employees cannot avoid becoming stressed because the environment is usually beyond the control of the individual (Kavitha, 2012).

Interpersonal counterproductive work behaviors such as workplace bullying; harassment; and aggression by leaders, coworkers or other employees are recent phenomena with regard to workplace stress (Tetrick and Campbell-Quick, 2011). Bullying refers to repeated inappropriate behavior that a person directs at one or more employees and that is unwanted by the victim because it causes humiliation, offense, or distress and leads to a poor work environment (Tambur and Vadi, 2012). Researchers have associated bullying and interpersonal conflicts with more frequent instances of illness and absenteeism and reduced job satisfaction, efficiency, and productivity, all of which negatively affect employees’ perceptions of equitable treatment and self-esteem (Kobussen et al., 2014; Mikkelsen et al., 2011). Karam (2011) noted that employees working under conditions of conflict or conflict-related stress continue to put in extra effort and help their coworkers and the organization to achieve their goals.

Bullying and conflict are as Foy (2015, p. 47) stated “symptoms of modern organizational life”, where: uncontrollable workloads; ineffective communication; inferior conflict management; inadequate work organization; Unreasonable monitoring; toxic leadership styles; organizational change; and inappropriate work assignments can lead to a complicated work environment and evaluated ERI (Almadi et al., 2013; Feldt et al., 2013; Fischer and Martinez, 2013; Kalliath and Kalliath, 2012; Tambur and Vadi, 2012). Organizational change that gives rise to greater psychological demands can have a adverse impact on employees’ mental health within a short time frame (Smith and Bielecky, 2012).

The following research question and the corresponding hypotheses guided this research:

RQ2.

What is the relationship between employees’ perceptions of social support and workplace stress while controlling for staff category, direct reports, age and gender in an Irish HEI?

H20.

There is no relationship between the independent variable of social support and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

H2a.

There is a relationship between the independent variable of social support, and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

## Work–life conflict

Everyday, individuals engage in many divergent roles that come with different responsibilities and challenges, which can lead to work–life conflict (Cheng and McCarthy, 2013). Work–life conflict does not have to be about one having supremacy over the other; work–life conflict can be about how work and non-work responsibilities can coexist in harmony (Lisson et al., 2013). As Foy (2015) and others (Jang et al., 2011; Murphy and Doherty, 2011) have noted, the relationship between work and life is, for example: family friendly; balanced; conflicted; and flexible. Individuals have limited time, energy and resources to deal with their multiple responsibilities; at times, one role can spill over into the other, which gives rise to conflict and high ERI (Cheng and McCarthy, 2013). Psychological capital relates to employee well-being, such as when individuals cognitively appraise stressful situations and adapt positively by maintaining resources (Avey et al., 2011; Luthans et al., 2013).

Individuals need symmetry between work and home life; when work responsibilities collide with an individual’s personal life, stress levels rise and productivity decreases (Evers et al., 2014). Work–life conflict can lead to negative consequences, such as: conflict; interference; interruptions; negative spillover; and high ERI (Carlson et al., 2011). Neither employees nor employers benefit from such an outcome. An examination of work–life conflict included the ability of employees to manage the many different aspects of their lives (Demerouti et al., 2014). The main aspects of work–life conflict to consider are: time for work and non-work activities; satisfaction gained from work and non-work activities; and psychological involvement in work and non-work activities (Demerouti et al., 2014). Employees need to manage work–life conflict in these three areas to reduce tension and maintain well-being. Work–life conflicts that originate in the workplace have a significantly greater negative impact on work satisfaction than on non-work satisfaction and vice versa (Amstad et al., 2011; Shockley and Singla, 2011).

As Foy (2015, p. 56) noted “leaders who are proactive in coping with work–life conflict using work–life policies and strategies create a positive work environment.” Specific benefits include: employee–company loyalty; a positive attitude among employees; enhanced employee well-being; reduced stress levels in the workplace; and reduced burnout (Bell, 2013). Work–life initiatives can lead to: facilitation; enhancement; enrichment; and positive spillover (Grawitch et al., 2013). Work–life policies and strategies are important in organizational life because of their benefits to both employees and employers (Sánchez-Vidal et al., 2012). Workplace initiatives that assist with work–life conflict include: flexible working hours; alternative working arrangements; atypical work arrangements; paid or unpaid leave; and access to care and support services (Demerouti et al., 2014). The focus of work–life conflict initiatives is structural and cultural support for employees; such initiatives include: job design; job sharing; teleworking; virtual arrangements; reduced workloads; absenteeism policies; child-care assistance; social support; and line manager support (Kossek et al., 2014).

The following research question and the corresponding hypotheses guided this research:

RQ3.

What is the relationship between employees’ perceptions work–life conflict and workplace stress while controlling for staff category, direct reports, age and gender in an Irish HEI?

H30.

There is no relationship between the independent variable of work–life conflict and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

H3a.

There is a relationship between the independent variable of work–life conflict and workplace stress while controlling for staff category, direct reports, age and gender.

## Theoretical framework

For this quantitative correlational research, ERI theory, expectation theory and equity theory formed the theoretical framework. Self-regulation is important for health and well-being and is dependent on successful social exchange (Siegrist, 2001). Since the purpose of this study was to examine the relationship between perceptions of social support, work–life conflict, job performance and workplace stress, the social reciprocity and social exchange principles inherent in ERI theory made this theory appropriate. Social reciprocity and social exchange reflect the norm of return expectancy in which separate rewards reciprocate efforts (Ganster and Perrewe, 2011). Failure to reciprocate this norm will lead to negative emotions and sustained stress. However, reciprocity will lead to positive emotions that will promote positive health and well-being (Parker, 2014). Based on the principles of ERI theory, a lack of reciprocity between costs and gains elicits negative emotions with a propensity to sustained autonomic and neuroendocrine activation (Siegrist, 1996).

Researchers using the ERI model have directly linked ERI with negative impacts for health (Olejniczak and Salmon, 2014). Employees often feel that leaders and managers do not reward them adequately for their efforts by way of: salary; promotion; esteem; and job security (Hyvonen et al., 2011; Olejniczak and Salmon, 2014). Little or no reciprocity leads to negative emotions and an increased risk of ill health as a consequence of increased stress (Hyvonen et al., 2011; Olejniczak and Salmon, 2014; Uusitalo et al., 2011). Researchers associated high ERI with employees who believe they receive a poor reward for their efforts (Hyvonen et al., 2011). In contrast, researchers associated low ERI with employees who believe they receive a fair reward for their efforts (Allisey et al., 2012). Employees with high ERI are more susceptible to stress and illness and have higher burnout and slower recovery rates than employees with low ERI (Feldt et al., 2013).

Vroom (1964) proposed expectancy theory to explain the decision-making process of individuals based on behavioral alternatives. Abadi et al. (2011) and Manolova et al. (2012) expressed expectancy theory as follows:

Motivation force = Expectancy × Instrumentality × Valence .

Because expectancy theory is a useful framework for assessing, interpreting and evaluating employee behavior in relation to attitude formation and decision making (Nasri, 2012), expectancy theory was a useful tool for examining aspects of workplace stress. Expectancy refers to the probability that effort will lead to good performance, instrumentality refers to the expectation that good performance will lead to preferred outcomes, and valence refers to the value individuals place on rewards (Abadi et al., 2011). Not having an expectation that management will recognize the efforts of members of the workforce will negatively affect the workforce and the organization as a whole (Branham, 2012). For optimal organizational performance, all members of staff should expect that their employers will recognize their efforts. Leaders who neither recognize effort nor reward employees fairly or who set expectations too high can create unfavorable situations, staff dissatisfaction and higher levels of stress (Sinha and Subramanian, 2012).

Adams (1963) developed equity theory in 1963 to explain the motivation of individuals in the context of their perceptions of the extent to which all individuals in the organization receive fair treatment by management (Kivimäki, 2014; Skiba and Rosenberg, 2011). Al-Zawahreh and Al-Madi (2012) noted that organizational leaders should consider equity theory in processes such as promotion, recognition and development. The many structural, procedural and cultural changes experienced by employees in public sector organizations as a result of greater managerialism result in increased levels of workplace stress (Rodwell et al., 2011). Through equity theory, Adams provided insight into how individuals view their recognition relative to their contribution when comparing themselves to others. Loughlin et al. (2012) noted there is growing evidence that the same organizational behavior by male and female leaders does not lead to the same results. Inequity will result in individuals becoming less committed and demonstrating less effort (Skiba and Rosenberg, 2011). Organizational leaders expend much time, effort and resources in developing their workforces. Leaders of organizations also promote self-management and autonomy as the binding force of teamwork (Al-Zawahreh and Al-Madi, 2012). Inequity will: lead to dissatisfaction and anger; disrupt teamwork; create inefficiencies; and alienate groups who feel aggrieved (Al-Zawahreh and Al-Madi, 2012). Leaders who understand equity theory will also recognize the sources and signs of stress in the workplace.

