- Email: [email protected]
Emotional job demands and the role of matching job resources: A cross-sectional survey study among health care workers
Available online at www.sciencedirect.com
International Journal of Nursing Studies 45 (2008) 1460–1469 www.elsevier.com/ijns
Emotional job demands and the role of matching job resources: A cross-sectional survey study among health care workers Jan de Jonge a,*, Pascale M. Le Blanc b, Maria C.W. Peeters b, Hanneke Noordam b a
Eindhoven University of Technology, Department of Technology Management, Subdepartment of Human Performance Management, P.O. Box 513, 5600 MB Eindhoven, The Netherlands b Utrecht University, Department of Social and Organizational Psychology, Utrecht, The Netherlands Received 27 August 2007; received in revised form 10 October 2007; accepted 10 November 2007
Abstract Background: Research on emotional labour in health care work has not yet revealed under what conditions emotional job demands have an impact on employee health and well-being. There is a need for more theory to unveil the black box of emotional labour processes. Objectives: To test the moderating role of matching (i.e. emotional) and non-matching (i.e. cognitive) job resources in the relation between emotional job demands and employee health/well-being (i.e. emotional exhaustion, employee creativity, and work motivation). Design: A cross-sectional survey with anonymous questionnaires was conducted. Settings: A large organization for residential elderly care with eight locations in an urban area in the Netherlands. Participants: Questionnaires were distributed to 1259 health care workers, of which 826 people returned the questionnaire (66% response rate). Methods: In addition to descriptive statistics, multivariate multiple regression analysis (LISREL 8.54) with cross-validation was conducted. Results: Findings showed that emotional job resources moderated the relation between emotional job demands and health/wellbeing outcomes. Firstly, emotional job resources were able to moderate the relation between emotional job demands and emotional exhaustion. Secondly, both emotional job resources and, to a lesser extent, cognitive job resources were able to moderate the relation between emotional job demands and positive well-being outcomes (i.e. employee creativity and work motivation). Finally, cross-validation showed that parameter estimates did not vary across subsamples. Conclusions: Job resources could compensate for resources lost through meeting the requirements of emotional job demands, thereby reducing stress-reactions and increasing well-being. Providing health care workers with more, preferably matching, job resources could make emotional job demands less stressful, and even stimulating and challenging. Future longitudinal studies should investigate the interplay of emotional job demands and (matching) job resources more profoundly. # 2007 Elsevier Ltd. All rights reserved. Keywords: Emotional labour; Occupational health; Questionnaires; Burnout; Health care staff
What is already known about the topic? * Corresponding author. Tel.: +31 40 2472243; fax: +31 40 2437161. E-mail address: [email protected] (J. de Jonge).
Health care workers are at risk for burnout due to emotional job demands.
0020-7489/$ – see front matter # 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijnurstu.2007.11.002
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
Job resources such as job control and social support seem to be able to moderate the negative impact of emotional job demands. Few recent studies have revealed beneficial consequences of emotional job demands such as increased job satisfaction and increased personal accomplishment. What this paper adds Study results support the hypothesis that job resources moderate the relation between emotional job demands and health/well-being outcomes. Specifically, findings show that moderating effects are found more often for matching, emotional, job resources than for non-matching, cognitive, job resources. Findings draw attention to the importance of providing health care workers with more, preferably matching, job resources that could make emotional job demands less stressful, and even stimulating and challenging. 1. Introduction Over the past few years, the phenomenon of emotional labour has received considerable attention due to its relevance for health care workers. One of the core job activities for health care workers is the social interaction with patients or clients, in which the requirement to regulate emotions plays a key role. In health care, emotional labour has been an important topic of debate because of its importance for both providers and recipients of care (cf. Hunter and Smith, 2007). Mitchell and Smith (2003), for instance, indicated that emotional labour has always been part of the image of health care workers. Emotional labour has usually been conceptualized in two main ways (Brotheridge and Grandey, 2002). First, employee-focused emotional labour denotes the employees’ efforts to manage their own emotions. Corresponding measures tap respondents’ efforts at ‘surface acting’ and ‘deep acting’ (e.g. Brotheridge and Lee, 2002; Kruml and Geddes, 2000). Second, job-focused emotional labour denotes the level of emotional job demands in occupations. This has usually been measured as job demands such as frequency of interactions with clients or dealing with death and dying (e.g. Morris and Feldman, 1996; Pugliesi, 1999; Zapf and Holz, 2006). Grandey (2000) provided a conceptual model of emotional labour that integrates both conceptualizations. According to her view, job-focused emotional labour refers to situational characteristics of the work situation (e.g. job demands) that cause employees to engage in employee-focused emotional labour. Emotional labour field studies and emotion regulation lab studies have demonstrated that the effortful processes of surface acting and deep acting are related to employee stress-reactions as well as their well-being (cf. Grandey, 2000; Zapf, 2002).
1461
In this article the construct of emotional labour refers exclusively to emotional demands at work (i.e. job-focused emotional labour). It is well known from the job stress literature that emotional job demands contribute to stressreactions such as burnout and psychosomatic health symptoms (e.g. Brotheridge and Grandey, 2002; Le Blanc et al., 2000; Zapf et al., 2001). The basic proposition here is that health care workers in emotionally demanding jobs report higher levels of burnout or other health problems than workers in less emotionally demanding jobs. The specific underlying mechanism of this relation is still part of the ‘black box’ of emotional labour. Brotheridge and Lee (2002) noted that there has been no overarching theoretical framework that can be used to explain the relation between emotional job demands and adverse health outcomes. Accordingly, the present study contributes to the literature by examining this relation using job stress theories developed by different scholars (e.g. Hobfoll, 1989, 2002; de Jonge and Dormann, 2003, 2006). By doing so, our findings not only shed light on the working of the relation between emotional job demands and health and well-being, but also have implications for ways of managing emotional job demands in health care work. As said before, there is ample empirical evidence that emotional job demands can be stressful (for an overview, see Zapf, 2002; Zapf and Holz, 2006). For instance, a longitudinal survey study by van Vegchel et al. (2004) showed that emotional job demands predicted emotional exhaustion (burnout’s key component) over time. It is therefore not surprising that the burnout concept was originally introduced as an individual reaction to high emotional job demands in human service work (Maslach, 1978). Drawing on the literature of emotion regulation (Gross, 1998), burnout can be considered as an indication that employees are no longer able to regulate their emotions adequately in patient or client interactions (Zapf and Holz, 2006). Emotional selfregulation at work seems to be critical in understanding stress-reactions among health care workers, because regulation of expressions (e.g. suppression of negative emotions) appears to be associated with prolonged effort which in turn has been linked to adverse health and well-being outcomes (e.g. Brotheridge and Lee, 2002; Zapf, 2002). However, there are also empirical findings that do not support this line of reasoning. Some studies did not find any association between emotional job demands and stress-reactions such as emotional exhaustion or adverse health symptoms (e.g. Cordes et al., 1997; Morris and Feldman, 1997; Rutter and Fielding, 1988). This has raised the question of what is going on in the black box: Under what conditions do emotional job demands have a negative impact on employee health and well-being? Classical job stress research has already demonstrated that the role of so-called job resources in the stress process should not be ignored in this respect (Kahn and Byosiere, 1992). Job resources can be conceptualized as cognitive–energetic reservoirs in the work environment that can be tapped, such as job control and workplace social
1462
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
support (cf. Hobfoll, 2002). Specifically, job control and social support seem to be able to moderate the negative impact of emotional job demands. A prospective cohort study among 2255 Swedish service workers showed, for example, that job control was able to reduce the impact of emotional job demands on burnout (van Vegchel et al., 2004). In a similar vein, Grandey et al. (2005) showed that when service workers reported low control in their job, emotional job demands and emotional exhaustion were positively related, whereas they were unrelated in case of high job control. According to the conservation of resources (COR) theory (Hobfoll, 1989, 2002), people strive to obtain, retain, protect and foster valued resources and minimize any threats of resource loss. Emotional job demands could be such a threat to resource loss. Employees usually invest their job resources in meeting job demands because they expect to receive positive outcomes in return. In response to emotional job demands, employees expend job resources. However, when an imbalance occurs between the emotional job demands and the available job resources to meet those demands, the end-result will be emotional stress-reactions. 1.1. Matching principle Recently, de Jonge and Dormann (2006) proposed a theoretical refinement of the moderating role of job resources. Next to describing the moderating effect of job resources in general, they argue that the type of job resources should correspond with the type of job demands. More specifically, they propose moderating effects to be most likely when there is a match between the kind of job demands and job resources. For instance, emotional support from colleagues (‘job resource’) may help to reduce stressreactions caused by emotional job demands at best. The function of emotional support as a job resource could facilitate employees’ coping with job stress, which accounts for a better state of health and well-being. The theoretical basis for the matching assumption is derived from so-called homeostatic regulation processes (cf. de Jonge et al., 2008). To survive, a living organism must maintain certain critical parameters within a bounded range. For instance, the human body must regulate its temperature, amount of fluids, and energy level. Maintaining each critical parameter requires that the body comes into contact with the corresponding satiatory stimulus (i.e. clothes, water, and food) at the right time. The process by which these critical parameters are maintained (i.e. to keep something regular) is generally referred to as homeostatic regulation. For instance, in the area of immune functioning, homeostatic regulation processes are known to cause an activation of internal resources (e.g. T- and B-cells) when particular demands occur (Lekander, 2002). Through evolutionary processes, the release of functional, matching internal resources is more likely than the release of dysfunctional, non-matching internal resources (Lekander, 2002). Similar homeostatic regulation processes can be found in the
nervous system as well, which in fact has much in common with the immune system. For example, homeostatic regulation processes engage the fine scale organization and operation of volumes of neural tissue to provide powerful and functional resources for neural signal integration and stable long-term storage of information (Montague, 1996). The idea of functional homeostatic regulation can easily be applied to organizational settings (e.g. Vancouver, 2000). Similar to homeostatic regulation in the immune and nervous system, it is proposed that employees activate functional, corresponding, job resources to mitigate the effects of specific job demands. Basically, a match exists if job resources provide functions that are similar to those provided by internal bodily resources in combating stress. For example, when emotional problems with patients arise (e.g. insolent patients), the availability of emotionally supportive colleagues as a job resource is likely to be helpful. If supportive colleagues are unavailable, other job resources can be useful to some extent, for instance control at work to handle a particular problematic patient. de Jonge and Dormann (2006) propose that job demands are firstly dealt with using easily available matching job resources. If such matching job resources are not available or when they are depleted (cf. Hobfoll, 2002), employees will search for other job resources and will even use job resources that do not correspond to the kind of job demands (cf. Vohs et al., 2005). Consequently, de Jonge and Dormann (2006) state that matching job resources are most likely to be successful in combating particular demands, followed by less-matching and non-matching job resources. 1.2. The present study The aim of the present study is to gain more insight into the moderating role of matching and non-matching job resources in the relation between emotional job demands and health/well-being outcomes among health care workers. We already mentioned that many scholars have considered the negative consequences of emotional job demands such as increased burnout. A few, more recent, studies have revealed beneficial consequences of emotional job demands such as increased job satisfaction and increased personal accomplishment (e.g. Brotheridge and Grandey, 2002; Zapf and Holz, 2006). As a consequence, we will explore both negative and positive consequences of emotional job demands. From the perspectives of Frederickson’s (2001) Broadenand-Build Theory of positive emotions as well as Isen’s (2000) Flexibility Hypothesis, experienced positive emotions are vehicles for individual growth, creativity and motivation. For that reason, we included two indicators of work-related positive well-being in the present study as well; that is, employee creativity and work motivation. Following de Jonge and Dormann’s (2006) line of reasoning, moderating effects between job demands and job resources should occur with greater likelihood among demands and resources from identical dimensions. For
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
instance, it is assumed that emotional job resources will show moderating effects on the relation between emotional job demands and burnout more often than other kinds of job resources such as cognitive job resources. To test this assumption, we will use both matching, i.e. emotional, job resources and non-matching, i.e. cognitive, job resources. Consequently, our hypotheses are as follows: Hypothesis 1. Job resources moderate the relation between emotional job demands and health/well-being outcomes (i.e. emotional exhaustion, employee creativity, and work motivation). There is a weaker association between emotional job demands and adverse health/well-being for employees with high job resources than for employees with low job resources. Hypothesis 2. The moderating effect is found more often for matching, emotional, job resources than for non-matching, cognitive, job resources.
