This article was downloaded by: [University of Otago] On: 10 July 2015, At: 15:11 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG

Anxiety, Stress, & Coping: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gasc20

Work overload, burnout, and psychological ill-health symptoms: a three-wave mediation model of the employee health impairment process a

a

a

Leon T. de Beer , Jaco Pienaar & Sebastiaan Rothmann Jr a

Click for updates

WorkWell Research Unit, North-West University, Private Bag X6001, Hoffman Street, Potchefstroom 2520, South Africa Accepted author version posted online: 16 Jun 2015.Published online: 08 Jul 2015.

To cite this article: Leon T. de Beer, Jaco Pienaar & Sebastiaan Rothmann Jr (2015): Work overload, burnout, and psychological ill-health symptoms: a three-wave mediation model of the employee health impairment process, Anxiety, Stress, & Coping: An International Journal, DOI: 10.1080/10615806.2015.1061123 To link to this article: http://dx.doi.org/10.1080/10615806.2015.1061123

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Downloaded by [University of Otago] at 15:11 10 July 2015

Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions

ANXIETY, STRESS, & COPING http://dx.doi.org/10.1080/10615806.2015.1061123

Work overload, burnout, and psychological ill-health symptoms: a three-wave mediation model of the employee health impairment process Leon T. de Beer, Jaco Pienaar and Sebastiaan Rothmann Jr

Downloaded by [University of Otago] at 15:11 10 July 2015

WorkWell Research Unit, North-West University, Private Bag X6001, Hoffman Street, Potchefstroom 2520, South Africa ABSTRACT

ARTICLE HISTORY

Background and Objectives: The study reported here investigated the causal relationships in the health impairment process of employee wellbeing, and the mediating role of burnout in the relationship between work overload and psychological ill-health symptoms, over time. The research is deemed important due to the need for longitudinal evidence of the health impairment process of employee well-being over three waves of data. Design: A quantitative survey design was followed. Participants constituted a longitudinal sample of 370 participants, at three time points, after attrition. Methods: Descriptive statistics and structural equation modeling methods were implemented. Results: Work overload at time one predicted burnout at time two, and burnout at time two predicted psychological ill-health symptoms at time three. Indirect effects were found between work overload time one and psychological ill-health symptoms time three via burnout time two, and also between burnout time one and psychological ill-health symptoms time three, via burnout time two. Conclusions: The results provided supportive evidence for an “indirect-only” mediation effect, for burnout’s causal mediation mechanism in the health impairment process between work overload and psychological ill-health symptoms.

Received 19 September 2014 Revised 7 June 2015 Accepted 8 June 2015 KEYWORDS

Longitudinal mediation; indirect effect; burnout; health impairment process; job demands

The job demands-resources (JD-R) model of burnout was constituted at the beginning of the new millennium (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), and led to the eventual operationalization of the dual process (Bakker & Demerouti, 2007). In the first process of the model, coined the motivational process, job resources lead to desirable organizational outcomes (e.g. retention and organizational commitment) through work engagement. In the second process, coined the health impairment process, inordinate job demands (e.g. work overload), through burnout, is linked to various employee and organizational outcomes of interest (health issues and reduced commitment), which, in turn, affect employee performance (Bakker & Demerouti, 2007; Bakker, Demerouti, & SanzVergel, 2014). This health impairment process is the focus of the current study. Research concerned with the health impairment process is important as it provides insights into the dynamics of employee motivation, employee health, and organizational outcomes, such as absenteeism, lack of job satisfaction, reduced commitment, turnover intention, psychological ill-health symptoms, and physical ill-health conditions (Bakker & Demerouti, 2007; Bakker et al., 2014). CONTACT Leon T. de Beer © 2015 Taylor & Francis

[email protected]

Downloaded by [University of Otago] at 15:11 10 July 2015

2

L.T. DE BEER ET AL.

Numerous cross-sectional studies have explored the mediating mechanism of burnout in the health impairment process of the JD-R framework, successfully, within various contexts (e.g. Bakker, Demerouti, De Boer, & Schaufeli, 2003; De Beer, Rothmann, & Pienaar, 2012; Hakanen, Bakker, & Schaufeli, 2006; Lewig, Xanthopoulou, Bakker, Dollard, & Metzer, 2007; Montgomery, Mostert, & Jackson, 2005; Rothmann & Essenko, 2007; Schaufeli & Bakker, 2004). However, the use of cross-sectional designs for mediation studies has, in recent years, come under increasing scrutiny (Maxwell, Cole, & Mitchell, 2011). In reality, a mediating effect cannot be conclusively supported with a measurement from a single point in time. Furthermore, the criticized mediation methods of Baron and Kenny (1986) and the Sobel test (1982) are still actively implemented even though more efficacious and novel methods have been proposed (MacKinnon, Lockwood, & Williams, 2004; Rucker, Preacher, Tormala, & Petty, 2011; Williams & MacKinnon, 2008). Moreover, a search of existing literature did not yield a single three-wave longitudinal mediation study that supports causal evidence for burnout as mediator in the health impairment process between job demands and psychological ill-health symptoms. However, Hakanen, Schaufeli, and Ahola (2008) investigated the mediating role of burnout in the health impairment process with two waves of data, and found evidence of partial mediation for burnout between job demands and psychological ill-health. The absence of a third point of measurement in the Hakanen et al. study restricted their investigation to only testing partial mediation, as it was not possible to investigate whether a potential mediating variable at time two (T2) fully mediated between time one (T1) and time three (T3) (Taris & Kompier, 2006). Indeed, Hakanen et al. suggest a three-wave measurement for investigations of burnout in future research. It must be noted that arguments have been presented that studies may only truly be considered longitudinal when there are a minimum of three time points available for analyses (cf. Ployhart & Ward, 2011). Therefore, a gap in the literature still remains for more specific causal evidence for the health impairment process over three waves of data. The current study endeavored to fill this gap and to search for said causal evidence in confirmatory support of the process of health impairment in practice.

