An Examination of Stressors, Strain, And Resilience in Academic and Non-Academic U.K. University Job Roles

International Journal of Stress Management An Examination Exami nation of Stres Stress sors, St Strain, rai n, and an d Resil Resilien ience ce in Academic and Non-academic U.K. University Job Roles Sheena J. Johnson, Sara M. Willis, and Jack Evans Online First Publication, April 16, 2018. http://dx.doi.org/10.1037/str0000096

CITATION John Johnso son n, S. J., Will Willis, S. M., M., & Evan Evans, s, J. J. (2018, (2018, April April 16). An Exam Examiinati nation on of Stres Stresso sors, rs, Stra Strain in,, and and Resilience in Academic and Non-academic U.K. University Job Roles. International International Journal of o f Stress Stress Management Manageme nt . Ad Advan vance ce onlin online e publicat publication ion.. http:/ http://dx.do /dx.doi. i.org/1 org/10.1037/s 0.1037/str0000096 tr0000096

International Journal of Stress Management © 2018 American Psychological Association 1072-5245/18/$12.00

2018, Vol. 25, No. 1, 000 http://dx.doi.org /10.1037/str0000096

An Examination of Stressors, Strain, and Resilience in Academic and Non-academic U.K. University Job Roles

. y l . d s a r e o r h s b i l d e b t u a p i n d m e i e l s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a i c i i v g d o n l o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l g y i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o c d l t s i r i h a s T i h T

Sheena J. Johnson and Sara M. Willis

Jack Evans

University of Manchester

Robertson Cooper Limited, Manchester, United Kingdom

Recent years have seen changes within the academic profession including decreased perceptions of autonomy and job security, increasing student numbers and teaching quality focus, and greater emphasis on high-quality research outputs. Such changes arguably lead to increased workplace stress, and given the potential negative impact of high workplace stress levels on health and work-related outcomes, a consideration of stressors and strain within academia is timely. In this article, we compared stressors and strain across U.K. academic and non-academic university job roles. The article also determines which stressors are the strongest drivers of poor health and considers the role of resilience in the stressor–strain relationship. The sample consisted of participants from three U.K. universities using the ASSET (A Shortened Stress Evaluation Tool) stress measure that gives information on eight stressors and two measures of strain (psychological and physical ill-health). As data sets varied across organizations, different subsamples were used for analysis, with sample sizes of N  2,779 to N  652, with the majority of the analysis using the smaller sample. Academics reported better physical health, higher levels of work overload, poorer work–life balance, better job conditions and work relationships, and less concern about pay and benefits in comparison with non-academic employees. For both academic and non-academic staff, the stressors work–life balance and aspects of the job were associated with psychological and physical ill-health, and stressors that impact ill-health did not differ by job type. Resilience had a direct effect on psychological and physical ill-health as well as an indirect effect by influencing perceptions of stressors. Keywords: workplace stress, resilience, health, mediation, academic

Academics work in a demanding environment and are required to perform complex tasks (Houston, Meyer, & Paewai, 2006). Perhaps

unsurprisingly then, there is a history of research indicating that an academic setting is likely to expose employees to high levels of workplace stress. A review in 2000 revealed that academic stress levels had increased over the preceding 15 years, and academic stress was reported to be higher than other occupations (Winefield, 2000). The high levels of stress in academia were further supported in 2005 through a comparison of 26 occupations, which revealed that lecturers had worse than average psychological well-being when compared with a norm score derived from a large data set of 25,000 working individuals (Johnson et al., 2005). An earlier study investigating stress in academia reported that academics believed work was their most significant cause of stress and placed three quarters of academic staff in a

Sheena J. Johnson and Sara M. Willis, Alliance Manchester Business School, University of Manchester; Jack Evans, Robertson Cooper Limited, Manchester, United Kingdom. Preliminary findings from the study were presented at a symposium at the Institute for Work Psychology Conference in Sheffield, United Kingdom, in June 2016. The presentation was entitled “Resilience in academic employees.” Correspondence concerning this article should be addressed to Sheena J. Johnson, Alliance Manchester Business School, University of Manchester, Booth Street East, Manchester M15 6PB, United Kingdom. E-mail: sheena [email protected] 1

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. y l . d s a r e o r h s b i l d e b t u a p i n d m e i e l s l s a i s d t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i d o l i n o h e c h t y s P f o n e a s c u i r l e a m n s A o r e e h t p e y t b h r d o e f t h l g y i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o d l c t s i r i h a s T i h T

