Running head: BURNOUT AND WORK ENGAGEMENT. Burnout and Work Engagement: Do Age, Gender, or Occupation Level Matter?

Transcription

1 Burnout and Work Engagement 1 Running head: BURNOUT AND WORK ENGAGEMENT Burnout and Work Engagement: Do Age, Gender, or Occupation Level Matter? Testing the Robustness of the Job Demands-Resources Model Christian Korunka Faculty of Psychology University of Vienna, Austria Bettina Kubicek Faculty of Psychology University of Vienna, Austria Wilmar B. Schaufeli Department of Psychology Utrecht University, The Netherlands Peter Hoonakker Center for Quality and Productivity Improvement University of Wisconsin Madison, USA

2 Burnout and Work Engagement 2 Abstract This study focuses on burnout and its positive antipode, work engagement, as well as their antecedents and consequences. According to the Job Demands-Resources (JD-R) model two different processes have to be distinguished: an energetic process that links job demands and health complaints via the mediating role of burnout and a motivational process that links job resources with turnover intention through work engagement. The robustness of the JD-R model was tested in a heterogeneous occupational sample (N = 956). Structural equation modeling analyses led to a slightly modified model with only exhaustion being indicative of burnout and vigor, dedication, absorption along with professional efficacy being indicative of engagement. The results provide evidence for the dipartite structure of the JD-R model. The mediating role of burnout and work engagement is, however, only partially confirmed because job demands and job resources directly affect health complaints and turnover intention. Multi-group analyses reveal the JD-R model to be invariant across age, gender and occupational level, underscoring the robustness of the model. Keywords: Job Demands-Resources model, work engagement, job burnout

3 Burnout and Work Engagement 3 Burnout and Work Engagement: Do Age, Gender, or Occupation Level Matter? Testing the Robustness of the Job Demands-Resources Model Introduction Throughout its history psychology has been preoccupied with the study and treatment of psychopathology, dysfunction, weakness, and damage, neglecting those aspects of the human condition that lead to well-being and fulfillment (Seligman & Csikszentmihalyi, 2000). Mirroring this almost exclusive focus on the negative, occupational health and work psychology emphasized performance deficits and workrelated stress, rather than workers happiness, health and betterment (Hill, 2003). Criticizing this biased view, the emerging field of positive psychology, tracing back to Martin E. P. Seligman s 1998 Presidential Address to the American Psychological Association, proposes to restore this imbalance in research by studying optimal functioning and positive experiences (Linley, Joseph, Harrington, & Wood, 2006). Thus, positive psychology aims to expand the implicit value basis of psychological research in order to integrate positive and negative aspects and span the whole human condition from disorder to well-being (Seligman, Steen, Park, & Peterson, 2005). According to this more general trend towards positive psychology a growing body of research into concepts such as occupational well-being (Warr, 1999), selfefficacy (Grau, Salanova, & Peiro, 2001; Salanova, Peiró, & Schaufeli, 2002), sense of coherence (Kivimäki, Feldt, Vahtera, & Nurmi, 2000), flow (Csikszentmihalyi, 1990; Salanova, Bakker, & Llorens, 2006), or resilience (Strumpfer, 2003) has begun to provide better understanding of positive work-related experiences. This interest in worker s well-being has recently also been put forward in burnout research. Researchers supplemented and extended the concept of burnout, which has dominated the research

4 Burnout and Work Engagement 4 agenda of occupational health psychology for over 25 years, by its positive opposite: work engagement (Maslach, Schaufeli, & Leiter, 2001). To integrate both work engagement and job burnout into a comprehensive model of work-related well-being, the Job Demands-Resources (JD-R) model has recently been introduced (Bakker, Demerouti, de Boer, & Schaufeli, 2003; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Hakanen, Bakker, & Schaufeli, 2006; Llorens, Bakker, Schaufeli, & Salanova, 2006; Schaufeli & Bakker, 2004). So far the JD-R model has been successfully tested in various countries such as Finland (Hakanen et al., 2006), the Netherlands and Spain (Llorens et al., 2006) whereby the model was shown to be invariant across Dutch and Spanish workers. Furthermore the model has been applied to various occupational groups such as home care professionals (Bakker, Demerouti, Taris, Schaufeli, & Schreurs, 2003), teachers (Hakanen et al., 2006) or white-collar (Schaufeli & Bakker, 2004) and blue-collar workers (Bakker et al., 2003). However, no simultaneous test, comparing white- and blue-collar workers, has yet been conducted, nor has the model been studied for age or gender differences. Therefore we examine the robustness of the JD-R model, using a heterogeneous sample of white- and blue-collar workers, both males and females of different age-groups. The Job Demands-Resources Model The Job Demands-Resources (JD-R) model (Demerouti et al., 2001; Schaufeli & Bakker, 2004) specifies how working conditions produce health impairments and motivation (Figure 1). It assumes that while employees in different organizations or occupations may be confronted with different working environments, the characteristics of these environments can always be classified in two general categories, namely, job

