Stressors, Resources, and Strain at Work: A Longitudinal Test of the Triple-Match Principle

Transcription

1 Journal of Applied Psychology Copyright 2006 by the American Psychological Association 2006, Vol. 91, No. 5, /06/$12.00 DOI: 137/ Stressors, Resources, and Strain at Work: A Longitudinal Test of the Triple-Match Principle Jan de Jonge Utrecht University and Eindhoven University of Technology Christian Dormann Johann Wolfgang Goethe University Frankfurt/Main and Johannes Gutenberg University Mainz Two longitudinal studies investigated the issue of match between job stressors and job resources in the prediction of job-related strain. On the basis of the triple-match principle (TMP), it was hypothesized that resources are most likely to moderate the relation between stressors and strains if resources, stressors, and strains all match. Resources are less likely to moderate the relation between stressors and strains if (a) only resources and stressors match, (b) only resources and strains match, or (c) only stressors and strains match. Resources are least likely to moderate the relation between stressors and strains if there is no match among stressors, resources, and strains. The TMP was tested among 280 and 267 health care workers in 2 longitudinal surveys. The likelihood of finding moderating effects was linearly related to the degree of match, with 33.3% of all tested interactions becoming significant when there was a triple match, 16.7% when there was a double match, and 0.0% when there was no match. Findings were most consistent if there was an emotional match or a physical match. Keywords: job stressors, job resources, job stress, triple-match principle, panel study The current article presents some theoretical predictions on cognitive, emotional, and physical processes that guide how certain forms of job resources relate to specific kinds of job stressors and strains. Broadly speaking, employee strain may be related to two core aspects of any job: stressors and resources (Frese & Zapf, 1994; Schaufeli & Bakker, 2004). Job stressors refer to the degree to which the work environment contains stimuli that require sustained cognitive, emotional, or physical effort (cf. Jones & Fletcher, 1996). Job resources are conceptually similar to coping options; they can be broadly conceptualized as a kind of energetic reservoir that an individual taps when he or she has to cope with job stressors (cf. Hobfoll, 1989, 2002). Basically, employees who experience intense cognitive, emotional, or physical job stressors are likely to experience strains unless they have the benefit of abundant external cognitive, emotional, or physical resources. Thus, job resources mitigate the impact of stressors on strains. In addition, job resources may have a main effect on strains. For instance, social support (a resource) may mitigate the effects of conflicts (a stressor) on anxiety (strain); support may directly reduce anxiety as well. Jan de Jonge, Department of Social and Organizational Psychology and Research Institute Psychology and Health, Utrecht University, Utrecht, the Netherlands, and Subdepartment of Human Performance Management, Department of Technology Management, Eindhoven University of Technology, Eindhoven, the Netherlands; Christian Dormann, Johann Wolfgang Goethe University Frankfurt/Main, Frankfurt, Germany, and Department of Work, Organizational, and Economic Psychology, Institute of Psychology, Johannes Gutenberg University, Mainz, Germany. Correspondence concerning this article should be addressed to Jan de Jonge, Subdepartment of Human Performance Management, Department of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, the Netherlands. j.d.jonge@tm.tue.nl Several theoretical frameworks have been advanced to explain the role of job resources in the stressor strain relation (cf. Cooper, 1998; Kahn & Byosiere, 1992). Most of these frameworks propose additive and interactive effects of job stressors and job resources (Cooper, Dewe, & O Driscoll, 2001). Additive effect models assume stressors and resources to independently impact strains, whereas interactive effect models propose resources to moderate the relation between stressors and strains. Prominent examples of interactive effect models are Karasek s (1979) demand-control model, the buffering model of social support (Viswesvaran, Sanchez, & Fisher, 1999), and Siegrist s (1996) effort reward imbalance model. Note that the terms interactive effect and moderating effect are used synonymously. Most existing theories provide the same one or two sorts of general explanation as to why moderating effects are expected. The mechanisms proposed loosely relate to primary and secondary appraisal processes (Lazarus & Folkman, 1984). In terms of primary appraisal, resources weaken stressor strain relations because stressors are not appraised as such. For instance, when insolent customers are around, emotional exhaustion is a likely consequence. However, when employees are adequately trained to handle difficult customers, they may not appraise their customers as particularly stressful. Consequently, strain will be lower. In terms of secondary appraisal, resources provide means to cope with stressors. For example, when a job requires an employee to lift heavy loads, back pain is a likely stress reaction. However, it is more or less obvious that this only applies if the heavy loads have to be lifted by a single employee, whereas there may be little problem if technical equipment or colleagues provide assistance whenever lifting of heavy loads is required. Both sorts of explanations imply that resources interact with stressors in the prediction of strain. Because job stressors often cannot be reduced, the idea to increase job resources instead to combat strain is appealing. Unfortunately, the empirical evidence is rarely in line with this proposition. Whereas there is little debate on additive effects of 1359