Defining and operationalizing burnout Burnout is described as a state of physical, mental, and emotional exhaustion induced by a depletion of the ability to cope with the work environment, resultant from the on-going demands of our daily lives (Maslach, 1982). Classically, burnout comprised three components: exhaustion, cynicism, and reduced professional efficacy (Maslach, Jackson, & Leiter, 1996). However, burnout is also operationalized as consisting of only the first two components as its core, that is, exhaustion and cynicism (Schaufeli, Bakker, Hoogduin, Schaap, & Kladler, 2001; Schaufeli & Taris, 2005). Recent research has also provided support for this operationalization in a bi-factor analysis, which found a global latent construct for burnout consisting of the exhaustion and cynicism items with professional efficacy as a divergent factor (Mészáros, Ádám, Szabó, Szigeti, & Urbán, 2014). Exhaustion denotes a depletion of energy which incapacitates performance (Schaufeli, 2003), and has been shown to be persistent over time (Toppinen-Tanner, Kalimo, & Mutanen, 2002); while Cynicism refers to an employee’s distant and indifferent (negative) attitude towards his or her work (Schaufeli, 2003).

Activation of the health impairment process and its consequences Chronic job demands (e.g. work overload) activate the health impairment process and are connected with psychological ill-health symptoms via burnout (Bakker et al., 2014). Job demands are not inherently problematic; every occupation has demands at its core without which there would be no reason for the job position to exist in the first instance. However, once an inordinate amount of job demands are perceived (overload) without sufficient recovery (Xanthopoulou, Sanz-Vergel, & Demerouti, 2014), demands result in distress.

Downloaded by [University of Otago] at 15:11 10 July 2015

ANXIETY, STRESS & COPING

3

Consequently, employees apply, invest, and exert themselves in order to meet these inordinate demands which affect their energetic capacity and slowly but surely this capacity becomes depleted – employees then experience exhaustion and cynical attitudes which result in burnout (Maslach, 1982). Indeed, meta-analysis has shown that job demands (and not a lack of job resources) are the strongest predictors of burnout (Lee & Ashforth, 1996). Burnout has been shown to be remarkably stable over time (Melamed, Shirom, Toker, & Shapira, 2006; Schaufeli & Buunk, 2002), which in turn leads to psychological ill-health symptoms and increases the likelihood of additional undesirable outcomes for the organization, such as absenteeism, turnover, and reduced commitment. Moreover, a systematic literature review has shown that key work factors associated with psychological ill-health and sickness absence are long hours worked, work overload, and work pressure (Michie & Williams, 2003). Research has confirmed that burnout is associated with heightened cortisol levels (Melamed et al., 1999), and evokes somatic responses that disturb the anabolic and catabolic (metabolic) processes in affected individuals (cf. Ekstedt, 2005; Ekstedt et al., 2006; Melamed et al., 1999). Furthermore, research has posited that burnout can also affect hypothalamic–pituitary–adrenal-axis functioning which is connected to other regulatory systems in the body that govern inter alia energy balance and mood states (cf. Mommersteeg, Heijnen, Verbraak, & Van Doornen, 2006; Raison & Miller, 2003). Moreover, research has also found that the costs incurred by a medical insurance provider for an employee group scoring high on burnout were approximately double the amount when compared with an employee group who scored low on burnout, and that on average the low burnout group visited a doctor (general practitioner) fewer times (De Beer, Pienaar, Rothmann, 2013). As can be inferred, the health impairment process is not an instantaneous occurrence, and the erosion of employee energetic capacity takes time to affect employee well-being status, that is, work overload takes time to lead to burnout and eventual psychological ill-health symptoms.

The research hypotheses Hypothesis 1 (H1): Burnout time two (T2) mediates the relationship between Work overload time one (T1) and Psychological ill-health symptoms time three (T3). Hypothesis 2 (H2): Burnout time two (T2) mediates the relationship between Burnout time one (T1) and Psychological ill-health symptoms time three (T3).

Method The data used in this research were collected at three points in time constituting a three-wave longitudinal data set from the financial services sector in South Africa. Measurement took place once a year over a three-year period (SD = 1.5 months between each 12-month period).

Research sample Employees of different ages and backgrounds were sampled from a large organization in the financial services sector with a web-based survey. Due to the electronic nature of the survey, missing data were negligible. Participation was voluntary and could be ceased at any point in time. Informed consent was obtained and the anonymity and confidentiality of the participants were assured. Only non-identifying information of participants was available, such as demographics and the answers to the survey questions. All participants were assigned a unique number which was connected to their email address in a secured database. Ethical guidelines for research with human subjects were adhered to during this study. Initially, 546 employees participated, but after attrition from T1 to T3 (32% total), the final sample comprised 370 participants at all of the time points, constituting the final research sample. The majority of the final three-wave sample was female, that is, 302 (81.6%)

4

L.T. DE BEER ET AL.

participants. The mean age of the sample participants was 40.8 years (SD = 11.2). The most prevalent home languages were Afrikaans (n = 237; 64.1%), followed by English (n = 67; 18.1%). Participants had to possess at least a completed general high school education in order to participate in the study.