2. Method 2.1. Procedure and participants A cross-sectional survey study was conducted among 1259 health care workers employed in a large organization for residential elderly care with eight locations in an urban area. Employees were given an anonymous questionnaire, which could be returned in sealed envelopes. In total, 826 people returned the questionnaire (66% response rate). All these workers were involved in patient or client work. Demographics showed that 89.9% of the sample was female, whereas the mean age was 41.2 years (S.D. = 10.7). Ages ranged from 16 to 64 years. Mean working time was 6.7 years (S.D. = 7.0), while 12.1% worked full-time (i.e. 36 h per week). 2.2. Measures 2.2.1. Emotional job demands Emotional demands at work were assessed by eight items, consisting of three subscales (cf. van Vegchel et al., 2001), scored on a 5-point rating scale ranging from (1) ‘‘never’’ to (5) ‘‘always’’. The first subscale was labeled ‘confrontation with death, dying, illness, and suffering’ and consisted of two items (r = .70, p < .001). For example, ‘In your work, are you confronted with death and dying?’. The second subscale was labeled ‘confrontation with awkward and aggressive patients or clients’ (three items, Cronbach’s alpha = .86). For instance, ‘In your work, are you confronted with aggressive patients or clients?’. The third subscale was labeled ‘general emotionally demanding work’ (three items, Cronbach’s alpha = .79). An example item is: ‘Does your work put you in emotionally disturbing situations?’. Confirmatory factor analysis (CFA) with correlated factors
1463
showed that a model with three factors representing the three subscales fitted well to the data (x2(17) = 128.73, p < .001, RMSR = .04, NNFI = .94, CFI = .96, AGFI = .90), and also fitted better than a one-factor solution ‘emotional job demands’ (x2(20) = 604.95, p < .001, RMSR = .07, NNFI = .84, CFI = .89, AGFI = .62). 2.2.2. Emotional job resources Emotional resources at work were operationalized as emotional support from supervisors and colleagues, and were measured using the socio-emotional part of a wellvalidated Dutch translation of the social support scale from Karasek’s (1985) Job Content Questionnaire (de Jonge et al., 2000a). Items were scored on a 4-point rating scale ranging from (1) ‘‘strongly disagree’’ to (4) ‘‘strongly agree’’. The scale consisted of four items (Cronbach’s alpha = .79) that refer to emotional support from supervisors and colleagues. An example item is: ‘My supervisor is concerned about my welfare’. 2.2.3. Cognitive job resources Cognitive job resources were assessed by a 5-item scale derived from de Jonge et al. (1996) measuring cognitive control opportunities at work (i.e. job control). For instance, ‘The extent to which the work offers the opportunity to determine the method of working yourself’. Items were scored on a 5-point rating scale, ranging from (1) ‘‘very little’’ to (5) ‘‘very much’’, with a Cronbach’s alpha of .81. 2.2.4. Employee creativity Employee creativity can be defined as the generation of novel and useful ideas by employees. This work-related construct was assessed by a 12-item scale developed by George and Zhou (2001), and translated/back-translated in a well-validated Dutch version. The scale could be scored on a 5-point rating scale ranging from (1) ‘‘never’’ to (5) ‘‘always’’, with a Cronbach’s alpha of .96. Example items are: ‘Comes up with new and practical ideas to improve performance’ and ‘Exhibits creativity on the job when given opportunity to’. 2.2.5. Work motivation Work motivation refers to the degree to which employees consider their job motivating and challenging (cf. Thierry, 1998). This construct was measured by two well-established items (de Jonge et al., 2000b) in which the respondents indicated how motivating and challenging their work is. The items were scored on a 5-point rating scale ranging from (1) ‘‘strongly disagree’’ to (5) ‘‘strongly agree’’. The intercorrelation was .49 ( p < .001). For instance, ‘My job is challenging’. 2.2.6. Emotional exhaustion Emotional exhaustion was measured by the well-validated Dutch version (Schaufeli and van Dierendonck, 2000) of the Maslach Burnout Inventory (Maslach and Jackson,
1464
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
1986). The scale contained five items with a 7-point response scale ranging from (0) ‘‘never’’ to (6) ‘‘always, daily’’, with a Cronbach’s alpha of .87. An example item is: ‘I feel emotionally drained from my work’. Finally, demographic characteristics such as gender (0 = female; 1 = male), age (in years), education (low till high), and employment status (working hours per week) were included as control variables as their relation with health/well-being outcomes is well-established (cf. Grandey, 2000; Totterdell and Holman, 2003). 2.3. Analytical strategy Hypotheses were tested with multivariate multiple regression analyses within the computer programme LISREL 8.54 (Jo¨reskog and So¨rbom, 1996). This method allows us to investigate all three outcome variables simultaneously, thus correcting for conceptual overlap (e.g. the Pearson correlation between employee creativity and work motivation was r = .38, p < .01) as well as decreasing Type I errors. Structural model tests were based upon covariance matrices1 and used maximum likelihood estimation. Postulated moderating effects were tested by adding multiplicative interaction terms (demands resources) of standardized emotional job demands and job resources into the regression analyses (cf. Aiken and West, 1991). In total 18 possible interaction effects between demands and resources were included (3 types of demands 2 types of resources 3 outcome variables). Also, the structural model was completely saturated because residuals among the three outcome variables were allowed to correlate. Error caused by misspecification of the model (e.g. due to unmeasured variables which might have some influence) would be reflected by these correlated residuals. Saturated models always have a perfect model fit, and therefore reporting fit indices is not necessary. Finally, in order to examine the robustness of the regression model, a cross-validation procedure was followed that has been suggested by Browne and Cudeck (1993). According to this procedure, our sample was randomly split into two equally sized subsamples (i.e. a test group and a calibration group). To investigate whether estimates of model parameters vary across both subsamples, a multisample analysis within LISREL 8.54 was conducted (cf. Kline, 1998).
3. Results Using the test group, a model M1 without interaction effects was specified (‘main effects model’), followed by a model M2 with interaction effects. The main effects model showed an acceptable model fit (x2(18) = 30.73, p < .05, RMSR = .02, NNFI = .93, CFI = .99, AGFI = .91). The pro1 Covariance matrices of both subsamples are available from the first author upon request.
portion of variance (R2) accounted for was .23 for emotional exhaustion, .31 for employee creativity, and .23 for work motivation. Accordingly, we tested the assumption that a regression model with interaction effects (M2) has a better statistical fit than a regression model without interaction effects (M1). Such nested models can be compared by a likelihood ratio test (cf. Bentler and Bonett, 1980). This test showed that the difference between the two chi-squares was significant (Dx2(18) = 30.73, p < .05), which means that the null hypothesis had to be rejected. The interactive model (M2) has a better statistical fit (in terms of chi-square) than a model without interactive effects (M1). As the superior model has a perfect model fit (i.e. no degrees of freedom left), reporting fit indices is superfluous. The proportion of variance (R2) accounted for in the interactive model was .26 for emotional exhaustion, .33 for employee creativity, and .25 for work motivation. 3.1. Emotional exhaustion Table 1 presents the results obtained from simultaneously testing the outcome variables in the test group. Unstandardized coefficients (B’s), standard errors (S.E.’s), and completely standardized coefficients (b’s) are presented in the table. In the first column, the independent variables are shown. The three outcome variables analyzed were emotional exhaustion, employee creativity, and work motivation. As far as emotional exhaustion is concerned, results showed one significant interaction effect ( p < .05) between general emotional job demands and emotional job resources. Significant interaction effects were graphically represented according to the method described by Aiken and West (1991). Values of the predictor variables were chosen one standard deviation below and above the mean. Two simple regression lines were then generated by entering these values in the equation. Dawson and Richter (2006) noted that interaction plots do not allow inferences as regards the significance of an individual slope. Therefore, a precise test of slope significance of the respective simple regression lines was carried out. The regression lines representing the interaction between general emotional job demands and emotional job resources in the prediction of emotional exhaustion are shown in Fig. 1. The figure indicates that in case of low emotional job resources, higher emotional job demands were associated with stronger feelings of exhaustion (simple slope test: t = 7.12, p < .001). Fig. 1 also shows that at high levels of emotional job resources, the positive relation between emotional job demands and emotional exhaustion is much weaker (simple slope test: t = 3.00, p < .01), implying a theoretically valid moderating effect of emotional job resources. Finally, Table 1 shows several main effects of demands and resources on emotional exhaustion. General emotional job demands were positively related to emotional exhaustion in both groups, whereas both emotional and cognitive job resources were negatively associated with exhaustion in both groups.
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
1465
Table 1 Multivariate multiple regression analyses (LISREL) in the test group (results Model 2) Source
Gender Employment status Education Age Emotional job demands (death) Emotional job demands (patient) Emotional job demands (general) Emotional job resources Cognitive job resources Interaction death emot. resources Interaction patient emot. resources Interaction general emot. resources Interaction death cogn. resources Interaction patient cogn. resources Interaction general cogn. resources
Dependent variable Emotional exhaustion
Employee creativity
Work motivation
B (S.E.)
B (S.E.)
B (S.E.)