2 1360 RESEARCH REPORTS stressors and resources on strains, evidence for synergistic effects mirrored in moderating effects has received mixed support, at best (cf. Cooper et al., 2001; van der Doef & Maes, 1999; van Vegchel, de Jonge, Bosma, & Schaufeli, 2005; Viswesvaran et al., 1999). There is at least one important reason why many studies failed to find moderating effects. Early research tended to treat stressors and resources as global and unidimensional constructs, obscuring the differential impact of specific components (e.g., Karasek, 1979; Viswesvaran et al., 1999). Subsequently, some researchers have argued that the associations among stressors, resources, and strains depend on the respective types of stressors, resources, and strains (e.g., Cohen & Wills, 1985; Cutrona & Russell, 1990; Sargent & Terry, 1998; Viswesvaran et al., 1999; Wall, Jackson, Mullarkey, & Parker, 1996). In particular, researchers have proposed that specific stressors and specific resources should match to show moderating effects in the prediction of strain. This line of thinking is referred to as the matching hypothesis: If the type of available resources (e.g., emotional) corresponds to existing stressors (e.g., irate customers), then those resources are best able to mitigate the effects of those stressors, and less strain (especially manifested through unwanted emotions) will result (cf. Viswesvaran et al., 1999). To put it differently, the discovery of optimal stressor resource combinations could help researchers to better understand how specific stressors threaten and how specific resources protect employees from developing strain or even enhance their well-being (cf. Cutrona & Russell, 1990). The current study adds value to existing theory and research by extending the idea of match in two respects. First, we propose that the idea of match should extend from stressors and resources to include matching strains, too. Second, we propose that the likelihood of finding moderating effects depends on the modality of human psychological processing: When stressors, resources, and strains are all of the same kind (either cognitive, emotional, or physical), moderating effects are most likely. Matching Hypothesis As far as job stressors are concerned, three types can be distinguished: (a) cognitive stressors, which impinge primarily on the brain processes involved in information processing (Hockey, 2000); (b) emotional stressors, which refer primarily to the effort needed to deal with organizationally desired emotions during interpersonal transactions (Morris & Feldman, 1996); and (c) physical stressors, which are primarily associated with the musculoskeletal system (i.e., motoric and physical aspects of behavior; cf. Hockey, 2000). Similarly, job resources may have a cognitive informational component (e.g., colleagues or computers providing information and control at work), an emotional component (e.g., colleagues providing sympathy and affection), and a physical component, such as instrumental help from colleagues or ergonomic aids (cf. Cutrona & Russell, 1990). Finally, in a similar vein, like stressors and resources, strains may also comprise cognitive, emotional, and physical dimensions (cf. Koslowski, 1998; Le Blanc, de Jonge, & Schaufeli, 2000). For instance, lack of active learning and low creativity represent cognitively laden strains (e.g., Taris & Kompier, 2005), emotional exhaustion (burnout) represents an emotionally laden strain variable (e.g., Maslach & Jackson, 1986), and physical health complaints can be reasonably assumed to mainly reflect bodily sensations. The idea of match between stressors and resources is about 20 years old. Cohen and McKay (1984; Cohen & Wills, 1985) proposed interaction effects to be highest when there is a match between specific kinds of stressors and certain forms of social support, which represent a resource. For instance, instrumental aid from colleagues may help reduce strain caused by heavy lifting. We call this double match (because two out of three constructs among the stressor resource strain triple match) of common kind (because this kind of match has been commonly proposed). The double match of common kind hypothesis has received some support (e.g., Terry & Jimmieson, 1999; Terry, Nielsen, & Perchard, 1993), but the overall evidence is mixed, at best. To better understand the mixed and seemingly inconsistent evidence regarding