Employees have expectancies when they engage in relationships, and the degree of equity in a relationship affects the outcomes of the relationship (Ganster and Perrewe, 2011). Employees expect a reward for their perceived contributions to the business, which translates into a contribution–reward ratio (Estes, 2011; Kobussen et al., 2014; Wei et al., 2012). Perceived equity in the contribution–reward ratio relative to peers depends on individuals’ perception of the value of contribution by their peers as opposed to actual contribution (Kobussen et al., 2014). Employees who have a high perception of contribution can also have a high expectation for reward and an expectation for greater reward than their peers (Estes, 2011; Kobussen et al., 2014).

## Methodology

The participants for this paper were the full-time and part-time academic, research and support staff of an Irish HEI. A data use agreement with the subject institution, provided access to the employee population through the subject institution’s standard operations, including access to institutional data sets. The institution’s staff included: academic staff (teaching assistants, lecturers, senior lecturers and professors); researcher staff (research assistants, postdocs, research fellows and senior research fellows); and support staff (leaders, managers, information technology professionals, librarians, administrators, laboratory technicians, grounds staff and catering staff). Participants of the study voluntarily completed the confidential online survey.

Finally, institutional leadership authorized the researchers to contact staff regarding the survey via the institution’s e-mail system. In addition, the president of the selected institution e-mailed all members of staff to request that they participate in the survey because the findings of the research could potentially help the institution’s leaders to cope proactively with workplace stress. Following the president’s e-mail, I sent e-mails to all members of staff inviting them to participate in the survey and providing them with a link to the survey (ASSET). We used the survey’s landing page to provide participants with answers to frequently asked questions about the nature and purpose of the study, to provide survey participants with assurances that their responses to the survey would remain anonymous and confidential, and to advise them that submitting their responses meant that they were giving their informed consent to participate. In agreement with the leadership of the selected institution, the survey remained open to participation for a three week period.

The paper employed a G*Power analysis tool to calculate the sample size as suggested by Faul et al. (2009). Using a two-tailed test for G*Power’s multiple regression random effects model, determined a minimum sample size of 92 participants to detect a coefficient of determination (R2) of 0.3, an α level of 0.05, 15 predictor variables, an effect size (f2) of 0.02, and a desired power of 0.95. For multiple regression linear models, where f2 is the effect size measure, Cohen (1992) suggested that f2 values of 0.02, 0.15, and 0.35 represent small, medium, and large effect sizes, respectively. Given the relationship between f2 and R2, the values for R2 (for small, medium and large standardized effect sizes) are, respectively, 0.0196, 0.1304, and 0.2592, and for R, 0.14, 0.36, and 0.51 (Cohen, 1992).

As identified by Foy (2015, p. 88) “Sheehan and McMillan (1999) reported that response rates for online surveys in HEIs are good (47.2 percent).” The literature was reviewed to confirm sample size standards and to determine the number of potential survey respondents required to obtain a minimum sample size of 92 required (Foy, 2015). Based on a review of the literature (Sheehan and McMillan, 1999) a survey distribution to at least 195 respondents were necessary to achieve the required sample size of 92. Thus, with a staff population of 1,420 potential participants, Foy (2015) determined the numbers were sufficient to achieve the desired sample size. All full-time and part-time academic, research and support staff were eligible to participate in the survey; all staff had access to work computers, which meant that participants were able to participate in the survey if they chose to do so.

Foy (2015, p. 89) determined “the results of the survey may be of interest and assistance to leaders of other institutions, both national and international, who wish to understand and manage issues related to workplace stress,” Foy (2015, p. 89) stated, it was not his “intention was not to generalize the results across other institutions.”

Finally, the management of the selected institution contracted the owners of the ASSET survey, Robertson Cooper Ltd, to administer the survey on behalf of the institution. All members of staff of the participating institution were sent an e-mail that included a link to the ASSET survey and an invitation to participate. The survey included questions on: demographics; perceived job performance; perceived coworker support; perceived leadership support; perceived work–life conflict; and perceived workplace stressors. A representative of Robertson Cooper Ltd sent the survey responses in anonymized format, thereby removing any risk of a breach in confidentiality and anonymity.

Multiple linear regression analysis was used to evaluate the relationships between the independent variables (social support, work–life conflict, job performance), the covariates (staff category, direct reports, age, gender), and the dependent variable (workplace stress). ASSET (Cartwright and Cooper, 2002) was the survey instrument used to collect data regarding the independent variables: social support; work–life conflict; job performance; and the dependent variable workplace stress. Developed with an occupational orientation, ASSET provides researchers with a robust and psychometrically tested instrument with which to diagnose work-related stress (American Psychological Association, 2014). ASSET was used to measure potential exposure to stress with respect to a range of common workplace stressors. The results from ASSET produced important information on levels of: physical health; psychological well-being; work-life conflict; workplace stressors; work-life conflict; social support; staff category; direct reports; age; and gender.

In relation to employees’ perceptions of their own job performance, ASSET measured this variable by means of a self-reported item on the extent to which individuals felt productive in their job over the previous three months (Donald et al., 2005). Measuring perceived job performance includes an 11-point scale ranging in steps of 10 from 100 percent productive to 0–9 percent productive, which is an objective and valid measure of productivity (Donald et al., 2005). Multiple linear regression was used to examine the relationships between the dependent variable, the covariates and the independent variables. The confidential ASSET survey instrument designed by Cartwright and Cooper (2002) was used to survey the entire population (n = 1,420) of academic, research and support staff of an Irish HEI. In total, 678 members of staff responded to the ASSET survey. This equates to a 48 percent response rate of the total population (n = 1,420).

The consistent replication of results indicates the reliability of the measurement instrument. As Foy (2015, p. 94) noted “Cartwright and Cooper (2002) used the Guttman split-half coefficient to determine the reliability of the ASSET instrument. ASSET coefficients ranged from 0.60 to 0.91, with all but two factors returning coefficients more than 0.70.” Johnson and Cooper (2003) found that the Psychological Well-Being subscale has good convergent validity with the General Health Questionnaire, which is an existing measure of psychiatric disorders (Goldberg and Williams, 1988). Tytherleigh (2003) used ASSET as an outcome measure of job satisfaction in a nationwide study of occupational stress levels in 14 English HEIs. Tytherleigh computed a series of Cronbach’s α on each of the questions for the five ASSET subscales to assess the reliability of the ASSET survey instrument. The values ranged from 0.64 to 0.94, which indicates good internal consistency reliability. Internal consistency is a common indicator of reliability in research, as it shows the degree to which items in a scale measure the same construct. The internal consistency coefficient α for ASSET are in Table I. Internal consistencies for the scales range from 0.71 to 0.92.

Straub et al. (2004) noted that the predictive validity technique serves the practitioner community well because it predicts given outcomes based on measures posited for constructs. Therefore, the predictive validity technique is an appropriate technique for practitioners and for a research project on business problems. ASSET has an established set of norms from a database of responses from 100,000 workers in public and private sector organizations in the UK.

ASSET presents scores in sten format. A sten is a standardized score based on a scale of 1 to 10, with a mean of 5.5 and a standard deviation of 2. Researchers use the sten system to make meaningful comparisons with the norm group. Most people (68 percent) scored between sten 4 and sten 7. Scores that fall further from the mean are more extreme. Approximately 16 percent of people score at the low end, and another 16 percent score at the high end. Figure 1 included an outline of the statistical validity of the ASSET instrument (Robertson Cooper, 2014).

Face validity refers to people’s perceptions of a test’s validity (Howitt and Cramer, 2011). Face validity represents the extent to which a measure looks like it measures what it purports to measure (Cohen et al., 2013). Face validity is an important concept because it can determine the extent to which respondents find the test acceptable and are willing to complete it (Howitt and Cramer, 2011). In the development of ASSET, it was important that the language and meaning of the items were acceptable to all grades and types of employees (Robertson Cooper, 2014). ASSET developers created an employee pool representing a range of different employee groups to pilot and test the instrument for meaning with the assistance of a panel of occupational health practitioners (Robertson Cooper, 2014). The designers of ASSET used feedback to develop the set of items that form ASSET and used face validity to test and evaluate scale construction (Robertson Cooper, 2014).