JOHNSON, WILLIS, AND EVANS

moderate stress category, and 10% in a serious well-being (Kinman & Jones, 2008; Tytherstress category (Abouserie, 1996), indicating leigh, Webb, Cooper, & Ricketts, 2005). Given that workplace stress was a significant concern the accepted potential negative impact of high in an academic setting 20 years ago. Recent workplace stress levels on health and workstudies continue to indicate high levels of stress related outcomes, a consideration of stressors in academic settings (Shin & Jung, 2014). It is and strain within academia is therefore timely. apparent then that workplace stress and the reThere have been numerous studies of stress in lated potential impact of such stress on em- academic settings; however, the focus of such ployee health and well-being is a significant risk studies is typically on academic employees and factor within the academic profession. not on the support staff working within acaA number of studies have investigated academia, although a study in 2002 compared job demic job roles in more depth to understand the roles and reported lower job stress levels in potential causes of stress in academia, reporting support staff compared with academic staff issues such as funding reductions, relatively low (Hogan et al., 2002). Given the significant pay, working long hours and heavy workloads, changes in the profession, as outlined earlier, increased student numbers, poor communica- academic support staff are also potentially negtion, role ambiguity, and publication expecta- atively affected by high levels of workplace tions (Kinman, 2008; Rutter, Herzberg, & stress, particularly in relation to increasing stuPaice, 2002; Winefield & Jarrett, 2001). It is dent numbers and related increased workload. It generally accepted that excessive workplace is pertinent then to include both academics and stress can lead to negative health outcomes such academic support staff in an investigation of as burnout (Schaufeli, 2003). This was con- workplace stress levels and related health outfirmed in an academic study by Barkhuizen, comes in academia in the United Kingdom. Rothmann, and van de Vijver (2014) who reA number of general workplace stressors ported that a lack of job-related resources and have been identified in the empirical literature job demands contributed to burnout. Other ac- as being potentially important with regard to an ademic-based studies have also supported links employee’s health and well-being. For example, between work stress and health and well-being Cooper and Marshall’s (1976) work stress outcomes; for example, physical health (Wine- model described five potential sources of workfield, Gillespie, Stough, Dua, & Hapuararchchi, place stress including intrinsic job factors (e.g., 2002), poor mental health (Doyle & Hind, poor working conditions or work overload), the 1998), reduced commitment to the organiza- employee’s role in the organization (e.g., role tions (Kinman, 2001), and medical symptoms conflict), career development (e.g., job insecu(Hogan, Carlson, & Dua, 2002) have all been rity), workplace relationships (e.g., workplace linked to workplace stress. bullying), and organizational climate (e.g., lack One reason for the reported increasing stress of involvement in decision-making). Workplace levels in academia is believed to be the signif- stress measures are designed to inform on such icant changes in the academic working environ- workplace stressors and are commonly used to ment that have occurred over the previous 2 provide an overview of how employees are bedecades (Kinman, 2008). Change continues to ing affected. One such measure, influenced by be a factor in academic working environments, established models of stress such as Cooper and and recent years have seen continued significant Marshall’s work stress model, is ASSET (A changes within the academic profession includ- Shortened Stress Evaluation Tool), which ining increasing student numbers, increasing forms on eight workplace stressors and physical workload, increasing focus on teaching quality, and psychological ill-health. The eight workcontinued focus on high-quality research out- place stressors measured by ASSET tool are puts, and decreased perceptions of autonomy overload, work–life balance, resources and and job security (University and College Union communication, job security, work relation[UCU], 2016). Such changes are arguably likely ships, control, pay and benefits, and aspects of to lead to increased levels of workplace stress job (referring to working conditions and unnecwithin the academic workplace, with studies essary change). ASSET tool has been used exshowing that continuous change in academic tensively, is a valid and reliable tool, and has settings can have a negative effect on employee been used to demonstrate the links between high

STRESSORS, STRAIN, & RESILIENCE IN UNIVERSITIES


workplace stress levels and poor employee health outcomes (Faragher, Cooper, & Cartwright, 2004; Johnson & Cooper, 2003). The ASSET tool measure has recently been expanded to include a Resilience scale drawing from the theoretical model of resilience proposed by Robertson and Cooper (2013) about which we provide more detail in the resilience section in the following text. The aforementioned review of the literature details the high levels of stress previously reported in an academic work setting, and the continuous changes reported in the profession that have the potential to negatively affect employee health and well-being. We argue that it is timely to review and better understand current levels of stressors and strain within a U.K. academic setting and propose the following two aims of the article, which will be investigated using the ASSET tool. Aim 1: Examine and compare reported

stressors and strain across U.K. academic and non-academic university job roles. Aim 2: Consider how stressors are related

to health outcomes and to determine which stressors are the strongest drivers of poor physical and psychological health across U.K. academic and non-academic job roles. Resilience

Resilience has been described as a phenomenon inferred from research findings that people are differentially affected by stressors (Rutter, 2013), and as “the ability to bounce back from negative emotional experiences and flexibly adapt to the changing demands of stressful experiences” (Hu, Zhang, & Wang, 2015, p. 18). One approach to resilience proposes that resilience is a personality trait that helps people successfully cope with stressors although Windle (2011) reviewed the evidence and argued that resilience should not be viewed as a stable personality trait, as it is dynamic and will change over time. Other conceptualizations of resilience include seeing it as a behavioral outcome, or as a dynamic process during which individuals can adapt and recover (Hu et al., 2015) and as a capacity that develops over time (Egeland, Carlson, & Sroufe, 1993). Fletcher

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and Sarkar (2013) reviewed resilience definitions and concluded resilience is best defined as “the role of mental processes and behavior in promoting personal assets and protecting an individual from the potential negative effect of stressors” that includes both process and trait resilience conceptualizations (Fletcher & Sarkar, 2012, p. 675). Despite the evident disagreement between researchers in defining and understanding resilience, which has hindered research (Hu et al., 2015), it is generally believed to be a multidimensional construct (Campbell-Sills, Cohan, & Stein, 2006). Robertson and Cooper (2013) discussed how resilience is unlikely to be a unidimensional construct. Drawing on different theories of resilience (e.g., as reviewed by Haglund, Nestadt, Cooper, Southwick, & Charney, 2007), they described a practical model of resilience that includes four components: adaptability (flexibility and adapting to changing situations), confidence (feelings of competence and effectiveness), purposefulness (having a clear sense of purpose), and social support (good relationships with others). Moreover, the model conceptualizes resilience as a malleable characteristic, as Robertson and Cooper (2013) argued that research indicates that although resilience might be related to personality, it is not a fixed characteristic (Cooper, Flint-Taylor, & Pearn, 2013). In support of this, Robertson, Cooper, Sarkar, and Curran (2015) showed in their review of resilience training studies that training can improve personal resilience and assist employee mental health and well-being development. In the present study, we use this multidimensional conceptualization of resilience (adaptability, confidence, purposefulness, and social support) and adopt Robertson and Cooper’s (2013) approach to resilience as an individual characteristic that can be developed. Despite the lack of clarity surrounding the conceptualization of resilience, it is apparent from research evidence that resilience can be practically important and it has been indicated as one explanation for why some employees exposed to high levels of workplace stressors do not experience burnout and are better able to manage challenges (Kinman & Grant, 2011). Resilient individuals are seen to have greater psychological well-being and reduced rates of depression (Burns, Anstey, & Windsor, 2011), and less psychological distress (Kinman &

