Testing reciprocal relationships between job characteristics and psychological well-being: A cross-lagged structural equation model. Jan de Jonge*. Department ...
Journal of Occupational and Organizational Psychology (2001), 74, 29–46 Ó 2001 The British Psychological Society
Printed in Great Britain
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Testing reciprocal relationships between job characteristics and psychological well-being: A cross-lagged structural equation model Jan de Jonge* Department of Social and Organizational Psychology, Utrecht University, The Netherlands Department of Work and Organizational Psychology, University of Nijmegen, The Netherlands
Christian Dormann Department of Work and Organizational Psychology, Johann Wolfgang Goethe University, Germany
Peter P. M. Janssen Department of Health Organization, Policy and Economics, Section Work and Health, Maastricht University, The Netherlands
Maureen F. Dollard School of Psychology, University of South Australia, Whyalla Campus, South Australia
Jan A. Landeweerd and Frans J. N. Nijhuis Department of Health Organization, Policy and Economics, Section Work and Health, Maastricht University, The Netherlands
This article describes a two-wave panel study which was carried out to examine reciprocal relationships between job characteristics and work-related psychological well-being. Hypotheses were tested in a sample of 261 health care professionals using structural equation modelling (LISREL 8). Controlling for gender, age, and negative aV ectivity, the results primarily supported the hypothesis that Time 1 job characteristics in uence Time 2 psychological well-being. More speci cally, Time 2 job satisfaction was determined by Time 1 job demands and workplace social support, respectively. Furthermore, there was also some preliminary but weak evidence for reversed cross-lagged eV ects since Time 1 emotional exhaustion seemed to be the causal dominant factor with respect to Time 2 (perceived) job demands. In conclusion, this study builds on earlier cross-sectional and longitudinal ndings by eliminating confounding factors and diminishing methodological de ciencies. Empirical support for the in uence of job characteristics on psychological well-being aYrms what several theoretical models have postulated to be the causal ordering among job characteristics and work-related psychological well-being. *Requests for reprints should be addressed to Jan de Jonge, Department of Social and Organizational Psychology, Utrecht University, P.O. Box 80.140, 3508 TC Utrecht, The Netherlands.
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The purpose of this study was to examine the causal relationship between job characteristics and employee psychological well-being. While there is some general agreement that these relationships do exist, there is still some disagreement about the speci c nature, magnitude, and in particular the causal direction of the relationships (e.g. Semmer, Zapf, & Greif, 1996; Zapf, Dormann, & Frese, 1996). For instance, do high job demands lead to job-related strain? Or, is strain primary such that high-strained workers are more likely to report their job demands as higher? Good examples of longitudinal panel studies are scarce (less than 10%, according to Zapf and colleagues), which means that evidence of strong causal relationships between job characteristics and well-being outcomes has been piecemeal and limited in scope. Moreover, comprehensive empirical tests of all postulated relationships by means of sophisticated statistical techniques, such as covariance structure models, are rather scarce as well (Zapf et al., 1996). Demonstrating causal relationships has become important from a theoretical, practical, and methodological perspective. For example, prominent job characteristics models like the demand–control–support (DCS) model (Karasek & Theorell, 1990) and the job characteristics (JC) model (Hackman & Oldham, 1980) assume that the causal ow is unidirectional, where job characteristics a V ect well-being outcomes, and not vice versa. If this is really the case, then there is reason to believe that some of the observed problems may have their roots in the properties of the job itself. Therefore, restructuring jobs or workplaces may be a useful starting-point for eV ective interventions. However, current transactional models like Edwards’ cybernetic model of stress, coping and well-being (Edwards, 1998) emphasize the reciprocal nature of the stress process, in which perceived job characteristics are also a V ected by employee well-being. Practically, the impact of stress management on employee well-being multiplies itself over time, as increases in well-being and decreases in job stressors mutually reinforce each other (Daniels & Guppy, 1997). There are still methodological pitfalls and de ciencies in longitudinal research, which make it very diYcult to detect causal as well as reciprocal relationships. According to Frese