A construct is an attribute, or a characteristic inferred from research (Straub et al., 2004). Establishing construct validity involves determining the extent to which a test is based on and measures a theory or model (Howitt and Cramer, 2011). Cooper and Marshall’s 1978 model of stress influenced ASSET (Robertson Cooper, 2014). However, since the time of Cooper and Marshall’s work, dramatic changes have occurred in career development and working arrangements, researchers have conducted extensive studies into models of stress, and a new set of stressors has emerged. By incorporating these new developments into ASSET, Cartwright and Cooper have ensured that the basis of the validated instrument is Cooper and Marshall’s theoretical model and that it reflects current research and the current workplace (Robertson Cooper, 2014).

The following assumptions for the multiple regression models were employed in this paper: variables are normally distributed; the relationships between the dependent and independent variables are linear; variables are measured without error; multicollinearity is not present; and heteroscedasticity is not present. The paper used SPSS to analyze the data and the Kolmogorov-Smirnov test to examine the data for normality prior to conducting the data analysis and used descriptive statistics (mean, standard deviation, skewness and kurtosis) to analyze the collected data from ASSET for normal distribution. The paper used boxplot diagrams to identify outliers for examination to decide whether to retain, transform or exclude the outliers (Green and Salkind, 2011). Outliers are data that have statistically significantly higher or lower values than other values in the collected data. The scatterplot of standardized residuals showed that the data met the assumptions of homogeneity of variance, and linearity.

The paper used the multiple linear regression module in SPSS to examine the relationships between the dependent variable and the independent variables and tested the study’s assumptions (discussed earlier) before completing the regression analysis. The tabular-format SPSS outputs provided me with information about the relationships between the variables, which I used to test the hypotheses. Data from the SPSS tables included values for: R; R2; adjusted R2; standard error of the estimate; sum of squares; degrees of freedom; mean squares; F statistics; p-values; unstandardized coefficients (β and standard error); standardized coefficients (β); and t test. SPSS provided the F statistic for determining the overall significance of the multiple regression model (Green and Salkind, 2011). Researchers consider values of R2 below 0.2 to be weak, values between 0.2 and 0.4 to be moderate, and values at 0.5 and above to be strong (Green and Salkind, 2011). Cohen (1992) noted that f2 values of 0.02, 0.15 and 0.35 represent small, medium and large effect sizes, respectively.

Furthermore, the paper deployed multiple linear regression analysis to evaluate whether correlations existed between employees’ perceptions of social support, work–life conflict, job performance and workplace stress. Additionally, the paper employed multiple linear regression analysis to conduct significance tests to evaluate whether social support, work–life conflict, and job performance correlated to workplace stress. The analysis related to the hypothesis since hypothesis were developed to determine if social support, work–life conflict and job performance correlated to workplace stress. In the analysis of the findings, the paper determined that social support, work–life conflict and job performance significantly related to workplace stress; therefore, the null hypothesis was rejected. The paper compared the p value with the actual significance level for the test; if it is smaller than the actual significance, then the result is significant. In the analysis of the findings, the paper tested the null hypotheses at the 5 percent significance level; and this was reported as p< 0.05. Smaller p-values provide stronger evidence for rejecting the null hypothesis.

Threats to statistical conclusion validity occur when researchers make incorrect inferences because of inadequate statistical power (Goodhue et al., 2012). Researchers using statistical conclusion validity techniques can check the quality of the statistical information and sources of statistical errors. I used known-groups validity to determine if the findings between different groups were valid (Howitt and Cramer, 2011). For example, if other researchers found consistently that HEI staff have high-stress profiles, so in this paper, we would use these findings to increase the assurance of statistical validity. Validity threats due to selection bias were not a concern because the entire staff population of the participating institution were surveyed and we did not seek to generalize the findings (Straub et al., 2004).

## Results

In model 1 (as outlined in Table II), the strength of the relationship (as shown in Table II) between the independent variables and the dependent variable was weak (R = 0.121, p< 0.05). In model 2, the strength of the relationship (as shown in Table I) between the independent variables and the dependent variable was moderate (R = 0.504, p < 0.01). The regression equation for model 1 (as shown in Tables II and III) was statistically significant: R2 = 0.015, adjusted R2 = 0.009, F(4, 649) = 2.418, p < 0.05. The regression equation for model 2 (as shown in Tables II and III) was statistically significant: R2 = 0.254, adjusted R2 = 0.246, F(7, 646) = 31.429, p < 0.01.

The first step of the linear regression involved entering four covariates: staff category, direct reports, age and gender. Model 1 was statistically significant F(4, 649) = 2.418, p < 0.05 (as shown in Tables II and III) and explained 1.5 percent of the variance in workplace stress. The second step of the multiple linear involved entering three predictors: social support, work–life conflict and job performance. After entry of social support, work–life conflict and job performance, the total variance explained by the model was 25.4 percent, F(7, 646) = 31.429, p < 0.01. The introduction of social support, work–life conflict and job performance explained an additional 23.1 percent of the variance in workplace stress, after controlling for staff category, direct reports, age and gender (R2 change = 0.239, p < 0.01).

Table IV includes the coefficient results for the independent variables and their coefficients. A positive or negative B coefficient indicates the direction of the relationship between the independent and the dependent variable. The unstandardized coefficient for social support was −1.475, which meant for every unit increase in social support, the resulting expectation was a −1.475 unit decrease in workplace stress. The unstandardized coefficient for work–life conflict was 0.869, which meant for every unit increase in work–life conflict, the resulting expectation was a 0.869 unit increase in workplace stress. The unstandardized coefficient for job performance was −0.422, which meant for every unit increase in job performance, the resulting expectation was a −0.422 unit decrease in workplace stress.

Using the unstandardized coefficients to make comparisons between the sizes of the various coefficients between the three independent variables was not possible, as the independent variables were measured on different scales. The standardized coefficients (β) in Table IV showed values of the transformed coefficients into standardized regression coefficients, which meant they were transformed to the same scale so measurement and comparison between the sizes of the various coefficients was possible. As shown in Table III, the values for the standardized coefficients (β): social support was −0.146; work–life conflict was 0.405; and job performance was −0.157. The largest coefficient (0.405) indicated the independent variable work–life conflict had the greatest relative influence on the dependent variable workplace stress.

As shown in Table IV, in the final (complete) model, all three predictor variables and two of the covariates (direct reports = p < 0.05, and gender = p < 0.01) were statistically significant, with standardized coefficients for work–life conflict recording a higher standardized β value (β = 0.405, p < 0.01) than job performance (β = −0.157, p < 0.01), social support (β = −0.146, p < 0.01), gender (β = 0.124, p < 0.01), and direct reports (β = −0.079, p < 0.05).

The null hypotheses for the research questions were as follows:

H10.

There is no relationship between the independent variable of job performance, and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

H20.

There is no relationship between the independent variable of social support and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

H30.

There is no relationship between the independent variable of work–life conflict and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender.

Thus, because there were statistically significant relationships between employees’ perceptions of social support, work–life conflict, job performance and workplace stress while controlling for staff category, direct reports, age and gender, all three of the null hypotheses were rejected.

In conclusion, the results showed that statistically significant (at the 0.05 level) correlations existed between employees’ perceptions of social support, work–life conflict, job performance, and workplace stress in the subject Irish HEI. In the analysis of the correlations, the strength of the relationships between the set of independent variables and the dependent variable based on the values of R were categorized as follows: weak = R ⩽ 0.40, moderate = R = 0.41–0.60, and strong = R > 0.60 (Cohen, 1992). The results (as shown in Tables II and IV) showed social support had a moderate negative relationship with workplace stress (R = 0.504; B = −1.475) after controlling for staff category, direct reports, age and gender. Work–life conflict (as shown in Tables II and IV) had a moderate positive relationship with workplace stress (R = 0.504, B = 0.869), and job performance (as shown in Tables II and IV) had a moderate negative relationship with workplace stress (R = 0.504, B = −0.422). Social support, work–life conflict, and job performance (as shown in Tables II and IV) had statistically significant relationships with workplace stress (p > 0.01). Therefore, staff with low levels of social support had higher than expected levels of workplace stress, staff with higher levels of job performance had lower than expected levels of workplace stress, and staff with higher work–life conflicts had higher than expected levels of workplace stress.