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Grant, 2011). Although these studies point to the importance of resilience, there is limited understanding of the explanatory mechanisms underlying the effects of resilience. Moreover, research on resilience mechanisms are drawn from studies of resilience in varied settings, for example, patients, children, students, and the elderly, and are not typically focused on workplace resilience. Workplace resilience is a relatively new concept, with research on workplace resilience steadily increasing over the past decade ( Crane & Searle, 2016; Vanhove, Herian, Perez, Harms, & Lester, 2016). However, researchers are only just beginning to investigate mechanisms through which resilience may be related to the experience of work stressors and strain (Windle, 2011). Work-based studies that have investigated resilience report varied findings, and despite the evidence linking resilience, work stressors, and burnout to the best of our knowledge, no studies have investigated the relationship between resilience, work stressors, physical health, and psychological well-being. Based on the evidence, we expect resilient individuals to be less negatively affected by workplace stressors. Given the lack of consensus of how resilience may interact with work stressors and employee well-being, we propose that resilience may play a role in both a moderating and mediating model. First, a moderator model assumes resilience may act as a buffer between stressors and health. It is suggested that employee resilience may play a (positive) buffering role between stressors and health, with some nonwork-based studies supporting this buffering hypothesis (Catalano, Chan, Wilson, Chiu, & Muller, 2011). In further support of a potential moderating role of resilience, there is extensive evidence to support personality variables playing a moderating role between stressors and strain (Grant & Langan-Fox, 2007), which, given that conceptualizations of resilience believe it to be related to personality (Cooper et al., 2013), may help to explain a potential moderating role of resilience. In one of the few work-based studies to date, Hao, Hong, Xu, Zhou, and Xie (2015) reported a moderator effect of resilience between work stressors and burnout, suggesting resilience acts as a buffer that can reduce the potential negative impact of work stress. Studies on nurses and doctors provide support for

this, showing resilience to buffer risk factors and burnout (Manzano-García & Ayala Calvo, 2012; Mealer et al., 2012; Taku, 2014). Thus, we argue that resilience may play a similar moderating role between stressors and strain and propose the following aim: Aim 3: Investigate the role of resilience in

the stressor–strain relationship by testing resilience as a moderator of the effect of workplace stressors on ill-health. An alternative mediator model tests whether resilience influences an individual’s perceptions of workplace stressors, which in turn will impact on health outcomes. Whereas some research has reported resilience as a partial mediator in the stress–strain relationship when investigating whether the experience of stressors can deplete one’s resilience (Hao et al., 2015), our study aims to explore resilience as a capacity to react more effectively to stressful events. Hao et al.’s (2015) study provides some support for this as, in addition to identifying resilience as a partial mediator, they also report that workplace stressors play a partial mediating role between resilience and burnout. This indicates that resilience can help prevent burnout development by influencing and relieving the experience of workplace stressors. In our mediation model, we therefore position resilience as an independent variable that affects employees’ perceptions of workplace stressors, which consequently impacts strain levels. This model is in line with conceptualizations that resilience affects employees’ reactions to workplace experiences and with individuals high in resilience processing stressful events as less threatening (Avey, Luthans, & Jensen, 2009). One explanation for this is that resilience can affect how individuals perceive and react to workplace stressors. Theoretically this is in line with Bolger and Zuckerman’s (1995) differential exposure–reactivity framework, which proposes that “personality affects both the exposure and reactivity stages of the stress process” (p. 891). Their study showed that, in comparison with low-neuroticism participants, high-neuroticism participants reported experiencing more daily conflicts and increased likelihood of reacting to conflicts with depression and anger. Using a similar framework for resilience would support the argument that highly resilient individuals



will perceive lower stressors due, for example, to their greater resources and confidence in dealing with workplace stress, and will have a decreased likelihood of reacting to workplace stressors with a negative strain response. Thus, the mediation model tested in our study proposes that individuals with high resilience levels will be less troubled by work stressors, which in turn results in better health outcomes (Figure 1). The final aim of the current study is therefore as follows: Aim 4: Investigate the role of resilience in

the stressor–strain relationship by testing whether resilience influences perceptions of workplace stressors, which in turn affect ill-health. Method Sample and Procedure

A total of 2,821 (1,025 male, 1,784 female, and 12 not reported) employees in three higher education institutions ( N  1,396 for Organization 1, N  764 for Organization 2, and N  661 for Organization 3) completed ASSET tool

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anonymously online. Respondents were categorized as either academic ( N  864) or nonacademic ( N  1,937) employees within their respective higher education institution (20 participants did not report their job type). Nonacademics consisted of non-teaching support staff, for example, working in administrative or clerical roles. A smaller sample of N  652 ( N academic  222; N non-academic  430) was used for most parts of the analysis, as not all variables were assessed in each participating organization. Surveys were conducted in the three institutions independently with data collected in Organization 1 in 2010, Organization 2 in 2014/2015, and Organization 3 in 2015. Due to organizational constraints, the psychological and physical ill-health measures were not included in Organization 2 and resilience was not measured in Organization 1, as the scale was still under development at the time. Hence, analysis relating to ill-health was only possible using the data from Organizations 1 and 3 ( N  2,014 with listwise deletion; i.e., 43 cases excluded due to missing values), and analysis including ill-health as well as resilience was based on data from Organization 3 only ( N  652 with

Work Relationships Work-Life Balance Overload

Resilience

Physical Health

Job Security Resources and Communication

Psychological Health

Control Pay and Benefits Aspects of Job

Mediation model with ASSET stressor variables as mediators in the relationship between resilience and physical and psychological health. Results for the model are reported in Table 5. Figure 1.