the double match of common kind, researchers have extended the idea of match: Frese (1999) argued that the third component of the stressor resource strain triple (i.e., the strain component) should be considered as a source of match or nonmatch, too. That is, here the match occurs between either job resources or job stressors, on the one hand, and strain, on the other, which we refer to as double match of extended kind. For instance, social types of resources (e.g., support from colleagues) or social types of stressors (e.g., public speaking) are proposed to cause social types of strain or dysfunction (e.g., social anxiety and irritation), whereas less social areas of strain (e.g., somatic symptoms; see also Dormann & Zapf, 1999) are not a likely consequence. When a double match of extended kind is investigated, it is still a theoretical challenge to predict the type of resource mitigating this relation (e.g., Viswesvaran et al., 1999). However, on the basis of the ideas we have mentioned regarding double match of common kind, double match of extended kind, and the multidimensionality of constructs, the triple-match principle (TMP) was developed (de Jonge & Dormann, 2003). The TMP proposes that the strongest interactive effects of stressors and resources are observed when stressors, resources, and strains are based on qualitatively identical dimensions. For instance, emotional support from colleagues is most likely to moderate (i.e., mitigate) the relation between emotional stressors (e.g., insolent customers) and emotional exhaustion. The TMP suggests not only that stressors and resources should match (i.e., double match of common kind; cf. Cohen & Wills, 1985) or that resources should match strains (i.e., double match of extended kind; cf. Frese, 1999) but also that stressors should match strains. For instance, insolent customers are more likely to cause emotional disorders than deficits in active learning or physical complaints. As the theoretical basis of the TMP we propose homeostatic regulation processes. In the area of immune functioning, homeostatic regulation processes are known to cause an activation of internal resources (e.g., hormones, neuropeptides, and cytokines) when particular stressors occur (Lekander, 2002). Through evolutionary processes, the release of functional, matching internal resources is more likely than the release of dysfunctional, nonmatching immune parameters (Lekander, 2002). The idea of functional homeostatic regulation can be transferred to organizational settings (e.g., Vancouver, 2000). Functional homeostatic regulation at work involves self-regulation processes to cope with states of psychological imbalance induced by stressors at work (cf. Pomaki & Maes, 2002). Thus, similar to homeostatic regulation, we propose individuals to activate functional, corresponding jobrelated resources to mitigate the effects of specific stressors. Ba-

3 RESEARCH REPORTS 1361 sically, a match exists if external resources provide similar functions as do internal resources when an individual is combating stress. For example, when emotional problems with customers arise (e.g., insolent customers), emotional self-regulation capability is likely to be quite helpful. When individuals lack this internal resource, emotionally supportive colleagues may do an almost similarly effective job. Even if supportive colleagues are unavailable, other job resources can be useful to some extent for instance, information provided by the supervisor about how to handle a particular problem customer. We propose that people deal with stressors first using easily available internal resources. If these resources are depleted, a demand for matching external resources is created, which may be of similar use (cf. Hobfoll, 1989, 2002). If such matching external resources are not available or if they are depleted, individuals search for other resources. They will then use even those resources that do not closely correspond to the stressor. Consequently, internal, matching resources are most powerful in combating stressors (if they exist at all), followed by corresponding external resources, followed by nonmatching external resources. Hypothesis The previous line of reasoning implies that interaction effects between stressors and resources should not exclusively appear for constructs reflecting identical dimensions. Rather, the effects should occur in general, but they should be more pronounced and should occur with greater likelihood among constructs from identical dimensions. Thus, next to so-called triple-match interactions (i.e., identical job stressors, resources, and strains), double-match interactions could exist as well. These double-match interactions could have different forms that is, (a) identical stressors and resources in the prediction of a qualitatively different, noncorresponding strain (double match of common kind), and (b) different kinds of stressors and resources in the prediction of a corresponding strain for either stressors or resources (double match of extended kind). Finally, so-called nonmatching interactions have little likelihood to occur (i.e., different kinds of stressors and