and Zapf (1988) as well as Kessler and Greenberg (1981), a plausible causal relationship exists between two variables if there is an association of some sort between them, if there is evidence about the direction of causality, and if other explanations can be ruled out. In short, we cannot demonstrate causality. We can only make causal relationships plausible by ruling out alternative explanations (see also Bollen, 1989). In this respect, several recommendations can be made, such as performing a full panel design with an adequately planned time lag, taking stabilities of variables into account, and using covariance structure modelling (e.g. Finkel, 1995; Frese & Zapf, 1988; Van der Kamp & Bijleveld, 1998; Williams & PodsakoV , 1989; Zapf et al., 1996). The focus of the present article is on the direction of relationships between job characteristics and work-related psychological well-being in a two-wave panel study. In this context, the DCS model or the JC model are useful vehicles to investigate speci c job characteristics and speci c well-being outcomes. Both models obviously favour the unidirectional perspective and allow for unambiguous predictions of the signs of the relationships between job characteristics and
Job characteristics and psychological well-being: reciprocal relationships
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employee well-being. For example, a two-wave panel study involving 180 student teachers by Parkes, Mendham, and Von Rabenau (1994) revealed that Time 2 somatic symptoms were predicted by a combination of high Time 1 demands, low Time 1 autonomy and low Time 1 social support. For this reason, we hypothesize that job characteristics at Measurement 1 in uence employee well-being at Measurement 2 (H1). There are methodological and substantive reasons to expect reversed eV ects of employee well-being on working conditions. According to the so-called drift hypothesis (Frese, 1982; Kohn & Schooler, 1983; Lorence & Mortimer, 1985; Williams & PodsakoV , 1989), one can postulate that highly motivated workers drift to better jobs accompanied with more job autonomy, more support, and fewer demands. In contrast, people with bad health or motivational de cits in particular may drift to worse jobs due to their bad personal record of sickness absenteeism or even disability (see also Waldron, Herold, Dunn, & Staum, 1982). As Daniels and Guppy (1997) have noted, ‘as the individual changes, so will his or her transactions with the environment’ (p. 157). For instance, unsatised people, people with a low motivation, or people who are emotionally exhausted may receive less social support because people with poor well-being may not be seen as being able to reciprocate by their supervisors and colleagues (Daniels & Guppy, 1997). Since supervisors may perceive them as also lacking drive, they may not be given autonomy either. Added to this, one could also argue that occupational burnout leads to cognitive and behavioural withdrawal reactions which, in turn, lead to lack of workplace social support (Schaufeli & Enzmann, 1998; see also Firth-Cozens & Hardy, 1992). Even if the actual amount of support, autonomy, and demands do not change, there may be poorer perceptions of these working conditions. It is well known that negative moods, in particular depression, lead to an increased recall of negative information (e.g. Mathews, 1993; Taris, Bok, & Calje´, 1998). Since uncontrollable events are supposed to be more aversive than controllable ones (Miller, 1980), following Daniels and Guppy it can be argued that people with reduced mood may recall more uncontrollable events and thus report less autonomy. Similarly, individuals with reduced aV ective well-being may recall more demands and fewer situations in which they received support so that they report less support than was actually available to them and more demands (see also Firth-Cozens, 1992). Nevertheless, in their overview of longitudinal studies by Zapf et al. (1996), only six out of 16 studies on organizational stress which tested for reversed causation, yielded evidence for reversed causation. Thus, when compared to the great number of theoretically proposed mechanisms, the empirical evidence for such eV ects is not strong. In conclusion, our second hypothesis is that psychological well-being at Measurement 1 in uences (perceived) job characteristics at Measurement 2 (H2). Finally, as several authors have stated (e.g. Caldwell & O’Reilly, 1982; James & Jones, 1980; James & Tetrick, 1986; Williams & PodsakoV , 1989; Zapf et al., 1996), there is also reason to believe that reciprocal relationships between (perceived) job characteristics and psychological well-being do exist in that Time 2 well-being is in uenced by Time 1 job characteristics, and Time 2 (perceived) job characteristics are aV ected by Time 1 psychological well-being. Williams and PodsakoV (1989), for
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Jan d e Jonge et al.