0.37 0.01 0.00 0.01 0.04 0.08 0.33 0.17 0.16 0.03 0.05 0.12 0.12 0.07 0.01
b
(0.18) (0.01) (0.04) (0.01) (0.07) (0.07) (0.06) (0.05) (0.05) (0.06) (0.07) (0.05) (0.07) (0.06) (0.05)
*
0.01 0.02 0.12 0.01 0.04 0.12 0.10 0.07 0.19 0.04 0.12 0.07 0.04 0.06 0.00
0.11 0.07 0.01 0.08 0.04 0.08 0.32* 0.17* 0.16* 0.03 0.05 0.13* 0.12 0.09 0.01
(0.13) (0.00) (0.03) (0.00) (0.05) (0.05) (0.05) (0.04) (0.04) (0.04) (0.05) (0.04) (0.05) (0.05) (0.04)
b 0.01 0.24* 0.22* 0.08 0.05 0.15* 0.12* 0.09 0.25* 0.06 0.16* 0.11 0.05 0.10 0.00
0.06 0.01 0.00 0.01 0.12 0.04 0.14 0.20 0.14 0.06 0.12 0.03 0.03 0.01 0.09
(0.13) (0.00) (0.03) (0.00) (0.05) (0.05) (0.05) (0.04) (0.04) (0.04) (0.05) (0.04) (0.05) (0.05) (0.04)
b 0.02 0.15* 0.00 0.09 0.16* 0.06 0.18* 0.26* 0.19* 0.09 0.16* 0.04 0.04 0.02 0.14*
Note: N = 334. *p < 0.05 (one-tailed) Cogn. = Cognitive, Emot. = Emotional.
3.2. Employee creativity For employee creativity Table 1 shows one significant interaction effect. Again, to study the shape of the interaction, a graphical representation was created. Fig. 2 shows the simple regression lines representing the interaction between emotional job demands (patients) and emotional resources. The figure indicates that at high levels of emotional job resources, higher emotional job demands were associated with more employee creativity (simple slope test: t = 4.14, p < .001). On the other hand, there was no significant relation between emotional job demands and creativity in case of low emotional resources (simple slope test: t = .14, p = n.s.). Finally, main effects of demands and resources on employee creativity were also detected (Table 1). There was a positive association between emotional job demands
Fig. 1. Interaction between emotional job demands (‘general’) and emotional job resources for emotional exhaustion (all metrics standardized). Emot. = emotional.
(both patients and general demands) and creativity. Further, cognitive job resources were positively associated with creativity, too. 3.3. Work motivation Table 1 presents two significant interaction effects with regard to work motivation. The first interaction was between emotional job demands (patients) and emotional job resources in the prediction of work motivation. The simple regression lines in Fig. 3 indicated a trend in case of high emotional job resources (simple slope test: t = 1.75, p < .10): confrontation with more emotional job demands from patients or clients seemed to be related to stronger work motivation. On the other hand, there is a negative relation between emotional job demands and work motivation in case of low emotional job resources (simple slope test: t = 3.33, p < .001). In that
Fig. 2. Interaction between emotional job demands (‘patient’) and emotional job resources for employee creativity (all metrics standardized). Emot. = emotional.
1466
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
significant (Dx2(9) = 18.57, p < .05), which means that the null hypothesis (i.e. equal interaction effects) had to be rejected. 3.4. Cross-validation
Fig. 3. Interaction between emotional job demands (‘patient’) and emotional job resources for work motivation (all metrics standardized). Emot. = emotional.
particular situation, more emotional job demands were associated with less work motivation. Further, Fig. 4 shows that at high levels of cognitive job resources, higher general emotional job demands were associated with stronger work motivation (simple slope test: t = 5.06, p < .001). No significant relation between general emotional job demands and work motivation could be found in case of low cognitive job resources (simple slope test: t = .63, p = n.s.). Main effects of demands and resources on work motivation were also found (Table 1). Generally, positive associations between either emotional job demands or job resources and work motivation were detected. However, emotional job demands from patients did not significantly predict work motivation. Finally, a more formal test of the matching hypothesis (Hypothesis 2) would be to constrain all emotional and cognitive interaction terms to be equal and to examine the impairment in model fit. Therefore, LISREL’s equality constraints option was used to test inequality between the emotional resources interaction terms and the cognitive resources interaction terms. The corresponding likelihood ratio test showed that the impairment in model fit was
Fig. 4. Interaction between emotional job demands (‘general’) and cognitive job resources for work motivation (all metrics standardized). Emot. = emotional. Cogn. = cognitive.
To investigate whether the superior model in the test group was also valid for the calibration group, we conducted a multisample analysis within LISREL. The significance of group differences was tested through the imposition of crossgroup equality constraints on the model regression coefficients. That is, only those coefficients which represent the superior model structure were constrained, and the other coefficients (e.g. error and residual terms) were allowed to be re-estimated. The rationale for this approach is that theoretical weights are assumed to be identical for every individual in the population, and therefore, would not be affected by sampling (cf. MacCallum and Tucker, 1991). Accordingly, the chi-square of the multisample model with its coefficients constrained to equality was then contrasted against that of the unconstrained multisample model. A likelihood ratio test showed that the difference between the two chi-squares was not significant (Dx2(45) = 47.16, p = n.s.), which implies that the null hypothesis could not be rejected. This means that the estimates of the superior model parameters did not vary across both subsamples. So, the interactive model in the test group was valid for the calibration group, too.
4. Discussion The present study investigated the moderating effects of matching and non-matching job resources in the relation between emotional job demands and employee health/wellbeing. Next to the postulated moderating effect of job resources on the relation between emotional job demands and health/well-being outcomes (e.g. see Grandey et al., 2005), we assumed that these effects were more likely to occur in case of a match between specific kinds of job demands and certain forms of job resources (cf. de Jonge and Dormann, 2006) than in case of a non-match between demands and resources. Specifically, we hypothesized that emotional job resources rather than cognitive job resources moderate the association between emotional job demands and health/well-being outcomes. Our hypotheses were tested in a sample of health care workers as prototypes of human service workers who have to perform emotional labour (Mann and Cowburn, 2005). The first hypothesis regarding the moderating role of job resources was partly confirmed. Results in the test group indeed showed that emotional job resources were able to moderate the relation between emotional job demands and our three indicators of employee health and well-being: emotional exhaustion, employee creativity, and work motivation. The second hypothesis regarding the likelihood of
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
moderating effects for emotional job resources was also partly confirmed. Moderating effects were detected more often in case of matching, emotional, job resources than in case of non-matching, cognitive, job resources, which was also confirmed by LISREL’s equality constraints test. Overall, we found four out of 18 interactions (i.e. 22.2%) to be significant, which is considerably more than could be expected by chance. Among these, three interactions were of matching kind (i.e. emotional job demands in combination with emotional job resources), while one interaction was of non-matching kind (i.e. emotional job demands in combination with cognitive job resources). The total amount of variance explained (ranging from R2 = .25 to R2 = .33) was satisfying, given methodological considerations as well as the multi-causal aetiology of employee health/well-being (cf. Semmer et al., 1996). Finally, cross-validation of results showed that these findings were robust in both subsamples. The significant moderating effect of emotional job resources (i.e. emotional support) on emotional exhaustion is consistent with earlier research (e.g., de Jonge and Dormann, 2006; Grandey et al., 2005; van Vegchel et al., 2004). For instance, in a two-wave panel study among 267 health care workers, de Jonge and Dormann found a time-lagged moderating effect of emotional support on the relation between emotional job demands and emotional exhaustion. The current findings also add to the literature on burnout. More recent research on burnout has shown that emotional job demands may lead to emotional exhaustion (cf. Zapf et al., 2001; Zapf and Holz, 2006). Although research on emotional job demands has been proliferating throughout the last decade, the question remains in what way the negative impact of this type of demands on employees’ health can be combated best. So, based upon our results, burnout (in terms of its key variable emotional exhaustion) seems to be a response to emotionally demanding tasks, which will be amplified in case particular job resources such as emotional support are absent. To put it differently, job resources such as emotional support could compensate for resources that are lost through meeting the requirements of emotional job demands, thereby reducing stress-reactions (see also Grandey, 2000; Totterdell and Holman, 2003). Furthermore, the current findings are also in line with recent research that revealed beneficial consequences of emotional job demands (e.g. Zapf and Holz, 2006), and positive emotions in general (e.g. Frederickson, 2001; Isen, 2000). Specifically, when health care workers reported high emotional support from colleagues and supervisors, emotional job demands were positively associated with creativity and work motivation. However, this was only true for specific emotional job demands, i.e. confrontation with awkward and aggressive patients or clients. Apparently, emotional job resources seem to be effective only in case of more specific emotional job demands, and not in case of more general types of emotional job demands. Evidence suggests that being engaged in several demanding tasks simultaneously may decrease the chance of success at each
1467
of them if an employee is using the same, limited, job resource (Vohs et al., 2005). Given this idea, it could be that employees effectively use (limited) emotional job resources in case of specific emotional job demands rather than in case of more general emotionally demanding tasks. In contrast, only one out of 9 interactions between emotional job demands and cognitive job resources (i.e. job control) was significant. More specifically, emotional job demands were positively associated with work motivation for health care workers who reported high job control. This finding is in line with Karasek’s (1998) well-known demandcontrol research, which assumes work motivation to be highest in case of high job demands and high job control. The availability of control at work makes it easier to cope with emotional job demands, which in turn could lead to experiencing motivation and challenge instead of stress. To conclude, the current moderating findings are in line with the proposed ranking order of de Jonge and Dormann (2006). Matching job resources seem to be successful in combating demands more often than non-matching job resources. Finally, main effects of the three types of emotional job demands showed positive associations with positive wellbeing, too. A possible interpretation of these associations is that many health care workers deliberately choose for this kind of (emotion) work (Zapf and Holz, 2006). As they like this kind of work, they consider the job demands challenging accordingly. However, our study showed in general that the pay-off in terms of positive well-being is stronger in case of (the availability of) matching job resources. The present findings should also be discussed in terms of strengths and limitations of the study. A particular methodological strength was the use of structural equation modeling in which we investigated three different outcomes simultaneously, hereby eliminating conceptual overlap and minimizing chance capitalization (i.e. reduction of Type I errors). In addition, the robustness of the regression model by means of cross-validation was proven as well. A first limitation concerns the cross-sectional design of the study and its reliance on only self-report measures. Although we suggested a particular causal order of the variables, other causal directions or even reciprocal relations could be possible as well (cf. Grandey, 2003). Future longitudinal studies should investigate this kind of relations more profoundly. Added to this, common method variance might have played a role, although recent studies showed that this influence is not as high as could be expected (cf. Spector, 2006). A second concern is the modest amount of variance explained by the interaction terms. However, in our opinion this does neither negate the theoretical importance nor mean that the interaction effects have little substantive significance. The results are important because the size of any interaction effect is attenuated by measurement error when interaction terms are formed by multiplying variables to form cross-product terms as is required in regression analysis (Aiken and West, 1991). Further, though our results
1468
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
suggest that investigating the moderating role of matching job resources is a useful approach for studying emotional job demands and employee health/well-being, future research should examine other types of resources as well. For instance, matching personal resources such as emotional intelligence (Giardini and Frese, 2006) and emotion-focused coping (Shimazu et al., 2008) could moderate the relation between emotional job demands and health/well-being outcomes, too. A final limitation is that the present study addressed the matching hypothesis in a limited subset of conditions only. Future studies should encompass all possible conditions to enable full support for the matching hypothesis to be obtained (cf. de Jonge and Dormann, 2006). The current findings have some practical implications for health care workers. Employees who are often confronted with high emotional job demands and have low job resources seem to be at risk for burnout. Enhancing specific job resources, such as emotional support from colleagues and supervisors, provide self-regulatory mechanisms to deal with emotional job demands, which enable a reduction in emotional exhaustion. The results also suggest that job resources that prevent demands for emotional self-regulation from becoming too high (i.e. emotional support by colleagues or supervisors and, to a lesser extent, job control), might actually contribute to more employee creativity and more work motivation, too. To conclude, work-related interventions on emotional demands in health care work should primarily focus on specific, emotional, job resources to diminish burnout and to stimulate positive well-being. Providing health care workers with more, preferably matching, job resources could make emotional job demands less stressful, and even stimulating and challenging.