resources in the prediction of a noncorresponding strain). In line with the conceptual discussion, we derive the following hypothesis: Hypothesis 1: The likelihood of interaction effects between stressors and resources in the prediction of strain increases as the number of matching variables increases. In particular, resources are most likely to mitigate the relation between stressors and strain if resources, stressors, and strains all match (i.e., triple match). Job resources are less likely to mitigate the relation between stressors and strain if (a) only resources and stressors match (i.e., double match of common kind), (b) only resources and strains match (i.e., double match of extended kind), or (c) only stressors and strains match (i.e., double match of extended kind). Job resources are least likely to mitigate the relations between job stressors and strain if there is no match among stressors, resources, and strains. The Present Study We designed the present study to achieve several objectives. First, we aimed at systematically examining the issues of triple match, double match, and nonmatch, using two 2-year panel studies. Second, we used a longitudinal model, which enables us to analyze lagged relations among job stressors, job resources, and job-related strain. Researchers commonly investigate lagged effects when investigating interaction effects using panel surveys; several methodological problems are mitigated, such as mutual contamination of constructs and problems related to third variables (cf. Zapf, Dormann, & Frese, 1996). Finally, we investigated several strains simultaneously, and we regressed each type of strain at Time 2 on all types of strain at Time 1, which eliminated conceptual overlap. For instance, by regressing Time 2 emotional exhaustion on Time 1 physical complaints, we have decontaminated the residual variance in Time 2 emotional exhaustion from potential physical symptoms. Method Procedure and Participants Analyses were based on longitudinal data gathered in two different studies. The first study was a two-wave panel survey conducted among employees of a Dutch health care foundation, including six different nursing homes for older adults. All employees were invited to participate on a voluntary basis. Questionnaires contained an administration number for second-round identification. For reasons of confidentiality, we knew the code of the administration number, and questionnaires could be returned in sealed envelopes. This self-reported questionnaire was distributed on two occasions: in April 2000 (Time 1) and in April 2002 (Time 2). In this way, possible seasonal fluctuations were controlled for. Moreover, Dormann and Zapf (2002) showed a time interval of 2 years to be most appropriate to demonstrate effects of job conditions on employee strain. At Time 1, 614 employees received a questionnaire, which 471 employees returned (response rate of 76.7%). At Time 2, 662 out of 918 returned the questionnaire, resulting in a response rate of 72.1%. Note that Time 2 questionnaires went out to everyone in the sample, regardless of whether they had completed and returned a Time 1 questionnaire. The final panel of the first study consisted of 280 persons (45.6% of the initial group) who responded to the questionnaire on both occasions. The demographic characteristics of the respondents in the final panel of the first study showed that the ages ranged from 19 to 58 years (M 42.3, SD 8.9). Most respondents were female (83.6%). The mean working time was 13.6 years (SD 7.5). Respondents worked 26.5 hr a week (SD 8.2) on average; 47.0% worked regular hours, and 53.0% worked variable hours. The most common job category of respondents was nurse or nurse s aide (49.3%). The remaining respondents were active in administration (30.4%), medical services (8.6%), management (6.4%), and other occupations (5.3%). Study 2 was also a two-wave panel study among employees of another Dutch health care foundation, encompassing six different nursing homes. The procedure was quite similar to the procedure in Study 1: In January 2001 (Time 1) and in January 2003 (Time 2), self-report questionnaires were distributed among all employees. At Time 1, 405 out of 554 employees filled out the self-report questionnaires, which is a response rate of 73.1%. At Time 2, 420 out of 624 persons participated (67.3% response rate). The final panel consisted of 267 persons, or 48.2% of the initial group. Most of the respondents were female (91.4%). The most common job category was nurse and nurse s aide (62.9%). The remaining respondents were active in administration (15.7%), medical services (9.0%), management (4.5%), and other jobs (7.9%). Ages ranged from 18 to 64 years (M 41.0, SD 8.7). The mean working time was 11.3 years (SD 7.5). Respondents worked 23.9 hr a week (SD 8.7) on average; 45.9% worked regular hours, and 54.1% worked variable hours. To determine whether attrition might have biased results, we used logistic regression analyses to test whether participation at Time 2 was