Downloaded by [University of Otago] at 15:11 10 July 2015

Measures The South African Employee Health and Wellness Survey (SAEHWS) (Rothmann & Rothmann, 2007) was used to measure the study variables at all of the time points. Work overload as the indicator of job demands was measured by three items which asked about workload as well as time pressure related to workload, for example, “I have too much work to do” on a four-point scale ranging from “Never” to “Always”. Burnout was measured by a total of eight items on a seven-point scale ranging from “Never” to “Always” based on items from its two core components, namely exhaustion (four items) and cynicism (four items). An example of an exhaustion item was: “I feel tired before I arrive at work”, and for cynicism: “I am uncertain whether my work is important”. Psychological illhealth symptoms as indicator variable of psychological unwell-being and psychological distress was measured by seven items: “Over the last three months how often have you experienced the following symptoms:” for example, “Mood swings” on a four-point scale ranging from “Never” to “Often”. The internal consistency of all the scales of the SAEHWS was satisfactory compared to generally accepted reliability guidelines – see Table 2 for alpha (α) and omega (ω) reliabilities.

Statistical analysis Structural equation modeling methods were implemented with Mplus 7.3 (Muthén & Muthén, 2014). Bayesian methodology was specifically chosen for the current research model as it does not necessitate normality assumptions in the sampling distribution of estimates, which is often the case when testing mediated effects with more traditional methods (Yuan & MacKinnon, 2009). Furthermore, Bayesian modeling is versatile in that priors from previous findings could be specified to current parameters, for example, values for correlations and regressions from previous studies; approximately zero cross-loadings, and even between-item residual correlations. No specific priors from previous studies were used. Parameters are estimated by way of Markov chain Monte Carlo (MCMC) iterations in parallel chains (two chains by default), and analogously it may be helpful to consider this iterative process similar to bootstrapping (cf. Zyphur & Oswald, 2013). Bayesian highest propensity density (HPD) credibility intervals (CI) were generated for values, and these CI are similar to bootstrapped 95% confidence intervals – but research has shown that Bayesian 95% CI’s can be more appropriate compared to bootstrapped CI’s (Manichaikul, Dupuis, Sen, & Broman, 2006). The most unusual aspect of Bayesian estimation to the frequentist reader is the absence of the use of the popular fit statistics (e.g. comparative fit index and Tucker–Lewis index); in Bayesian estimation model fit and convergence is assessed by considering MCMC chain mixing, the potential scale reduction (PSR) factor, the posterior predictive p-value (PPP), and visual inspection of the parameter distribution plots (Van de Schoot & Depaoli, 2014). When the PSR convergence criterion is successfully satisfied, the implication is that the parameter values in the chains are so similar that it is no longer possible to distinguish from which chain the parameter values come from; indicating convergence of the chains (Asparouhov & Muthén, 2010a; Kaplan & Depaoli, 2012). The default convergence criterion for the PSR was set at 1.05, and the PPP-value should be larger than .05 to avoid model rejection (Asparouhov & Muthén, 2010b; Zyphur & Oswald, 2013), but ideally close to .50 and above (Kaplan & Depaoli, 2012). Furthermore, parameter trace plots need to be visually inspected (Cumming & Finch, 2005; Van de Schoot & Depaoli, 2014), in order to investigate the distribution of the parameters throughout the estimation process. If inordinate variations are clearly evident the model should be discarded and additional iterations should be specified – see Figure 3 with examples of the resultant graphs.

Downloaded by [University of Otago] at 15:11 10 July 2015

ANXIETY, STRESS & COPING

5

To investigate the indirect effects in the model, that is, the mediating effect of Burnout T2 between Work overload T1 and Psychological ill-health symptoms T3, the model constraint function was used, that is, the indirect effect was specified by the formula m1 = a × b; where m1 is the label for the first indirect effect, a is the regression path from Work overload T1 to Burnout T2, and b is the regression path from Burnout T2 to Psychological ill-health symptoms T3. For the other indirect effect of H2, the path from Burnout T1 to Burnout T2 was labeled d resulting in the specified formula in the model constraint function: m2 = d × b. Longitudinal mediation models of three waves or more are superior to cross-sectional models due to the added ability of longitudinal models to address the issue of time (Ford et al., 2014). Additionally, longitudinal models offer the opportunity to investigate additional indirect effects that are not possible with simple three-variable mediation models due to baseline control variable specifications (i.e. the same variable regressed on its corresponding counterpart over time) (Selig & Preacher, 2009). Furthermore, in their recommendations, Selig and Preacher state that it is important to report all of the possible individual indirect effects tested in the model, and also the total indirect effect for a variable; in this case: burnout. Age and gender were included as control variables (Schaufeli, Leiter, & Maslach, 2009). No item parceling methods were implemented and therefore each latent variable was estimated by its corresponding observed indicators (items). The loadings and intercepts of each observed indicator were also parameterized to its corresponding indicator, assuming measurement invariance over time. As a pragmatic option, a test for measurement invariance (configural, metric, and scalar invariance) was conducted with contemporary methods. Dropout analysis was also performed on the sample. Furthermore, the effect sizes for the correlation coefficients were considered as per Cohen’s guidelines of r ≥ .30 for a medium effect, and r ≥ .50 for a large effect (Cohen, 1992). Additionally, in line with the suggestions by Ford et al. (2014) to also consider the potential dynamics of reverse causation effects in longitudinal models, this study also reversed the relationships of the hypothesized model to test a reverse causation model, that is, Burnout T1 predicting Work overload T2, Psychological ill-health symptoms T1 predicting Burnout T2, Burnout T2 predicting Work overload T3, Psychological ill-health symptoms T2 predicting Burnout T3, and also a direct path from Psychological ill-health symptoms T1 to Work overload T3. The Bayesian information criterion (BIC) and deviance information criterion (DIC) were used to compare the hypothesized model to the reverse causal model in order to ascertain the best fitting model; of which the lowest value indicated the best fitting model (Van de Schoot, Lugtig, & Hox, 2012). Figure 1 presents the final research model specified based on the longitudinal mediation recommendations prescribed by Selig and Preacher (2009).