## Discussion

The theoretical framework for this research consisted of a combination of ERI theory, expectancy theory and equity theory. The theoretical framework reflects the expectations of employees and managers of an equitable reward and recognition for expended effort (Al-Zawahreh and Al-Madi, 2012). Researchers using the ERI model have directly linked ERI with negative impacts on health (Olejniczak and Salmon, 2014). Social reciprocity and social exchange reflect the norm of return in which separate rewards reciprocate efforts (Ganster and Perrewe, 2011). Researchers have predicted that failure to reciprocate this norm will lead to negative emotions and sustained stress (Branham, 2012). However, reciprocity is likely to lead to positive emotions that will promote positive health and well-being (Parker, 2014).

Social support is a critical feature of the workplace reflected in the reciprocation of good relationships among employees and between employees and leaders (Chandra, 2012). Social support refers to an individual’s belief that he or she is: valued; informed; communicated with; emotionally cared for; and part of a relationship group or network (Fernandes and Tewari, 2012). In particular, support from leadership and coworkers has a positive effect on well-being; employees who feel supported are likely to feel less stressed and believe they receive fair rewards for their efforts (Demerouti et al., 2014; Fischer and Martinez, 2013; Thi Giang et al., 2013).

The results from this study (as shown in Tables II and IV) depicted a moderate negative relationship (R = 0.504; B = −1.475) between social support and workplace stress, which means that employees with low levels of social support are likely to have higher levels of workplace stress, which endorses the findings of previous research and the theoretical framework. Furthermore, the results (as shown in Tables II and IV) showed a moderate negative relationship (R = 0.504; B = −0.422) between job performance and workplace stress; employees with higher levels of job performance are likely to have lower levels of workplace stress. Therefore, if leaders enable and empower staff to improve job performance levels, they should witness a reduction in workplace stress. The findings on job performance are not in keeping with previous research, which showed a curvilinear relationship between job performance and workplace stress (Adaramola, 2012; Savage and Torgler, 2012). The results of this study do not support such a curvilinear relationship between job performance and workplace stress. Researchers had previously identified a positive relationship between stress and performance on the basis that employees sometimes work better under pressure, that is, when there is enough pressure on individuals to focus their attention but not so much that it disrupts their performance (Domínguez, 2013; Leung et al., 2011). The results do not support a positive relationship between job performance and workplace stress.

The results (as shown in Tables II and IV) showed higher levels of social support predicted reduced levels of workplace stress. Workplace stressors include poor relationships between managers and staff, inadequate communication and lack of support (McVicar et al., 2013). The results supported the findings of researchers who have shown that a lack of support and poor relationships (social support) reflect higher levels of workplace stress (McVicar et al., 2013) and that regular interactions between managers and employees (social support) have a direct positive effect on employee work output (Evers et al., 2014).

Olejniczak and Salmon (2014) noted that a lack of management recognition for employee effort leads to high ERI. An employee who experiences stressful working conditions or job insecurity can benefit from social support and employee–environment fit. Social support can give rise to perceptions of reward and reciprocity and reduce employees’ perception of high ERI (Schreurs et al., 2012, p. 624). The results showed a negative relationship between social support levels and levels of workplace stress, which indicated that high levels of social support should reduce workplace stress levels. This finding supported research by Olejniczak and Salmon (2014) and Schreurs et al. (2012).

Researchers have described the relationship between work and life as being, for example: family friendly; balanced; conflicted; and flexible (Jang et al., 2011; Murphy and Doherty, 2011). Across the globe, for both employees and leaders of organizations, work–life conflict relates to increased workplace stress arising from the globalization of markets and demands for greater productivity and efficiency (Bell, 2013). The results as depicted in Table IV indicate the independent variable work–life conflict had the greatest influence on the dependent variable workplace stress (β = 0.405). Staff with higher levels of work–life conflict had higher than expected levels of workplace stress. The results also showed a moderate negative relationship (R = 0.504; B = −0.422) between job performance and workplace stress, which means employees with higher levels of job performance had lower than expected levels of workplace stress. Employers often find it difficult to strike the right balance between accommodating flexible work arrangements and eliciting job performance from workers to deliver value for money for the business (Kossek et al., 2011). The results (as shown in Table IV) clearly depicted that high levels of work–life conflict resulted in higher than expected levels of workplace stress.

Prolonged exposure to workplace stress can negatively affect job performance by reducing interest in work activities and can lead to physical ill health and psychological symptoms of distress (Spurgeon et al., 2012). The results as noted in Table IV showed a negative relationship between job performance and workplace stress (B = −0.422). Exposure to an environment conducive to high levels of job performance can reduce levels of workplace stress (Olejniczak and Salmon, 2014). The results depicted that increased levels of job performance reflected lower levels of workplace stress. Abugre (2012) found that high-performing organization leaders succeeded in reducing levels of workplace stress by fostering and nurturing a climate of social interaction whereby managers and team members engaged meaningfully, and team members participated in organizational activities and decision-making processes. Lopez (2011) determined that employees tended to have lower levels of workplace stress as a result of leaders paying attention to the work environment and creating a climate conducive to high levels of job performance by encouraging social support from peers, family and managers.

The theoretical framework supported the interpretation of the findings because perceptions of equity, reciprocation, and expectancy of fairness influence perceptions of social support, work–life conflict, job performance and workplace stress (Olejniczak and Salmon, 2014). The findings supported the findings of Hansen et al. (2014) and Smith et al. (2012), who demonstrated that employees with robust interpersonal networks, quality coworker relationships, and low levels of work–life conflict are more likely to have lower levels of workplace stress. Furthermore, the results (as shown in Tables II–IV) showed that employees with high levels of job performance exhibited lower than expected levels of workplace stress.

## Conclusions

Based on the principles of ERI theory, a lack of reciprocity between costs and gains elicits negative emotions with a propensity to sustained autonomic and neuroendocrine activation (Siegrist, 1996). Therefore, the social reciprocity and social exchange principles that are inherent in ERI theory made this theory appropriate for this study. The expectancy and reward aspects of ERI is consistent with expectancy theory, which also formed part of the theoretical framework of this study. Vroom (1964) proposed expectancy theory to explain the decision-making process of individuals based on behavioral alternatives. Further to this, equity theory, Adams (1963) explains the motivation of individuals in the context of their perceptions of the extent to which all individuals in the organization receive fair treatment by management (Kivimäki, 2014; Skiba and Rosenberg, 2011).

It is evident from the interpretation of the findings that ERI theory, expectancy theory, and equity theory through perceptions of equity, effort, reward, reciprocation and expectancy of fairness influence perceptions of social support, work–life conflict, job performance and workplace stress. Managers implementing policies and practices that increase social support, improve performance and reduce work-life conflict should be reciprocated with lower levels of workplace stress. Dealing with workplace stress makes good business sense because lowering workplace stress levels should result in: improved employee punctuality; reduced absenteeism; higher productivity; lower employee turnover; reduced training costs for replacement staff for sick leave; less depression, aggression and violence; improved job satisfaction; and enhanced image of the organization. Leaders who understand the dynamics and principles of ERI, expectancy and equity theories will also recognize the sources and signs of stress in the workplace. With this knowledge leaders should be better placed to develop interventions to manage work-life conflict and social support to reduce workplace stress and increase performance.

The examination and establishment of particular relationships between social support, work–life conflict and job performance with workplace stress is significant for the leaders of the subject institution. The results provide leaders with information about these relationships, which they can use to develop and deploy strategies to cope with workplace stress. In turn, these strategies can increase productivity, reduce costs, increase profits and improve the quality of people’s everyday lives. The results showed that the extent of the relationships between the independent variables social support, work–life conflict, and job performance and the dependent variable workplace stress while controlling for staff category, direct reports, age and gender. In conclusion, the results from this research provide information to leaders, professional practitioners, researchers and managers, for developing and deploying strategies for reducing workplace stress in organizations.