References Aiken, L.S., West, S.G., 1991. Multiple Regression: Testing and Interpreting Interactions. Sage Publications, Newbury Park, CA. Bentler, P.M., Bonett, D.G., 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88, 588–606. Brotheridge, C.M., Grandey, A.A., 2002. Emotional labor and burnout: comparing two perspectives of ‘‘people work’’. J. Vocat. Behav. 60, 17–39. Brotheridge, C.M., Lee, R.T., 2002. Testing a conservation of resources model of the dynamics of emotional labor. J. Occup. Health Psychol. 7, 57–67. Browne, M.W., Cudeck, R., 1993. Alternative ways of assessing model fit. In: Bollen, K.A., Long, J.S. (Eds.), Testing Structural Equation Models. Sage Publications, Newbury Park, CA, pp. 136–162. Cordes, C.L., Dougherty, T.W., Blum, M., 1997. Patterns of burnout among managers and professionals: a comparison of models. J. Organ. Behav. 18, 685–701. Dawson, J.F., Richter, A.W., 2006. Probing three-way interactions in moderated multiple regression: development and applica-
tion of a slope difference test. J. Appl. Psychol. 91 (4), 917– 926. de Jonge, J., Dollard, M.F., Dormann, C., Le Blanc, P.M., Houtman, I.L.D., 2000b. The Demand-Control Model: specific demands, specific control, and well-defined groups. Int. J. Stress Manage. 7 (4), 269–287. de Jonge, J., Dormann, C., 2003. The DISC Model: demandinduced strain compensation mechanisms in job stress. In: Dollard, M.F., Winefield, A.H., Winefield, H.R. (Eds.), Occupational Stress in the Service Professions. Taylor & Francis, London, pp. 43–74. de Jonge, J., Dormann, C., 2006. Stressors, resources, and strain at work: a longitudinal test of the Triple Match Principle. J. Appl. Psychol. 91 (6), 1359–1374. de Jonge, J., Dormann, C., van den Tooren, M., 2008. The DemandInduced Strain Compensation Model: renewed theoretical considerations and empirical evidence. In: Na¨swall, K., Hellgren, J. (Eds.),The Individual in the Changing Working Life. Cambridge University Press, Oxford, pp. 67–87. de Jonge, J., Janssen, P.P.M., van Breukelen, G.J.P., 1996. Testing the Demand-Control-Support Model among health care professionals: a structural equation model. Work Stress 10 (3), 209– 224. de Jonge, J., Reuvers, M.M.E.N., Houtman, I.L.D., Bongers, P.M., Kompier, M.A.J., 2000a. Linear and nonlinear relations between psychosocial job characteristics, subjective outcomes, and sickness absence: baseline results from SMASH. J. Occup. Health Psychol. 5 (2), 256–268. Frederickson, B.L., 2001. The role of positive emotions in positive psychology: the broaden and build theory of positive emotions. Am. Psychol. 56, 218–226. George, J.M., Zhou, J., 2001. When openness to experience and conscientiousness are related to creative behaviour: an interactional approach. J. Appl. Psychol. 86 (3), 513–524. Giardini, A., Frese, M., 2006. Reducing the negative effects of emotion work in service occupations: emotional competence as a psychological resource. J. Occup. Health Psychol. 11 (1), 63–75. Grandey, A.A., 2000. Emotion regulation in the workplace: a new way to conceptualize emotional labor. J. Occup. Health Psychol. 5, 95–110. Grandey, A.A., 2003. When the ‘‘show must go on’’: surface acting and deep acting as determinants of emotional exhaustion and peer-rated service delivery. Acad. Manage. J. 46, 86–96. Grandey, A.A., Fisk, G.M., Steiner, D.D., 2005. Must ‘service with a smile’ be stressful? The moderating role of personal control for American and French employees. J. Appl. Psychol. 90 (5), 893– 904. Gross, J., 1998. Antecedent- and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. J. Pers. Soc. Psychol. 74 (1), 224–237. Hobfoll, S.E., 1989. Conservation of resources: a new attempt at conceptualizing stress. Am. Psychol. 44, 513–524. Hobfoll, S.E., 2002. Social and psychological resources and adaptation. Rev. Gen. Psychol. 6 (4), 307–324. Hunter, B., Smith, P., 2007. Emotional labour: just another buzz word? Int. J. Nurs. Stud. 44, 859–861. Isen, A.M., 2000. Positive affect and decision making. In: Lewis, M., Haviland-Jones, J. (Eds.), Handbook of Emotions. 2nd ed. Guilford, New York, pp. 417–435. Jo¨reskog, K.G., So¨rbom, D., 1996. LISREL 8: User’s Reference Guide. In: 2nd ed.. Scientific Software International, Chicago.
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469 Kahn, R.L., Byosiere, P., 1992. Stress in organizations. In: Dunnette, M.D., Hough, L.M. (Eds.), Handbook of Industrial and Organizational Psychology, 2nd ed., vol. 3. Consulting Psychologists Press, Palo Alto, CA, pp. 571–650. Karasek, R.A., 1985. Job Content Instrument: Questionnaire and User’s Guide. Department of Industrial and Systems Engineering, University of Southern California, Los Angeles. Karasek, R.A., 1998. Demand/Control Model: a social, emotional, and physiological approach to stress risk and active behaviour development. In: Stellman, J.M. (Ed.), Encyclopaedia of Occupational Health and Safety. ILO, Geneva, pp. 34.6–34.14. Kline, R.B., 1998. Principles and Practice of Structural Equation Modelling. The Guilford Press, New York. Kruml, S.M., Geddes, D., 2000. Exploring the dimension of emotional labor. Manage. Commun. Quart. 14 (1), 8–49. Le Blanc, P.M., de Jonge, J., Schaufeli, W.B., 2000. Job stress and health. In: Chmiel, N. (Ed.), Introduction to Work and Organisational Psychology: A European Perspective. Blackwell Publishers, Oxford, pp. 148–177. Lekander, M., 2002. Ecological immunology: the role of the immune system in psychology and neuroscience. Eur. Psychol. 7, 98–115. MacCallum, R.C., Tucker, L.R., 1991. Representing sources of error in the common-factor model: implications for theory and practice. Psychol. Bull. 109, 502–511. Maslach, C., 1978. The client role in staff burn-out. J. Soc. Issues 34 (4), 111–124. Maslach, C., Jackson, S.E., 1986. Maslach Burnout Inventory: Manual, 2nd ed. Consulting Psychologists Press, Palo Alto, CA. Mann, S., Cowburn, J., 2005. Emotional labour and stress within mental health nursing. J. Psychiat. Ment. Health Nurs. 12, 154– 162. Mitchell, D., Smith, P., 2003. Learning from the past: emotional labour and learning disability nursing. J. Learn. Disabil. 7, 109– 117. Montague, P.R., 1996. The resource consumption principle: attention and memory in volumes of neural tissue. In: Proceedings of the National Academy of Sciences of the United States of America 93. pp. 3619–3623. Morris, J.A., Feldman, D.C., 1996. The dimensions, antecedents, and consequences of emotional labour. Acad. Manage. Rev. 21 (4), 986–1010. Morris, J.A., Feldman, D.C., 1997. Managing emotions in the workplace. J. Manage. Issues 9 (3), 257–274. Pugliesi, K., 1999. The consequences of emotional labor: effects on work stress, job satisfaction, and well-being. Motiv. Emotion 23, 125–154.
1469
Rutter, D.R., Fielding, P.J., 1988. Sources of occupational stress: an examination of British prison officers. Work Stress 2 (4), 291– 299. Schaufeli W.B., van Dierendonck, D., 2000. Utrechtse Burnout Schaal (UBOS): handleiding [Manual Utrecht Burnout Scale]. Swets & Zeitlinger, Lisse. Semmer, N., Zapf, D., Greif, S., 1996. ‘Shared job strain’: a new approach for assessing the validity of job stress measurements. J. Occup. Organ. Psychol. 69, 293–310. Shimazu, A., de Jonge, J., Irimajiri, H., 2008. Lagged effects of active coping within the Demand-Control Model: a three-wave panel study among Japanese employees. Int. J. Behav. Med. 1 (1), 11–20. Spector, P.E., 2006. Method variance in organizational research: truth or urban legend? Organ. Res. Methods 9, 221–232. Thierry, Hk., 1998. Motivation and satisfaction. In: Drenth, P.J.D., Thierry, Hk., de Wolff, Ch.J. (Eds.), Handbook of Work and Organizational Psychology, 2nd ed., vol. 4. Psychology Press, Hove, pp. 253–289. Totterdell, P., Holman, D., 2003. Emotion regulation in customer service roles: testing a model of emotional labor. J. Occup. Health Psychol. 8 (1), 55–73. Vancouver, J.B., 2000. Self-regulation in organizational settings: a tale of two paradigms. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (Eds.), Handbook of Self-regulation. Academic Press, San Diego, CA, pp. 303–341. van Vegchel, N., de Jonge, J., Meijer, T., Hamers, J.P.H., 2001. Different effort constructs and effort-reward imbalance: effects on employee well-being in ancillary health care workers. J. Adv. Nurs. 34 (1), 128–136. van Vegchel, N., de Jonge, J., So¨derfeldt, M., Dormann, C., Schaufeli, W., 2004. Quantitative versus emotional demands among Swedish human service employees: moderating effects of job control and social support. Int. J. Stress Manage. 10 (1), 21–40. Vohs, K.D., Baumeister, R.F., Ciarocco, N.J., 2005. Self-regulation and self-presentation: regulatory resource depletion impairs impression management and effortful self-presentation depletes regulatory resources. J. Pers. Soc. Psychol. 88 (4), 632–657. Zapf, D., 2002. Emotion work and psychological well-being: a review of the literature and some conceptual considerations. Hum. Resour. Manage. Rev. 12, 237–268. Zapf, D., Holz, M., 2006. On the positive and negative effects of emotion work in organizations. Eur. J. Work Organ. Psychol. 15 (1), 1–28. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., Holz, M., 2001. Emotion work and job stressors and their effects on burnout. Psychol. Health 16, 527–545.