instance, have argued that at a conceptual level, such reciprocal relationships are prevalent in organizational behaviour research like job design and job stress research. Bi-directional in uences which imply some sort of vicious circle do not correspond entirely to the nature of most social and psychological systems. Hence, a few longitudinal studies have demonstrated that simultaneous reciprocal causality does occur (e.g. Bateman & Strasser, 1983; James & Jones, 1980; James & Tetrick, 1986; Kohn & Schooler, 1983; Schwarzer, Hahn, & Jerusalem, 1993; Taris et al., 1998). For example, Schwarzer et al. (1993) found in a three-wave panel study that poor mental health leads to lower (perceived) social support which, in turn, leads to poorer mental health. Taken together, we nally hypothesize that, in addition to Hypotheses 1 and 2, (perceived) job characteristics and psychological well-being mutually in uence each other (H3). In addition to all this, the present study was designed to address some of the above-mentioned methodological issues (e.g. performing a full panel design and using covariance structure modelling). Method Design and participants We conducted a full panel design with two panel waves: participants supplied data at two measurement points in time. The aim was to have about a one-year interval between the two measurements. This time appears to be long enough for possible changes in individual scores, but not too long for too much non-response in our study sample (see also Frese & Zapf, 1988; Vermaat, 1994). Moreover, in this way possible seasonal uctuations in work were controlled for. Self-report questionnaires were administered and could be returned by mail. All questionnaires contained an administration number for second round identi cation. As part of an extensive research project (De Jonge, 1995), the initial sample consisted of health care workers of one general hospital and three nursing homes in The Netherlands. At Time 1, 457 health care workers received the questionnaires, and 380 respondents returned the questionnaire (83% response rate). At Time 2, 363 out of 454 health care workers returned the questionnaire, giving an 80% response rate. The nal sample re ects those persons who participated at both times. The number of respondents who completed both questionnaires was 261, or 57% of the initial group. This percentage is not very unusual according to the literature about panel non-response (Hagenaars, 1990). A breakdown of the demographic characteristics of the sample shows that 89% of the health care workers were women. The mean age of the group was 31.87 years (SD = 8·52). The mean work experience was 10.68 years (SD = 7·17), and 45% of the respondents worked full-time. A comparison of continuous participants with drop-outs showed that our data did not appear to su V er from serious selection problems. The panel group was signi cantly older (t = 2.04, p < .05), and had signi cantly higher mean work experience (Mann–Whitney Z = 4.02, p < .001). These results are not surprising, because a substantial number of the drop-outs were (young) student nurses, who change units twice a year. An important question is whether disappearance from the sample is an outcome of a causal dynamic that is diV erent from that of the survivors. To nd out whether this was the case or not, it is advisable to check for causal homogeneity in the sample (Hagenaars, 1990; Kessler & Greenberg, 1981). In other words, causal relationships should be (nearly) the same for the panel group and the drop-outs. Cross-sectional multi-sample structural equation analyses (Jo¨reskog & So¨rbom, 1993) indicated that disappearance from the sample was not likely to be the result of diV erent causal dynamics (group comparison: D v 2(12) = 9.09, p = n.s.). Therefore both groups were quite comparable in terms of internal consistency as well as in terms of the pattern of relationships between job characteristics and psychological well-being.
Job characteristics and psychological well-being: reciprocal relationships
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Measures The questionnaire comprised three main sections: demographic/personal characteristics, job characteristics and employee psychological well-being. Demographic and personal characteristics. These refer to background factors such as gender, age and negative aV ectivity. These variables may confound the results (Burke, Brief, & George, 1993; Karasek & Theorell, 1990; Spector, 1997). Therefore, we included these variables to control for their possible in uence. Negative aV ectivity was measured by trait anxiety, as recommended by several authors (Clark, Watson, & Mineka, 1994; Dollard & Wine eld, 1998; Watson & Clark, 1984). The scale was derived from the Dutch Organizational Stress Questionnaire (Reiche & Van Dijkhuizen, 1979), and consists of four items with a response scale ranging from 1 ‘never’ to 4 ‘always’. An example item is ‘I feel nervous’. The job characteristics, demands, autonomy, and social support, included in this study were guided by Karasek’s DCS model (Karasek & Theorell, 1990). In order to minimize bias, the subjective indictors of the job characteristics contained items with a minimum of cognitive processing. In other words, these items were precisely de ned and were worded as neutrally as possible (cf. Frese, 1999; Frese & Zapf, 1988). Job d emands. These were measured by an eight-item questionnaire (5-point response scale ranging from 1 ‘never’ to 5 ‘always’) that included a wide range of qualitative and quantitative demanding aspects of the job, such as working under time pressure, working hard, and job complexity. The psychological job demands scale has been well validated in Dutch samples of health care professionals (e.g. De Jonge, Janssen, & Van Breukelen, 1996; De Rijk, Le Blanc, Schaufeli, & De Jonge, 1998). An example item is: ‘In the unit where I work, work is carried out under pressure of time.’ Job autonomy. This was assessed by the Maastricht Autonomy Questionnaire (MAQ; De Jonge, Landeweerd, & Van Breukelen, 1994; De Jonge, 1995), which consisted of ten Likert-items with a 5-point response scale ranging from 1 ‘very little’ to 5 ‘very much’. The MAQ measures the worker’s opportunity to determine a variety of task elements, like the method of working, the pace of work and the work goals. For instance, ‘The opportunity that the work o V ers to determine the method of working yourself.’ Workplace social support. A ten-item scale of total work-related social support (from the senior nursing oYcer as well as from colleagues) was used to measure workplace social support. The scale was derived from a Dutch questionnaire on organizational stress (VOS-D; Bergers, Marcelissen, & De WolV , 1986). A 4-point response scale was used, ranging from 1 ‘never’ to 4 ‘always’. For instance, ‘If there are problems at work, can you talk about them with your senior nursing oYcer/colleagues?’ The choice of the three psychological well-being variables (cf. Warr, 1987) was also guided by the DCS model (see also Van der Doef & Maes, 1999). That is, emotional exhaustion re ected outcomes of the job-strain hypothesis of the DCS model, whereas job satisfaction and work motivation re ected outcomes of the active behaviour hypothesis (for more details, see Karasek & Theorell, 1990). Emotional exhaustion. We measured emotional exhaustion by means of the Dutch version of the Maslach Burnout Inventory (Schaufeli & Van Dierendonck, 1993). This instrument is particularly suitable for use in human services professions like nursing. Moreover, emotional exhaustion is the most characteristic burnout dimension that is closest to more orthodox job-strain variables (Enzmann, Schaufeli, Janssen, & Rozemann, 1998; Maslach, 1998). In its original form, the scale consists of nine items, scored on a 7-point scale (ranging from 0 ‘never’ to 6 ‘always’). Because of insuYcient factorial validity in earlier burnout studies, one of the original items (i.e. ‘Working with people directly puts too much stress on me’) was eliminated in the Dutch version of the MBI. Job satisfaction. This was assessed by a single item (i.e. ‘I am satis ed with my present job’) that was scored on a 5-point rating scale, ranging from 1 ‘strongly disagree’ to 5 ‘fully agree’. It has been shown that a global index of overall job satisfaction is a valid measure of general job satisfaction (e.g.