## Figures

#### Figure 1

Statistical validity of the ASSET instrument

## Table I

ASSET internal consistency

Scale α value (n = 32,500)
Perceptions of job
Resources and communication 0.71
Control 0.85
Work-life balance 0.73
Job security and change 0.74
Work relationships 0.84
Job conditions 0.74
Physical health 0.79
Psychological health 0.92
Psychological well-being
Positive psychological well-being 0.91
Sense of purpose 0.82
Engagement and related scales
Engagement 0.79
Commitment of employee 0.85
Perceived commitment of organization toward employee 0.76

Source: Adapted from “Introducing ASSET” by Robertson Cooper, 2014, available at: www.robertsoncooper.com/how-we-do-it/our-products/asset#what-is-asset

## Table II

Multiple linear regression model

Change statistics
Model R R2 Adjusted R2 SE of the estimate R2 change F change df1 df2 Sig. F change
1 0.121a 0.015 0.009 9.86112 0.015 2.418 4 649 0.047
2 0.504b 0.254 0.246 8.60007 0.239 69.095 3 646 0.000

Notes: aPredictors: (constant), staff category, direct reports, age, and gender; bpredictors: (constant), staff category, direct reports, age, gender, social support, work–life conflict and job performance

## Table III

ANOVA for multiple linear regression model

Sum of squares df Mean square F Sig.
Model 1
Regression 940.45 4 235.113 2.418 0.047a
Residual 63,109.90 649 97.242
Model 2
Regression 16,271.46 7 2,324.494 31.429 0.000b
Residual 47,778.90 646 73.961

Notes: Dependent variable is workplace stress. aPredictors: (constant), staff category, direct reports, age, and gender; bpredictors: (constant), staff category, direct reports, age, gender, social support, work–life conflict and job performance

## Table IV

Coefficients for the multiple linear regression model

Unstandardized coefficients Standardized coefficients Collinearity statistics
B SE β t Sig. Tolerance VIF
Model 1
(Constant) 36.22 1.25 28.89 0.000
Staff category −0.77 0.48 −0.063 −1.59 0.113 0.997 1.003
Direct reports −0.37 0.41 −0.036 −0.89 0.374 0.991 1.009
Age −0.183 0.38 −0.019 −0.47 0.635 0.993 1.007
Gender 2.03 0.83 0.097 2.43 0.016 0.997 1.003
Model 2
(Constant) 43.63 3.10 14.06 0.000
Staff category 0.39 0.43 0.032 0.90 0.367 0.991 1.009
Direct reports −0.81 0.36 −0.079 −2.23 0.026 0.983 1.017
Age 0.20 0.34 0.021 0.61 0.544 0.991 1.009
Gender 2.61 0.75 0.124 3.50 0.000 0.987 1.013
Social support −1.47 0.37 −0.146 −4.00 0.000 0.909 1.100
Work–life conflict 0.87 0.08 0.405 11.43 0.000 0.960 1.041
Job performance −0.42 0.10 −0.157 −4.41 0.000 0.904 1.106

Note: Dependent variable is workplace stress

## References

Abadi, F.E., Jalilvand, M.R., Sharif, M., Salimi, G.A. and Khanzadeh, S.A. (2011), “A study of influential factors on employees’ motivation for participating in the in-service training courses based on modified expectancy theory”, International Business and Management, Vol. 2 No. 1, pp. 157-169, doi: 10.3968/j.ibm.1923842820110201.011.

Abugre, J.B. (2012), “How managerial interactions affect employees’ work output in Ghanaian organizations”, African Journal of Economic and Management Studies, Vol. 3 No. 2, pp. 204-226, doi: 10.1108/20400701211265009.

Adams, J.S. (1963), “Towards an understanding of inequity”, Journal of Abnormal and Social Psychology, Vol. 67 No. 5, pp. 422-436, doi: 10.1037/h0040968.

Adaramola, S.S. (2012), “Job stress and productivity increase”, Work, Vol. 41 Nos 8-9, pp. 2955-2958, doi: 10.3233/WOR-2012-0547-2955.

Allisey, A., Rodwell, J. and Noblet, A. (2012), “Personality and the effort-reward imbalance model of stress: individual differences in reward sensitivity”, Work & Stress, Vol. 26 No. 3, pp. 230-251, doi: 10.1080/02678373.2012.714535.

Almadi, T., Cathers, I. and Chow, C.M. (2013), “An Arabic version of the effort-reward imbalance questionnaire: translation and validation study”, Psychological Reports, Vol. 113 No. 1, pp. 275-290, doi: 10.2466/08.14.PR0.113x10z7.

Al-Zawahreh, A. and Al-Madi, F. (2012), “The utility of equity theory in enhancing organizational effectiveness”, European Journal of Economics, Finance and Administrative Sciences, Vol. 46 No. 2012, pp. 158-170, available at: www.europeanjournalofeconomicsfinanceandadministrativesciences.com/

American Psychological Association (2014), “Measures of organizational stressors”, available at: http://supp.apa.org/books/ (accessed January 15, 2014).

Amstad, F.T., Meier, L.L., Fasel, U., Elfering, A. and Semmer, N.K. (2011), “A meta-analysis of work-family conflict and various outcomes with a special emphasis on cross-domain versus matching-domain relations”, Journal of Occupational Health Psychology, Vol. 16 No. 2, pp. 151-169, doi: 10.1037/a0022170.

Avey, J.B., Reichard, R.J., Luthans, F. and Mhatre, K.H. (2011), “Meta analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance”, Human Resource Development Quarterly, Vol. 22 No. 2, pp. 127-152, doi: 10.1002 /hrdq.20070.

Avey, J.B., Luthans, F., Hannah, S.T., Sweetman, D. and Peterson, C. (2012), “Impact of employees’ character strengths of wisdom on stress and creative performance”, Human Resource Management Journal, Vol. 22 No. 2, pp. 165-181, doi: 10.1111/j.1748-8583.2010.00157.x.

Bell, G. (2013), “Cary Cooper on engagement, wellbeing, and the persistence of the glass ceiling”, Human Resource Management International Digest, Vol. 21 No. 1, pp. 41-44, doi: 10.1108/hrmid-04-2013-0024.

Billing, T.K., Bhagat, R.S., Babakus, E., Krishnan, B., Ford, D.L., Srivastava, B.N. and Nasurdin, A.M. (2014), “Work–family conflict and organizationally valued outcomes: the moderating role of decision latitude in five national contexts”, Applied Psychology, Vol. 63 No. 1, pp. 62-95, doi: 10.1111/j.1464-0597.2012.00526.x.

Biron, C. and Karanika-Murray, M. (2014), “Process evaluation for organizational stress and well-being interventions: implications for theory, method, and practice”, International Journal of Stress Management, Vol. 21 No. 1, pp. 85-111, doi: 10.1037/a0033227.

Boscolo, P., Forcella, L., Reale, M., Vianale, G., Battisti, U., Bonfiglioli, R. and Salerno, S. (2012), “Job strain in different types of employment affects the immune response”, Work, Vol. 41 No. 1, pp. 2950-2954, doi: 10.3233/WOR-2012-0546-2950.

Branham, L. (2012), The 7 Hidden Reasons Employees Leave: How to Recognize the Subtle Signs and Act Before It’s Too Late, AMACOM A Division of American Management Association, New York, NY.

Bucurean, M. and Costin, M.-A. (2011), “Organization stress and its impact on work performance”, Annals of the University of Oradea, Economic Science Series, Vol. 1, Special Issue, pp. 333-337, available at: http://anale.steconomiceuoradea.ro/en/

Burton, J.P., Hoobler, J.M. and Scheuer, M.L. (2012), “Supervisor workplace stress and abusive supervision: the buffering effect of exercise”, Journal of Business & Psychology, Vol. 27 No. 3, pp. 271-279, doi: 10.1007/s10869-011-9255-0.

Carlson, D.S., Ferguson, M., Kacmar, K.M., Grzywacz, J.G. and Whitten, D. (2011), “Pay it forward: the positive crossover effects of supervisor work–family enrichment”, Journal of Management, Vol. 37 No. 3, pp. 770-789, doi: 10.1177/0149206310363613.

Cartwright, S. and Cooper, C.L. (2002), ASSET: An Organizational Stress Screening Tool: The Management Guide, Robertson Cooper, Manchester.

Chandra, V. (2012), “Work-life balance: eastern and western perspectives”, International Journal of Human Resource Management, Vol. 23 No. 5, pp. 1040-1056, doi: 10.1080/09585192.2012.651339.

Cheng, B.H. and McCarthy, J.M. (2013), “Managing work, family, and school roles: disengagement strategies can help and hinder”, Journal of Occupational Health Psychology, Vol. 18 No. 3, pp. 241-251, doi: 10.1037/a0032507.

Cohen, J. (1992), “A power primer”, Psychological Bulletin, Vol. 112 No. 1, pp. 155-159, doi: 10.1037/0033-2909.112.1.155.

Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. (2013), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Routledge, New York, NY.