International Journal of Nursing Studies 45 (2008) 1460–1469 www.elsevier.com/ijns
Emotional job demands and the role of matching job resources: A cross-sectional survey study among health care workers Jan de Jonge a,*, Pascale M. Le Blanc b, Maria C.W. Peeters b, Hanneke Noordam b a
Eindhoven University of Technology, Department of Technology Management, Subdepartment of Human Performance Management, P.O. Box 513, 5600 MB Eindhoven, The Netherlands b Utrecht University, Department of Social and Organizational Psychology, Utrecht, The Netherlands Received 27 August 2007; received in revised form 10 October 2007; accepted 10 November 2007
Abstract Background: Research on emotional labour in health care work has not yet revealed under what conditions emotional job demands have an impact on employee health and well-being. There is a need for more theory to unveil the black box of emotional labour processes. Objectives: To test the moderating role of matching (i.e. emotional) and non-matching (i.e. cognitive) job resources in the relation between emotional job demands and employee health/well-being (i.e. emotional exhaustion, employee creativity, and work motivation). Design: A cross-sectional survey with anonymous questionnaires was conducted. Settings: A large organization for residential elderly care with eight locations in an urban area in the Netherlands. Participants: Questionnaires were distributed to 1259 health care workers, of which 826 people returned the questionnaire (66% response rate). Methods: In addition to descriptive statistics, multivariate multiple regression analysis (LISREL 8.54) with cross-validation was conducted. Results: Findings showed that emotional job resources moderated the relation between emotional job demands and health/wellbeing outcomes. Firstly, emotional job resources were able to moderate the relation between emotional job demands and emotional exhaustion. Secondly, both emotional job resources and, to a lesser extent, cognitive job resources were able to moderate the relation between emotional job demands and positive well-being outcomes (i.e. employee creativity and work motivation). Finally, cross-validation showed that parameter estimates did not vary across subsamples. Conclusions: Job resources could compensate for resources lost through meeting the requirements of emotional job demands, thereby reducing stress-reactions and increasing well-being. Providing health care workers with more, preferably matching, job resources could make emotional job demands less stressful, and even stimulating and challenging. Future longitudinal studies should investigate the interplay of emotional job demands and (matching) job resources more profoundly. # 2007 Elsevier Ltd. All rights reserved. Keywords: Emotional labour; Occupational health; Questionnaires; Burnout; Health care staff
What is already known about the topic? * Corresponding author. Tel.: +31 40 2472243; fax: +31 40 2437161. E-mail address: [email protected] (J. de Jonge).
Health care workers are at risk for burnout due to emotional job demands.
0020-7489/$ – see front matter # 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijnurstu.2007.11.002
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
Job resources such as job control and social support seem to be able to moderate the negative impact of emotional job demands. Few recent studies have revealed beneficial consequences of emotional job demands such as increased job satisfaction and increased personal accomplishment. What this paper adds Study results support the hypothesis that job resources moderate the relation between emotional job demands and health/well-being outcomes. Specifically, findings show that moderating effects are found more often for matching, emotional, job resources than for non-matching, cognitive, job resources. Findings draw attention to the importance of providing health care workers with more, preferably matching, job resources that could make emotional job demands less stressful, and even stimulating and challenging. 1. Introduction Over the past few years, the phenomenon of emotional labour has received considerable attention due to its relevance for health care workers. One of the core job activities for health care workers is the social interaction with patients or clients, in which the requirement to regulate emotions plays a key role. In health care, emotional labour has been an important topic of debate because of its importance for both providers and recipients of care (cf. Hunter and Smith, 2007). Mitchell and Smith (2003), for instance, indicated that emotional labour has always been part of the image of health care workers. Emotional labour has usually been conceptualized in two main ways (Brotheridge and Grandey, 2002). First, employee-focused emotional labour denotes the employees’ efforts to manage their own emotions. Corresponding measures tap respondents’ efforts at ‘surface acting’ and ‘deep acting’ (e.g. Brotheridge and Lee, 2002; Kruml and Geddes, 2000). Second, job-focused emotional labour denotes the level of emotional job demands in occupations. This has usually been measured as job demands such as frequency of interactions with clients or dealing with death and dying (e.g. Morris and Feldman, 1996; Pugliesi, 1999; Zapf and Holz, 2006). Grandey (2000) provided a conceptual model of emotional labour that integrates both conceptualizations. According to her view, job-focused emotional labour refers to situational characteristics of the work situation (e.g. job demands) that cause employees to engage in employee-focused emotional labour. Emotional labour field studies and emotion regulation lab studies have demonstrated that the effortful processes of surface acting and deep acting are related to employee stress-reactions as well as their well-being (cf. Grandey, 2000; Zapf, 2002).
1461
In this article the construct of emotional labour refers exclusively to emotional demands at work (i.e. job-focused emotional labour). It is well known from the job stress literature that emotional job demands contribute to stressreactions such as burnout and psychosomatic health symptoms (e.g. Brotheridge and Grandey, 2002; Le Blanc et al., 2000; Zapf et al., 2001). The basic proposition here is that health care workers in emotionally demanding jobs report higher levels of burnout or other health problems than workers in less emotionally demanding jobs. The specific underlying mechanism of this relation is still part of the ‘black box’ of emotional labour. Brotheridge and Lee (2002) noted that there has been no overarching theoretical framework that can be used to explain the relation between emotional job demands and adverse health outcomes. Accordingly, the present study contributes to the literature by examining this relation using job stress theories developed by different scholars (e.g. Hobfoll, 1989, 2002; de Jonge and Dormann, 2003, 2006). By doing so, our findings not only shed light on the working of the relation between emotional job demands and health and well-being, but also have implications for ways of managing emotional job demands in health care work. As said before, there is ample empirical evidence that emotional job demands can be stressful (for an overview, see Zapf, 2002; Zapf and Holz, 2006). For instance, a longitudinal survey study by van Vegchel et al. (2004) showed that emotional job demands predicted emotional exhaustion (burnout’s key component) over time. It is therefore not surprising that the burnout concept was originally introduced as an individual reaction to high emotional job demands in human service work (Maslach, 1978). Drawing on the literature of emotion regulation (Gross, 1998), burnout can be considered as an indication that employees are no longer able to regulate their emotions adequately in patient or client interactions (Zapf and Holz, 2006). Emotional selfregulation at work seems to be critical in understanding stress-reactions among health care workers, because regulation of expressions (e.g. suppression of negative emotions) appears to be associated with prolonged effort which in turn has been linked to adverse health and well-being outcomes (e.g. Brotheridge and Lee, 2002; Zapf, 2002). However, there are also empirical findings that do not support this line of reasoning. Some studies did not find any association between emotional job demands and stress-reactions such as emotional exhaustion or adverse health symptoms (e.g. Cordes et al., 1997; Morris and Feldman, 1997; Rutter and Fielding, 1988). This has raised the question of what is going on in the black box: Under what conditions do emotional job demands have a negative impact on employee health and well-being? Classical job stress research has already demonstrated that the role of so-called job resources in the stress process should not be ignored in this respect (Kahn and Byosiere, 1992). Job resources can be conceptualized as cognitive–energetic reservoirs in the work environment that can be tapped, such as job control and workplace social
1462
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
support (cf. Hobfoll, 2002). Specifically, job control and social support seem to be able to moderate the negative impact of emotional job demands. A prospective cohort study among 2255 Swedish service workers showed, for example, that job control was able to reduce the impact of emotional job demands on burnout (van Vegchel et al., 2004). In a similar vein, Grandey et al. (2005) showed that when service workers reported low control in their job, emotional job demands and emotional exhaustion were positively related, whereas they were unrelated in case of high job control. According to the conservation of resources (COR) theory (Hobfoll, 1989, 2002), people strive to obtain, retain, protect and foster valued resources and minimize any threats of resource loss. Emotional job demands could be such a threat to resource loss. Employees usually invest their job resources in meeting job demands because they expect to receive positive outcomes in return. In response to emotional job demands, employees expend job resources. However, when an imbalance occurs between the emotional job demands and the available job resources to meet those demands, the end-result will be emotional stress-reactions. 1.1. Matching principle Recently, de Jonge and Dormann (2006) proposed a theoretical refinement of the moderating role of job resources. Next to describing the moderating effect of job resources in general, they argue that the type of job resources should correspond with the type of job demands. More specifically, they propose moderating effects to be most likely when there is a match between the kind of job demands and job resources. For instance, emotional support from colleagues (‘job resource’) may help to reduce stressreactions caused by emotional job demands at best. The function of emotional support as a job resource could facilitate employees’ coping with job stress, which accounts for a better state of health and well-being. The theoretical basis for the matching assumption is derived from so-called homeostatic regulation processes (cf. de Jonge et al., 2008). To survive, a living organism must maintain certain critical parameters within a bounded range. For instance, the human body must regulate its temperature, amount of fluids, and energy level. Maintaining each critical parameter requires that the body comes into contact with the corresponding satiatory stimulus (i.e. clothes, water, and food) at the right time. The process by which these critical parameters are maintained (i.e. to keep something regular) is generally referred to as homeostatic regulation. For instance, in the area of immune functioning, homeostatic regulation processes are known to cause an activation of internal resources (e.g. T- and B-cells) when particular demands occur (Lekander, 2002). Through evolutionary processes, the release of functional, matching internal resources is more likely than the release of dysfunctional, non-matching internal resources (Lekander, 2002). Similar homeostatic regulation processes can be found in the
nervous system as well, which in fact has much in common with the immune system. For example, homeostatic regulation processes engage the fine scale organization and operation of volumes of neural tissue to provide powerful and functional resources for neural signal integration and stable long-term storage of information (Montague, 1996). The idea of functional homeostatic regulation can easily be applied to organizational settings (e.g. Vancouver, 2000). Similar to homeostatic regulation in the immune and nervous system, it is proposed that employees activate functional, corresponding, job resources to mitigate the effects of specific job demands. Basically, a match exists if job resources provide functions that are similar to those provided by internal bodily resources in combating stress. For example, when emotional problems with patients arise (e.g. insolent patients), the availability of emotionally supportive colleagues as a job resource is likely to be helpful. If supportive colleagues are unavailable, other job resources can be useful to some extent, for instance control at work to handle a particular problematic patient. de Jonge and Dormann (2006) propose that job demands are firstly dealt with using easily available matching job resources. If such matching job resources are not available or when they are depleted (cf. Hobfoll, 2002), employees will search for other job resources and will even use job resources that do not correspond to the kind of job demands (cf. Vohs et al., 2005). Consequently, de Jonge and Dormann (2006) state that matching job resources are most likely to be successful in combating particular demands, followed by less-matching and non-matching job resources. 1.2. The present study The aim of the present study is to gain more insight into the moderating role of matching and non-matching job resources in the relation between emotional job demands and health/well-being outcomes among health care workers. We already mentioned that many scholars have considered the negative consequences of emotional job demands such as increased burnout. A few, more recent, studies have revealed beneficial consequences of emotional job demands such as increased job satisfaction and increased personal accomplishment (e.g. Brotheridge and Grandey, 2002; Zapf and Holz, 2006). As a consequence, we will explore both negative and positive consequences of emotional job demands. From the perspectives of Frederickson’s (2001) Broadenand-Build Theory of positive emotions as well as Isen’s (2000) Flexibility Hypothesis, experienced positive emotions are vehicles for individual growth, creativity and motivation. For that reason, we included two indicators of work-related positive well-being in the present study as well; that is, employee creativity and work motivation. Following de Jonge and Dormann’s (2006) line of reasoning, moderating effects between job demands and job resources should occur with greater likelihood among demands and resources from identical dimensions. For
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
instance, it is assumed that emotional job resources will show moderating effects on the relation between emotional job demands and burnout more often than other kinds of job resources such as cognitive job resources. To test this assumption, we will use both matching, i.e. emotional, job resources and non-matching, i.e. cognitive, job resources. Consequently, our hypotheses are as follows: Hypothesis 1. Job resources moderate the relation between emotional job demands and health/well-being outcomes (i.e. emotional exhaustion, employee creativity, and work motivation). There is a weaker association between emotional job demands and adverse health/well-being for employees with high job resources than for employees with low job resources. Hypothesis 2. The moderating effect is found more often for matching, emotional, job resources than for non-matching, cognitive, job resources.