Figure 1. The research model.

6

L.T. DE BEER ET AL.

Table 1. Mean comparison of the final sample mean with the dropout sample mean. Variable

Sample mean

Sample SD

Dropout mean

Dropout SD

Cohen’s d

Work overload Burnout Psychological ill-health symptoms Note: SD, standard deviation.

0.00 0.00 0.00

0.54 0.46 0.32

0.04 0.07 0.06

0.53 0.49 0.36

0.07 0.14 0.18

Results

Downloaded by [University of Otago] at 15:11 10 July 2015

Dropout analysis Dropout analysis was performed based on age (ANOVA) and gender (cross-tabulation chi-square difference testing). Results showed no significant difference based on age between the sample participants and the dropouts (p = .77). Similarly, there was also no significant difference based on gender between the dropouts of the study participants (p = .54). Furthermore, as can be seen from Table 1, there were also no practically significant mean differences between the final sample and the dropouts on any of the variables (Cohen’s d < 0.30).

Measurement invariance, means, and correlation matrix for the variables The test for measurement invariance showed that strong measurement invariance was apparent for the participants on all variables for T1 measurement to T2 measurement and this was also the case for T2 measurement to T3 measurement. This ensured more accurate comparability of mean values over time. Figure 2 presents the means of the latent variables at the specific time intervals. As can be seen from Table 1, the time one (T1) variables are all standardized to zero for meaningful comparison over the time points. All of the variables had statistically higher mean values at T2, with a slight drop at T3. However, there were no practically significant differences found when calculating Cohen’s d (all values d < 0.30), except for Burnout T2 (M = 0.16, SD = 0.50) which had a small practically significant increase from Burnout T1 (M = 0.00, SD = 0.42, d = 0.35). Table 2 presents the correlation matrix for the variables. The correlation matrix revealed that the constructs were practically significantly correlated with large effects to their corresponding counterparts over time, especially when these correlations were considered chronologically, for example: Burnout T1 with Burnout T2 (r = .70) and Burnout T2

Figure 2. Mean values for the variables at the specific time intervals.

ANXIETY, STRESS & COPING

7

Table 2. Correlation matrix for the latent variables. Reliability

R

Downloaded by [University of Otago] at 15:11 10 July 2015

Variable α ω 1 2 3 4 5 6 7 1. Work overload T1 .71 .72 – – 2. Work overload T2 .72 .72 .71a .79a – 3. Work overload T3 .70 .72 .57a b .26 .20 – 4. Burnout T1 .84 .89 .36 .27 .21 .70a – 5. Burnout T2 .86 .90 .38b .74a – 6. Burnout T3 .86 .90 .23 .11 .21 .51a .23 .18 .78a .59a .44b – 7. Psychological ill-health symptoms T1 .87 .91 .32b a b .43 .32b .70a 8. Psychological ill-health symptoms T2 .89 .92 .23 .17 .13 .61 a b a .44 .61 .55a 9. Psychological ill-health symptoms T3 .88 .91 .16 .11 .26 .51 10. Age n/a n/a .01 −.09 .01 −.01 −.09 −.03 .01 11. Gender n/a n/a .01 .04 .06 .12 .23 .15 .13 Notes: T1, time 1; T2, time 2; T3, time 3; α, alpha coefficient; ω, omega coefficient; n/a, not applicable. a Large effect. b Medium effect.

8

9

10

– .73a −.04 .18

– −.01 .18

– .13

with Burnout T3 (r = .74). This indicated the stability of the constructs over time, and was deemed advantageous for the purposes of the current study. Work overload T1 was positively correlated with both Burnout T1 (r = .36) and Burnout T2 (r = .38) with a medium effect. Furthermore, all the Burnout variables and all the Psychological ill-health symptoms variables had practically significant positive correlations at all of the various time points.

Model estimation and convergence The model was set as per the specification guidelines (Selig & Preacher, 2009; see Figure 1). Chain convergence was achieved after 48,800 MCMC iterations. However, due to the Mplus default burn in of 50% (which discards the first 50% of iterations) with Bayesian estimation and to increase confidence, the iterations were doubled and increased to execute 100,000 iterations to investigate if the PSR value still remained below the cutoff. The PSR value for indication of convergence among the parallel MCMC chains was shown to be ≤1.05 – indicating acceptable chain convergence. The parameter trace plots were also visually inspected to ascertain if adequate mixing took place between the two MCMC chains. These trace plots revealed that mixing took place with no noticeable or concerning inordinate variations (see Figure 3 for trace plots and parameter distributions). The PPP was .11 indicating that the model should not be rejected (Asparouhov & Muthén, 2010b). Figure 3 presents graphical representation of the two chains converging for a visual indication of the parameter trace plots (evidence of the mixing between the chains to prove convergence) and for the histograms (evidence for the distribution of the specific parameters of interest as acceptable) for the a-path (regression of Burnout T2 on Work overload T1) and b-path (regression of Psychological ill-health symptoms T3 on Burnout T2) in the longitudinal model.