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Jan d e Jonge et al.
Scarpello & Campbell, 1983; Wanous, Reichers, & Hudy, 1997). Wanous et al. (1997) assumed a reliability of .57 for such a single-item measure. Work motivation. This was measured by ve items in which the respondents were asked how stimulating, interesting and challenging their work was (De Jonge, 1995). The questions were answered on a 5-point scale with a response scale ranging from 1 (‘strongly disagree’ to 5 ‘fully agree’. For example, ‘My work stimulates me to perform better all the time’.
Data analysis We performed covariance structure modelling (LISREL 8; Jo¨reskog & So¨rbom, 1993) in order to analyse our panel data. Because of the large number of items used to operationalize all the variables in our model, simultaneous consideration of all observed variables (i.e. items) would result in unreliable parameter estimates and insuYcient power (Bentler & Chou, 1987; Jaccard & Wan, 1996). Therefore, each of the components in the panel model was included in the structural equation analyses as a latent variable. In other words, the covariance structure model was simpli ed by assuming that the observed and latent variables were identical (each construct had only one operationalization). Furthermore, a two-step approach was followed (cf. Anderson & Gerbing, 1988). First we tested the measurement models, and secondly we tested the structural equation models. The measurement models used showed the variables to be valid and reliable (De Jonge, 1995; see also Table 1). Using structural equation modelling may lead to a slightly complex variant of the general panel model (e.g. see Finkel, 1995; Kessler & Greenberg, 1981). By means of such a cross-lagged structural equation model (see Fig. 1), a number of competing structural equation models were tted to the data in several steps. First of all, a model without cross-lagged structural paths but with temporal stabilities (model M 1) was speci ed. Second, this stability model was compared with three more complex models that were nearest in likelihood to the hypothesized structural model: (1) a model with cross-lagged structural paths from Time 1 job characteristics to Time 2 psychological well-being (model M 2; re ecting Hypothesis H1 in arrow 1); (2) a model with cross-lagged structural paths from Time 1 psychological well-being to Time 2 job characteristics (model M 3; re ecting Hypothesis H2 in arrow 2); (3) a model with both cross-lagged structural patterns (model M 4) representing reciprocal eV ects (re ecting Hypothesis H3 in arrows 1 and 2). Full information maximum likelihood (FIML) estimation was used to assess the t of this cross-lagged structural equation model. Note that the model consists of regression coeYcients representing the diV erential cross-lagged structural paths, test–retest coeYcients between the measurement scales, covariances between the background variables, residual covariances between the job characteristics, and errors in equations. The latter are allowed to correlate, because error caused by mis-speci cation of the model would be re ected in these correlations. The existence of an additional variable that is not included in the model might be responsible for this error-correlation (called occasion-factor by Dwyer, 1983), and is necessary in order to explain the outcome variables more fully (Long, 1983, MacCallum, Wegener, Uchino, & Fabrigar, 1993). Moreover, Time 1 gender (dummy variable), Time 1 age, and Time 1 negative aV ectivity (NA) were introduced into the panel model as potential confounders. Consequently, these variables were labelled as exogenous variables (see Bollen, 1989, p. 126), and all other variables were labelled as endogenous variables (i.e. the job characteristics and psychological well-being). Finally, we assume that gender, age, and negative aV ectivity were directly related to the Time 1 variables, and only indirectly to the Time 2 variables (i.e. by way of test–retest coeYcients from Time 1 variables to Time 2 variables).
