The Demand-Control Model: Specific Demands, Specific Control, and Well-Defined Groups

Transcription

1 International Journal of Stress Management, Vol. 7, No. 4, 2000 The Demand-Control Model: Specific Demands, Specific Control, and Well-Defined Groups Jan de Jonge, 1,2,6 Maureen F. Dollard, 3 Christian Dormann, 4 Pascale M. Le Blanc, 1 and Irene L. D. Houtman 5 The purpose of this study was to test the Demand-Control Model (DCM), accompanied by three goals. Firstly, we used alternative, more focused, and multifaceted measures of both job demands and job control that are relevant and applicable to today s working contexts. Secondly, this study intended to focus on particular demands in human services work and to incorporate these demands in the DCM. Finally, this occupation-based study investigated relatively large well-defined subgroups compared to a total sample. Workers from five human service sectors (n = 2,485) were included in a cross-sectional survey (i.e., health care, transport, bank/insurance, retail trade, and warehouse). Results showed that job demands and job control are able to show several interaction effects on employee well-being and health, but only in specific occupational groups. In conclusion, the current findings provide renewed empirical support for the view that high-strain jobs (high demand, low control) are conducive to ill health (i.e., emotional exhaustion, psychosomatic health complaints). Further, it appears that active jobs (high demands, high control) give rise to positive outcomes (i.e., job challenge, job satisfaction). KEY WORDS: Demand-Control Model; human service workers; multi-sample analysis; employee well-being. 1 Department of Social and Organizational Psychology, Utrecht University, The Netherlands. 2 Department of Work and Organizational Psychology, University of Nijmegen, The Netherlands. 3 School of Psychology, University of South Australia, Whyalla Campus, South Australia. 4 Department of Work and Organizational Psychology, Johann Wolfgang Goethe University, Frankfurt/Main, Germany. 5 TNO Work and Employment, Hoofddorp, The Netherlands. 6 Corresponce should be directed to Jan de Jonge, Department of Social and Organizational Psychology, Utrecht University, P.O. Box , 3508 TC Utrecht, The Netherlands; j.dejonge@ fss.uu.nl /00/ /$18.00/ Human Sciences Press, Inc.

2 270 de Jonge, Dollard, Dormann, Le Blanc, and Houtman Globally, the nature of work is changing rapidly (Dunnette, 1998; Marmot, Siegrist, Theorell, & Feeney, 1999). The resultant work environment is determined more by economic imperatives and cost/benefit market-based approaches than by a consideration of the human implications of these changes (Karasek, Siegrist, & Theorell, 1998). One theory, the Demand-Control Model (DCM), pays attention to the human factor in the work environment and conceptualises the work environment as purely one of human construction, capable of change to an optimal active learning environment (Karasek, 1979, 1998; Karasek & Theorell, 1990). This model theorizes that the range of control over one s environmental situation is a crucial dimension in determining health on the one hand, and active behavior/learning on the other (Karasek et al., 1998). Specifically, jobs combining high demands and low control (i.e., high strain jobs) are the worst context for a worker in terms of health (job strain hypothesis). Alternatively, jobs combining high (but not overwhelming) demands as well as high control (i.e., active jobs) provide the context for workers to have some latitude regarding how and when to deal with current and new challenges. This context leads to active behavior in workers, to new learning, to challenge, to a sense of mastery, and self-efficacy (the active learning hypothesis; cf. Karasek & Theorell, 1990; Theorell & Karasek, 1996). A persistent problem in a wide variety of current enterprises is that demands cannot be reduced easily if survival is to be ensured in the competitive global market. The DCM is among the few theories that predicts how employee health and well-being problems can nevertheless be avoided by increasing employee control. The philosophy of the DCM has captured the attention of many researchers and has dominated the empirical research on job stress and health and on job redesign research over the past fifteen years (Cordery, 1997; de Jonge & Kompier, 1997). There is some empirical evidence showing that the combination of high job demands and low job control is an important predictor for psychological strain and illness. Epidemiological studies examining cardiovascular disease offer the most support for this model (Schnall, Landsbergis, & Baker, 1994). However, true interactions (demands control) have not been found consistently. Further, the active learning hypothesis has seldom been examined; that is, the majority of empirical studies investigated health-related dependent variables and did not consider variables such as motivation, learning behavior, or challenge (de Jonge & Kompier, 1997; Karasek, 1998). Two possible reasons for mixed evidence for true interactions are related to measurement and validity; a third reason is concerned with sampling issues (Kristensen, 1995, 1996; Wall, Jackson, Mullarkey, & Parker, 1996). Taking job demands first, there are some doubts regarding the concept and measurement of job demands in empirical tests of the model. First, job demands typically have been conceptualized and operationalized at a general level with different demanding aspects involved. Second, job demands like complexity