5 Burnout and Work Engagement 5 demands and job resources. Job demands refer to those physical, psychological, social or organizational aspects of the job that require sustained physical and/or psychological (i.e., cognitive or emotional) effort and are therefore associated with certain physiological and/or psychological costs (Demerouti et al., 2001). Although job demands can be experienced as challenging (Lazarus, 1999; Lazarus & Folkman, 1984), they may become stressors in situations that require prolonged high effort to maintain an expected performance level. When confronted with demanding working conditions employees are at risk of experiencing a discrepancy between work capacity and workload. This mismatch providing it persists over a period of time may lead to negative responses, including burnout. Given the potential negative effects of job demands on individuals health and well-being, the basic question is, what keeps people healthy even when they encounter high levels of workload (Richter & Hacker, 1998)? The answer is health-protecting factors called resources. Thus, job resources refer to those physical, psychological, social or organizational aspects of the job that (1) may reduce job demands and the associated physiological and psychological costs, (2) are functional in achieving work goals, and (3) simulate personal growth, learning, and development (Demerouti et al., 2001). Richter and Hacker (1998) distinguish two types of resources, namely external or job resources and internal or personal resources. In the present study we focus on external resources, which can be influenced by adequate job (re-)design. Regarding the differentiation of job demands and job resources the JD-R model resembles the well-known and influential Job Demand-Control (JD-C) model (Karasek, 1979), but overcomes some of its limitations. The JD-C model posits an interaction between job demands (work overload and time pressure) and job control predicting

6 Burnout and Work Engagement 6 psychological strain. More specific, the JD-C model assumes that job stress is caused by high job demands and low job control and in particular by the interaction between high demands and low control. The literature has shown that high job demands and low control independently do predict psychological strain, but has failed to support the interaction hypothesis. For example, in a review of 63 studies of the Job Demand- Control model with psychological strain as the outcome only 15 studies out of 31 at least partially supported the interaction hypothesis (Van der Doef & Maes, 1999). But if control and demands are only two independent stressors, the theory looses its innovative virtue and there is not much reason to focus on these two specific variables (Beehr, Glaser, Canali, & Wallwey, 2001). Although social support at work has later been added as a third dimension (Johnson & Hall, 1988), critics of the JD-C model point out that the model considers only a limited number of potential predictors of job stress. To put it another way, by concentrating on demands and control the JD-C model reduces the complex reality of working conditions to only two or three predictor variables. Research on job stress and burnout has, however, identified and successfully tested a long list of job demands and job resources as potential antecedents of job strain beyond those in the JD-C model (Lee & Ashforth, 1996). Taking these critical remarks into account, the Job Demands-Resources model includes various job demands and job resources that independently contribute to explaining work-related well-being, namely job burnout and work engagement. Burnout as a reaction to chronic occupational stress is usually defined as a syndrome of exhaustion, cynicism, and reduced professional efficacy (Maslach, Jackson, & Leiter, 1996). Exhaustion refers to feelings of strain, particularly chronic fatigue resulting from overtaxing work. The second dimension, cynicism reflects an

7 Burnout and Work Engagement 7 indifferent or a distant attitude towards work in general. Finally, lack of professional efficacy refers to reduced feelings of competence, successful achievement, and accomplishment both in one s job and in the organization. During the past decade, evidence has accumulated that lack of professional efficacy plays a divergent role, compared with exhaustion and cynicism. More specific, professional efficacy shows low correlations with both of the other burnout components (e.g. see Lee & Ashforth, 1996). It therefore seems that exhaustion and cynicism constitute the core of the burnout syndrome, which is loosely related to professional efficacy (Schaufeli & Bakker, 2004). Current work on the concept of engagement can be distinguished in two different, but related paths. Maslach and Leiter (Maslach & Leiter, 1997) redefined burnout as an erosion of engagement with the job. What started as meaningful and challenging work becomes meaningless and unpleasant. In their view energy turns into exhaustion, involvement turns into cynicism, and efficacy turns into ineffectiveness (Maslach & Leiter, 1997, p. 24). Thus engagement is characterized by energy, involvement, and efficacy the direct opposites of the three burnout dimensions. Consequently, following Maslach and Leiter (1997), work engagement can be assessed using the Maslach Burnout Inventory General Survey (MBI-GS). Employees displaying a profile of engagement score low on exhaustion and cynicism whereas they depict high scores on efficacy. Schaufeli, Salanova, González-Romá and Bakker (2002) have taken a different approach to the concept of engagement, defining and operationalizing engagement in its own right. Even though engagement is still conceptualized as the positive antipode of burnout, Schaufeli et al. (2002) do not presume that engagement is adequately measured by the opposite profile of MBI scores. Rather burnout and engagement are considered