4 1362 RESEARCH REPORTS related to any Time 1 variable (cf. Goodman & Blum, 1996). These two analyses produced no significant terms, indicating that the attrition was random. Measures Cognitive stressors. Cognitive stressors were assessed with two items (i.e., In the unit where I work, the work is mentally exacting, and In the unit where I work, the work is complicated ; cf. de Jonge, 1995). These items were scored on a 5-point frequency scale, ranging from 1 (never) to 5 (always). Table 1 presents the psychometric properties of the measures included as well as their correlations. Emotional stressors. Two items were used to assess emotional stressors (i.e., In your work, are you confronted with death? and In your work, are you confronted with illness and other human suffering? ; cf. de Jonge, Mulder, & Nijhuis, 1999). Items were scored similar to the previous scale. Physical stressors. Physical stressors were measured with a 3-item scale consisting of static and dynamic physical exertion (cf. de Jonge et al., 1999). An example item is In my work, I have to lift or move heavy persons or objects (more than 10 kg). Items were scored similar to the previous scales. The three stressor measures were taken from well-validated larger scales (de Jonge et al., 1999), because the current items most clearly referred to cognitive, emotional, or physical content. Time 1 correlations of the short scales with the full scales were.75 for cognitive stressors,.78 for emotional stressors, and.95 for physical stressors in Study 1 (all ps.001). Time 1 correlations in Study 2 were.71,.67, and.95 (all ps.001), respectively. Cognitive resources. Cognitive resources were assessed with a 5-item scale developed by de Jonge (1995). The scale includes items that refer to the worker s opportunities to determine a variety of task aspects, such as the method of working and the work goals. An example item is the opportunity that the work offers to determine the work goals yourself. Items were scored on a 5-point rating scale, ranging from 1 (very little) to 5 (very much). Emotional and physical resources. Emotional resources and physical resources were measured with a well-validated Dutch translation of the social support scale from Karasek s (1985) Job Content Questionnaire (de Jonge, Reuvers, Houtman, Bongers, & Kompier, 2000). Items were scored on a 4-point rating scale ranging from 1 (strongly disagree) to 4 (strongly agree). Emotional resources consisted of four items that refer to emotional support from supervisors and colleagues. An example item is My supervisor pays attention to what I am saying. Physical resources consisted of four items that refer to instrumental support from the supervisor as well as from colleagues. For example, My colleagues help to get the job done. Active learning behavior. Lack of cognitive strain was measured in terms of active learning behavior, which refers to the degree to which employees indicate that their job motivates them to learn new behavior patterns and skills or that they are keen to solve problems at their job (Karasek, 1998; Taris & Kompier, 2005). This scale consists of four items that can be scored on a 4-point frequency scale ranging from 1 (almost) never to 4 (nearly) always. For instance, At work, I view problems as puzzles that can be solved. Emotional exhaustion. We assessed emotional exhaustion as a type of emotional strain with the Dutch version (Schaufeli & van Dierendonck, 2000) of the Maslach Burnout Inventory (Maslach & Jackson, 1986). The scale contains five items with a 7-point response scale ranging from 0 (never) to 6 (always, daily). An example item is I feel emotionally drained from my work. Physical health symptoms. Four items were used to assess physical strain. They were derived from a well-validated questionnaire developed by Hildebrandt and Douwes (1991), with the possible responses ranging from 1 (no) to 3 (yes). An example item is During the past six months, did you have trouble with your low back? Table 1 Coefficient Alpha, Test Retest Reliability, and Pearson Intercorrelations of the Variables in Both Studies at Both Waves Study 2 (n 267) Study 1 (n 280) r t 1 2 r t Measure Items 1 Cognitive stressors 2.45**.46**.39**.42**.46**.52**.06.26**.03.23** **.21**.06.22**.17** 2 Cognitive resources ** **.12.19**.07.29**.06.27** **.19**.11 3 Emo stressors 2.53**.59**.54**.41**.40**.59**.52**.18**.07.28** *.16* * 4 Emo resources ** **.22**.15*.17**.13*.77**.40**.26**.15*.35** Phys stressors ** **.20**.34**.29**.14*.07.25**.20**.29**.24**.19**.27** 6 Phys resources ** **.23** **.03.34**.29**.11.31**.13*.08 7 Active learning T **.83.62**.12.19**.06.34**.22**.23**.16**.12.62** Emotional exhaustion T **.86.50**.45**.23**.34**.25**.36**.21**.13*.36**.14*.49**.26** 9 Physical symptoms T **.72.63**.06.16*.04.15*.29**.11.20**.38**.08.24**.62** 10 Active learning T **.10.22**.16*.19**.47**.15*.18**.20** Emotional exhaustion T **.14*.22**.19**.20**.18**.08.55**.33**.21**.39** 12 Physical symptoms T **.08.13*.27** **.61**.16*.46** Note. Study 1 correlations are presented above the diagonal; and Study 2 correlations are presented below the diagonal. In the case of two items, Pearson intercorrelations instead of coefficients alphas are presented. Emo Emotional; Phys Physical; T Time. p.10. * p.05. ** p.01 (two-tailed).