Demerouti, E., Derks, D., Lieke, L. and Bakker, A.B. (2014), “New ways of working: impact on working conditions, work–family balance, and well-being”, in Korunka, C. and Hoonakker, P. (Eds), The Impact of ICT on Quality of Working Life, Springer, Dordrecht, pp. 123-141, doi: 10.1007/978-94-017-8854-0_8.

Department of Public Expenditure and Reform (2017), “HR management in the civil service”, available at: https://hr.per.gov.ie/sick-leave/ (accessed June 14, 2018).

DeTienne, K., Agle, B., Phillips, J. and Ingerson, M.C. (2012), “The impact of moral stress compared to other stressors on employee fatigue, job satisfaction, and turnover: an empirical investigation”, Journal of Business Ethics, Vol. 110 No. 3, pp. 377-391, doi: 10.1007/s10551-011-1197-y.

Dhabhar, F.S. (2014), “Effects of stress on immune function: the good, the bad, and the beautiful”, Immunologic Research, Vol. 53 Nos 2-3, pp. 193-210, doi: 10.1007/s12026-014-8517-0.

Domínguez, E.S. (2013), “Work stressors and creativity”, Management, Vol. 16 No. 4, pp. 479-504, available at: www.management-aims.com

Donald, I., Taylor, P., Johnson, S., Cooper, C., Cartwright, S. and Robertson, S. (2005), “Work environments, stress, and productivity: an examination using ASSET”, International Journal of Stress Management, Vol. 12 No. 4, pp. 409-423, doi: 10.1037/1072-5245.12.4.409.

Dwamena, M.A. (2012), “Stress and its effects on employees productivity – A case study of Ghana ports and habours authority”, Master’s thesis, Kwame Nkrumah University of Science and Technology, Kumasi and Takoradi, available at: http://ir.knust.edu.gh/bitstream/ (accessed October 10, 2018).

Estes, B.C. (2011), “Predicting productivity in a complex labor market: a sabermetric assessment of free agency on major league baseball player performance”, Business Studies Journal, Vol. 3 No. 1, pp. 23-58, available at: www.alliedacademies.org/public /journals/JournalDetails.aspx?jid=26

Evers, K.E., Castle, P.H., Prochaska, J.O. and Prochaska, J.M. (2014), “Examining relationships between multiple health risk behaviors, well-being, and productivity 1, 2”, Psychological Reports, Vol. 114 No. 3, pp. 843-853, doi: 10.2466/13.01.PR0.114k25w4.

Faul, F., Erdfelder, E., Buchner, A. and Lang, A.G. (2009), “Statistical power analyses using G* Power 3.1: tests for correlation and regression analyses”, Behavior Research Methods, Vol. 41 No. 4, pp. 1149-1160, doi: 10.3758/BRM.41.4.1149.

Fearon, C., McLaughlin, H. and Morris, L. (2013), “Conceptualising work engagement: an individual, collective and organisational efficacy perspective”, European Journal of Training and Development, Vol. 37 No. 3, pp. 244-256, doi: 10.1108/03090591311312723.

Feldt, T., Huhtala, M., Kinnunen, U., Hyvonen, K., Mäkikangas, A. and Sonnentag, S. (2013), “Long-term patterns of effort-reward imbalance and over-commitment: Investigating occupational well-being and recovery experiences as outcomes”, Work & Stress, Vol. 27 No. 1, pp. 64-87, doi: 10.1080/02678373.2013.765670.

Fernandes, C. and Tewari, K. (2012), “Organizational role stress: impact of manager and peer support”, Journal of Knowledge Globalization, Vol. 5 No. 1, pp. 1-28, available at: www.kglobal.org/journal.html

Fischer, F.M. and Martinez, M.C. (2013), “Individual features, working conditions and work injuries are associated with work ability among nursing professionals”, Work: A Journal of Prevention, Assessment and Rehabilitation, Vol. 45 No. 4, pp. 509-517, doi: 10.3233/WOR-131637.

Foy, T. (2015), “Managing workplace stress for increased performance in an Irish higher education institution”, doctoral dissertation, Walden University, MN.

Gachter, M., Savage, D.A. and Torgler, B. (2011), “The relationship between stress, strain and social capital”, International Journal of Police Strategies & Management, Vol. 34 No. 3, pp. 515-540, doi: 10.1108/13639511111157546.

Ganster, D.C. and Perrewe, P.L. (2011), “Theories of occupational stress”, in Campbell-Quick, J. and Tetrick, L.E. (Eds), Handbook of Occupational Health Psychology, 2nd ed., American Psychological Association, Washington, DC, pp. 37-53.

Gharib, M., Jamil, S., Ahmad and Ghouse, S. (2016), “The impact of job stress on job performance: a case study on academic staff at Dhofar University”, International Journal of Economic Research, Vol. 13 No. 1, pp. 21-33, available at: https://mafiadoc.com/the-impact-of-job-stress-on-job-performance-a-case-study-on-_599ff5ad1723dd0a40e06ca0.html (accessed October 17, 2018).

Goldberg, D.P. and Williams, P. (1988), A User’s Guide to the GHQ, NFER, Nelson and London.

Goodhue, D.L., Lewis, W. and Thompson, R. (2012), “Does PLS have advantages for small sample size or non-normal data?”, Mis Quarterly, Vol. 36 No. 3, pp. 891-1001, available at: www.misq.org (accessed June 21, 2014).

Goswami, T. (2015), “Job stress and its effects on employee performance in banking sector”, Indian Journal of Commerce & Management Studies, Vol. 6 No. 2, pp. 51-56, available at: www.scholarshub.net/ijcms/vol6/issue2/Paper_08.pdf (accessed October 15, 2018).

Grawitch, M.J., Maloney, P.W., Barber, L.K. and Mooshegian, S.E. (2013), “Examining the nomological network of satisfaction with work-life balance”, Journal of Occupational Health Psychology, Vol. 18 No. 3, pp. 276-284, doi: 10.1037/a0032754.

Green, S.B. and Salkind, N.J. (2011), Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6th ed., Pearson, Upper Saddle River, NJ.

Hancock, F. and Page, F. (2013), “Family to work conflict and the usefulness of workplace support”, Occupational Medicine, Vol. 63 No. 5, pp. 373-376, doi: 10.1093/occmed/kqt053.

Hansen, A., Byrne, Z. and Kiersch, C. (2014), “How interpersonal leadership relates to employee engagement”, Journal of Managerial Psychology, Vol. 29 No. 8, pp. 953-972, doi: 10.1108/JMP-11-2012-0343.

Howitt, D. and Cramer, D. (2011), Introduction to Research Methods in Psychology, 3rd ed., Pearson, Harlow.

Hyvonen, K., Feldt, T., Kinnunen, U. and Tolvanen, A. (2011), “Changes in personal work goals in relation to the psychosocial work environment: a two-year follow-up study”, Work & Stress, Vol. 25 No. 4, pp. 289-308, doi: 10.1080/02678373.2012.630587.

Ipsen, C. and Jensen, P.L. (2012), “Organizational options for preventing work-related stress in knowledge work”, International Journal of Industrial Ergonomics, Vol. 42 No. 4, pp. 325-334, doi: 10.1016/j.ergon.2012.02.006.

Jain, A.K., Giga, S.I. and Cooper, C.L. (2013), “Perceived organizational support as a moderator in the relationship between organizational stressors and organizational citizenship behaviors”, International Journal of Organizational Analysis, Vol. 21 No. 3, pp. 313-334, doi: 10.1108/IJOA-Mar-2012-0574.

Jamal, M. (2013), “Job stress among hospital employees in middle east: social support and type a behavior as moderators”, Middle East Journal of Business, Vol. 8 No. 3, pp. 7-16, doi: 10.5742/MEJB.2013.83282.

Jang, S.J., Park, R. and Zippay, A. (2011), “The interaction effects of scheduling control and work-life balance programs on job satisfaction and mental health”, International Journal of Social Welfare, Vol. 20 No. 2, pp. 135-143, doi: 10.1111/j.1468-2397.2010.00739.x.

Johnson, S. and Cooper, C. (2003), “The construct validity of the ASSET stress measure”, Stress and Health, Vol. 19 No. 3, pp. 181-185, doi: 10.1002/smi.971.

Kalliath, T. and Kalliath, P. (2012), “Changing work environments and employee well-being: an introduction”, International Journal of Manpower, Vol. 33 No. 7, pp. 729-737, doi: 10.1108/01437721211268285.