3 Demand-Control Model 271 should not be treated as stressors per se. Highly complex jobs may have even positive effects on employee health if they match the qualifications of those who carry out the complex job (Frese & Zapf, 1994). However, if complexity exceeds the respective employee s knowledge, skills, and abilities, the employee will suffer. Thus, the measurement of demands in terms of complexity should take qualifications at least implicitly into account. A third often neglected issue is that particular demands should match the control measure used (van der Doef & Maes, 1999; Frese, 1999). That is, interaction effects are more likely to be observed if demands are investigated that are theoretically likely to interact with control in the proposed fashion. Role ambiguity, for example, may be better reduced if goals and working methods are clearly prescribed, rather than if too much control is given to the employee. A fourth problem is concerned with the use of affective judgments in the measurement of job demands. If affective judgments are used to measure demands, demands and strains may be confounded, which will also make it difficult to detect interaction effects. For instance, when measuring demands, one should avoid asking individuals how stressful the proposed demands are (Begley & Czajka, 1993), but should instead ask how often the demands occur. The measurement of job control is not without problems either. First, it has been argued that job decision latitude as measured in the original DCM (Karasek, 1979) is much broader than job control (e.g., Ganster & Fusilier, 1989; Wall et al., 1996). Job decision latitude comprises control and skill usage, as well as complexity. This causes several problems. Demands and decision latitude are confounded if demands are also measured in terms of complexity. Methodologically, this increases their correlation, thereby making it more difficult to find interactions (McClelland & Judd, 1993). Theoretically, decision latitude may not fit with several demands. For instance, theoretical reasons are lacking as to why complexity (one aspect of decision latitude) should moderate the effect of time pressure demands (Wall et al., 1996). Second, the previous line of argumentation leads to another problem. At best, job control should be measured specifically because different aspects of control may interact with different types of demands. On the one hand, different facets of job control, such as choice of methods (plans), scheduling (time frame), and criteria (goals) of work can be distinguished (cf. Breaugh, 1985; de Jonge, 1995; Frese, 1989). This suggests that only particular interactions may be found. For example, choosing working methods that fit one s abilities may help in complex situations, adequate distribution of workload over time may help when physical demands are high, and changing one s qualitative or quantitative criteria may help depending on the amount of time pressure. On the other hand, new concepts of job control may be required if particular demands such as emotional stressors (Zapf, Vogt, Seifert, Mertini, & Isic, 1999) are considered (see also Söderfeldt, 1997). Emotional demands require

4 272 de Jonge, Dollard, Dormann, Le Blanc, and Houtman individuals to express or suppress certain emotions in order to get their job done well (Söderfeldt et al., 1996). In such instances, a common, nonspecific measure of job control may not show the interactive effects predicted by the DCM, and new measures incorporating different facets of control may have to be developed. Third, job control should be measured without using affective judgments. As it applies for job demands, using affective judgments leads to confounding of control and strains, thereby reducing the probability of finding interactions. A third possible reason for mixed evidence regarding the postulated interaction effect is concerned with sampling issues. Most DCM studies have been population-based or occupation-based. Often the criterion applied for positive study characteristics are representativeness of the sample and a high response rate (Kristensen, 1995). Less attention has been given to the potential problem of well-defined occupational groups in population-based studies. This problem as outlined by several authors (Ganster & Fusilier, 1989; de Jonge & Kompier, 1997; Dollard, 1997; Kristensen, 1995, 1996; Sparks & Cooper, 1999), highlights the reduced likelihood of uncovering interaction effects in large heterogeneous samples due to a tension between (1) the diversity of individual occupations and (2) the diversity in job characteristics (closely related to the specificity of instruments). As an illustration, in an occupational strain classification system as proposed by Karasek and Theorell (1990), a single occupational group such as nurses would have been classified in the same quadrant of the DCM. Ganster and Fusilier (1989) argued that analyses based on occupational classifications are probably too crude in their neglect of variability in job characteristics within such occupational groups. Furthermore, they stated that the presumption of variability in job characteristics within occupations is the only basis for us to hope that we could intervene (e.g., through job redesign) to make the worker s job less stressful (Ganster & Fusilier, 1989, p. 263; also Kristensen, 1995). Another reason why diversity among occupational groups may reduce the likelihood of finding interactions is due to additional higher-order moderating variables. For instance, perceived control may be put more easily into action in some occupations than in others. The purpose of the present study is threefold. Firstly, we intend to use alternative, more focused, and multifaceted measures of both job demands and job control relevant and equally applicable to current working contexts. Modern workers are exposed to job demands including increasing time pressure and job complexity due to the performance of more complex tasks, precise quality standards and quality control, and more complex problem solving (Hockey, 2000). Secondly, this study proposes to focus on specific job demands in the current study group (i.e., human services workers) and to incorporate these demands in the DCM. For example, emotional demands particularly due to the direct demands of clients and patients are becoming increasingly important with the everexpanding service sector (cf. Söderfeldt et al., 1996; Zapf et al., 1999). In the