Results Preliminary results Prior to the LISREL analyses, the means, standard deviations, coeYcient alphas and Pearson correlations (including test–retest coeYcients) were computed (see
SD
a
Gendera (1) — — Age (1) 31.87 8.52 AVectivity (1) 1.44 .41 .75 Demands (1) 3.16 .68 .88 Autonomy (1) 2.73 .59 .81 Support (1) 3.34 .32 .75 Satisfaction (1) 4.02 .85 — Motivation (1) 3.82 .69 .86 Exhaustion (1) 1.68 .92 .85 Demands (2) 3.16 .71 .89 Autonomy (2) 2.78 .58 .84 Support (2) 3.28 .38 .82 Satisfaction (2) 3.94 .83 — Motivation (2) 3.63 .66 .86 Exhaustion (2) 1.61 .88 .85
M
2
2
2
2
2
.08 .08 .02 .06 .02 .16* .13* .06 .11 .01
2 .19*
2 .08
2 .05
1
2 .11
*p# .05, two-tailed. a Gender was coded 0 (males) and 1 (females). Note. N=261. Key. (1)=Time 1; (2)=Time 2.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
Measures
2
2
2
2
2
2
2
2
2
.01 .03 .13* .04 .09 .16* .07 .01 .04 .00 .03 .06 .12
2
4
5
.24* .25* .19* .45* .52* 2 .13 .20* .72* 2 .35* .64* 2 .04 2 .27* .23* 2 .18* 2 .21* .24* 2 .26* 2 .34* .17* 2 .09 2 .20* .37* .33* 2 .17*
2 .30* 2 .19* 2 .24* 2 .34* 2 .31* 2 .07 2 .26*
.21*
2 .03
3
7
.46* .34* .52* 2 .25* 2 .40* 2 2 .21* 2 .28* 2 .29* .22* .59* .36* .38* .55* .25* .40* 2 .24* 2 .35* 2
6
.27* .21* .25* .32* .43* .62* .25*
8
.66*
.50*
11
12
13
14
.39* .39* .53* .27* .42* .55* .50* 2 .33* 2 .44* 2 .54* 2 .36*
10
2 .16* 2 .43* 2 .26* 2 .34* 2 .43* 2 .40* 2 .25* 2 .20*
9
Table 1. Means, standard deviations (SD), coeYcient alphas (a ) and Pearson correlations of the study variables
Job characteristics and psychological well-being: reciprocal relationships 35
Job characteristics and psychological well-being: reciprocal relationships
37
Table 2. Goodness-of- t indices and chi-square diVerence tests of nested structural models of psychological well-being Model No cross-lagged (M1) Cross JCT1–PWT2 (M2) Cross PWT1–JCT2 (M3) Both cross (M4)
v
2
98.86*** 81.85*** 86.95*** 68.49***
d.f. 48 39 39 30
Comparison
M1 M1 M1 M2 M3
vs. vs. vs. vs. vs.
M2 M3 M4 M4 M4
D v
2
17.01* 11.91 30.37* 13.36 18.46*
D d.f.
9 9 18 9 9
*p# .05; ***p# .001. Note. N=261. Key. JC=Job characteristics; PW=Psychological well-being; T1=Time 1; T2=Time 2.
The rst chi-square diV erence test showed that the diV erence between the stability model and the model with cross-lagged eV ects from job characteristics to well-being outcomes was signi cant (M1 vs. M 2: D v 2(9) = 17.01, p# .05). This means that the unconstrained model with lagged eV ects (M2) better accounts for the data than the constrained model with no lagged eV ects (M1). In other words, there is statistical evidence that Time 1 job characteristics in uence Time 2 psychological well-being. Alternatively, a second chi-square diV erence test showed that the diV erence between the stability model and the model with cross-lagged structural paths from Time 1 well-being to Time 2 (perceived) job characteristics was not signi cant (M1 vs. M 3: D v 2(9) = 11.91, p = n.s.). Thus, model M 3 has no better statistical t than model M 1. Generally, psychological well-being in Measurement 1 was not able to in uence (perceived) job characteristics in Measurement 2. The chi-square di V erence test between the stability model and the model with all cross-lagged structural paths was signi cant (M1 vs. M 4: D v 2(18) = 30.37, p# .05). However, model t did not improve if reversed structural paths were added to the conventional paths (M2 vs. M 4: D v 2(9) = 13.36, p = n.s.). On the contrary, there was a clear improvement in model t if usual structural paths were added to the reversed paths (M3 vs. M 4: D v 2(9) = 18.46, p# .05). Generally, in terms of chi-square relative to the degrees of freedom, model M 2 showed the best t of all competing models (see Table 2). Model M 2 also had a relatively good t when the most important practical t indices were reviewed (i.e. GFI = .96, AGFI = .86, RMSEA = .07, NNFI = .91, AIC = 240.31, PNFI = .35, and CFI = .97), according to criteria presented by Hu and Bentler (1998) or by Schumacker and Lomax (1996). For instance, in terms of model comparisons, model M 2 showed the best combination of NNFI and CFI. In terms of parsimony, model M 2 showed the lowest AIC compared to the other lagged models (i.e. models M 3 and M 4). If one were to take these t indices as the most important ones, it would also lead to the preference of model M 2.