Structural regressions and lagged effects Table 3 presents the regression results from the model estimation. Results showed that the stability paths had the strongest relationships over time, for instance, Burnout T1 → Burnout T2 (β = .53, 95% CI [0.44, 0.62]) and Burnout T2 → Burnout T3 (β = .54, 95% CI [0.46, 0.61]). This was also the case for work overload and for psychological ill-health symptoms over time. In terms of the causal relationships of interest, Work overload T1 predicted Burnout T2 (β = .14, 95% CI [0.05, 0.24]) and Burnout T2 predicted Psychological ill-health symptoms T3 (β = .12, 95% CI [0.03, 0.22]) – providing evidence for a positive causal relationship over the three time points, and the potential of a mediating effect.

Downloaded by [University of Otago] at 15:11 10 July 2015

8

L.T. DE BEER ET AL.

Figure 3. The parameter trace plots and histograms for the a- and b-paths.

Indirect effects and longitudinal mediation Concerning the mediation investigation, the direct path from Work overload T1 to Psychological ill-health symptoms T3’s estimate’s 95% CI crossed zero. However, the a-path and b-path did not cross zero, and the indirect effect could therefore still be investigated (cf. Rucker et al., 2011; Zhao, Lynch, & Chen, 2010). Results revealed an indirect effect of .02 (95% CI [0.01, 0.04]) from Work overload T1 to Psychological ill-health symptoms T3 via Burnout T2, which supported H1. The other indirect effect that could be investigated was that of Burnout T2 between Burnout T1 and Psychological ill-health symptoms T3, which also revealed an indirect effect of .05 (95% CI [0.01, 0.09]), supporting H2. Figure 4 presents the mediation results without the stability paths for ease of interpretation. The total indirect effect (the sum of the indirect effects) for Burnout T2 was therefore .07. These results provided supportive evidence for Burnout T2’s mediating effect between work overload and psychological ill-health symptoms, over time.

Table 3. Structural regressions with HPD values. Structural regression

Standardized estimate

Lower 95% CI

Upper 95% CI

Work overload T1 → Work overload T2 Work overload T2 → Work overload T3 Burnout T1 → Burnout T2 Burnout T2 → Burnout T3 Psychological ill-health symptoms T1 → Psychological ill-health symptoms T2 Psychological ill-health symptoms T2 → Psychological ill-health symptoms T3 Work overload T1 → Psychological ill-health symptoms T3 Work overload T1 → Burnout T2 Work overload T2 → Burnout T3 Burnout T2 → Psychological ill-health symptoms T3 Burnout T1 → Psychological ill-health symptoms T2

.39a .44a .53a .54a .50a .47a −.04 .14a −.03 .12a −.09

.31 .35 .44 .46 .41 .39 −.13 .05 −.12 .03 −.20

.47 .51 .62 .61 .58 .55 .04 .24 .06 .22 .02

Note: CI, credibility interval. a Estimate did not cross zero.

Downloaded by [University of Otago] at 15:11 10 July 2015

ANXIETY, STRESS & COPING

9

Figure 4. Mediation paths with indirect effects.

Reverse causation model The model comparison information criteria values for the reverse causation model (BIC = 54,053.49; DIC = 47,430.18) were shown to be larger than the values for the hypothesized model (BIC = 53,999.53; DIC = 47,407.90), indicating that the reverse causation model was not a better model compared to the hypothesized model. Furthermore, barring the stability paths, all of the reverse causation regression coefficients’ 95% CI’s crossed zero.

Discussion The aim of this study was to investigate supportive evidence for the employee health impairment process over time, that is, the relationship from work overload to psychological ill-health symptoms, through burnout, with three waves of data. Evidence was found for the causal relationships in the health impairment process, that is, Work overload T1 predicted Burnout T2 (a-path), which, in turn, predicted Psychological ill-health symptoms T3 (b-path). The first hypothesis was therefore supported as an indirect effect between Work overload T1 and Psychological ill-health symptoms T3 through Burnout T2 was evident. This provided three-wave longitudinal support for previous cross-sectional studies that investigated the health impairment process and found this mediating effect (e.g. Bakker et al., 2003; De Beer et al., 2012; Hakanen et al., 2006; Lewig et al., 2007; Montgomery et al., 2005; Rothmann & Essenko, 2007; Schaufeli & Bakker, 2004). Additionally, this finding also supported the findings of Hakanen et al. (2008) who were limited to investigating the partial mediating effect with two waves of data. Furthermore, Burnout T1 predicted Burnout T2, which, in turn, also predicted Burnout T3. This is in line with literature that suggests that burnout is relatively stable over time (Melamed et al., 2006; Schaufeli & Buunk, 2002). Additionally, there was also a predictive relationship from Burnout T2 to Psychological ill-health symptoms T3. The second hypothesis for this study was therefore supported: Burnout T2 also mediated the relationship between Burnout T1 and Psychological ill-health symptoms T3 with an indirect effect that did not cross zero. This indirect effect was larger compared to the first indirect effect, and provided additional support for the progression of burnout into psychological ill-health symptoms, over time. These indirect effects could be classified as indirect-only mediation (analogous to full mediation) and as such an omitted mediating variable was unlikely (Zhao et al., 2010), providing supportive evidence for burnout’s causal mediation between work overload and psychological ill-health symptoms, over time. Given this causal evidence, the logical assertion would be to suggest that practitioners should aim to monitor the workload of employees in order to guard against overload and thereby attempt to