5 Demand-Control Model 273 European Union, for instance, clients account for most demands on the time of workers, as compared to colleagues and production norms (e.g., Houtman, Smulders, & Klein Hesselink, 1999; Paoli, 1997). Further, exposure to physical hazards is still very prevalent (Houtman et al., 1999; Paoli, 1997). Finally, this study will examine carefully chosen, occupation-based study samples with relatively large, well-defined groups and sufficient variability in job characteristics. Specifically, the study hypotheses are that (1) different types of job demands (i.e., workload, physical demands, emotional demands) in combination with multifaceted job control have an interaction effect with respect to psychological well-being, and (2) interactions will be found only in well-described, relatively homogeneous groups with sufficient variability in job characteristics (Ganster & Fusilier, 1989; Hockey, 2000; Kristensen, 1995, 1996). METHOD Sample and Procedure Respondents participated in a large risk assessment project (i.e., Maastricht Risk Inventory) in The Netherlands, commissioned as part of the Dutch Labour Legislation (N = 11,636). The data were collected by means of a survey as part of an ongoing investigation in Dutch companies. The data collection procedure involved the distribution and return of self-report questionnaires with a prestamped addressed return envelope. The samples selected for this study formed large well-defined groups (Ganster & Fusilier, 1989; de Jonge & Kompier, 1997; Kristensen, 1996). Workers from five human service sectors (n = 2,485) were included in the study: (1) health care (n = 242); (2) transport (n = 462); (3) bank/insurance (n = 638); (4) retail trade (n = 396); and (5) warehouse (n = 747). The changes in modern work that were described previously (i.e., mental and emotional demands) are particularly relevant for these workers (see also Marmot et al., 1999). Generally, 72.0 percent of the workers were men. The mean age of the entire group was 35.5 years (SD = 9.6). The mean work experience was 7.6 years (SD = 7.1), and 83.4 percent of the employees had full-time employment. A breakdown of the demographic characteristics of the selected subgroups is shown in Table 1. It appears that there were significant differences between the five subgroups with respect to all four demographic characteristics. For example, the number of men and full-time employment status are lowest in health care, whereas the number of men, the number of full-time employees, and age are highest in transport.

6 274 de Jonge, Dollard, Dormann, Le Blanc, and Houtman Table 1. Demographic Characteristics of the Five Subgroups Health Bank/ Retail Characteristic care Transport Insurance trade Warehouse Significance test Gender (% men) χ 2 (4) = * Employment (% full-time) χ 2 (4) = * Age (mean) F(4, 2455) = 33.69* Work experience (mean) F(4, 1960) = 15.18* *p <.05. Measures We used several demographic characteristics (i.e., gender, age, and employment status) in order to control for their possible influence. In line with the DCM, we used four job characteristics as predictor variables; that is, psychological demands (workload), physical demands, emotional demands, and job control (see also, Hockey, 2000). In addition, to minimize bias and cognitive processing, the corresponding items were precisely defined, and were as neutral as possible with frequency-based response categories ranging from 1 (never) to 5(always) (Frese & Zapf, 1988; Wall et al., 1996). Principal factor analyses (PAF) with oblique rotation clearly showed four factors with eigenvalues ranging from 5.52 to Workload was measured by an 8-item scale that includes both quantitative and qualitative demanding aspects of the job, such as time pressure and high complexity (de Jonge, Landeweerd, & Nijhuis, 1993). An example item is: In the unit where I work, work is carried out under pressure of time. Physical demands were assessed by a 7-item scale (de Jonge, Mulder, & Nijhuis, 1999). The items measure aspects such as carrying heavy loads, constrained standing, stooping deeply, and carrying shoulder high. For instance, In my work, I have to carry shoulder high for a long time. Emotional demands were measured by a 4-item scale that contained items about aggressive and awkward clients or patients, and social problems like human suffering (cf. de Jonge et al., 1999). An example is: In my work, I am confronted with aggressive clients. Job control was assessed by 10 items and measured the worker s opportunities to determine multiple facets of task elements, like method of working, pace of work, work goals, amount of work, working hours, and work evaluation (de Jonge, 1995). For instance, The opportunity that the work offers to determine the method of working yourself. The choice of the four well-being indicators was also guided by the DCM. That is, emotional exhaustion and psychosomatic health complaints reflect out-