5 RESEARCH REPORTS 1363 Demographics. Finally, demographic characteristics such as gender, age, and occupational status (low, middle, high) were included as control variables. Analytical Strategy Data were separately analyzed for each study because variance covariance matrices of the two samples were statistically different, 2 (153) , p.01. We conducted all analyses with structural equation modeling (LISREL 8; Jöreskog & Sörbom, 1993) to simultaneously cross the three content domains (cognition, emotion, and physical/ bodily sensations) with the three conceptual domains (stressors, resources, and strain). Thus, basically, each analysis comprised nine variables (3 content areas 3 conceptual variables). Furthermore, interaction effects between stressors and resources were also included, but, because of the large number of possible interaction effects (3 stressors 3 resources 3 strains 27 interaction effects), we decided to split the analysis according to our theoretical assumptions and to analyze each data set twice. The first analysis of each data set included all triple-match effects (e.g., cognitive stressors cognitive resources 3 cognitive strain). Because all variables were analyzed simultaneously, a couple of double-match interactions of common kind had to be included, too (e.g., cognitive stressors cognitive resources 3 emotional strain). Therefore, in the first analysis, we simultaneously tested 3 triple matches and 6 double matches of common kind (i.e., 9 out of 27 possible interaction effects). The second analysis of each data set included the remaining match and nonmatch conditions. In this case, there was no match between stressors and resources at all, but double matches of stressors and strains as well as double matches of resources and strains were included (extended kind; e.g., cognitive stressors emotional resources 3 cognitive strain). Because all variables were analyzed simultaneously, a couple of nonmatch interactions had to be included, too (e.g., cognitive stressors emotional resources 3 physical strain). Therefore, in the second analysis, we simultaneously tested 12 double matches of extended kind and 6 nonmatches (i.e., 18 out of 27 possible interaction effects; we explain this further in the Results section). Lagged interaction effects were analyzed. The multiplicative variables at Time 1 were used to predict strains at Time 2. In addition, stressors, resources, and strains at Time 1 predicted each type of strain at Time 2. For example, Time 1 emotional strain not only was used to predict Time 2 emotional strain but also was used to predict Time 2 cognitive and physical strains. This represents a very conservative strategy, because true variance in one outcome variable is always removed when some conceptual overlap with another outcome variable exists. However, this also contributes to an empirical alignment of outcomes according to their conceptual meaning, and, thus, we expected this to support our theoretical predictions. In addition, we used active learning behavior as a positive outcome, whereas emotional exhaustion and physical health symptoms were negative outcomes. Hence, the expected sign of interaction effects is different for active learning behavior. 1 Finally, we note that our initial analyses included occupational status (dummy coded) as a further control. Because including the dummy variables did not affect the pattern of significant findings, we omit them from the analyses presented here. Study 1 Results Analysis 1 (triple match and stressor resource double match). Table 2 shows the results obtained from simultaneously testing triple-match effects and stressor resource double-match effects (common kind). In the first column, the independent variables are shown. The three outcomes analyzed were active learning (lack of cognitive strain), emotional exhaustion (emotional strain), and physical symptoms (physical strain). As far as the lack of cognitive strain (active learning) is concerned, no interaction effect reached significance, as shown in Table 2. For the emotional strain (emotional exhaustion), there was one significant interaction that is, a double-match interaction effect of physical stressors and physical resources. For the physical strain (physical symptoms), the expected triple-match interaction effect was significant (i.e., Physical Stressors Physical Resources). Significant interaction effects are graphically represented according to the method described by Aiken and West (1991). We chose values of the predictor variables one standard deviation below and above the mean. We then generated two simple regression lines by entering these values in the equation. The regression lines representing the double-match interaction of common kind between physical stressors and physical resources in the prediction of emotion exhaustion are shown in Figure 1. The figure indicates that the combination of high physical stressors and low physical resources led to the highest feelings of exhaustion 2 years later. Figure 1 also shows that at high levels of physical resources (plus one standard