Downloaded by [University of Otago] at 15:11 10 July 2015

10

L.T. DE BEER ET AL.

offset the progression of the health impairment process. However, this gives rise to a valid recurring question, that is, when is workload too high, or how much work and time pressure is too much? (cf. Demerouti & Bakker, 2011). Accordingly, various strategies have been proposed in order to offset the negative effect of inordinate job demands. Specifically, it has been found that job resources assist in buffering the effects of inordinate demands and should therefore be provided to employees (Bakker, Demerouti, & Euwema, 2005) – but adding job resources to an inordinate workload might not necessarily alleviate time pressure due to the quantity of work versus set deadlines. In that instance, perhaps the resource should be time itself by reducing load or investigating more favorable deadlines which would allow for more recovery time. Objectively, if workload is a concern, staff-to-workload ratios could be considered to investigate whether the cause might be insufficient staffing, which could then be addressed by sourcing more candidates if financially feasible. Furthermore, research has found that interventions that improved psychological health and lowered absenteeism used training and other organizational approaches to increase job resources, such as participation in decision-making, feedback, social support, and communication (Michie & Williams, 2003). Regarding intervention programs that seek to address burnout, two approaches can generally be followed. The first approach is the person-directed approach in which affected individual(s) are worked with directly, usually via an employee assistance program or other relevant professionals. Recent research has also found that employees suffering from burnout need assistance in restructuring their working conditions (Bakker & Costa, 2014). The second is an organization-directed approach (changing or addressing issues within the organizational context). In a review of burnout intervention programs, it was found that the person-directed approach had shorter term positive effects (six months or less), but that the combination of directing the intervention(s) at both the employee and the organization level had the longest lasting effect at approximately 12 months (cf. Awa, Plaumann, & Walter, 2010; Kuoppala, Lamminpää, Husman, 2008). Therefore, it is important that an inclusive approach is considered and followed in order to offset the health impairment process, that is, practitioners, consultants, occupational health psychologists, employee assistance providers, employees, and management should work together in order to effectively address the underlying antecedents of sub-optimal employee well-being in organizations.

Limitations and directions for future research The three-wave longitudinal nature of this study is considered its key strength. However, some authors question if even three waves are sufficient to analyze mediating effects due to insufficient time lapses to investigate these effects (Reichardt, 2011). Thus, an extension to four or five ways in future research should be considered. At face value the indirect effects in this study appear to be small, and decisions on the time lapse used between measurements are important and can also mask potential mediating effects (Timmons & Preacher, 2015). This concern has also been questioned as there is no apparent correct or incorrect time lag and effect sizes will vary over time (Reichardt, 2011). In most situations, the decision on the time lapses between measurements is not fully under the control of the researcher(s), specifically in organizational research; permission is mostly granted to implement surveys in the workforce on an annual basis – as was the case with the current study. Therefore, there is a strong practical component to be considered and not just the role of time when designing future research studies. It may very well be that if shorter time intervals were considered, for example, three or six months between measurements, that the indirect effect could have been larger. Conversely, it could also be that the indirect effect might have disappeared if the time lapse was widened further (cf. Taris & Kompier, 2014). Future studies are encouraged to replicate and expand on these findings based on the aforementioned considerations on time lapse(s) in other sample contexts, and with other measures of burnout, such as the Maslach Burnout Inventory (Maslach et al., 1996), which is used in the majority of studies

ANXIETY, STRESS & COPING

11

investigating burnout (Bianchi, Schonfeld, & Laurent, 2015a). Furthermore, given the questions surrounding the nosological value of burnout and its overlap with depression (Bianchi et al., 2015a; Bianchi, Schonfeld, & Laurent, 2015b), it is important to consider depression as a control variable in future studies. As for the generalizability of the results in the current study, even though longitudinal support for the health impairment process was found, generalization is cautioned due to the country, industryorganization homogeneity, and potential gender bias present in the sample.

Disclosure statement No potential conflict of interest was reported by the authors.