7 Demand-Control Model 275 comes of the job strain hypothesis, whereas job satisfaction and job challenge reflect outcomes of the active learning hypothesis. Emotional exhaustion was measured by means of four items of the Dutch version of the Maslach Burnout Inventory (Schaufeli & van Dierendonck, 1993). This instrument is particularly suitable for use in human service work (Maslach, 1998). The scale was scored on a 5-point scale, ranging from 1 (never) to5 (always). An example item is: I feel emotionally drained from my work. Psychosomatic health complaints were measured by 10 specific items dealing with all kinds of complaints (Dirken, 1969). This scale was scored on a 3- point scale: 1 (no), 2 (seldom), 3(yes). For instance, Are you troubled by headaches in the last six months? Job satisfaction was assessed by a single item (i.e., I am satisfied with my present job ) that was scored on a 5-point rating scale, ranging from 1 (strongly disagree) to 5(fully agree). A meta-analysis by Wanous, Reichers, and Hudy (1997) has shown that single-item measures are highly correlated with multi-item job satisfaction scales. A global indicator of overall job satisfaction, therefore, seems to be a valid and economic measure of general job satisfaction (see also Scarpello & Campbell, 1983). Job challenge was also measured by one item in which the respondents were asked how challenging their work is. This question was also answered on a 5-point scale, ranging from 1 (strongly disagree) to 5(fully agree). Data Analysis In order to analyze the data from the five groups, we performed multisample structural equation modeling (LISREL 8; Jöreskog & Sörbom, 1993). In this way, we were able to judge the fit of a postulated model as a whole, taking into account all equations as well as all groups simultaneously. Furthermore, it is possible to test empirically the hypothesized relationships against several conceivable alternatives. Each of the components in our research model was included in the structural equation analyses as a latent variable. In other words, the covariance structure model was simplified by assuming that the observed and latent variables are identical (each construct had only one operationalization). This was particularly true for job satisfaction and job challenge. First of all, gender (dummy variable), employment status (dummy variable), and age were introduced into the structural model as potential confounders. Therefore, they were labelled as exogenous variables, and all other variables were labelled as endogenous variables (i.e., job characteristics and outcome variables). In addition, the DCM suggests interactions between job demands and job control. Such relationships can be appropriately modelled by including two