8 Burnout and Work Engagement 8 two independent states of employee s well-being. Based on this theoretical reasoning Schaufeli and his colleagues have defined engagement as a persistent positive, fulfilling, work-related state of mind that comprises three components: energy or vigor, involvement or dedication, and absorption (Schaufeli et al., 2002). Vigor refers to high levels of energy and resilience, the willingness to invest effort in one s job, the ability to not be easily fatigued, and persistence in the face of difficulties. Dedication refers to a strong involvement in one s work, accompanied with feelings of enthusiasm and significance, and by a sense of pride and inspiration. Finally, absorption refers to a pleasant state of total immersion in one s work, which is characterizes by time passing quickly and being unable to detach oneself from the job. Thus, being fully absorbed in one s job bears resemblance to the concept of flow, a state of optimal experience that is characterized by focused attention, loss of self-consciousness, distortion of time, and intrinsic enjoyment (Csikszentmihalyi, 1990). But flow is a more comprehensive concept that includes many aspects and refers to short-term peak experiences. Absorption, on the other hand, delineates a more persistent as well as pervasive state of mind (Schaufeli et al., 2002). Using confirmatory factor analysis, various studies found evidence for a twofactor model composed of a core burnout factor, including emotional exhaustion and cynicism, and an extended engagement factor, which included the three engagement dimensions plus professional efficacy (e.g. Schaufeli & Bakker, 2004; Schaufeli et al., 2002). Hence, in contrast to the assumptions of Maslach and Leiter (1997), burnout and engagement do not seem to merge into one single dimension. Instead professional efficacy (as measured with the MBI-GS) along with vigor, dedication and absorption (as

9 Burnout and Work Engagement 9 measured with the UWES) load on an engagement factor whereas exhaustion and cynicism (as measured with the MBI-GS) form a burnout factor (Schaufeli et al., 2002). To summarize the JD-R model assumes that job demands and job resources evoke two different albeit related processes (see Fig. 1): (1) an energetic process of wearing out in which high job demands deplete employees mental and physical resources and may thus lead to job burnout, health impairments (Schaufeli & Bakker, 2004), and sick leave (Bakker et al., 2003); (2) A motivational process in which job resources stimulate employees motivation and foster engagement and organizational commitment (Schaufeli & Bakker, 2004). More specific, the energetic process links job demands with health impairments via the mediating role of job burnout. This process can be further elucidated using Hockey s (1997) compensatory regulatory-control model on the effects of stress on performance. The model delineates two modes of regulation in dealing with high working demands representing different forms of tradeoffs between performance protection and the mental effort that has to be invested in the job: In the so-called strain coping mode employees increase the energy spent under demanding working conditions to accommodate the new level of demands (Hockey, 1997, p. 81). Compensatory effort is mobilized in order to maintain performance levels at the expense of physiological and psychological costs such as increased levels of adrenalin, increases in heart rate and blood pressure, fatigue or loss of motivation. In cases of short-term rises in demands an active coping response facilitates performance protection and is therefore adequate. Chronic use of the strain coping mode may, however, be maladaptive, since employees have little opportunity for recovery. Continuous activation of compensatory effort thus drains the employee s energy and may consequently lead to burnout and in the long run to ill health (Hockey, 1997). An

10 Burnout and Work Engagement 10 alternative response to excessive demands is to adopt a so called passive coping mode, involving adjustment in performance targets, for instance by reducing required levels of accuracy or speed (Hockey, 1997, p. 82). By leaving the maximum effort budget at its present level this strategy does not cause further psychological or physiological costs but may, in extreme cases, lead to complete disengagement from the pursuit of task goals (Hockey, 1997). The motivational process links job resources with organizational outcomes (e.g., turnover intention) through work engagement. Job resources may play both an intrinsic and/or extrinsic motivational role, leading in both cases to a positive, work-related state of mind, namely work engagement. In the former case job resources foster employees growth, learning, and development. According to self-determination theory (Deci & Ryan, 1985) work environments that satisfy the basic human needs for autonomy, relatedness (social support) and competence enhance well-being and increase commitment, thus preventing employees from leaving the organization. The Job Characteristics Model (Hackman & Oldham, 1980) also emphasizes this intrinsic motivational potential of job resources. On the other hand job resources may develop an extrinsic motivational effect in that they contribute to the successful completion of the work task, be it by providing required information or by fostering an innovative climate (Meijman & Mulder, 1998). Therefore it can be assumed that engaged workers are committed to the organization and remain with their current employer, because the organization provides them with the resources needed not only to achieve work goals but also to experience growth and development (Houkes, Janssen, de Jonge, & Bakker, 2003).

11 Burnout and Work Engagement 11 Relations with age, gender and occupational level Previous studies have shown that burnout may be related to particular demographic variables, such as age or gender (Maslach & Jackson, 1985; Maslach et al., 2001). Numerous studies with inconclusive results have considered age as a factor associated with job burnout. Some studies reported no correlations whereas others found negative correlations between age and burnout (e.g. Tuuli & Karisalmi, 1999). Integrating the results of 35 studies reporting correlations between job burnout and age, Brewer and Sharpard (2004) found a small but significant negative relationship between employees age and burnout in general and emotional exhaustion in particular. Thus younger employees were more likely to experience burnout than their older counterparts. One possible explanation for negative correlations between age and burnout is that employees develop more effective coping skills with age or with experience. Because of their work experience, older employees may have developed more coping resources and therefore appraise problems as less stressful and thus report fewer burnout symptoms and higher levels of well-being. Support for this interpretation was found in a study carried out by Siu, Spector, Cooper, and Donald (2001). Older managers reported fewer sources of stress, better coping and higher levels of occupational well-being compared to their younger counterparts. In accordance with these findings small but statistically significant positive correlations were found between work engagement and age (Schaufeli, Bakker, & Salanova, 2006). Another explanation for negative associations between age and burnout is selection or the socalled healthy worker effect (McMichael, 1976) which refers to the consistent tendency for the actively employed to exhibit a better health status since the severely or chronically ill are ordinarily excluded from employment. Accordingly, older employees