Downloaded by [University of Otago] at 15:11 10 July 2015

References Asparouhov, T., & Muthén, B. O. (2010a). Bayesian analysis using Mplus. Technical appendix. Retrieved from https://www. statmodel.com/download/Bayes3.pdf Asparouhov, T., & Muthén, B. (2010b). Bayesian analysis of latent variable models using Mplus. Retrieved from http://www. statmodel.com/download/BayesAdvantages18.pdf Awa, W. L., Plaumann, M., & Walter, U. (2010). Burnout prevention: A review of intervention programs. Patient Education and Counseling, 78, 184–190. doi:10.1016/j.pec.2009.04.008 Bakker, A. B., & Costa, P.L. (2014). Chronic job burnout and daily functioning: A theoretical analysis. Burnout Research, 1, 112–119. doi:10.1016/j.burn.2014.04.003 Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22, 309–328. doi:10.1108/02683940710733115 Bakker, A. B., Demerouti, E., De Boer, E., & Schaufeli, W. (2003). Job demands and job resources as predictors of absence duration and frequency. Journal of Vocational Behavior, 62, 341–356. doi:10.1016/S0001-8791(02)00030-1 Bakker, A. B., Demerouti, E., & Euwema, M. C. (2005). Job resources buffer the impact of job demands on burnout. Journal of Occupational Health Psychology, 10, 170–180. doi:10.1037/1076-8998.10.2.170 Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annual Reviews in Organizational Psychology and Organizational Behavior, 1, 389–411. doi:10.1146/annurev-orgpsych031413-091235 Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. doi:10.1037/h0046016 Bianchi, R., Schonfeld, I. S., & Laurent, E. (2015a). Burnout-depression overlap: A review. Clinical Psychology Review, 36, 28– 41. doi:10.1016/j.cpr.2015.01.004 Bianchi, R., Schonfeld, I. S., & Laurent, E. (2015b). Burnout: Absence of binding diagnostic criteria hampers prevalence estimates. International Journal of Nursing Studies, 52, 789–790. doi:10.1016/j.ijnurstu.2014.12.008 Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1), 155–159. doi:10. 1037/0033-2909.112.1.155 Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. American Psychologist, 60, 170–180. doi:10.1037/0003-66X.60.2.170 De Beer, L., Pienaar, J., & Rothmann Jr., S. (2013). Linking employee burnout to medical aid provider expenditure. South African Medical Journal, 103, 89–93. doi:10.7196/samj.6060 De Beer, L., Rothmann Jr., S., & Pienaar, J. (2012). A confirmatory investigation of a job demands-resources model using a categorical estimator. Psychological Reports, 111, 528–544. doi:10.2466/01.03.10.PR0.111.5.528-544 Demerouti, E., & Bakker, A. B. (2011). The job demands-resources model: Challenges for future research. SA Journal of Industrial Psychology, 37, 1–9. doi:10.4102/sajip.v37i2.974 Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86, 499–512. doi:10.1037/0021-9010.86.3.499 Ekstedt, M. (2005). Burnout and sleep. Retrieved from http://perski.se/pub/thesisMiriam.pdf Ekstedt, M., Söderström, M., Åkerstedt, T., Nilsson, J., Søndergaard, H. P., & Aleksander, P. (2006). Disturbed sleep and fatigue in occupational burnout. Scandinavian Journal of Work, Environment & Health, 32, 121–131. doi:10.5271/sjweh.987 Ford, M. T., Matthews, R. A., Wooldridge, J. D., Mishra, V., Kakar, U. M., & Strahan, S. R. (2014). How do occupational stressorstrain effects vary with time? A review and meta-analysis of the relevance of time lags in longitudinal studies. Work & Stress, 28, 9–30. doi:10.1080/02678373.2013.877096 Hakanen, J., Bakker, A. B., & Schaufeli, W. B. (2006). Burnout and work engagement among teachers. The Journal of School Psychology, 43, 495–513. doi:10.1016/j.jsp.2005.11.001

Downloaded by [University of Otago] at 15:11 10 July 2015

12

L.T. DE BEER ET AL.