Job characteristics and psychological well-being: reciprocal relationships
39
coeYcients of the best tting model (M2). It should be noted that the FIML estimates have been standardized and that only signi cant relationships between job characteristics and psychological well-being and test–retest stability coeYcients are shown. Regarding the signi cant cross-lagged parameters, it appears that Time 1 job demands in uence Time 2 job satisfaction. Higher levels of Time 1 job demands cause lower levels of Time 2 job satisfaction. Furthermore, Time 1 workplace social support in uences Time 2 job satisfaction as well; that is, higher levels of social support lead to higher levels of job satisfaction. Taken together, both job demands and social support appeared to be the causal factors. As mentioned before, evidence exists that reciprocal relationships are present since the unconstrained model with both types of lagged eV ects (M4) better accounts for the data than the constrained model with no lagged eV ects (M1). However, we also concluded that the chi-square di V erence test was mainly caused by the eV ects of job characteristics on psychological well-being. This means that, in general, the reciprocal model can exist without reversed eV ects, but it cannot exist without eV ects of job characteristics on psychological well-being. Referring to Rogosa’s (1980) statement again, we tried to explore those reciprocal cross-lagged patterns in addition to model M 2 by examining the individual pathways. Results from model M 4 showed one additional cross-lagged eV ect from Time 1 emotional exhaustion to Time 2 (perceived) job demands (standardized path coeYcient: .11, p# .05). This reversed structural path indicated that higher levels of Time 1 emotional exhaustion seemed to in uence higher levels of (perceived) psychological job demands at Time 2. Discussion The main purpose of the present study was to test and evaluate the direction of relationships between job characteristics and employee psychological well-being. We used a two-wave panel design which allowed more rigorous interpretation of causality and reciprocity than cross-sectional designs. Additionally, we tried to avoid the possible methodological pitfalls of standard statistical techniques by testing cross-lagged structural equation models. The results primarily supported Hypothesis H1 such that, after controlling for gender, age and negative a V ectivity, job characteristics in uenced psychological well-being. More speci cally, both job demands and workplace social support appeared to be the causally dominant factors with regard to job satisfaction. Furthermore, there was also some preliminary but weak evidence for reversed cross-lagged eV ects since emotional exhaustion seemed to be the causally dominant factor with respect to (perceived) job demands. The present ndings are consistent with the scarce longitudinal studies in this research area. For instance, James and Tetrick (1986) performed a two-stage least squares analysis of job characteristics and job satisfaction. After comparing three alternative causal models, they concluded that job characteristics appeared to be a stronger cause of job satisfaction than vice versa. A closer inspection of the lagged relationships indicated additional support for job characteristics–well-being relationships, as far as job demands and workplace
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Jan d e Jonge et al.
social support were concerned. Both variables showed a lagged pattern of relation similar to cross-sectional ndings presented in the literature. The nding that psychological job demands are negatively associated with job satisfaction was replicated in the present study (Spector, 1997). Interestingly, though we found a lagged eV ect which strengthens earlier cross-sectional ndings in health care work (e.g. De Jonge et al., 1996; Landsbergis, 1988; McLaney & Hurrell, 1988). The lagged relationship between social support and job satisfaction seems to re ect the nature of work of nurses and nurses’ aides. A key characteristic feature of their jobs is that workplace social support plays an important role in daily work, because for the most part nurses and nurses’ aides work closely together in teams (e.g. Shinn, Rosario, Morch, & Chestnut, 1984). This nding also underlines the assumptions of the DCS model concerning highly motivated and satis ed employees (Karasek & Theorell, 1990). It is therefore not surprising that this model has been expanded with workplace social support as a key predictor (Johnson & Hall, 1988). Finally, a number of empirical studies indicate that supportive relationships generally enhance outcomes such as job satisfaction and work motivation (e.g. see Boumans & Landeweerd, 1992; Parkes et al., 1994; Peeters, 1994). In all, these results provide additional empirical evidence for these two job characteristics in the prediction of employee well-being. Conversely, there was weak evidence for only one lagged reversed relationship with emotional exhaustion as a predictor for (perceived) psychological job demands. This contradicts the research that does nd evidence of reversed causation (e.g. Zapf et al., 1996). Reasons for this can only be speculative. For instance, Daniels and Guppy (1997) found only a weak eV ect of poor well-being for the appraised stressfulness of job demands, not their frequency. This carries the risk that relationships between demands and well-being are a V ected by self-report or method variance (Wall, Jackson, Mullarkey, & Parker, 1996). We tried to avoid evaluative components like intensity in the measurement of demands, and used descriptive as well as frequency-based measures instead (e.g. Frese & Zapf, 1988), and this might be a reason for lack of corroboration. A similar way of reasoning could be true for job autonomy to explain lack of reversed causation (e.g. De Jonge, 1995; Wall et al., 1996). Previous longitudinal studies that detected reversed eV ects on (perceived) social support covered either a very short time lag (e.g. Daniels & Guppy, 1997; Fisher, 1985) or longer time lags (Marcelissen, Winnubst, Buunk, & De WolV , 1988). However, a panel study by Billings and Moos (1982) which used a similar time lag as our study (i.e. one year), failed to demonstrate lagged eV ects of psychological symptoms on support. It might be that in general the eV ect of aspects of well-being on (perceived) social support takes place within a few months rather than a one-year period. Another reason might be that reversed eV ects of well-being on (perceived) support are speci c rather than global, and that the support measure used in our study was not speci c enough to detect reversed eV ects. For example, Daniels and Guppy (1997) found a diV erential pattern of associations between aspects of well-being and subsequent reported diV erent dimensions of social support. More speci cally, they found eV ects on help support and social dependability, but not on esteem support. In a similar vein, Marcelissen et al.