Karam, C.M. (2011), “Good organizational soldiers: conflict-related stress predicts citizenship behavior”, International Journal of Conflict Management, Vol. 22 No. 3, pp. 300-319, doi: 10.1108/10444061111152982.

Kavitha, P. (2012), “Organizational role stress among college faculties: an empirical study”, Global Management Review, Vol. 6 No. 4, pp. 36-50, available at: www.sonamgmt.org/gmr.html

Kelloway, E.K., Turner, N., Barling, J. and Loughlin, C. (2012), “Transformational leadership and employee psychological well-being: the mediating role of employee trust in leadership”, Work & Stress, Vol. 26 No. 1, pp. 39-55, doi: 10.1080/02678373 .2012.660774.

Khoury, L., Tang, Y.L., Bradley, B., Cubells, J.F. and Ressler, K.J. (2010), “Substance use, childhood traumatic experience, and posttraumatic stress disorder in an urban civilian population”, Depress Anxiety, Vol. 27 No. 12, pp. 1077-1086, doi: 10.1002/da.20751.

Kivimäki, M. (2014), “Effort-reward imbalance”, in Cockerham, W., Dingwall, R. and Quah, S.R. (Eds), The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society, John Wiley & Sons, Hoboken, NJ, pp. 450-454, doi: 10.1002/9781118410868.wbehibs001.

Kobussen, G., Kalagnanam, S. and Vaidyanathan, G. (2014), “The impact of better than average bias and relative performance pay on performance outcome satisfaction”, Accounting Perspectives, Vol. 13 No. 1, pp. 1-27, doi: 10.1111/1911-3838.12022.

Kossek, E.E., Baltes, B.B. and Mathews, R.A. (2011), “How work-family research can finally have an impact in organizations”, Industrial and Organizational Psychology, Vol. 4 No. 3, pp. 352-369, doi: 10.1111/j.1754-9434.2011.01353.x.

Kossek, E.E., Hammer, L.B., Kelly, E.L. and Moen, P. (2014), “Designing work, family and health organizational change initiatives”, Organizational Dynamics, Vol. 42 No. 1, pp. 53-63, doi: 10.1016/j.orgdyn.2013.10.007.

Kossek, E.E., Pichler, S., Bodner, T. and Hammer, L.B. (2011), “Workplace social support and work-family conflict: a meta-analysis clarifying the influence of general and work-family-specific supervisor and organizational support”, Personnel Psychology, Vol. 64 No. 2, pp. 289-313, doi: 10.1111/j.1744-6570.2011.01211.x.

Ladegård, G. (2011), “Stress management through workplace coaching: the impact of learning experiences”, International Journal of Evidence Based Coaching & Mentoring, Vol. 9 No. 1, pp. 29-43, available at: http://ijebcm.brookes.ac.uk/

Leung, K., Huang, K.-L., Su, C.-H. and Lu, L. (2011), “Curvilinear relationships between role stress and innovative performance: moderating effects of perceived support for innovation”, Journal of Occupational & Organizational Psychology, Vol. 84 No. 4, pp. 741-758, doi: 10.1348/096317910x520421.

Leung, M.Y., Chan, Y.S.I. and Dongyu, C. (2011), “Structural linear relationships between job stress, burnout, physiological stress, and performance of construction project managers”, Engineering, Construction and Architectural, Vol. 18 No. 3, pp. 312-328, doi: 10.1108/09699981111126205.

Lisson, S., Mee, L. and Gilbert, K. (2013), “The influence of work-life balance, choice and a meaningful location on work transitions”, Work: Journal of Prevention, Assessment & Rehabilitation, Vol. 44 No. 1, pp. 77-79, doi: 10.3233/WOR-2012-01564.

Lopez, A. (2011), “Posttraumatic stress disorder and occupational performance: building resilience and fostering occupational adaptation”, Work, Vol. 38 No. 1, pp. 33-38, doi: 10.3233/wor-2011-1102.

Loughlin, C., Arnold, K. and Crawford, J.B. (2012), “Lost opportunity: is transformational leadership accurately recognized and rewarded in all managers?”, Equality, Diversity and Inclusion: An International Journal, Vol. 31 No. 1, pp. 43-64, doi: 10.1108/02610151211199218.

Luthans, F., Youssef, C.M., Sweetman, D.S. and Harms, P.D. (2013), “Meeting the leadership challenge of employee well-being through relationship PsyCap and health PsyCap”, Journal of Leadership & Organizational Studies, Vol. 20 No. 1, pp. 118-133, doi: 10.1177/1548051812465893.

McVicar, A., Munn-Giddings, C. and Seebohm, P. (2013), “Workplace stress interventions using participatory action research designs”, International Journal of Workplace Health Management, Vol. 6 No. 1, pp. 18-37, doi: 10.1108/17538351311312303.

Manolova, T.S., Brush, C.G., Edelman, L.F. and Shaver, K.G. (2012), “One size does not fit all: entrepreneurial expectancies and growth intentions of US women and men nascent entrepreneurs”, Entrepreneurship & Regional Development: An International Journal, Vol. 24 Nos 1‐2, pp. 7-27, doi: 10.1080/08985626.2012.637344.

Mansour, R. and Elmorsey, R. (2016), “Occupational stress: measuring its impact on employee performance and turnover”, European Journal of Business and Management, Vol. 8 No. 21, pp. 12-21, available at: https://iiste.org/Journals/index.php/EJBM/article/viewFile/31667/32539 (accessed October 24, 2018).

Mikkelsen, E.G., Hogh, A. and Puggaard, L.B. (2011), “Prevention of bullying and conflicts at work: process factors influencing the implementation and effects of interventions”, International Journal of Workplace Health Management, Vol. 4 No. 1, pp. 84-100, doi: 10.1108/17538351111118617.

Murphy, F. and Doherty, L. (2011), “The experience of work life balance for Irish senior managers”, Equality, Diversity and Inclusion: An International Journal, Vol. 30 No. 4, pp. 252-277, doi: 10.1108/02610151111135732.

Nair, P. and Xavier, M. (2012), “Initiating employee assistance program (EAP) for a corporate: an experiential learning”, IUP Journal of Organizational Behavior, Vol. 11 No. 2, pp. 67-76, available at: www.iupindia.in/Organizational_Behavior.asp

Nasr, L. (2012), “The relationship between the three components model of commitment, workplace stress and career path application to employees in medium sized organizations in Lebanon”, Journal of Organizational Culture, Communications and Conflict, Vol. 16 No. 1, pp. 71-87, available at: http://alliedacademies.org/public/Journals /JournalDetails.aspx?jid=11

Nasri, W. (2012), “Motivating salespeople to contribute to marketing intelligence activities: an expectancy theory approach”, International Journal of Marketing Studies, Vol. 4 No. 1, pp. 168-175, doi: 10.5539/ijms.v4nlpl68.

Noblet, A., Maharee-Lawler, S. and Rodwell, J. (2012), “Using job strain and organizational justice models to predict multiple forms of employee performance behaviours among Australian policing personnel”, International Journal of Human Resource Management, Vol. 23 No. 14, pp. 3009-3026, doi: 10.1080/09585192.2012.656989.

Olejniczak, M. and Salmon, D. (2014), “Workers in German UB II job centers: stress caused by new public management?”, Journal of Workplace Rights, Vol. 17 Nos 3-4, pp. 255-282, doi: 10.2190/WR.17.3-4.b.

Parker, S.K. (2014), “Beyond motivation: job and work design for development, health, ambidexterity, and more”, Annual Review of Psychology, Vol. 65 No. 1, pp. 661-691, doi: 10.1146/annurev-psych-010213-115208.

Pridgeon, A. and Whitehead, K. (2013), “A qualitative study to investigate the drivers and barriers to healthy eating in two public sector workplaces”, Journal of Human Nutrition and Dietetics, Vol. 26 No. 1, pp. 85-95, doi: 10.1111/j.1365-277X.2012.01281.x.

Robertson Cooper (2014), “Introducing ASSET”, available at: www.robertsoncooper.com/how-we-do-it/our-products/asset#what-is-asset (accessed January 12, 2014).

Rockett, P., Fan, S.K., Dwyer, R.J. and Foy, T. (2017), “A human resource management perspective of workplace bullying”, Journal of Aggression, Conflict and Peace Research, Vol. 9 No. 2, pp. 116-127, doi: 10.1108/JACPR-11-2016-0262.

Rodwell, J.J., Noblet, A.J. and Allisey, A.F. (2011), “Improving employee outcomes in the public sector: the beneficial effects of social support at work and job control”, Personnel Review, Vol. 40 No. 3, pp. 383-397, doi: 10.1108/00483481111118676.