Hakanen, J. J., Schaufeli, W. B., & Ahola, K. (2008). The job demands-resources model: A three-year cross-lagged study of burnout, depression, commitment, and work engagement. Work & Stress, 22, 224–241. doi:10.1080/ 02678370802379432 Kaplan, D., & Depaoli, S. (2012). Bayesian structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 650–673). New York, NY: Guilford Press. Kuoppala, J., Lamminpää, A., & Husman, P. (2008). Work health promotion, job well-being, and sickness absences – a systematic review and meta-analysis. Journal of Occupational and Environmental Medicine, 50, 1216–1227. doi:10.1097/ JOM.0b013e31818dbf92 Lee, R. T., & Ashforth, B. E. (1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology, 81, 123–133. doi:10.1037/0021-9010.81.2.123 Lewig, K. A., Xanthopoulou, D., Bakker, A. B., Dollard, M. F., & Metzer, J. C. (2007). Burnout and connectedness among Australian volunteers: A test of the job demands–resources model. Journal of Vocational Behavior, 71, 429–445. doi:10.1016/j.jvb.2007.07.003 MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi:10.1207/s15327906mbr3901_4 Manichaikul, A., Dupuis, J., Sen, Ś., & Broman, K. W. (2006). Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics, 174, 481–489. doi:10.1534/genetics.106.061549 Maslach, C. (1982). Burnout – The cost of caring. Englewood Cliffs, NJ: Spectrum. Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach burnout inventory manual (3rd ed.). Palo Alto, CA: Consulting Psychologists Press. Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46, 816–841. doi:10.1080/ 00273171.2011.606716 Melamed, S., Shirom, A., Toker, S., & Shapira, I. (2006). Burnout and risk of type 2 diabetes: A prospective study of apparently healthy employed persons. Psychosomatic Medicine, 68, 863–869. doi:10.1097/01.psy.0000242860.24009.f0 Melamed, S., Ugarten, U., Shirom, A., Kahana, L., Lerman, Y., & Froom, P. (1999). Chronic burnout, somatic arousal and elevated salivary cortisol levels. Journal of Psychosomatic Research, 46, 591–598. doi:10.1016/S0022-3999(99)00007-0 Mészáros, V., Ádám, S., Szabó, M., Szigeti, R., & Urbán, R. (2014). The bifactor model of the Maslach Burnout Inventory– Human Services Survey (MBI-HSS) – An alternative measurement model of burnout. Stress and Health, 30, 82–88. doi:10.1371/journal.pone.0114987 Michie, S., & Williams, S. (2003). Reducing work related psychological ill health and sickness absence: A systematic literature review. Occupational and Environmental Medicine, 60, 3–9. doi:10.1136/oem.60.1.3. PMCID: PMC1740370 Mommersteeg, P., Heijnen, C. J., Verbraak, M. J., & van Doornen, L. J. (2006). A longitudinal study on cortisol and complaint reduction in burnout. Psychoneuroendocrinology, 31, 793–804. doi:10.1016/j.psyneuen.2006.03.003 Montgomery, A., Mostert, K., & Jackson, L. (2005). Burnout and health of primary school educators in the North West Province. South African Journal of Education, 25, 266–272. doi:10.1002/smi.1098 Muthén, L. K., & Muthén, B. O. (2014). Mplus user’s guide (7th ed.). Los Angeles, CA: Author. Ployhart, R. E., & Ward, A. K. (2011). The ‘quick start guide’ for conducting and publishing longitudinal research. Journal of Business and Psychology, 26, 413–422. doi:10.1007/S10869-01.1-9209-6 Raison, C. L., & Miller, A. H. (2003). When not enough is too much: the role of insufficient glucocorticoid signaling in the pathophysiology of stress-related disorders. American Journal of Psychiatry, 160, 1554–1565. doi:10.1176/appi.ajp.160. 9.1554 Reichardt, C. S. (2011). Commentary: Are three waves of data sufficient for assessing mediation. Multivariate Behavioral Research, 46, 842–851. doi:10.1080/00273171.2011.606740 Rothmann, S., & Essenko, N. (2007). Job characteristics, optimism, burnout, and ill health of support staff in a higher education institution in South Africa. South African Journal of Psychology, 37, 135–152. doi:10.1177/008124630703700110 Rothmann, S., & Rothmann, J. C. (2007). The South African employee health and wellness survey (SAEHWS) user manual (4th ed.). Potchefstroom: Afriforte. Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5, 359–371. doi:10.1111/j.1751-9004.2011. 00355.x Schaufeli, W. B. (2003). Past performance and future perspectives of burnout research. South African Journal of Industrial Psychology, 29, 1–15. doi:10.4102/sajip.v29i4.127 Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25, 293–315. doi:10.1002/job.248 Schaufeli, W. B., Bakker, A. B., Hoogduin, K., Schaap, C., & Kladler, A. (2001). On the clinical validity of the Maslach Burnout Inventory and the Burnout Measure. Psychology & Health, 16, 565–582. doi:10.1007/s11325-015-1162-6 Schaufeli, W. B., & Buunk, B. P. (2002). Burnout: An overview of 25 years of research and theorizing. In M. Schabracq, J. A. M. Winnubst, & C. L. Cooper (Eds.), The handbook of work & health psychology (2nd ed., pp. 282–424), Chichester: Wiley.

Downloaded by [University of Otago] at 15:11 10 July 2015

ANXIETY, STRESS & COPING

13

Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Development International, 14, 204–220. doi:10.1108/13620430910966406 Schaufeli, W. B., & Taris, T. W. (2005). The conceptualization and measurement of burnout: Common ground and worlds apart. Work & Stress, 19, 256–262. doi:10.1080/02678370500385913 Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144–164. doi:10.1080/15427600902911247 Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco, CA: Jossey-Bass. Taris, T. W., & Kompier, M. A. (2006). Games researchers play – Extreme-groups analysis and mediation analysis in longitudinal occupational health research. Scandinavian Journal of Work, Environment & Health, 32, 463–472. doi:10.5271/ sjweh.1051 Taris, T. W., & Kompier, M. A. (2014). Cause and effect: Optimizing the designs of longitudinal studies in occupational health psychology. Work & Stress, 28, 1–8. doi:10.1080/02678373.2014.878494 Timmons, A. C., & Preacher, K. J. (2015). The importance of temporal design: How do measurement intervals affect the accuracy and efficiency of parameter estimates in longitudinal research? Multivariate Behavioral Research, 50, 41– 55. doi:10.1080/00273171.2014.961056 Toppinen-Tanner, S., Kalimo, R., & Mutanen, P. (2002). The process of burnout in white-collar and blue-collar jobs: Eightyear prospective study of exhaustion. Journal of Organizational Behavior, 23, 555–570. doi:10.1002/job.155 Van de Schoot, R., & Depaoli, S. (2014). Bayesian analyses: Where to start and what to report. European Health Psychologist, 16, 75–84. doi:10.1016/j.bodyim.2013.03.002 Van de Schoot, R., Lugtig, P., & Hox, J. (2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9, 486–492. doi:10.1080/17405629.2012.686740 Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling: A Multidisciplinary Journal, 15, 23–51. doi:10.1080/10705510701758166 Xanthopoulou, D., Sanz-Vergel, A. I., & Demerouti, E. (2014). Reconsidering the daily recovery process: New insights and related methodological challenges. In S. Leka & R. R. Sinclair (Eds.), Contemporary occupational health psychology (Vol. 3, pp. 51–67). Chichester: Wiley. Yuan, Y., & MacKinnon, D. P. (2009). Bayesian mediation analysis. Psychological Methods, 14, 301–322. doi:10.1037/ a0016972 Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37, 197–206. doi:10.1086/651257 Zyphur, M. J., & Oswald, F. L. (2013). Bayesian probability and statistics in management research a new horizon. Journal of Management, 39, 5–13. doi:10.1177/0149206312463183