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listwise deletion, i.e., nine cases deleted due to missing values). Table 1 shows the sample information for each of the three organizations. With regards to assessing the study aims, this means that analyses conducted for Aim 1 were based on the complete sample from Organizations 1, 2, and 3 ( N  2,779) to compare total stressor levels (all eight stressor variables combined) between academics and non-academics and on a subsample ( N  2,014) from Organizations 1 and 3 to compare levels of individual stressors and ill-health between the two job groups. Aims 2, 3, and 4 were tested using data from Organization 3 only ( N  652). In all parts of the analysis, sample sizes can be considered large, and exact sample sizes are reported in the tables’ notes. Measures

The data for this study were collected using items from the ASSET tool (Faragher et al., 2004), which includes scales on stressors, illhealth, and resilience. Previous research has demonstrated good psychometric properties for the ASSET tool questionnaire (Donald et al., 2005; Faragher et al., 2004; Johnson & Cooper, 2003). To measure stressors, ASSET tool includes 37 items designed to assess how psychologically troubled respondents are by certain work pressures. There are eight subscales within this, which are as follows: Resources and Communication (four items), Control (four items), Overload (four items), Work–Life Balance (four items), Job Security (four items), Work Relationships (eight items), Pay and Benefits (one item), and Aspects of the Job (eight items; referring to working conditions and change at work). Items are preceded by the phrase, “I am troubled that . . .,” for example, “I

am troubled that I am given unmanageable workloads.” Responses are given on a 6-point Likert scale, ranging from 1 ( strongly disagree ) to 6 (strongly agree). Stressor scores showed good reliability ranging between   .72 to .85 (Table 2). Pay and benefits is measured using a single item; therefore, no reliability is reported (Faragher et al., 2004). The eight stressor subscales can be combined into a total workplace stressor index, which has been supported by previous research (Nikolaou & Tsaousis, 2002; Vakola & Nikolaou, 2005) and showed good reliability in the current study (   .93). ASSET tool also includes two Self-Reported Health scales. Respondents report the extent to which they have experienced specific physical ill-health (six items, e.g., “Insomnia—sleep loss”) and psychological ill-health symptoms (11 items, e.g., “Panic or anxiety attacks”) during the previous 3 months (4-point scale: 1  never to 4  often). The two ill-health scales have been shown to be reliable and valid in previous research (Faragher et al., 2004; Johnson & Cooper, 2003) and had good reliability in the present study (Psychological Ill-Health:   .92; Physical Ill-Health:   .77). Finally, the ASSET tool includes a Resilience scale that measures participants’ current levels of psychological resilience on four items that were designed to correspond with the theoretical model and four components of resilience proposed by Robertson and Cooper (2013): confidence, adaptability, purposefulness, and the use of social support (e.g., “Right now at work I feel confident that I can deal with difficulties when they arise”). Respondents indicate the extent to which they agree with the four statements on a 0 –100-point scale, with higher scores indicative of higher levels of resilience. The scale showed

Table 1 Sample Size Information for the Three Higher Education Institutions Sample details Total sample size N non-academic N Academic Variables measured

University 1 1,396 894 487 (15 missing) 8 ASSET stressors

University 2

University 3

764 610 153 (1 missing) 8 ASSET stressors Resilience

661 433 224 (4 missing) 8 ASSET stressors Resilience Psychological health Physical health

Psychological health Physical health Note.

ASSET  A Shortened Stress Evaluation Tool.