8 276 de Jonge, Dollard, Dormann, Le Blanc, and Houtman linear terms as well as an interaction term (Bollen, 1989; Kenny & Judd, 1984). This was done by performing two analytic steps (Aiken & West, 1991; Jaccard, Turrisi, & Wan, 1990): (1) the job characteristics were mean-centered in order to prevent multicollinearity, and (2) the interaction terms of the job characteristics were computed from these centered variables. Next, two structural models were fitted to the data in two steps. First, a model M 1 without interaction effects was specified, followed by a model M 2 with interaction effects. Accordingly, the assumptions of the DCM were tested against the null hypothesis (H 0 ) that there are no interactive relationships. Different nested models can be compared by a chi-square differences test (Bentler & Bonett, 1980; Jöreskog & Sörbom, 1993). Second, LISREL can also be used to analyse several samples simultaneously to determine the extent to which a structural equation model fits for several different groups, as well as to examine whether model differences exist between these groups in the analysis. So, using this technique, it is possible to test whether the proposed pattern of relationships is identical in all five subgroups (Schumacker & Lomax, 1996). Subsequently, several commonly used fit indices have been used to assess the overall model fit (Jöreskog, 1993; Schumacker & Lomax, 1996): the chisquare statistic (χ 2 ), the LISREL adjusted goodness-of-fit index (AGFI), the root mean square error of approximation (RMSEA), and finally the comparative fit index (CFI). RESULTS Prior to the LISREL analyses, the means, standard deviations, and coefficient alphas were computed for both the five separate subgroups and the total group (see Table 2). Table 2 shows that all multiple-item measurement instruments had a coefficient alpha equal to or higher than.75. This also demonstrates that the instruments applied were equally valid (reliable) across the different subgroups. Furthermore, we computed coefficients of variation (standard deviation divided by mean; see Ganster & Fusilier, 1989) for each of the job characteristics and displayed the results in Table 3. In general, it is apparent that there is as much variability in the five subgroups as there is in the total group. Covariance matrices were used in order to analyse the structural equation models (available from the first author). We first analysed the covariance matrix obtained for the whole sample (n = 2,485). Model M 1 was considered without interaction effects and model M 2 was considered with interaction effects. The corresponding chi-square differences test showed that the difference between the two chi-squares was not significant ( χ 2 (12) = 15.51, n.s.), which means that H 0 is not rejected. In other words, an interactive model (M 2 ) has no better statistical fit (in terms of chi-square) than an additive model (M 1 ) within the total

9 Demand-Control Model Table 2. Means (M), Standard Deviations (SD), and Coefficient Alphas (α) of the Study Variables Health care Transport Bank/Insurance Retail trade Warehouse Total Measures M SD α M SD α M SD α M SD α M SD α M SD α Job control Workload Phys. demands Emot. demands Job satisfaction Job challenge Emot. exhaustion Health complaints

10 278 de Jonge, Dollard, Dormann, Le Blanc, and Houtman Table 3. Comparison of Coefficients of Variation (SD/M) for the Four Job Characteristics Health Bank/ Retail care Transport Insurance trade Warehouse Total Measures SD/M SD/M SD/M SD/M SD/M SD/M Job control Workload Phys. demands Emot. demands sample. Next, we performed multi-sample analysis to investigate model differences between the five groups. This analysis showed a significant chi-square differences test ( χ 2 (112) = , p <.05), indicating that our multi-sample model is not invariant between the five groups. In other words, interaction effects (i.e., the paths relating the product term to the dependent variables), were significantly different across the subgroups. Therefore, model variance between sectors was allowed, and (interactive) model testing was repeated for each subgroup separately. The results of the model comparisons at subgroup level are presented in Table 4. It appeared that three out of five sectors showed a significant model comparison test (i.e., health care, transport, and warehouse), which means that in those cases, the interactive model was superior to the additive model. In addition, all superior models showed acceptable fit indices in terms of AGFI, RMSEA, and CFI (Schumacker & Lomax, 1996). A closer look at the three superior interactive models showed that 6 out of 12 interaction effects were significant, and all but 1 of them in the expected Table 4. LISREL Model Comparisons at Subgroup Level Bank/ Retail Sector Health care Transport Insurance trade Warehouse Model additive (M 1 ) (21) (21) (21) (21) (21) interactive (M 2 ) 9.77 (9) (9) (9) (9) (9) χ 2 ( df) 28.68* (12) 19.39* (12) (12) (12) 22.88* (12) Superior model Interactive Interactive Additive Additive Interactive Fit measures AGFI RMSEA CFI *p <.05, one-tailed.