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(1988) found that strains a V ected only support by co-workers, and not by the supervisor. The reversed association between emotional exhaustion and (perceived) job demands was found in earlier burnout studies. For instance, a two-wave panel study among health care professionals (3 months’ time interval) also showed that Time 1 emotional exhaustion predicted Time 2 (perceived) work overload (Leiter & Durup, 1996). In addition, in a two-wave longitudinal study (10 months’ time interval) among teachers, Shirom and Oliver (1986) found that burnout was a predictor rather than a consequence of (perceived) job demands. There are several reasons why one may expect that emotional exhaustion in particular a V ects (perceived) working conditions rather than job satisfaction and work motivation. First, the drift hypothesis, which is rather non-speci c in suggesting that any kind of poor health may cause bad working conditions by workers drifting to a new and worse job, does not apply because job changes were excluded from the present study. Secondly, perceived working conditions rather than objective ones were considered, so that perceptual mechanisms are more likely to apply than eV ects of well-being on actual (objective) working conditions. Thirdly, the argument that people with poor a V ective health recall more unfavourable working conditions particularly applies to depression (Daniels & Guppy, 1997). Among the variables investigated in the present study, emotional exhaustion is most closely linked to depression as indicated by items such as ‘feeling depressed’, ‘feeling hopeless’, and ‘feeling worthless’. Fourthly, emotional exhaustion has been conceived as a general and comprehensive dimension of well-being (Enzmann et al., 1998). Hence, if emotional exhaustion or any other broad well-being variable is included, further health-related variables may have only weak additional eV ects on the perceptions of job conditions. Therefore, emotional exhaustion may have stronger eV ects on working conditions than job satisfaction and work motivation. Schaufeli and Enzmann (1998), however, explained such reversed causal eV ects from burnout to (perceived) job characteristics from a methodological point of view. They stated that regression approaches to control statistically for the initial burnout scores may be inappropriate as a method to study predictors of change, especially if the stability of burnout scores is high (which often seems to be the case). Therefore, they proposed promising alternative methods for the study of change, such as growth curve modelling. We did not apply this technique, as more than two waves are required for this approach. In conclusion, the present results underline the importance of studying reversed causal eV ects in this kind of study (see also Zapf et al., 1996). We believe that reversed eV ects should always be conceived as a plausible alternative explanation for relations between (perceived) job characteristics and employee well-being. The concept of negative a V ectivity (NA) as a confounder of the association between (perceived) job characteristics and job-related strain has been widely investigated (for an overview, see Spector, Zapf, Chen, & Frese, 2000). There are important, diverging, reasons for assessing NA in job stress research. First, NA may spuriously in ate the associations between the variables which have been measured
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by self-report methods. Secondly, controlling for NA may also mean that variance in job-related strain that could be due to the work environment is removed with the NA measure. In other words, not controlling for NA could lead to an exaggeration of the job-strain association, whereas controlling for NA in attempting to re ne methodology could lead to an underestimation of the relative importance of job characteristics (Dollard & Wine eld, 1998). Except for job autonomy and work motivation, the present study showed that NA was correlated with all job characteristics and psychological well-being. In the structural equation models, however, controlling for NA had no impact on job-strain relationships. There are at least three plausible interpretations of this nding. First, although measures used were self-reports, job characteristics measures have been operationalized in such a way that a certain degree of objectivity would be derived (cf. Frese, 1999; Frese & Zapf, 1988). Secondly, if perfect stability of NA is assumed, NA need not be considered in panel studies, because its eV ect is automatically partialled out if the structural equation model is in a state of equilibrium (Spector et al., 2000). Finally, a number of studies have found that NA is not (or weakly) associated with job satisfaction (e.g. Chen & Spector, 1991; Dormann & Zapf, 1998; Moyle, 1995; Williams, Gavin, & Williams, 1996). Because the signi cant cross-lagged patterns were mainly found with regard to job satisfaction, this study underlines the use of job satisfaction as an index of strain to help overcome the confounding eV ects produced by NA. At least two remarks regarding the present study can be made. First, our data are collected in discrete