2. Method 2.1. Procedure and participants A cross-sectional survey study was conducted among 1259 health care workers employed in a large organization for residential elderly care with eight locations in an urban area. Employees were given an anonymous questionnaire, which could be returned in sealed envelopes. In total, 826 people returned the questionnaire (66% response rate). All these workers were involved in patient or client work. Demographics showed that 89.9% of the sample was female, whereas the mean age was 41.2 years (S.D. = 10.7). Ages ranged from 16 to 64 years. Mean working time was 6.7 years (S.D. = 7.0), while 12.1% worked full-time (i.e. 36 h per week). 2.2. Measures 2.2.1. Emotional job demands Emotional demands at work were assessed by eight items, consisting of three subscales (cf. van Vegchel et al., 2001), scored on a 5-point rating scale ranging from (1) ‘‘never’’ to (5) ‘‘always’’. The first subscale was labeled ‘confrontation with death, dying, illness, and suffering’ and consisted of two items (r = .70, p < .001). For example, ‘In your work, are you confronted with death and dying?’. The second subscale was labeled ‘confrontation with awkward and aggressive patients or clients’ (three items, Cronbach’s alpha = .86). For instance, ‘In your work, are you confronted with aggressive patients or clients?’. The third subscale was labeled ‘general emotionally demanding work’ (three items, Cronbach’s alpha = .79). An example item is: ‘Does your work put you in emotionally disturbing situations?’. Confirmatory factor analysis (CFA) with correlated factors
1463
showed that a model with three factors representing the three subscales fitted well to the data (x2(17) = 128.73, p < .001, RMSR = .04, NNFI = .94, CFI = .96, AGFI = .90), and also fitted better than a one-factor solution ‘emotional job demands’ (x2(20) = 604.95, p < .001, RMSR = .07, NNFI = .84, CFI = .89, AGFI = .62). 2.2.2. Emotional job resources Emotional resources at work were operationalized as emotional support from supervisors and colleagues, and were measured using the socio-emotional part of a wellvalidated Dutch translation of the social support scale from Karasek’s (1985) Job Content Questionnaire (de Jonge et al., 2000a). Items were scored on a 4-point rating scale ranging from (1) ‘‘strongly disagree’’ to (4) ‘‘strongly agree’’. The scale consisted of four items (Cronbach’s alpha = .79) that refer to emotional support from supervisors and colleagues. An example item is: ‘My supervisor is concerned about my welfare’. 2.2.3. Cognitive job resources Cognitive job resources were assessed by a 5-item scale derived from de Jonge et al. (1996) measuring cognitive control opportunities at work (i.e. job control). For instance, ‘The extent to which the work offers the opportunity to determine the method of working yourself’. Items were scored on a 5-point rating scale, ranging from (1) ‘‘very little’’ to (5) ‘‘very much’’, with a Cronbach’s alpha of .81. 2.2.4. Employee creativity Employee creativity can be defined as the generation of novel and useful ideas by employees. This work-related construct was assessed by a 12-item scale developed by George and Zhou (2001), and translated/back-translated in a well-validated Dutch version. The scale could be scored on a 5-point rating scale ranging from (1) ‘‘never’’ to (5) ‘‘always’’, with a Cronbach’s alpha of .96. Example items are: ‘Comes up with new and practical ideas to improve performance’ and ‘Exhibits creativity on the job when given opportunity to’. 2.2.5. Work motivation Work motivation refers to the degree to which employees consider their job motivating and challenging (cf. Thierry, 1998). This construct was measured by two well-established items (de Jonge et al., 2000b) in which the respondents indicated how motivating and challenging their work is. The items were scored on a 5-point rating scale ranging from (1) ‘‘strongly disagree’’ to (5) ‘‘strongly agree’’. The intercorrelation was .49 ( p < .001). For instance, ‘My job is challenging’. 2.2.6. Emotional exhaustion Emotional exhaustion was measured by the well-validated Dutch version (Schaufeli and van Dierendonck, 2000) of the Maslach Burnout Inventory (Maslach and Jackson,
1464
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
1986). The scale contained five items with a 7-point response scale ranging from (0) ‘‘never’’ to (6) ‘‘always, daily’’, with a Cronbach’s alpha of .87. An example item is: ‘I feel emotionally drained from my work’. Finally, demographic characteristics such as gender (0 = female; 1 = male), age (in years), education (low till high), and employment status (working hours per week) were included as control variables as their relation with health/well-being outcomes is well-established (cf. Grandey, 2000; Totterdell and Holman, 2003). 2.3. Analytical strategy Hypotheses were tested with multivariate multiple regression analyses within the computer programme LISREL 8.54 (Jo¨reskog and So¨rbom, 1996). This method allows us to investigate all three outcome variables simultaneously, thus correcting for conceptual overlap (e.g. the Pearson correlation between employee creativity and work motivation was r = .38, p < .01) as well as decreasing Type I errors. Structural model tests were based upon covariance matrices1 and used maximum likelihood estimation. Postulated moderating effects were tested by adding multiplicative interaction terms (demands resources) of standardized emotional job demands and job resources into the regression analyses (cf. Aiken and West, 1991). In total 18 possible interaction effects between demands and resources were included (3 types of demands 2 types of resources 3 outcome variables). Also, the structural model was completely saturated because residuals among the three outcome variables were allowed to correlate. Error caused by misspecification of the model (e.g. due to unmeasured variables which might have some influence) would be reflected by these correlated residuals. Saturated models always have a perfect model fit, and therefore reporting fit indices is not necessary. Finally, in order to examine the robustness of the regression model, a cross-validation procedure was followed that has been suggested by Browne and Cudeck (1993). According to this procedure, our sample was randomly split into two equally sized subsamples (i.e. a test group and a calibration group). To investigate whether estimates of model parameters vary across both subsamples, a multisample analysis within LISREL 8.54 was conducted (cf. Kline, 1998).
3. Results Using the test group, a model M1 without interaction effects was specified (‘main effects model’), followed by a model M2 with interaction effects. The main effects model showed an acceptable model fit (x2(18) = 30.73, p < .05, RMSR = .02, NNFI = .93, CFI = .99, AGFI = .91). The pro1 Covariance matrices of both subsamples are available from the first author upon request.
portion of variance (R2) accounted for was .23 for emotional exhaustion, .31 for employee creativity, and .23 for work motivation. Accordingly, we tested the assumption that a regression model with interaction effects (M2) has a better statistical fit than a regression model without interaction effects (M1). Such nested models can be compared by a likelihood ratio test (cf. Bentler and Bonett, 1980). This test showed that the difference between the two chi-squares was significant (Dx2(18) = 30.73, p < .05), which means that the null hypothesis had to be rejected. The interactive model (M2) has a better statistical fit (in terms of chi-square) than a model without interactive effects (M1). As the superior model has a perfect model fit (i.e. no degrees of freedom left), reporting fit indices is superfluous. The proportion of variance (R2) accounted for in the interactive model was .26 for emotional exhaustion, .33 for employee creativity, and .25 for work motivation. 3.1. Emotional exhaustion Table 1 presents the results obtained from simultaneously testing the outcome variables in the test group. Unstandardized coefficients (B’s), standard errors (S.E.’s), and completely standardized coefficients (b’s) are presented in the table. In the first column, the independent variables are shown. The three outcome variables analyzed were emotional exhaustion, employee creativity, and work motivation. As far as emotional exhaustion is concerned, results showed one significant interaction effect ( p < .05) between general emotional job demands and emotional job resources. Significant interaction effects were graphically represented according to the method described by Aiken and West (1991). Values of the predictor variables were chosen one standard deviation below and above the mean. Two simple regression lines were then generated by entering these values in the equation. Dawson and Richter (2006) noted that interaction plots do not allow inferences as regards the significance of an individual slope. Therefore, a precise test of slope significance of the respective simple regression lines was carried out. The regression lines representing the interaction between general emotional job demands and emotional job resources in the prediction of emotional exhaustion are shown in Fig. 1. The figure indicates that in case of low emotional job resources, higher emotional job demands were associated with stronger feelings of exhaustion (simple slope test: t = 7.12, p < .001). Fig. 1 also shows that at high levels of emotional job resources, the positive relation between emotional job demands and emotional exhaustion is much weaker (simple slope test: t = 3.00, p < .01), implying a theoretically valid moderating effect of emotional job resources. Finally, Table 1 shows several main effects of demands and resources on emotional exhaustion. General emotional job demands were positively related to emotional exhaustion in both groups, whereas both emotional and cognitive job resources were negatively associated with exhaustion in both groups.
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
1465
Table 1 Multivariate multiple regression analyses (LISREL) in the test group (results Model 2) Source
Gender Employment status Education Age Emotional job demands (death) Emotional job demands (patient) Emotional job demands (general) Emotional job resources Cognitive job resources Interaction death emot. resources Interaction patient emot. resources Interaction general emot. resources Interaction death cogn. resources Interaction patient cogn. resources Interaction general cogn. resources
Dependent variable Emotional exhaustion
Employee creativity
Work motivation
B (S.E.)
B (S.E.)
B (S.E.)