11 Demand-Control Model 279 direction (see Figure 1). More specifically, three interaction effects represented the strain diagonal of the DCM, whereas the other three interactions reflected the active learning diagonal. In four out of six interactions, emotional demands and job control represented the interaction effect. With one exception, these interactive findings showed in general that the combination of high job demands and low job control gives rise to high emotional exhaustion and high psychosomatic health complaints, while the combination of high demands and high control is conducive to high job challenge and, to a lesser extent, high job satisfaction. Remarkable, however, was the interaction effect between emotional demands and job control in the prediction of psychosomatic health complaints (health care sector). This interaction effect showed that emotional demands and psychosomatic health complaints were negatively related at low levels of job control, whereas they were positively related at high levels of job control. Finally, Table 5 shows the explained variance (R 2 ) for the four psychological outcomes within each subgroup, as well as within the total group. The explained variance was highest for the burnout component emotional exhaustion and lowest for job satisfaction. In general, explained variance was somewhat higher at the subgroup level when compared to the total sample level. As far as the first three outcomes are concerned, the explained variance was higher at the subgroup level than at the total sample level in all but one of the cases (i.e., four out of five). With regard to psychosomatic health complaints, in three out of five cases the explained variance appeared to be higher at the subgroup level. DISCUSSION The main purpose of this study was to test the Demand-Control Model, accompanied by three secondary goals. First, we used alternative, more focused, and multifaceted measures of both job demands and job control relevant and applicable to present-day working contexts. Second, this study intended to focus on emotional demands in human services work and to incorporate these demands in the DCM. Finally, this occupation-based study investigated relatively large well-defined subgroups compared to a total sample. More specifically, the study hypotheses were that (1) different types of job demands (i.e., workload, physical demands, emotional demands) in combination with multifaceted job control have an interaction effect with respect to psychological well-being, and (2) interactions will be found only in well-described, relatively homogeneous groups with sufficient variability in job characteristics. In some cases, joint moderating effects of demands and control on psychological well-being exist, thereby providing some evidence for the two DCM hypotheses. Specifically, it appears that job demands and job control are able to show several interaction effects, but only in specific occupational groups. These

12 280 de Jonge, Dollard, Dormann, Le Blanc, and Houtman Fig. 1. Interactions among different kinds of job demands and job control in the prediction of job satisfaction, job challenge, emotional exhaustion, and psychosomatic health complaints.

13 Demand-Control Model 281 Table 5. Explained Variance (R 2 ) for the Four Psychological Outcomes Health Bank/ Retail Measures care Transport Insurance trade Warehouse Total Job satisfaction Job challenge Emot. exhaustion Health complaints results also suggest that occupation-specific measurement of demands and control (at least covering important dimensions or facets) could improve the predictive power of the DCM (see also Kasl, 1996). Furthermore, the results are in accordance with the critique and suggestions of Ganster and Fusilier (1989) and Kristensen (1996). According to them, job strain (i.e., high demand, low control) should be seen as a structural characteristic, and the sample unit should be specific jobs or occupations. Added to this, the variability in the exposure variables does not differ much in the five subgroups compared to the total group. This might also be a reason why the explained variance at subgroup level is in general equal to or even higher than at total sample level. From a job strain point of view, very few studies have used a similar occupation-based approach. Moreover, none of these studies seem to be optimal. For instance, Theorell et al. (1990) performed a study on six contrasting occupational groups and found some support for both the job strain and active hypotheses (only additive effects). The number of people included in the six groups, however, was rather small (range: persons). Another, more elaborated, study by Sparks and Cooper (1999) among 7,099 employees from 13 occupations showed that stressor-strain relationships were different in each occupation. However, they neither tested this conclusion statistically (as we did), nor tested the interactive assumptions as proposed by the DCM. As in our study, several (single) occupation-based studies found similar interaction effects of high workload and low job control in the prediction of adverse health outcomes (e.g., Fox, Dwyer, & Ganster, 1993; Landsbergis, Schnall, Deitz, Friedman, & Pickering, 1992; Parkes, Mendham, & von Rabenau, 1994). In contrast, significant interactions between physical or emotional demands and job control have not been often reported in the literature (Andries, Kompier, & Smulders, 1996; de Jonge et al., 1999; Söderfeldt et al., 1996; Söderfeldt, 1997). Sixty-seven percent of the significant interaction effects were caused by emotional demands, probably due to the nature of human service work. In addition, the interaction of emotional demands and job control with respect to psychosomatic health complaints (health care sector) is not in line with the assumptions of the DCM. An explanation for this inconsistent effect is documented by several authors (Fox, Dwyer, & Ganster, 1993; de Jonge et al.,