12 Burnout and Work Engagement 12 that are burnt-out or exhausted may leave the field trough various paths like early retirement or work disability. We therefore expect that, depending on their age, employees differ in the impact of job resources and job demands on burnout and engagement. Technically speaking we expect the path coefficients between latent variables as well as the variances and covariances of the structural model, but not the factor loadings and measurement errors to differ between age groups. That is, we assume the structural model, but not the measurement model to vary across age groups. Apart from age, gender was systematically used as a classifier in a number of burnout and engagement studies. Early findings indicate that women were more prone to burnout than men (for an overview see Greenglass, 1991; Schaufeli & Greenglass, 2001) since they often take on family and caring responsibilities in addition to their gainful employment. However, these findings can not be interpreted straightforward because men and women often occupy different occupational roles, which may result in a confounding effect of gender and occupation. Therefore, differences between men and women may in fact reflect differences in occupations. On the other hand, available data suggest that rather than experiencing more burnout, women report lower scores on depersonalization (which was initially used as an indicator of burnout in lieu of cynicism) than men (Greenglass, Burke, & Ondrack, 1990). Explanations for the finding that men are more prone to depersonalization focus either on gender roles or on differences in coping strategies (Greenglass, 1991). The masculine gender role emphasizes strength, independence as well as invulnerability. From this perspective depersonalization may reflect a man s repressed emotionality. The second explanation focuses on women s greater ability to cope with interpersonal stress referring to

13 Burnout and Work Engagement 13 women s greater interpersonal skills and social competence. Furthermore recent findings indicate that resources play divergent roles in the development of burnout among men and women. For example, Greenglass, Burke and Konarski (1998) reported that while co-worker support led to lower emotional exhaustion in women, supervisory and co-workers support resulted in higher levels of professional efficacy among men. It is therefore expected that job resources offer greater protection against burnout among women whereas they foster the development of engagement among men. Again we assume structural path coefficients, but not factor loadings or measurement errors to differ among subsamples. Finally, the influence of job resources and job demands on burnout and work engagement has been more often studied in white-collar jobs, in which qualitative and quantitative work overload are assumed to result in the development of burnout, whereas resources contribute to feelings of work engagement. Comparisons between various occupational groups indicate that blue-collar workers score significantly lower on all engagement dimensions (vigor, dedication and absorption) than the highest scoring occupational groups such as police officers, managers and educators (Schaufeli et al., 2006). The JC-R model itself has however not yet been tested simultaneously among various occupational groups. Therefore we conduct a multi-group comparison between two heterogeneous samples of white and blue-collar workers, to further elucidate the impact of occupational levels on burnout and engagement. Hypotheses The present study intends to investigate the robustness of the JD-R model across various occupational and age groups as well as across gender. Before conducting multi-

14 Burnout and Work Engagement 14 group analysis the research model as depicted in Figure 1 was tested for the entire sample. Accordingly, the following relations were assumed: Burnout mediates the relationship between high job demands and health complaints, whereas work engagement mediates the relationship between job resources and turnover intention. In addition to the two hypothesized processes, five cross-links (two directional and three non-directional associations) are assumed (see Fig. 1): Job resources are negatively related to burnout and burnout is negatively associated with turnover intention. Furthermore, we expect job demands and job resources as well as burnout and engagement to be negatively correlated. Finally, according to Baba, Jamal and Tourigny (1998) we expect a positive relationship between health complaints and employees intention to leave the organization. After fitting the research model to the entire sample we examined whether the model is invariant across various demographic variables. Accordingly, the hypotheses of the study were the following: Hypothesis 1. Path coefficients between latent variables as well as the variances and covariances of the structural model, but not the factor loadings and measurement errors will vary across age groups. Hypothesis 2. The structural path linking job resources and burnout will reach a higher value among women whereas path coefficients linking job resources and engagement will assume higher values among men. That is, again we assume structural path coefficients, but not factor loadings or measurement errors to differ among subsamples. Hypothesis 3. Coefficients of the structural model will differ between white- and bluecollar workers. Method Participants and procedure

15 Burnout and Work Engagement 15 Between September and December 2005 questionnaires were distributed among employees of eight different companies in Austria. Participating organizations belong both to service and to manufacturing industries. Subsequently the sample consisted of white- and blue-collar workers although white-collar workers were stronger represented (77%) than blue-collar workers (21%). A total of 964 employees filled out and returned the questionnaire within the data collection phase. We had to exclude the scores of eight participants because of missing data in the turnover intention variable, resulting in an overall return rate of 55.3% (N = 956). The sample population was 59 percent male (N = 562) and ranged in age from 15 to 65 years, with a mean age of 36.4 years (SD = 11.1). Thirty-two percent of the participants were younger than 30 years old (N = 306), 41% were between 30 and 44 years (N = 397), and 27% were over 45 years of age (N = 253). The observed (relatively small) proportion of employees older than 45 years is typical for the Austrian working population (Statistik Austria, 2005). Education levels were slightly above the Austrian average, with 23% of the employees having a university degree (Statistik Austria, 2005). Most participants worked full-time (89%). The average tenure was 11 years (SD = 9.75). Measurement instruments Job Resources and Job Demands. To measure job resources and job demands we used the Salutogenic subjective work analysis questionnaire (Fragebogen Salutogenetische subjektive Arbeitsanalyse, SALSA) developed by Rimann and Udris (1997; Udris & Rimann, 1999). The SALSA-questionnaire assesses perceived salutogenic and pathogenic work characteristics. Job resources assessed in this study