. e s good reliability (  .81). The Resilience scale c i m a s m has been recently added to the ASSET tool, and e e d  h a t evidence of scale structure has not previously c a d been published. We therefore conducted a con r e ) o w 1 8 5 0 0 3 3 0 7 3 0 2 2 f o 1 4 firmatory factor analysis with the four items . 6 . 4 . 4 . 4 . 2 . 4 . 4 . 4 . 1 . 5 . 9 . l ( a h  n s n loading onto one overall Resilience factor. As o o g i t a i e resilience was only measured in Organizations 2 ) 0 0 5 1 7 1 4 2 1 8 1 1 7 d l e e 1 6 8 4 3 5 2 6 6 6 1 6 3 d and 3, the confirmatory factor analysis was . . . . . . . . . ( . . . t h e            s i based on a sample size of N  1,418 (listwise w w o r l i e deletion; N missing  9). The model indicated 8 0 3 1 2 7 4 5 8 4 b a p 9 4 — 6 2 2 3 3 3 3 3 2 1 1 . . . . . . . . . . . d good fit, with 2  26.87, p  .001, compara h n i a t  w tive fit index  .98, Tucker–Lewis index  .93, s c x i i r root mean square error of approximation  .09, ) 3 5 4 7 2 4 3 1 3 7 2 m e t a 8 6 7 7 4 3 6 2 7 7 3 6 5 4 . . . . . d . . . . . . . ( standardized root mean square residual  .02. m a c  n a Each of the four items loaded significantly on - o n i t a o l ) the Resilience factor with factor loadings of .77 5 4 8 1 7 5 1 2 3 6 2 n r 7 2 r e 8 7 4 6 4 3 r . . 4 . 3 . 7 . 3 . 7 . 8 . . . . . o (Resilience— confidence item), .74 (Resil ( f o c  e r a ience—social support item), .73 (Resilience— a h l t i ) a purposefulness item), and .67 (Resilience— 1 2 3 7 3 5 9 7 4 6 2 n w 6 0 8 7 6 3 6 4 3 o s . . 5 . 4 . 7 . 2 . 8 . . . . . . adaptability item). g ( a i s  i y d l e a n h ) t Results 0 4 4 3 8 0 4 7 5 8 9 e a 5 5 5 . 2 . 3 . 2 . 3 . 7 . 4 . 3 . 4 . 2 . 2 . 1 . v n o ( s o i  b a a r Descriptive statistics and zero-order correla s p n ) tions for the study variables are displayed in o m o 4 7 2 3 9 7 3 5 9 3 6 t i 4 1 c 8 3 6 7 6 3 5 4 2 . . 5 . 3 . 7 . . . . . . . . a ( Table 2. As expected all eight stressor variables l A e  r . r 0 were positively correlated with psychological o 3 C 4 ) and physical ill-health. Using the data from all . 1 7 8 9 9 4 2 1 1 7 5 s 3 0  6 2 1 2 3 4 2 3 4 4 e . 6 . 7 . . . . . . . . . . s ( c three organizations, comparison through t test e i  h t m of the total stressor score showed higher levels e n d e ) r a of stress for academics ( N  852) compared c 1 1 3 2 8 4 6 9 1 2 2 a 2 0 p a 7 6 5 2 4 5 5 2 5 5 4 . . 8 . . . . . . . . . . ( n n with non-academics ( N  1,927; missing data i  o d n l were deleted listwise, N missing  42), al o ) 2 7 8 8 6 4 7 8 3 6 2 b N though differences were small (academics total n d 1 3 n i 9 7 5 7 5 7 8 7 6 6 5 4 . . . . . . . . . . . . a l ( stress M  2.89, SD  .81; non-academics total a 2  n 2 o 2 stress M  2.78, SD  .79), t (2777)  3.41, 0 4 4 6 6 2 2 2 2 5 6 7 g a  8 . 1 . 1 . 0 . 1 . 9 . 2 . 8 . 7 . 4 . 6 . 6 . i D p  .001, 95% confidence interval for mean S 0 1 1 1 1 0 1 0 1 8 0 0 d i c 1 e h m difference [0.04, 0.17]. To further investigate e ) ) ) ) ) ) ) ) ) ) ) ) t d 8 7 5 5 5 7 8 0 3 8 1 9 n a differences between academic and non-aca 1 1 1 1 1 1 2 0 1 5 4 o c ) 9 7 , 8 , 8 , 8 , 8 , 8 , 8 , 8 , 8 , 4 , 0 , 0 , d a N demic employees (Aim 1), a one-way multivar ( 2 ( 2 ( 2 ( 2 ( 2 ( 2 ( 2 ( 2 ( 2 ( 1 ( 2 ( 2 ( e y a N . 2 2 6 9 2 6 0 3 1 1 0 9 l M 8 iate analysis of variance was computed on the n . 9 . 6 . 8 . 8 . 4 . 1 . 5 . 2 . 1 . 1 . 1 . p o s i . 2 2 2 2 2 2 3 2 3 3 2 2 i t 7 d l e 1 0 eight stressor variables (work–life balance, e e 0 r . work overload, job security, job control, re n a d o e s i t s  sources and communication, work relation e i a i s t c i w p r i l t i o n ships, aspects of job, and pay and benefits) and s h s i b t u l l s a e a i n m l r e . the two ill-health outcomes (psychological and t o 1 e e m s r S p H c h d - t e e n o i l l . physical ill-health) based on the data from Or s l  e 0 l t a C h a b s s c I l . s fi e a l a n  ganizations 1 and 3 ( N  2,014; N academic  a a d n b ’ b l i o e r o J r H h a p B n i n e p y a k c c a t e l t t e i l a r t e i a f 697; N non-academic  1,317). A significant V r l e I r g B a b o o d f i s e e c l u o l n s a p c a L l s d n c a t n i W o i e h . multivariate effect was detected, Wilk’s  F (10, o – r e R o c o r n u S l o n r c l a l k k o 5 i r t e s C o i i r r c t a 0 t  52.61, p  .001, 2  .21. Means, 2003) n p t e s s y . y a o o y b a v s l e o e o a s h l o W T O W R J C A P R P P t . e r r  standard deviations, and univariate F values for r e . . . . . . . . . . . . e p 1 2 3 4 5 6 7 8 9 0 1 2 o o r all dependent variables are reported in Table 3. 1 1 1 N C o c   

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8


Table 3 Univariate Statistics for Mean Comparison Between Academics and Non-Academics on Stressor and Health Variables


Variable

Academic M ( SD)

Non-academic M ( SD)

F value

p value

2

Overload Work–Life Balance Resources and Communication Job Security Work Relationships Control Aspects of Job Pay and Benefits Psychological Ill-Health Physical Ill-Health

3.19 (1.15) 3.23 (1.18) 2.93 (1.07) 2.76 (1.21) 2.39 (0.88) 3.09 (1.18) 2.35 (0.78) 3.10 (1.71) 2.12 (0.65) 2.11 (0.67)

2.76 (1.09) 2.43 (1.02) 2.93 (1.06) 2.81 (10.13) 2.52 (0.93) 3.14 (1.24) 2.58 (0.81) 3.33 (1.74) 2.08 (0.66) 2.24 (0.66)

66.75 249.21 0.01 1.00 9.22 0.75 38.15 8.18 1.62 18.02

.001 .001 .924 .318 .002 .385 0 .004 .203 .001

.03 .11 .001 .001 .001 .001 .02 .001 .001 .01

Note. 2  estimated effect size. Bonferroni-corrected level of significance with  at .05 is p  .005. Total N  2,014; N academic  697; N non-academic  1,317. Missing data are deleted listwise.