0.37 0.01 0.00 0.01 0.04 0.08 0.33 0.17 0.16 0.03 0.05 0.12 0.12 0.07 0.01
b
(0.18) (0.01) (0.04) (0.01) (0.07) (0.07) (0.06) (0.05) (0.05) (0.06) (0.07) (0.05) (0.07) (0.06) (0.05)
*
0.01 0.02 0.12 0.01 0.04 0.12 0.10 0.07 0.19 0.04 0.12 0.07 0.04 0.06 0.00
0.11 0.07 0.01 0.08 0.04 0.08 0.32* 0.17* 0.16* 0.03 0.05 0.13* 0.12 0.09 0.01
(0.13) (0.00) (0.03) (0.00) (0.05) (0.05) (0.05) (0.04) (0.04) (0.04) (0.05) (0.04) (0.05) (0.05) (0.04)
b 0.01 0.24* 0.22* 0.08 0.05 0.15* 0.12* 0.09 0.25* 0.06 0.16* 0.11 0.05 0.10 0.00
0.06 0.01 0.00 0.01 0.12 0.04 0.14 0.20 0.14 0.06 0.12 0.03 0.03 0.01 0.09
(0.13) (0.00) (0.03) (0.00) (0.05) (0.05) (0.05) (0.04) (0.04) (0.04) (0.05) (0.04) (0.05) (0.05) (0.04)
b 0.02 0.15* 0.00 0.09 0.16* 0.06 0.18* 0.26* 0.19* 0.09 0.16* 0.04 0.04 0.02 0.14*
Note: N = 334. *p < 0.05 (one-tailed) Cogn. = Cognitive, Emot. = Emotional.
3.2. Employee creativity For employee creativity Table 1 shows one significant interaction effect. Again, to study the shape of the interaction, a graphical representation was created. Fig. 2 shows the simple regression lines representing the interaction between emotional job demands (patients) and emotional resources. The figure indicates that at high levels of emotional job resources, higher emotional job demands were associated with more employee creativity (simple slope test: t = 4.14, p < .001). On the other hand, there was no significant relation between emotional job demands and creativity in case of low emotional resources (simple slope test: t = .14, p = n.s.). Finally, main effects of demands and resources on employee creativity were also detected (Table 1). There was a positive association between emotional job demands
Fig. 1. Interaction between emotional job demands (‘general’) and emotional job resources for emotional exhaustion (all metrics standardized). Emot. = emotional.
(both patients and general demands) and creativity. Further, cognitive job resources were positively associated with creativity, too. 3.3. Work motivation Table 1 presents two significant interaction effects with regard to work motivation. The first interaction was between emotional job demands (patients) and emotional job resources in the prediction of work motivation. The simple regression lines in Fig. 3 indicated a trend in case of high emotional job resources (simple slope test: t = 1.75, p < .10): confrontation with more emotional job demands from patients or clients seemed to be related to stronger work motivation. On the other hand, there is a negative relation between emotional job demands and work motivation in case of low emotional job resources (simple slope test: t = 3.33, p < .001). In that
Fig. 2. Interaction between emotional job demands (‘patient’) and emotional job resources for employee creativity (all metrics standardized). Emot. = emotional.
1466
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
significant (Dx2(9) = 18.57, p < .05), which means that the null hypothesis (i.e. equal interaction effects) had to be rejected. 3.4. Cross-validation
Fig. 3. Interaction between emotional job demands (‘patient’) and emotional job resources for work motivation (all metrics standardized). Emot. = emotional.
particular situation, more emotional job demands were associated with less work motivation. Further, Fig. 4 shows that at high levels of cognitive job resources, higher general emotional job demands were associated with stronger work motivation (simple slope test: t = 5.06, p < .001). No significant relation between general emotional job demands and work motivation could be found in case of low cognitive job resources (simple slope test: t = .63, p = n.s.). Main effects of demands and resources on work motivation were also found (Table 1). Generally, positive associations between either emotional job demands or job resources and work motivation were detected. However, emotional job demands from patients did not significantly predict work motivation. Finally, a more formal test of the matching hypothesis (Hypothesis 2) would be to constrain all emotional and cognitive interaction terms to be equal and to examine the impairment in model fit. Therefore, LISREL’s equality constraints option was used to test inequality between the emotional resources interaction terms and the cognitive resources interaction terms. The corresponding likelihood ratio test showed that the impairment in model fit was
Fig. 4. Interaction between emotional job demands (‘general’) and cognitive job resources for work motivation (all metrics standardized). Emot. = emotional. Cogn. = cognitive.
To investigate whether the superior model in the test group was also valid for the calibration group, we conducted a multisample analysis within LISREL. The significance of group differences was tested through the imposition of crossgroup equality constraints on the model regression coefficients. That is, only those coefficients which represent the superior model structure were constrained, and the other coefficients (e.g. error and residual terms) were allowed to be re-estimated. The rationale for this approach is that theoretical weights are assumed to be identical for every individual in the population, and therefore, would not be affected by sampling (cf. MacCallum and Tucker, 1991). Accordingly, the chi-square of the multisample model with its coefficients constrained to equality was then contrasted against that of the unconstrained multisample model. A likelihood ratio test showed that the difference between the two chi-squares was not significant (Dx2(45) = 47.16, p = n.s.), which implies that the null hypothesis could not be rejected. This means that the estimates of the superior model parameters did not vary across both subsamples. So, the interactive model in the test group was valid for the calibration group, too.
4. Discussion The present study investigated the moderating effects of matching and non-matching job resources in the relation between emotional job demands and employee health/wellbeing. Next to the postulated moderating effect of job resources on the relation between emotional job demands and health/well-being outcomes (e.g. see Grandey et al., 2005), we assumed that these effects were more likely to occur in case of a match between specific kinds of job demands and certain forms of job resources (cf. de Jonge and Dormann, 2006) than in case of a non-match between demands and resources. Specifically, we hypothesized that emotional job resources rather than cognitive job resources moderate the association between emotional job demands and health/well-being outcomes. Our hypotheses were tested in a sample of health care workers as prototypes of human service workers who have to perform emotional labour (Mann and Cowburn, 2005). The first hypothesis regarding the moderating role of job resources was partly confirmed. Results in the test group indeed showed that emotional job resources were able to moderate the relation between emotional job demands and our three indicators of employee health and well-being: emotional exhaustion, employee creativity, and work motivation. The second hypothesis regarding the likelihood of
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
moderating effects for emotional job resources was also partly confirmed. Moderating effects were detected more often in case of matching, emotional, job resources than in case of non-matching, cognitive, job resources, which was also confirmed by LISREL’s equality constraints test. Overall, we found four out of 18 interactions (i.e. 22.2%) to be significant, which is considerably more than could be expected by chance. Among these, three interactions were of matching kind (i.e. emotional job demands in combination with emotional job resources), while one interaction was of non-matching kind (i.e. emotional job demands in combination with cognitive job resources). The total amount of variance explained (ranging from R2 = .25 to R2 = .33) was satisfying, given methodological considerations as well as the multi-causal aetiology of employee health/well-being (cf. Semmer et al., 1996). Finally, cross-validation of results showed that these findings were robust in both subsamples. The significant moderating effect of emotional job resources (i.e. emotional support) on emotional exhaustion is consistent with earlier research (e.g., de Jonge and Dormann, 2006; Grandey et al., 2005; van Vegchel et al., 2004). For instance, in a two-wave panel study among 267 health care workers, de Jonge and Dormann found a time-lagged moderating effect of emotional support on the relation between emotional job demands and emotional exhaustion. The current findings also add to the literature on burnout. More recent research on burnout has shown that emotional job demands may lead to emotional exhaustion (cf. Zapf et al., 2001; Zapf and Holz, 2006). Although research on emotional job demands has been proliferating throughout the last decade, the question remains in what way the negative impact of this type of demands on employees’ health can be combated best. So, based upon our results, burnout (in terms of its key variable emotional exhaustion) seems to be a response to emotionally demanding tasks, which will be amplified in case particular job resources such as emotional support are absent. To put it differently, job resources such as emotional support could compensate for resources that are lost through meeting the requirements of emotional job demands, thereby reducing stress-reactions (see also Grandey, 2000; Totterdell and Holman, 2003). Furthermore, the current findings are also in line with recent research that revealed beneficial consequences of emotional job demands (e.g. Zapf and Holz, 2006), and positive emotions in general (e.g. Frederickson, 2001; Isen, 2000). Specifically, when health care workers reported high emotional support from colleagues and supervisors, emotional job demands were positively associated with creativity and work motivation. However, this was only true for specific emotional job demands, i.e. confrontation with awkward and aggressive patients or clients. Apparently, emotional job resources seem to be effective only in case of more specific emotional job demands, and not in case of more general types of emotional job demands. Evidence suggests that being engaged in several demanding tasks simultaneously may decrease the chance of success at each
1467
of them if an employee is using the same, limited, job resource (Vohs et al., 2005). Given this idea, it could be that employees effectively use (limited) emotional job resources in case of specific emotional job demands rather than in case of more general emotionally demanding tasks. In contrast, only one out of 9 interactions between emotional job demands and cognitive job resources (i.e. job control) was significant. More specifically, emotional job demands were positively associated with work motivation for health care workers who reported high job control. This finding is in line with Karasek’s (1998) well-known demandcontrol research, which assumes work motivation to be highest in case of high job demands and high job control. The availability of control at work makes it easier to cope with emotional job demands, which in turn could lead to experiencing motivation and challenge instead of stress. To conclude, the current moderating findings are in line with the proposed ranking order of de Jonge and Dormann (2006). Matching job resources seem to be successful in combating demands more often than non-matching job resources. Finally, main effects of the three types of emotional job demands showed positive associations with positive wellbeing, too. A possible interpretation of these associations is that many health care workers deliberately choose for this kind of (emotion) work (Zapf and Holz, 2006). As they like this kind of work, they consider the job demands challenging accordingly. However, our study showed in general that the pay-off in terms of positive well-being is stronger in case of (the availability of) matching job resources. The present findings should also be discussed in terms of strengths and limitations of the study. A particular methodological strength was the use of structural equation modeling in which we investigated three different outcomes simultaneously, hereby eliminating conceptual overlap and minimizing chance capitalization (i.e. reduction of Type I errors). In addition, the robustness of the regression model by means of cross-validation was proven as well. A first limitation concerns the cross-sectional design of the study and its reliance on only self-report measures. Although we suggested a particular causal order of the variables, other causal directions or even reciprocal relations could be possible as well (cf. Grandey, 2003). Future longitudinal studies should investigate this kind of relations more profoundly. Added to this, common method variance might have played a role, although recent studies showed that this influence is not as high as could be expected (cf. Spector, 2006). A second concern is the modest amount of variance explained by the interaction terms. However, in our opinion this does neither negate the theoretical importance nor mean that the interaction effects have little substantive significance. The results are important because the size of any interaction effect is attenuated by measurement error when interaction terms are formed by multiplying variables to form cross-product terms as is required in regression analysis (Aiken and West, 1991). Further, though our results
1468
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469
suggest that investigating the moderating role of matching job resources is a useful approach for studying emotional job demands and employee health/well-being, future research should examine other types of resources as well. For instance, matching personal resources such as emotional intelligence (Giardini and Frese, 2006) and emotion-focused coping (Shimazu et al., 2008) could moderate the relation between emotional job demands and health/well-being outcomes, too. A final limitation is that the present study addressed the matching hypothesis in a limited subset of conditions only. Future studies should encompass all possible conditions to enable full support for the matching hypothesis to be obtained (cf. de Jonge and Dormann, 2006). The current findings have some practical implications for health care workers. Employees who are often confronted with high emotional job demands and have low job resources seem to be at risk for burnout. Enhancing specific job resources, such as emotional support from colleagues and supervisors, provide self-regulatory mechanisms to deal with emotional job demands, which enable a reduction in emotional exhaustion. The results also suggest that job resources that prevent demands for emotional self-regulation from becoming too high (i.e. emotional support by colleagues or supervisors and, to a lesser extent, job control), might actually contribute to more employee creativity and more work motivation, too. To conclude, work-related interventions on emotional demands in health care work should primarily focus on specific, emotional, job resources to diminish burnout and to stimulate positive well-being. Providing health care workers with more, preferably matching, job resources could make emotional job demands less stressful, and even stimulating and challenging.