16 Burnout and Work Engagement 16 refer to task characteristics (i.e., decision latitude) and interpersonal relations (i.e., social support by co-workers and supervisors). Decision latitude, defined as the working individual s opportunity to decide and to engage in organizational decision-making processes was measured with 4 items (for example, This work allows for making a lot of decisions on my own ). The internal consistency (Cronbach s α) of this scale was.69. Supervisory and co-worker support were assessed using 8 and 7 items, respectively. Examples of the items are: My supervisor supports me in carrying out work tasks. (supervisory support, α =.90) and The people I work with treat me in a friendly way. (co-worker support, α =.84). Two types of job demands were measured: quantitative and qualitative workload. Quantitative workload refers to demands arising from tight deadlines or shortage of time as well as the parallel execution of several tasks. The scale consists of three items (e.g., There is so much work to be done at once that one can only hardly master it ). Likewise, three items were used to measure qualitative workload. According to Rimann and Udris (1997; Udris & Rimann, 1999) qualitative workload originates from tasks which require other than the acquired qualifications. (e.g., One must do things for which one is not trained or prepared enough ). Internal consistencies for the two scales were.76 and.64, respectively. Respondents had to rate the extent to which each item described their job. Items were scored on a five-point Likert scale ranging from 1 = Do not agree at all to 5 = Agree completely. Burnout. The German language version of the Maslach Burnout Inventory- General Scale (MBI-GS) was used to assess job burnout (Schaufeli, Leiter, Maslach, & Jackson, 1996). The factorial validity of the MBI-GS has been confirmed across occupational groups and across nations (Schutte, Toppinen, Kalimo, & Schaufeli, 2000). We used two subscales, namely exhaustion and cynicism which both consist of

17 Burnout and Work Engagement 17 five items. Examples of the items are I feel used up at the end of a working day and I doubt the significance of my work. All items were scored on a six-point rating scale ranging from 1 (never) to 6 (always). The internal consistencies (Cronbach s α) of the two scales were:.86 for exhaustion and.81 for cynicism. Work Engagement. This was measured by a short German version of the Utrecht Work Engagement Scale (UWES-9; Schaufeli et al., 2006). The psychometric properties of the short version have recently been studied using a large international database and were found to be satisfactory (Schaufeli et al., 2006). The items of the UWES-9 are grouped into three subscales, each comprising three items. The items were scored on a seven-point rating scale, ranging from 1 (never) to 7 (always). Examples of the items are When I get up in the morning, I feel like going to work (Vigor, α =.83), I am enthusiastic about my job (Dedication, α =.88), and I get carried away when I m working (Absorbtion, α =.86). In addition professional efficacy was measured using the corresponding subscale of the MBI-GS. One of the six items reads I can effectively solve the problems that arise in my work. Items were scored on a six-point rating scales. Internal consistency for the scale was.72. Health complaints. A psychosomatic complaints scale comprising 8 items was adapted from Ducki (Ducki, 2000). An example of the items is: How often do you have neck or shoulder pain? (1 = never, 5 = always). Although Ducki conceptualized the scale as one-dimensional, an explanatory factor analysis indicated a three-factor model (explaining 72% of the variance). We characterized the three factors as musculoskeletal (2 items) gastro-intestinal (3 items, α =.75), and cardiovascular complaints. After removing one unsound item the cardiovascular complaints scale comprised two items.

18 Burnout and Work Engagement 18 Turnover intention. A dichotomous turnover intention index was calculated from four items measuring turnover plans and turnover behavior, based on the questionnaire experience and evaluation of work (Vragenlijst Belevingen Beoordeling van de Arbeid, VBBA; Van Veldhoven & Meijman, 1994). For example, I intend to change jobs during the next year. Response categories for these items were dichotomous (yes or no). Missing data Within the data set, there was a small amount of missing observations on some variables. The frequency of missing data on the study variables ranged from 0 to 1.7% per variable. Given the small number of missing values and the potential negative effects of not including all available data in the analysis we used imputation techniques to calculate estimated scores (Bollen, 1989). That is, we substituted missing observations on a particular variable with the overall sample mean on that variable. Supplemental analyses were conducted to prove the robustness of study results. Similar patterns of results were found in the analysis of datasets with listwise deletion of missing observations and in the analysis of the imputed dataset. Analyses Structural equation modeling (SEM) was employed using the AMOS 5.0 software package (Arbuckle & Wothke, 1999). SEM allows more rigorous tests of formalized hypotheses than usual procedures of bivariate and multivariate statistics. Compared with the (historically older) path analysis, the advantage of this method consists of the fact that SEM models distinguish between observed and unobserved (latent) variables (Reinecke, 2005).