Compared with their non-academic colleagues, academics reported poorer work–life balance, higher levels of work overload, better job conditions and work relationships as well as lower levels of concern regarding pay and benefits. Thus, these results suggest that the main sources of stress might be different for academics and non-academics. With regards to health, academics reported better physical health. No significant difference emerged between academics’ and non-academics’ psychological ill-health. To address Aim 2 and explore which stressors are most strongly related to ill-health in academics and non-academics, hierarchical multiple regression was conducted whereby all independent variables (i.e., eight stressors, job type, resilience) were entered in a first block to assess the main effects, and interaction terms between stressor variables and job type (academic/non-academic) were entered in a second step. Separate regression models were conducted for psychological ill-health and physical ill-health respectively. Hence, this analysis investigated whether unique stressors drive psychological and physical ill-health for academics compared with employees in non-academic positions (Table 4). To aid interpretation of the results, independent variables were standardized before analysis. Main effects revealed that out of the eight stressor variables, work–life balance, aspects of job, work overload, and job security were related to psychological ill-health (Table 4, Block 1). Work–life balance and aspects of job were also related to physical illhealth. When entering the interaction terms be-

tween the stressor variables and job type (academic/non-academic), no significant effects were found (see Table 4, Block 2). Thus, there was no support for job type to influence the relationship between individual stressor variables and psychological and physical ill-health. In other words, the results from moderation analysis suggest that the sources of stress that most strongly impact on ill-health do not differ for academics and non-academics. Aim 3 proposed resilience as a moderator of the relationship between workplace stressors and psychological and physical ill-health. To test this, we extended the regression model that addressed the second study aim by adding interaction terms between resilience and the eight stressor variables in a third step. Given the large sample size of the study, it was appropriate to test both moderators (i.e., job type and resilience) within the same model. This analysis showed that resilience emerged as a significant moderator for the relationship between work– life balance and psychological ill-health, but was not a significant moderator for any other stressor variables (Table 4, Block 3). Simple slope analysis of the moderation effect showed that the relationship between work–life balance stressors and psychological ill-health was significant when resilience was low ( b  .28, t  8.82, p  .001), but was nonsignificant when resilience was high (b  .08, t  1.12, p  .26). Hence, if resilience is low, experiencing stress from poor work–life balance has a negative effect on psychological health. However, if resilience is high, this effect becomes nonsignif-


9

Table 4 Hierarchical Regression for Stressors, Resilience, and Job Type Main Effects (Block 1) and Academic Job (Block 2) and Resilience (Block 3) as Moderators With Psychological Health and Physical Health as Dependent Variables Dependent variables Psychological Ill-Health Variable . y l . d s a r e o r h s b i l d e b t u a p i n d m e i e l s l s a i s d t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i d o l i n o h e c h t y s P f o n e a s c u i r l e a m n s A o r e e h t p e y t b h r d o e f t h l g y i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o d l c t s i r i h a s T i h T

Constant Block 1 Main effects Overload Work–Life Balance Resources and Communication Job Security Work Relationships Control Aspects of Job Pay and Benefits Resilience Academic/Non-academic Block 2 Moderator: Academic/Non-academic Overload  Academic Job Work–Life Balance  Academic Job Resources and Communication  Academic Job Job Security  Academic Job Work Relationships  Academic Job Control  Academic Job Aspects of Job  Academic Job Pay and Benefits  Academic Job Block 3 Moderator: Resilience Overload  Resilience Work–Life Balance  Resilience Resources and Communication  Resilience Job Security  Resilience Work Relationships  Resilience Control  Resilience Aspects of Job  Resilience Pay and Benefits  Resilience R2adjusted

Physical Ill-Health



SE



SE

1.89

.05

1.28

.05

.16 .18 .001 .14 .04 .13 .25 .08 .30 .03

.05 .04 .05 .04 .06 .06 .05 .04 .03 .05

.15 .27 .09 .08 .01 .02 .20 .06 .08 .12

.004

.06 .05 .07 .05 .07 .08 .07 .04











.004

.001 .07 .03

.06 .03 .004 .07 .10 .001 .03 .03 .08 .08 .02 .40 

.03 .02 .03 .02 .03 .03 .04 .02

.05 .13

.05 .03 .02 .04

.08 .05 .02

0 .05

.06 .02 .07 .04 .07







.05 .06 .05 .06 .05 .06 .07 .06 .04 .04 .07 .06 .08 .06 .08 .09 .08 .05 .03 .03 .04 .03 .03 .04 .04 .02

R2adjusted  adjusted R-square for the overall model; N  652; N academic  222; N non-academic  430. Missing data are deleted listwise. p  .05. p  .01. p  .001.

Note. 





icant, suggesting that resilience acts as a buffer (Figure 2). It should be noted though that we only found one significant interaction, so that resilience might only be an effective buffer with regards to specific sources of stress (i.e., work– life balance) and might not protect employees from the detrimental effects of other stress sources. Finally, in a separate regression model, we tested whether resilience predicts employees’ stressor perceptions, which in turn predict physical and psychological ill-health (Aim 4). Thus, we

assessed whether resilience has an indirect effect on health outcomes through influencing how individuals perceive stressors in their environment (Figure 1). For this, we conducted path analysis in MPlus software Version 7.1 using the INDIRECT command (Muthén & Muthén, 2010). The advantage of assessing mediation in MPlus is that estimates for direct effects, indirect effects for individual mediators and all mediators combined as well as total effects are provided. Thus, for Aim 4, we tested the eight stressor variables as mediators in the relationship between resilience and psycho-

10

JOHNSON, WILLIS, AND EVANS 5 4.5 4


h t l a e 3.5 H l l I 3 l a c i g 2.5 o l o h 2 c y s P

Low Resilience High Resilience

1.5

1 Low Work Life Balance Stressor

High Work Life Balance Stressor

Resilience as moderator of the relationship between work life balance stressor and psychological ill-health. High and low values are plotted at 1 SD above and below the variable mean. Figure 2.

logical and physical ill-health. A path model was specified with direct effects between resilience and psychological and physical ill-health as well as indirect effects through the eight stressor variables. Results revealed that resilience showed a significant direct as well as an indirect effect on both ill-health outcomes (Table 5). When examining the eight stressor variables as individual mediators, results showed that the indirect effect was mainly mediated by aspects of job for both

psychological and physical ill-health (.14 and .18, respectively). Resilience also had significant indirect effects through overload and work– life balance on physical and psychological illhealth, but the effects were small. Discussion

The present research set out to determine the main workplace stressor sources for academics

Table 5 Indirect Effects of Resilience on Psychological Ill-Health and Physical Ill-Health Through ASSET (A Shortened Stress Evaluation Tool) Stressor Variables Psychological Ill-Health Effect Mediated effect of resilience Direct effect (resilience on health outcomes) Total indirect effect (through eight stressor variables combined) Total effect (direct and indirect effects combined) Specific indirect effects through individual stressor variables Overload Work–Life Balance Resources and Communication Job Security Work Relationships Control Aspects of Job Pay and Benefits Note.