Saade, S.L. and Marchand, A. (2013), “Work organisation conditions, alcohol misuse: the moderating role of personality traits”, Work, Vol. 44 No. 2, pp. 191-200, doi: 10.3233/WOR-2012-1408.

Sánchez-Vidal, M.E., Cegarra-Leiva, D. and Cegarra-Navarro, J.G. (2012), “Gaps between managers’ and employees’ perceptions of work-life balance”, International Journal of Human Resource Management, Vol. 23 No. 4, pp. 645-661, doi: 10.1080/09585192 .2011.561219.

Savage, D.A. and Torgler, B. (2012), “Nerves of steel? Stress, work performance and elite athletes”, Applied Economics, Vol. 44 No. 19, pp. 2423-2435, doi: 10.1080/00036846.2011.564150.

Schreurs, B.H.J., Hetty van Emmerik, I.J., Günter, H. and Germeys, F. (2012), “A weekly diary study on the buffering role of social support in the relationship between job insecurity and employee performance”, Human Resource Management, Vol. 51 No. 2, pp. 259-279, doi: 10.1002/hrm.21465.

Sheehan, K.B. and McMillan, S.J. (1999), “Response variation in e-mail surveys: an exploration”, Journal of Advertising Research, Vol. 39 No. 4, pp. 45-54, available at: www.journalofadvertisingresearch.com/ (accessed February 17, 2014).

Shockley, K.M. and Singla, N. (2011), “Reconsidering work-family interactions and satisfaction: a meta-analysis”, Journal of Management, Vol. 37 No. 3, pp. 861-886, doi: 10.1177/0149206310394864.

Siegrist, J. (1996), “Adverse health effects of high-effort/low reward conditions”, Journal of Occupational Health Psychology, Vol. 1 No. 1, pp. 27-41, doi: 10.1037/1076-8998.1.1.27.

Siegrist, J. (2001), “A theory of occupational stress”, in Dunham, J. (Ed.), Stress in the Workplace: Past, Present, and Future, Whurr, Philadelphia, PA, pp. 52-66.

Sinha, V. and Subramanian, K.S. (2012), “Organizational role stress across three managerial levels: a comparative study”, Global Business & Organizational Excellence, Vol. 31 No. 5, pp. 70-77, doi: 10.1002/joe.21443.

Skiba, M. and Rosenberg, S. (2011), “The disutility of equity theory in contemporary management practice”, Journal of Business & Economic Studies, Vol. 17 No. 2, pp. 1-19, available at: http://som.njit.edu/jbes/index.php

Smith, M.R., Mills, M.J., Rasmussen, J.L., Wefald, A.J. and Downey, R.G. (2012), “Stress and performance: do service orientation and emotional energy moderate the relationship?”, Journal of Occupational Health Psychology, Vol. 17 No. 1, pp. 116-128, doi: 10.1037/a0026064.

Smith, P.M. and Bielecky, A. (2012), “The impact of changes in job strain and its components on the risk of depression”, American Journal of Public Health, Vol. 102 No. 2, pp. 352-358, doi: 10.2105/AJPH.2011.300376.

Solanki, K. (2013), “Association of job satisfaction, productivity, motivation, stress levels with flextime”, Journal of Organization and Human Behaviour, Vol. 2 No. 2, pp. 1-10, available at: www.publishingindia.com/johb/

Spurgeon, P., Mazelan, P. and Barwell, F. (2012), “The organizational stress measure: an integrated methodology for assessing job-stress and targeting organizational interventions”, Health Services Management Research, Vol. 25 No. 1, pp. 7-15, doi: 10.1258/hsmr .2011.011016.

Straub, D., Boudreau, M.C. and Gefen, D. (2004), “Validation guidelines for IS positivist research”, Communications of the Association for Information Systems, Vol. 13 No. 1, pp. 380-427, available at: http://aisel.aisnet.org/cais (accessed March 23, 2014).

Swayze, J.S. and Burke, L.A. (2013), “Employee wellness program outcomes: a case study”, Journal of Workplace Behavioral Health, Vol. 28 No. 1, pp. 46-61, doi: 10.1080/15555240.2013.755448.

Tambur, M. and Vadi, M. (2012), “Workplace bullying and organizational culture in a post-transitional country”, International Journal of Manpower, Vol. 33 No. 7, pp. 754-768, doi: 10.1108/01437721211268302.

Tetrick, L.E. and Campbell-Quick, J. (2011), “Overview of occupational health psychology: Public health in occupational settings”, in Campbell-Quick, J. and Tetrick, L.E. (Eds), Handbook of Occupational Health Psychology, 2nd ed., American Psychological Association, Washington, DC, pp. 3-20.

Thi Giang, H., Corbière, M., Neg, A., Minh Khuê, P. and Reinharz, D. (2013), “Validation of the Karasek job content questionnaire to measure job strain in Vietnam”, Psychological Reports, Vol. 113 No. 2, pp. 363-379, doi: 10.2466/01.03.PR0.113x20z3.

Tytherleigh, M.Y. (2003), “What employers may learn from English higher education institutions: a fortigenic approach to occupational stress”, South African Journal of Industrial Psychology, Vol. 29 No. 4, pp. 101-106, available at: http://sajip.co.za/index.php/sajip (accessed April 10, 2014).

Uusitalo, A., Mets, T., Martinmäki, K., Mauno, S., Kinnunen, U. and Rusko, H. (2011), “Heart rate variability related to effort at work”, Applied Ergonomics, Vol. 46 No. 2, pp. 830-838, doi: 10.1016/j.apergo.2011.01.005.

van Scheppingen, A.R., de Vroome, E.M., ten Have, K.C., Bos, E.H., Zwetsloot, G.I. and van Mechelen, W. (2013), “The associations between organizational social capital, perceived health, and employees’ performance in two Dutch companies”, Journal of Occupational and Environmental Medicine, Vol. 55 No. 4, pp. 371-377, doi: 10.1097/JOM.0b013e31828acaf2.

Vroom, V.H. (1964), Work and Motivation, Wiley, Oxford.

Walinga, J. and Rowe, W. (2013), “Transforming stress in complex work environments: exploring the capabilities of middle managers in the public sector”, International Journal of Workplace Health Management, Vol. 6 No. 1, pp. 66-88, doi: 10.1108/17538351311312420.

Wang, P., Walumbwa, F.O., Wang, H. and Aryee, S. (2013), “Unraveling the relationship between family-supportive supervisor and employee performance”, Group & Organization Management, Vol. 38 No. 2, pp. 258-287, doi: 10.1177/1059601112472726.

Wei, Y.S., Frankwick, G.L. and Nguyen, B.H. (2012), “Should firms consider employee input in reward system design? The effect of participation on market orientation and new product performance”, Journal of Product Innovation Management, Vol. 29 No. 4, pp. 546-558, doi: 10.1111/j.1540-5885.2012.00924.x.

Wolever, R.Q., Bobinet, K.J., McCabe, K., Mackenzie, E.R., Fekete, E., Kusnick, C.A. and Baime, M. (2012), “Effective and viable mind-body stress reduction in the workplace: a randomized controlled trial”, Journal of Occupational Health Psychology, Vol. 17 No. 2, pp. 246-258, doi: 10.1037/a0027278.

Deery, S., Walsh, J. and Zatzick, C.D. (2014), “A moderated mediation analysis of job demands, presenteeism, and absenteeism”, Journal of Occupational and Organizational Psychology, Vol. 87 No. 2, pp. 352-369, doi: 10.1111/joop.12051.

Ho, V. (2012), “Interpersonal counterproductive work behaviors: distinguishing between person-focused versus task-focused behaviors and their antecedents”, Journal of Business & Psychology, Vol. 27 No. 4, pp. 467-482, doi: 10.1007/s10869-012-9256-7.

Safaria, T. (2014), “Are leadership practices, role stressor, religious coping, and job insecurity predictors of job stress among university teachers? A moderated-mediated model”, International Journal of Research Studies in Psychology, Vol. 3 No. 3, pp. 87-99, doi: 10.5861/ijrsp.2014.750.

#### Supplementary materials

IJPPM_68_6.pdf (7.5 MB)

## Acknowledgements

The authors thank Dr Tommy Foy for providing the seminal research, which significantly contributed to development of the paper.

## Corresponding author

Rocky J. Dwyer is the corresponding author and can be contacted at: rocky.dwyer@mail.waldenu.edu