19 Burnout and Work Engagement 19 The latent exogenous variables (i.e. job resources and job demands) were measured with three and two indicators, respectively. Decision latitude, supervisory support and co-workers support were used to estimate the latent job resource variable whereas quantitative and qualitative work load represented the latent job demands variable. In accordance to recent findings (Schaufeli & Bakker, 2004; Schaufeli et al., 2002) burnout was measured by two indicators, exhaustion and cynicism, whereas an extended engagement factor was assumed, consisting of vigor, dedication, absorption, and professional efficacy. Health problems comprised three indicators, namely, musculoskeletal, gastro-intestinal and cardiovascular complaints. Turnover intention was measured with a single indicator. To account for potential measurement errors the error variance was fixed by setting it equal to the product of the variance and the quantity one minus the estimated reliability as indicated by Cronbach s alpha (Bollen, 1989). Given the minimal skewness and kurtosis of the study variables maximum likelihood estimation was employed to test the various structural equation models. The adequacy of the models was estimated by using the following goodness-of fit statistics (Hoyle & Panter, 1995): The χ² goodness-of-fit statistic, the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the root mean square error of approximation (RMSEA) as well as the Tucker-Lewis Index (TLI) and the comparative fit index (CFI). The χ² goodness-of-fit statistic assesses the magnitude of the discrepancy between the covariance matrix implied by the hypothesized model and the observed sample covariance matrix. A large χ² relative to the degrees of freedom indicates that the model significantly deviates from the sample covariance matrix and needs to be modified in order to better fit the data. The sensitivity of the χ² test to

20 Burnout and Work Engagement 20 sample size (that is the increasing probability of rejecting a hypothesized model with growing sample size), however, limits its usefulness in evaluating the adequacy of a specified model in a large sample. To address this problem the goodness-of fit index (GFI) and the AGFI as absolute fit indices were employed, with values close to 1.00 being indicative of good fit (Hu & Bentler, 1995). Both indices compare the hypothesized model with no model at all (Hu & Bentler, 1995) and were found to be invariant against sample sizes (Reinecke, 2005). In contrast to the GFI, the AGFI additionally adjusts for the number of degrees of freedom in the specified model and is therefore able to indicate model parsimony (Reinecke, 2005). RMSEA, another absolute fit index, assesses how well the model would fit the population covariance matrix if it were available (Byrne, 2001). According to Hu and Bentler (1999) a cutoff value close to.06 represents a relatively good fit between the hypothesized model and the observed data. In contrast to absolute fit indices incremental fit indices measure the improvement in fit by comparing the model with an independent model, that is a model in which all the observed variables are uncorrelated (Hu & Bentler, 1999). Bentler s CFI represents such an incremental fit index that takes sample size into account (Byrne, 2001). Although a value higher than.90 was originally considered representative of a wellfitting model, a revised cutoff value close to.95 has recently been advised (Hu & Bentler, 1999). The Tucker-Lewis Index (TLI), another incremental though nonnormed fit index that compensates for the effect of model complexity, yields values that can fall outside the zero to 1.00 range. For large samples values close to.95 are indicative of good model fit (Hu & Bentler, 1999). Results Descriptive statistics

21 Burnout and Work Engagement 21 Table 1 presents the means, standard deviations and intercorrelations of all study variables as well as internal consistencies. Results show that all significant relationships between the variables were in the expected direction. Job demands were positively associated with burnout as well as health complaints. On the other hand, job resources were positively related to the various components of work engagement but negatively related to turnover intention. In addition, while burnout symptoms were positively related to turnover intention, work engagement scales showed negative associations with turnover intention. Test of the Job Demands-Resources Model In order to test the adequacy of the Job Demands-Resources Model we performed a series of structural equation modeling analyses. As indicated in Table 2 the estimation of the original model (M1) yielded an overall χ² value of with 82 degrees of freedom which suggests that model fit was only marginally adequate. Given the known sensitivity of this statistic to sample size, however, the χ² index provides little guidance in determining the extent to which the model does not fit. Thus, it is more useful to rely on additional indices of fit. Interestingly, while the RMSEA value of.08 represents a reasonably fitting model none of the other fit indices meet its cutoff criterion. Values related to GFI, AGFI, CFI and TLI were.93,.89,.92 and.89, respectively. Hence, the model fits marginally well and therefore modifications are advisable in order to determine a model that better represents the sample data. Model re-specification suggested in the following draws upon a recent conceptual and causal discussion in burnout research. In line with Cox, Kulk and Leiter (1993) we consider emotional exhaustion to be the central part of the burnout concept. Only recently this theoretical perspective on burnout has been strengthened by