.32 .23 .55







.07 .06



.01 .02 .002 .03 .14 .01 



SE

Estimate

.04 .04 .03

.13 .24 .37



.02 .02 .03 .01 .04 .04 .03 .01

.08 .04









.05 .01 .01 .03 .18 .003



SE

.05 .04 .04 .02 .02 .04 .01 .04 .04 .03 .007

N  652; N academic  222; N non-academic  430. Missing data are deleted listwise. The estimates in this table

correspond to the model displayed in Figure 1. p  .05. p  .01. p  .001. 

Estimate

Physical Ill-Health







in comparison with non-academic staff (Aim 1), to understand the main stressors that influence psychological and physical ill-health for academics and non-academics (Aim 2), as well as to advance the understanding of the role of resilience in the stressor–strain relationship (Aims 3 and 4). The study brought about several important findings by identifying unique stressors for academic and non-academic staff and demonstrating that resilience can influence how employees perceive psychosocial risks in their work environment. Results revealed that the main stressors within a work environment can be specific to particular occupations or job groups. It has been argued that occupation-specific investigations into stress are more likely to produce concrete and useful guidance for practitioners (de Jonge, Dollard, Dormann, Le Blanc, & Houtman, 2000; Sparks & Cooper, 1999). Thus, by identifying overload and work–life balance as two stressors that are particularly prevalent for academics, and aspects of job, work relationships, and pay and benefits as salient stressors for non-academic staff, our study provides insights where universities might target stress prevention efforts for these two core staff groups. Recent changes in the academic working environment have involved growing student numbers, an increased emphasis on teaching quality together with strong expectations regarding research outputs, and securing of research funding (UCU, 2016). Our finding that overload and poor work–life balance are two key areas of concern for academics, fits with such demands. In a similar vein, one area of concern reported by non-academics related to aspects of the job, referring to working conditions and unnecessary change, which may also be a result of the changes occurring in academic settings such as increasing student numbers and related workload that will impact on non-academic as well as academic staff (Kinman, 2008; UCU, 2016). The concerns about work relationships reported by non-academic staff possibly relate to the fact that non-academic staff will typically work in a team-based environment in comparison with academics who work in a more solitary fashion and who may be less concerned about work relationships as a result. The concerns about pay and benefits reported by non-academic staff are perhaps unsurprising given their typically lower pay levels in comparison with academic staff.

11

The different concerns reported by academics and non-academics indicate the importance for universities to monitor the effects of work environment changes on psychosocial risk factors for specific job groups. However, results also showed that there was no significant difference between academic and non-academic employees in psychological health and that academics had slightly better physical health. Thus, although sources of stress might differ for job groups, overall strain levels might be similar. With regards to the study’s second aim, findings indicated that the main stressors that influence ill-health are similar for academics and non-academics. Job group did not moderate any of the relationships between the different stressors and psychological and physical ill-health. In both job groups, aspects of jobs and work– life balance were related to both health outcomes. Job overload and job security were further related to psychological ill-health. This finding is in contrast with some research that has demonstrated that the stressor–strain relationship can vary for different occupations (Sparks & Cooper, 1999). One possible explanation why we did not find specific predictors of health issues for academics and non-academics, is that these two job groups still operate within the same wider work environment (i.e., higher education). More broadly our findings are congruent with existing research that has identified aspects of job and work–life balance as major risk factors for health issues (Amstad, Meier, Fasel, Elfering, & Semmer, 2011; Pindek & Spector, 2016). In particular, the finding that aspects of job (referring to working conditions and unnecessary change) showed a strong relationship with psychological and physical illhealth is noteworthy. In a recent meta-analysis, Pindek and Spector (2016) found that organizational constraints, which are related to the aspects of job stressor dimension in our study, significantly impair physical and psychological health beyond other stressors. The present findings further attest that aspects of the work environment should be more central in stress research and are to be considered alongside other commonly studied stressors such as work–life conflict. The third and fourth aims of the study investigated the role of resilience in the stressor– strain relationship. Our results showed little support for resilience as a moderator of the

12



association between stressors and ill-health, as we only found support for resilience to interact with work–life balance as a source of stress but not with any of the other stressor variables. This suggests that resilience might not be a “global buffer,” but might only mitigate against certain sources of stress such as poor work–life balance. The measure of stress sources used in the present study might explain why we found an indirect effect for resilience on health through stressor perceptions, but less support for a buffering moderator effect. The ASSET tool measure assesses the extent to which individuals are troubled by a series of workplace stressors, rather than the extent to which these are present. Our findings suggest that levels of resilience influence whether an individual perceives a stressor as troublesome, but once an employee is troubled by workplace stressors he or she is likely to report poor health regardless of his or her level of resilience. In other words, resilience seems to take effect earlier on in the process by influencing how troublesome the presence of stressors is perceived, but once an individual has become burdened by a stressor, resilience does not seem to be able to mitigate effects on health. It is acknowledged that stress lies in the eye of the beholder (e.g., Faragher et al., 2004), and the present results indicate that resilience can help individuals to cope better with the presence of stressors and subsequently to perceive these as less troublesome. This makes sense if the benefits of resilience work through a “cognitive appraisal mechanism” in which resilient employees evaluate potential stress sources as less threatening than individuals with low resilience. As discussed previously, this is in line with Bolger and Zuckerman’s (1995) differential exposure–reactivity framework. Findings demonstrated that resilience has a direct effect on psychological and physical illhealth as well as an indirect effect through the perceptions of stressors. Overall, our results suggest that resilience might act through several moderator, mediator, and direct mechanisms—as a buffer against some sources of stress, as well as a resource that directly boosts health outcomes, and an indirect force that protects health by influencing how employees perceive and experience stressors in their work environment in the first place. Employees with high levels of resilience might not perceive sources of stress as depleting and are therefore