References Aiken, L.S., West, S.G., 1991. Multiple Regression: Testing and Interpreting Interactions. Sage Publications, Newbury Park, CA. Bentler, P.M., Bonett, D.G., 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88, 588–606. Brotheridge, C.M., Grandey, A.A., 2002. Emotional labor and burnout: comparing two perspectives of ‘‘people work’’. J. Vocat. Behav. 60, 17–39. Brotheridge, C.M., Lee, R.T., 2002. Testing a conservation of resources model of the dynamics of emotional labor. J. Occup. Health Psychol. 7, 57–67. Browne, M.W., Cudeck, R., 1993. Alternative ways of assessing model fit. In: Bollen, K.A., Long, J.S. (Eds.), Testing Structural Equation Models. Sage Publications, Newbury Park, CA, pp. 136–162. Cordes, C.L., Dougherty, T.W., Blum, M., 1997. Patterns of burnout among managers and professionals: a comparison of models. J. Organ. Behav. 18, 685–701. Dawson, J.F., Richter, A.W., 2006. Probing three-way interactions in moderated multiple regression: development and applica-
tion of a slope difference test. J. Appl. Psychol. 91 (4), 917– 926. de Jonge, J., Dollard, M.F., Dormann, C., Le Blanc, P.M., Houtman, I.L.D., 2000b. The Demand-Control Model: specific demands, specific control, and well-defined groups. Int. J. Stress Manage. 7 (4), 269–287. de Jonge, J., Dormann, C., 2003. The DISC Model: demandinduced strain compensation mechanisms in job stress. In: Dollard, M.F., Winefield, A.H., Winefield, H.R. (Eds.), Occupational Stress in the Service Professions. Taylor & Francis, London, pp. 43–74. de Jonge, J., Dormann, C., 2006. Stressors, resources, and strain at work: a longitudinal test of the Triple Match Principle. J. Appl. Psychol. 91 (6), 1359–1374. de Jonge, J., Dormann, C., van den Tooren, M., 2008. The DemandInduced Strain Compensation Model: renewed theoretical considerations and empirical evidence. In: Na¨swall, K., Hellgren, J. (Eds.),The Individual in the Changing Working Life. Cambridge University Press, Oxford, pp. 67–87. de Jonge, J., Janssen, P.P.M., van Breukelen, G.J.P., 1996. Testing the Demand-Control-Support Model among health care professionals: a structural equation model. Work Stress 10 (3), 209– 224. de Jonge, J., Reuvers, M.M.E.N., Houtman, I.L.D., Bongers, P.M., Kompier, M.A.J., 2000a. Linear and nonlinear relations between psychosocial job characteristics, subjective outcomes, and sickness absence: baseline results from SMASH. J. Occup. Health Psychol. 5 (2), 256–268. Frederickson, B.L., 2001. The role of positive emotions in positive psychology: the broaden and build theory of positive emotions. Am. Psychol. 56, 218–226. George, J.M., Zhou, J., 2001. When openness to experience and conscientiousness are related to creative behaviour: an interactional approach. J. Appl. Psychol. 86 (3), 513–524. Giardini, A., Frese, M., 2006. Reducing the negative effects of emotion work in service occupations: emotional competence as a psychological resource. J. Occup. Health Psychol. 11 (1), 63–75. Grandey, A.A., 2000. Emotion regulation in the workplace: a new way to conceptualize emotional labor. J. Occup. Health Psychol. 5, 95–110. Grandey, A.A., 2003. When the ‘‘show must go on’’: surface acting and deep acting as determinants of emotional exhaustion and peer-rated service delivery. Acad. Manage. J. 46, 86–96. Grandey, A.A., Fisk, G.M., Steiner, D.D., 2005. Must ‘service with a smile’ be stressful? The moderating role of personal control for American and French employees. J. Appl. Psychol. 90 (5), 893– 904. Gross, J., 1998. Antecedent- and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. J. Pers. Soc. Psychol. 74 (1), 224–237. Hobfoll, S.E., 1989. Conservation of resources: a new attempt at conceptualizing stress. Am. Psychol. 44, 513–524. Hobfoll, S.E., 2002. Social and psychological resources and adaptation. Rev. Gen. Psychol. 6 (4), 307–324. Hunter, B., Smith, P., 2007. Emotional labour: just another buzz word? Int. J. Nurs. Stud. 44, 859–861. Isen, A.M., 2000. Positive affect and decision making. In: Lewis, M., Haviland-Jones, J. (Eds.), Handbook of Emotions. 2nd ed. Guilford, New York, pp. 417–435. Jo¨reskog, K.G., So¨rbom, D., 1996. LISREL 8: User’s Reference Guide. In: 2nd ed.. Scientific Software International, Chicago.
J. de Jonge et al. / International Journal of Nursing Studies 45 (2008) 1460–1469 Kahn, R.L., Byosiere, P., 1992. Stress in organizations. In: Dunnette, M.D., Hough, L.M. (Eds.), Handbook of Industrial and Organizational Psychology, 2nd ed., vol. 3. Consulting Psychologists Press, Palo Alto, CA, pp. 571–650. Karasek, R.A., 1985. Job Content Instrument: Questionnaire and User’s Guide. Department of Industrial and Systems Engineering, University of Southern California, Los Angeles. Karasek, R.A., 1998. Demand/Control Model: a social, emotional, and physiological approach to stress risk and active behaviour development. In: Stellman, J.M. (Ed.), Encyclopaedia of Occupational Health and Safety. ILO, Geneva, pp. 34.6–34.14. Kline, R.B., 1998. Principles and Practice of Structural Equation Modelling. The Guilford Press, New York. Kruml, S.M., Geddes, D., 2000. Exploring the dimension of emotional labor. Manage. Commun. Quart. 14 (1), 8–49. Le Blanc, P.M., de Jonge, J., Schaufeli, W.B., 2000. Job stress and health. In: Chmiel, N. (Ed.), Introduction to Work and Organisational Psychology: A European Perspective. Blackwell Publishers, Oxford, pp. 148–177. Lekander, M., 2002. Ecological immunology: the role of the immune system in psychology and neuroscience. Eur. Psychol. 7, 98–115. MacCallum, R.C., Tucker, L.R., 1991. Representing sources of error in the common-factor model: implications for theory and practice. Psychol. Bull. 109, 502–511. Maslach, C., 1978. The client role in staff burn-out. J. Soc. Issues 34 (4), 111–124. Maslach, C., Jackson, S.E., 1986. Maslach Burnout Inventory: Manual, 2nd ed. Consulting Psychologists Press, Palo Alto, CA. Mann, S., Cowburn, J., 2005. Emotional labour and stress within mental health nursing. J. Psychiat. Ment. Health Nurs. 12, 154– 162. Mitchell, D., Smith, P., 2003. Learning from the past: emotional labour and learning disability nursing. J. Learn. Disabil. 7, 109– 117. Montague, P.R., 1996. The resource consumption principle: attention and memory in volumes of neural tissue. In: Proceedings of the National Academy of Sciences of the United States of America 93. pp. 3619–3623. Morris, J.A., Feldman, D.C., 1996. The dimensions, antecedents, and consequences of emotional labour. Acad. Manage. Rev. 21 (4), 986–1010. Morris, J.A., Feldman, D.C., 1997. Managing emotions in the workplace. J. Manage. Issues 9 (3), 257–274. Pugliesi, K., 1999. The consequences of emotional labor: effects on work stress, job satisfaction, and well-being. Motiv. Emotion 23, 125–154.
1469
Rutter, D.R., Fielding, P.J., 1988. Sources of occupational stress: an examination of British prison officers. Work Stress 2 (4), 291– 299. Schaufeli W.B., van Dierendonck, D., 2000. Utrechtse Burnout Schaal (UBOS): handleiding [Manual Utrecht Burnout Scale]. Swets & Zeitlinger, Lisse. Semmer, N., Zapf, D., Greif, S., 1996. ‘Shared job strain’: a new approach for assessing the validity of job stress measurements. J. Occup. Organ. Psychol. 69, 293–310. Shimazu, A., de Jonge, J., Irimajiri, H., 2008. Lagged effects of active coping within the Demand-Control Model: a three-wave panel study among Japanese employees. Int. J. Behav. Med. 1 (1), 11–20. Spector, P.E., 2006. Method variance in organizational research: truth or urban legend? Organ. Res. Methods 9, 221–232. Thierry, Hk., 1998. Motivation and satisfaction. In: Drenth, P.J.D., Thierry, Hk., de Wolff, Ch.J. (Eds.), Handbook of Work and Organizational Psychology, 2nd ed., vol. 4. Psychology Press, Hove, pp. 253–289. Totterdell, P., Holman, D., 2003. Emotion regulation in customer service roles: testing a model of emotional labor. J. Occup. Health Psychol. 8 (1), 55–73. Vancouver, J.B., 2000. Self-regulation in organizational settings: a tale of two paradigms. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (Eds.), Handbook of Self-regulation. Academic Press, San Diego, CA, pp. 303–341. van Vegchel, N., de Jonge, J., Meijer, T., Hamers, J.P.H., 2001. Different effort constructs and effort-reward imbalance: effects on employee well-being in ancillary health care workers. J. Adv. Nurs. 34 (1), 128–136. van Vegchel, N., de Jonge, J., So¨derfeldt, M., Dormann, C., Schaufeli, W., 2004. Quantitative versus emotional demands among Swedish human service employees: moderating effects of job control and social support. Int. J. Stress Manage. 10 (1), 21–40. Vohs, K.D., Baumeister, R.F., Ciarocco, N.J., 2005. Self-regulation and self-presentation: regulatory resource depletion impairs impression management and effortful self-presentation depletes regulatory resources. J. Pers. Soc. Psychol. 88 (4), 632–657. Zapf, D., 2002. Emotion work and psychological well-being: a review of the literature and some conceptual considerations. Hum. Resour. Manage. Rev. 12, 237–268. Zapf, D., Holz, M., 2006. On the positive and negative effects of emotion work in organizations. Eur. J. Work Organ. Psychol. 15 (1), 1–28. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., Holz, M., 2001. Emotion work and job stressors and their effects on burnout. Psychol. Health 16, 527–545.