22 Burnout and Work Engagement 22 Kristensen, Borritz, Villadsen and Christensen (2005) who in accordance with Pines and Aronson (1988) as well as Shirom (Shirom, 2003) conceptualize burnout as a form of fatigue or exhaustion that can be attributed to work-related activities such as work in general or client work. Moreover Taris, Le Blanc, Schaufeli and Schreurs (2005) showed that the three burnout dimensions described by Maslach, Jackson and Leiter (Maslach, Jackson, & Leiter, 1996) represent separate concepts that affect each other s development across time supporting the notion that exhaustion triggers withdrawal behavior (i.e. depersonalization/cynicism). A similar pattern in the development of burnout symptoms was found by Toppinen-Tanner, Kalimo and Mutanen (Toppinen-Tanner, Kalimo, & Mutanen, 2002) among blue-collar and whitecollar workers. Their analyses revealed that exhaustion precedes the development of cynicism, which in turn leads to lack of professional efficacy. Moreover, they indicated that symptoms of exhaustion were persistent over time. These findings suggest that burnout unfolds across time, with exhaustion playing a crucial role as initiator of this process. We therefore re-specified the model by focusing on exhaustion as the core component of burnout (M2). Thus, burnout was measured by one indicator, namely, exhaustion, with adjustment for measurement errors (error variance = variance * (1-α)). As shown in Table 2, the modification resulted in a significant improvement of fit compared to the initial model (M1). The estimation yielded an overall χ² (70) value of , with GFI =.96, CFI =.95, LTI =.94 and RMSEA =.06.Comparing the fit indices with the above mentioned cutoff criteria the values are consistently indicative of a relatively well-fitting model. Although standardized residuals partly exceed the proposed value of 2.58 (maximal discrepancy: 3.89) the modification indices showed no modification that would provide a significant improvement in model fit.

23 Burnout and Work Engagement 23 To further test the mediating role of burnout and work engagement a partial mediation model (M3) was fitted to the data, which, in addition to the full mediation model (M2), included direct paths from job demands to health complaints and from job resources to turnover intention. As suggested by Table 2, adding direct paths did significantly improve the model fit. The estimation yielded an overall χ² (68) value of , with GFI =.96, CFI =.96, LTI =.94 and RMSEA =.06. Although the improvement in model fit for the partially mediated model, compared with the fully mediated model, would appear to be trivial on the basis of the GFI, LTI or RMSEA values, the model difference nonetheless was statistically significant ( χ² (2) = 25.44). The modified partial mediation model, thus, was used for the following examination of age and gender differences as well as differences among white- and blue-collar workers. Multi-group analyses To examine whether findings based upon the entire sample were invariant across age groups, simultaneous multiple-group comparisons were conducted using three age groups: years (N = 304), years (N = 392), and 45+ years (N = 252). In a first step the baseline model was estimated simultaneously for all age groups, assuming group equality (M3a). That is, all parameter estimates were constrained to be equal across age-groups. As apparent in Table 3 the constrained model fits the data well. Nonetheless further models were analyzed to test for age invariance. To assess the improvement in model fit the differences in χ² values ( χ²) were evaluated relative to differences in degrees of freedom ( df). According to Reinecke (Reinecke, 2005) a significant amelioration of fit is achieved when the comparison between the initial and the less constrained model yields at least a χ² difference value of 5 for each degree of freedom lost. In a second step, structural paths between latent factors were freely

24 Burnout and Work Engagement 24 estimated across groups (M3b). That is, the relations between the latent variables were estimated for all age groups independently. Since the difference in χ² values between this model and the constrained model was not statistically significant, no improvement in model fit was achieved ( χ² (16) = 9.63). Therefore, age invariance regarding the structural paths is assumed. Next, structural covariances (i.e. the covariance between job resources and job demands as well as those between burnout and engagement, and between health and turnover intention) were set free (M3c). Finally, a model was tested with all structural parameters (structural paths, structural covariances, structural variances and structural residuals) freely estimated for the three age groups (M3d). Both models, however, showed no significant improvement in model fit. As apparent in Table 3 χ² values did not significantly ameliorate in the less constrained models (M2c: χ² (22) = 18.21; M2d: χ² (34) = 41.91). Hence, relationships between latent variables were shown to be invariant across age groups. Subsequently one final model that allows all parameters of the measurement model as well as those of the structural model to be freely estimated (M3e) was tested against the constrained (age-invariant) model. Again, no significant improvement in fit was achieved ( χ² (76) = ), suggesting that relations between latent and observed variables are age invariant. Thus Hypothesis 1 is not confirmed: the model works similar for all age groups. Using the same procedure we tested whether the model is invariant across male (N = 562) and female (N = 394) employees. As apparent in Table 4 the constrained model (M4a) fitted the data well. The estimation yielded an overall χ² (175) value of , with CFI =.95 and RMSEA =.04. And again, testing increasingly less constrained models did not result in a significant improvement of model fit. Neither the structural nor the measurement components were shown to vary across gender.

25 Burnout and Work Engagement 25 Therefore we also had to reject Hypothesis 2: the model works similar for men and women. In a final series of structural equation modeling analyses we evaluated the invariance of the partially mediated Job demands-resources Model across occupational levels (white-collar workers: N = 743, blue-collar workers: N = 199). Table 5 presents the results of these analyses. Again, no significant improvement in fit was achieved when comparing the freely estimated model with the constrained model leading to the conclusion that the model is invariant across incumbents of white- and blue-collar jobs as well as a rejection of Hypothesis 3: the model works similar for white-collars and blue-collars. In sum, we did show the robustness of the re-specified, partially mediated Job Demands-Resources model across age, gender and occupational level. Since no differences among various subsamples were found, Figure 2 depicts the re-specified model for the entire sample. As compared to Schaufeli and Bakker (2004) we confirmed the mediating role of burnout and engagement only to some extent. Instead of being full mediators, burnout and engagement are only partial mediators in the relationship between job demands and health problems and job resources and turnover intention, respectively. As can be seen in Figure 2, path coefficients were not equally strong in both mediating processes. Whilst job demands and burnout as well as burnout and health complaints were strongly associated (.63 and.73, respectively), associations between work engagement and turnover intention were only moderate (-.26). In fact the value of the direct path from job resources to turnover intention (-.29) did slightly exceed the indirect effect. Accordingly, more variance is explained in burnout (49 per cent) and health complaints (40 per cent) compared to engagement (37