14 282 de Jonge, Dollard, Dormann, Le Blanc, and Houtman 1999; Söderfeldt, Söderfeldt, Bejerot, & Axtelius, 1999). Many health care workers feel some ambivalence in curing and/or caring for intensely suffering patients or clients. In fact, employees who reported low control may have felt better knowing that they could not prevent the suffering or dying of very ill or chronic patients. In other words, these workers have an opportunity to avoid the internal attributions of failure that might be more likely if they had high job control. This result highlights the potential negative side of possessing high job control in the case of emotional demands. As Karasek and Theorell recently have argued (see Levi et al., 2000), high job control seems to be the primary psychological demand in such a case, and then the DCM fails. Furthermore, the present finding also stresses the use of new concepts of job control to deal with this kind of demand such as emotional control (Zapf et al., 1999). All other findings, however, corroborate those in recent reviews on the DCM (e.g., van der Doef & Maes, 1998, 1999; Jones, Bright, Searle, & Cooper, 1998; Jones & Fletcher, 1996; Schnall et al., 1994). Generally, the current findings provide renewed empirical support for the view that high-strain jobs (high demand, low control) are conducive to ill health (Karasek, 1998; Karasek & Theorell, 1990; Schnall et al., 1994; Theorell & Karasek, 1996). Furthermore, it is interesting that we also found some support for the active learning hypothesis (Landsbergis et al., 1992). In the latter case, it appears that active jobs (i.e., high demands, high control) give rise to positive outcomes (i.e., high job challenge, high job satisfaction). The current results have conceptual and methodological implications for the DCM. The present study indicates that there is merit in using more focused measures of job demands and job control, and even using broader formulations of job demands (e.g., workload, physical demands, and particularly emotional demands), as well as job control (like multiple facets or levels; de Jonge & Kompier, 1997; Frese, 1989; Karasek, 1998). Further, using well-defined occupational groups seems to enable meaningful analyses. As Kristensen (1995, 1996) has noted, in this type of study the variation in exposure is the decisive factor, rather than the representativeness of the sample (see also Table 3). However, a shortcoming is that we cannot determine empirically the psychological differences among the five subgroups. That is, we are unable to investigate what job-related variables moderate the interaction effect. For instance, we can only speculate why interactions are significant for health care, transport, and warehouse employees, but not for bank/insurance and retail trade workers. One explanation may be that supervision is much closer in banks/insurance and retail trade than in health care settings, transport, and warehouses because the latter are usually large and autonomous organizations. Perceived control, which was essentially investigated in the present study, may thus be more easily put into action in those sectors. With respect to the interactions which were not found in the present study,

15 Demand-Control Model 283 it may be that the kind of control investigated does only moderate certain but not all stressors depending on the kind of match (Frese, 1989, 1999; Wall et al., 1996). For example, since the workload measure used comprises mainly complexity issues, control in terms of negotiating distribution of tasks among colleagues depending on qualifications may be more adequate than the simple opportunity to determine the method of working by oneself. The latter may not be enough to cope with complexity. Similarily, rather than having control over work schedules in general, the most appropriate moderator for the effects of emotional demands may be emotional control (e.g., Zapf et al., 1999). That is, the display rules of emotions (Hochschild, 1983) should be less prescribed by the organization in order to devolve emotional control and to buffer the adverse effect of emotional demands. Notwithstanding, this study has also several limitations. First, finding 6 out of 24 possible interaction effects (i.e., 25%) statistically implies little support for the central DC hypotheses. However, the support is quite meaningful as the interactions were found in different occupational groups (i.e., health care, transport, and warehouse) and dealt with different types of job demands (emotional demands in particular). Moreover, two interactions support the active learning hypothesis of the DCM, which has been rarely investigated or confirmed. Second, the cross-sectional design limits conclusions about causation (Zapf, Dormann, & Frese, 1996). Several longitudinal studies, however, provided some evidence for the postulated causal relationships (e.g., de Jonge, 1995; Karasek, 1979; Parkes et al., 1994). So, generalization and replication of the current findings awaits further empirical examination. Third, the present study incorporated four different job characteristics to examine the stressor-strain relationship. However, these four job characteristics are obviously not exhaustive (e.g., see de Jonge, Bosma, Peter, & Siegrist, 2000; Karasek & Theorell, 1990; Sparks & Cooper, 1999). As almost every occupational group has its own idiosyncratic stressors, other characteristics like workplace social support, work-home interference, and occupational rewards need to be addressed as well to increase the variance in job-related strain in different occupational groups. Finally, we did not control for negative affectivity as a biasing factor that affects subject reports of stressors and strains. However, a recent meta-analysis by Spector, Zapf, Chen, and Frese (2000) has shown that controlling for negative affectivity seems like throwing out the baby with the bath water (p. 91). In other words, the overlap of negative affectivity with other variables may not be attributed to bias. Others have argued that control for negative affectivity may also lead to an underestimation of the relationship between demanding aspects of the work situation and stress reactions (Dollard & Winefield, 1998; Karasek et al., 1998). The results also have practical implications. For instance, psychological well-being can be improved by increasing job control, more or less indepen-

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