more able to retain positive emotions and subsequently better health. In line with our finding, Avey et al. (2009) suggested that resilient individuals are better equipped to deal with stressful work situations, allowing higher levels of health. Specifically, our results showed that resilience influences employees’ perceptions of workload, work–life balance, aspects of the job, and job security as troublesome stressors, which subsequently influenced health outcomes. However, resilience did not have any indirect effects on health outcomes through the remaining stressors (i.e., resources and communication, work relationships, job control, and pay and benefits). This suggests that resilience impacts on health by enabling individuals to cope better with the demands that originate from certain stressors, but that this is not a mechanism that applies to all types of workplace stressors. The four stressors (i.e., workload, work–life balance, job security, and aspects of job) that did significantly mediate the relationship between resilience and ill-health are also those that emerged as significant predictors of ill-health for academics and non-academics in the main effects analysis. Hence, when considering resilience as an approach to build individuals capacity to respond to stressors, it seems important to first consider which stressors are most relevant for ill-health in the particular work context such as academia. Overall, it should be noted that the mediation analysis indicated that the indirect effect is limited to certain stressors and as noted earlier that we only found resilience to be a moderator for one stressor. Therefore, resilience should not be viewed as a ubiquitous protector against any source of stress. Further research is needed to better understand when and for which particular stressors resilience offers a protective resource. Nevertheless, given the evidence suggesting resilience can be developed (Robertson et al., 2015; Vanhove et al., 2016), the present results suggest that it offers a proximal resource through which universities can bolster staff health. Although the mediating effect of resilience, as discussed earlier, has the potential to protect against diminished health by affecting how employees evaluate potential stress sources, the direct effect of resilience on health suggests one benefit of increasing resilience might be through directly strengthening individuals’ health. This is evident, for example, when re-



13

viewing the content of resilience training low-up studies with multiple measurement time programs in which building high levels of resil- points to measure change in academic working ience commonly involves enhancing psychoso- environments, and to assess the impact of both cial factors such as Self-Efficacy, Optimism, change and particular stress sources on academand a Sense of Social Competence (Vanhove et ics’ and non-academics’ health. Moreover, it al., 2016). Such psychosocial factors are likely was discussed earlier that the measure of workto contribute toward robust levels of psycholog- place stressors, which asked participants how ical well-being. Others have discussed that troubled they are by various workplace streswhen investigating workplace stress, it is essen- sors, might explain the lack of support for a tial to include factors outside the workplace moderator role of resilience in the stressor– (Mak, Ng, & Wong, 2011). Our results suggest strain relationship (only one significant moderthat resilience is an important concept to under- ation effect was found). Future research that stand how wider individual characteristics in- compares measures that assess how troubled terplay with work-specific factors to determine individuals are by stressors with more objective health and well-being. assessments of the presence of stressors (e.g., Our study has several strengths such as a hours worked per week) would be valuable to large sample from different universities and in- further enlighten the role of resilience for health vestigating resilience in a nonstudent, working and well-being. Finally, the organizations insample. However, a number of limitations cluded in our sample included researchshould be noted. As with most studies of work- intensive institutions (Organizations 1 and 2) as place stress there is the potential influence of the well as a more teaching-focused university (Orhealthy worker effect, where the employees ganization 3). Thus, the results from the study most negatively affected by workplace stress have relevance for both types of higher educaleave employment and are not represented in the tion institutions. It is possible though that the research. The study used a cross-sectional de- type of stressors experienced by academics as sign, which prevents any conclusions about the well as non-academics are influenced by the direction of effects. For example, the associa- type of university that they operate in. The tion between resilience and health might be focus of our study was to assess differences in bidirectional, with better health enabling indi- stressors and strain between academics and nonviduals to build resilience, which in turn en- academics. Future research should extend this hances their health further. We also did not find investigation by exploring whether the research significant differences in psychological health or teaching status of a university influences the between academic and non-academic staff and demands that staff experience. indeed academics reported slightly better physical health compared with non-academics. Conclusions There was also no support for job role to moderate the type of stressors that most strongly The present study identified that academics’ influence health. If the generic occupational main sources of stress emerge from excessive changes in academic work environments that workloads and work–life conflict. Non-acawe outlined in the introduction, for example, demic support staff reported to be more troubled increased student numbers and decreased per- than academics by pay and benefits, aspects of ceptions of autonomy and job security ( Kinman, job, and work relationships. These findings 2008; UCU, 2016) have relatively recently in- demonstrate that core stressors might vary for creased job overload and led to poorer work– different job groups within the same work enlife balance for academics and poorer job con- vironment, and underline the importance of ocditions (aspects of job) for non-academic staff, cupation-oriented approaches for effective it might take some time until diminishing ef- stress prevention. Moreover, resilience emerged fects on employees’ health are observable. as a protective resource that indirectly influHowever, the degree and the timeline of change ences perceptions of stressors and directly bolexperienced by the participants in this study sters health. Thus, our study made an important were not measured and therefore the effect of contribution to the workplace stress literature any change cannot be determined. It would be by establishing the positive effect of resilience of value for future research to conduct fol- in a working sample and advanced the under-

14


standing of the mechanisms through which resilience impacts health outcomes.

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An Examination of Stressors, Strain, And Resilience in Academic and Non-Academic U.K. University Job Roles

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