26 Burnout and Work Engagement 26 per cent) and turnover intention (29 per cent). Taken together these results suggest that the energetic process is more pervasive than the motivational process. As far as non-directional and directional cross-links between the two processes are concerned, hypotheses were also partly confirmed. Job resources and job demands were shown to be moderately negatively related (-.33) as were burnout and engagement (-.41). No statistically significant association was found between health complaints and the intention to leave one s employer. As expected, job resources were negatively related to burnout. This path coefficient is, however, rather weak (-.16). Finally the path form burnout to turnover intention was not statistically significant. Discussion The Job Demands-Resources Model The aim of the present study was to test the robustness of the Job Demands- Resources model as proposed by Schaufeli and Bakker (2004) in an Austrian sample of white- and blue-collar workers, both males and females of different age groups. The model assumes two different processes: (1) an energetic process that starts with high job demands which may lead to burnout and consequently to health complaints; and (2) a motivational process that starts with job resources and may lead to work engagement and consequently to employees intention to remain with their current employer. Although we failed to entirely confirm the original model, theoretical considerations led to a slightly modified model that fits the data reasonably well. Given the importance of exhaustion in developing burnout symptoms (Cox et al., 1993; Toppinen-Tanner et al., 2002), we concentrated on a core model, with only exhaustion being indicative of burnout. Furthermore, according to previous findings (Schaufeli & Bakker, 2004; Schaufeli et al., 2002) we assumed an extended engagement dimension comprising

27 Burnout and Work Engagement 27 vigor, dedication, absorption and professional efficacy, which fitted the data rather well. These findings question the original definition of burnout as a syndrome combining three different albeit interrelated dimensions, namely exhaustion, depersonalization/cynicism and professional efficacy. As Kristensen and his colleagues (2005) pointed out in their comprehensive critique of the Maslach burnout concept it is unclear why three independent dimensions which have their own precursors and consequences (e.g. see Lee & Ashforth, 1996) should be viewed as facets of the same phenomenon. Furthermore, as noted by Taris et al. (2005) the three components are not based on sound theorizing or clinical observations rather they have been inductively developed by factor-analyzing. Thus, in lieu of conceptualizing burnout as a threedimensional syndrome Kristensen et al. (2005) consider fatigue or exhaustion to be the core dimension of burnout. Consistent with recent causal models (Leiter, 1993; Taris et al., 2005; Toppinen-Tanner et al., 2002) they interpret depersonalization or cynicism as a (dysfunctional) coping strategy and reduced professional efficacy as one of many consequences of burnout. Our findings support these assumptions as far as the significance of exhaustion to burnout is concerned. More work on the theoretical foundations of burnout is needed to further elucidate the construct (Shirom, 2005). Despite these modifications we were able to confirm the main assumption of the original JC-R model, namely the differentiation between two underlying processes. The energetic process linking job demands with psychosomatic health complaints via burnout, however, shows stronger path coefficients than the motivational process in which engagement plays a mediating role. The weaker path coefficients in the latter process may partly be due to deficits in the operationalization of organizational commitment by using turnover intention as indicator. Future studies should therefore

28 Burnout and Work Engagement 28 measure turnover intention more detailed or consider other organizational outcomes. The result is, however, in line with previous findings. Schaufeli and Bakker (2004) as well as Hakanen et al. (2006) found the energetic process to be more prominent than the motivational process. Referring to conversation of resources theory, Hakanen et al. (2006) pointed out that employees are more sensitive to the loss of resources compared with the option of gaining resources. Thus, the energetic process as a loss process seems to be more prominent than the motivational process that reflects a gain process. However, the mediating role of burnout and work engagement was not completely confirmed because job demands and job resources were found to directly affect health complaints and turnover intention, respectively. While the direct effect of job demands on health complaints assumed a low, but statistically significant negative value, the direct effect of job resources on turnover intention was about the same size as the indirect effect. The observed additional direct effects of job demands and job resources agree with a recent study conducted among Dutch and Spanish workers (Llorens et al., 2006). In order to test the robustness of the JD-R model we conducted multi-group analyses, comparing different groups of employees. First, in contrasting three age groups (15-29, 30-44, and 45+ years) the JD-R model proved to be age invariant. In addition SEM analyses revealed that the structural as well as the measurement model of the re-specified model were similar across male and female workers. Finally, we tested for invariance across white- and blue-collar workers. Although the JD-R model has been tested in various occupational samples, so far the generalizability of the model has not been studied across blue- and white-collar workers simultaneously. And again, we found the model to be invariant across subsamples. Thus, the basic structure of the Job