time samples while the processes to be observed are continuous. Therefore, we cannot obtain valid parameter estimates until the measurement period matches the causal lag (Engel & Meyer, 1996; Frese & Zapf, 1988). Moreover, time mis-speci cations may lead to serious problems if the time lag is too short (Kessler & Greenberg, 1981). We have tried to estimate this period by means of a pilot study (Vermaat, 1994), but the only remedy seems to be the estimation of distributed lag models afterwards in multi-wave studies (Engel & Meyer, 1996). In contrast, one could argue that discrete time approximations of such continuous processes are in practice quite adequate since in some instances changes do not occur continuously but in distinct stages (Van der Kamp & Bijleveld, 1998). A second point involves the study population. Studying people in just one occupation has advantages as well as disadvantages. An advantage of a singleoccupation group like health care professionals is that we had virtually no variance in socio-economic status, which precludes confounding eV ects. The challenge is, however, to obtain enough variance on the variables of interest to allow hypothesis tests. Compared to large multi-occupation studies, we might have some restriction in range in our variables. But health care professionals as an occupational group have the advantage of providing much natural variance because of di V erent types of health care areas, and because diV erent specialties exist within the same general hospital or nursing home (Fox, Dwyer, & Ganster, 1993; Ganster & Fusilier, 1989). Nevertheless, generalization of the current results to other occupations awaits further empirical examination.
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In conclusion, this study builds on earlier cross-sectional and longitudinal ndings by eliminating confounding factors and diminishing several methodological de ciencies. Empirical support for the in uence of job characteristics on work-related psychological well-being aYrms what several prominent theoretical models have postulated to be the causal ordering among job characteristics and a V ective responses to jobs (see Hackman & Oldham, 1980; Karasek & Theorell, 1990; Siegrist, 1998; Warr, 1987). However, this study does not invalidate the evidence that supports a reverse causal relationship (i.e. from psychological well-being to (perceived) job characteristics), although this evidence is less conclusive. It may preliminarily indicate that (perceived) job characteristics and pyschological well-being in uence each other reciprocally rather than unidirectionally (cf. Edwards, 1998). So, there is a plea for studying more complex models of the job stress process, including reciprocal relationships. Practically, the present study indicated that job characteristics themselves are relatively important predictors of employee well-being. Worksite interventions— decreasing or stabilizing job demands and increasing social support—are useful starting-points which could improve employee well-being. References Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two step approach. Psychological Bulletin, 103, 411–423. Bateman, T. S., & Strasser, S. (1983). A cross-lagged regression test of the relationships between job tension and employee satisfaction. Journal of Applied Psychology, 68, 439–445. Bentler, P. M., & Bonett, D. G. (1980). Signi cance tests and goodness of t in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modelling. Sociological Methods and Research, 16, 78–117. Bergers, G. P. A., Marcelissen, F. H. G., & WolV , Ch. J. de (1986). VOS-D. Vragenlijst Organisatiestress-D: handleiding [VOS-D. Work stress questionnaire Doetinchem]. Nijmegen: University of Nijmegen. Billings, A. G., & Moos, R. H. (1982). Social support and functioning among community and clinical groups: A panel model. Journal of Behavioral Medicine, 5, 295–311. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Boumans, N. P. G., & Landeweerd, J. A. (1992). The role of social support and coping behaviour in nursing work: Main or bu V ering eV ect? Work and Stress, 6, 191–202. Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445–455. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model t. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage. Burke, M. J., Brief, A. P., & George, J. M. (1993). The role of negative aV ectivity in understanding relations between self-reports of stressors and strains: A comment on the applied psychology literature. Journal of Applied Psychology, 78, 402–412. Caldwell, D. F., & O’Reilly, C. A. (1982). Task perceptions and job satisfaction: A question of causality. Journal of Applied Psychology, 67, 361–391. Chen, P. Y., & Spector, P. E. (1991). Negative aV ectivity as the underlying cause of correlations between stressors and strain. Journal of Applied Psychology, 76, 398–407. Clark, L. A., Watson, D., & Mineka, S. (1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology, 103, 103–116. Daniels, K., & Guppy, A. (1997). Stressors, locus of control, and social support as consequences of aV ective psychological well-being. Journal of Occupational Health Psychology, 2, 156–174.
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