deviation), physical stressors impact on emotional exhaustion became mitigated. Next, Figure 2 shows the triple-match interaction between physical stressors and physical resources in the prediction of physical symptoms. This figure shows that, in particular, the combination of high physical stressors and low physical resources led to more physical symptoms 2 years later. Added to this, the figure also shows that at high levels of physical resources (plus one standard deviation), the impact of physical stressors on physical symptoms was reduced. Analysis 2 (nonmatch, stressor outcome double match, and resource outcome double match). The second analysis of Study 1 data investigated the remaining interaction effects not considered in Analysis 1. These were all nonmatching interaction effects as well as those double-match effects in which strains matched stressors or strains matched resources (extended kind). Among the six rows at the bottom of Table 3 showing the effects of the multiplicative interaction variables, there were three significant effects. Two effects involved a double match of extended kind. First, the interaction of physical stressors with emotional resources predicted emotional exhaustion. Second, this interaction also predicted physical symptoms. Third, there was a significant 1 It should also be noted that we used structural equation models for the purpose of simultaneously testing different types of outcomes. Several researchers have pointed out that to test for specificity of stressors resources effects, it is necessary to analyze residualized (rather than raw) outcomes, in which the effects of the other outcomes are controlled (Parkes, 1991; Terry & Jimmieson, 1999). We did not include measurement models because they would have implied unfavorable ratios of participants per estimated parameter (e.g., Bentler & Chou, 1987; Jaccard & Wan, 1996). In line with this, we did not use latent interactions based on multiple cross-products of items (e.g., Dormann & Zapf, 1999), because models including latent interactions frequently lead to estimation problems even if only a single moderating effect is investigated. Therefore, we used ordinary interactions based on single cross-products of the respective two scales involved. Note that we followed the common suggestion to compute multiplicative interaction terms out of standardized stressors and resources (cf. Aiken & West, 1991; van Vegchel, de Jonge, & Landsbergis, 2005). Also, the structural models were fully saturated because residuals among the outcome variables were allowed to correlate. Saturated models always fit perfectly, and, thus, reporting fit indexes is superfluous.

6 1364 RESEARCH REPORTS Table 2 Lagged Structural Equation Models of Active Learning Behavior, Emotional Exhaustion, and Physical Symptoms With Triple-Match and Double-Match (Common Kind) Interactions Dependent variable Active learning Time 2 Emotional exhaustion Time 2 Physical symptoms Time 2 Source B SE T B SE T B SE T Control variables Gender * * Age ** Stressors and resources Cogn stressors Cogn resources Phys stressors Phys resources Emo stressors Emo resources Time 1 outcome variables Cogn outcome (learning) Time ** Emotional exhaustion Time ** Physical symptoms Time ** **.58 Interaction effects Cogn Stressors Cogn Resources T D D Emo Stressors Emo Resources D T D Phys Stressors Phys Resources D **.14 D *.10 T Note. Study 1, N 280. Cogn cognitive; Emo emotional; Phys physical; T triple match; D double match. * p.05. ** p.01 (two-tailed). Emot. Exhaustion 2 on Phys. Stressors Phys. Resources 1 SD Emot. Exhaustion Phys. Stressors Phys. Resources + 1 SD Figure 1. Double-match interaction of common kind between physical stressors and physical resources for emotional exhaustion (Study 1). Phys. physical; Emot. emotional.

7 RESEARCH REPORTS 1365 Phys. Symptoms 2 on Phys. Stressors Phys. Symptoms 2 - Phys. Resources + 1 SD Phys. Resources 1 SD - - Phys. Stressors Figure 2. Triple-match interaction between physical stressors and physical resources for physical health symptoms (Study 1). Phys. physical. Table 3 Lagged Structural Equation Models of Active Learning Behavior, Emotional Exhaustion, and Physical Symptoms With Nonmatch or Double-Match (Extended Kind) Interactions Dependent variable Active learning Time 2 Emotional exhaustion Time 2 Physical symptoms Time 2 Source B SE T B SE T B SE T Control variables Gender Age Stressors and resources Cogn stressors Cogn resources Phys stressors Phys resources Emo stressors Emo resources Time 1 outcome variables Cogn outcome (learning) Time ** Emo exhaustion Time ** Phys symptoms Time ** **.59 Interaction effects Cogn Stressors Emo Resources D D N Cogn Stressors Phys Resources D N D Emo Stressors Cogn Resources D D N Emo Stressors Phys Resources N D D Phys Stressors Cogn Resources D *.13 N D Phys Stressors Emo Resources N **.16 D **.13 D Note. Study 1, N 280. Cogn cognitive; Emo emotional; Phys physical; D double match; N nonmatch. * p.05. ** p.01 (two-tailed).

8 1366 RESEARCH REPORTS interaction between physical stressors and cognitive resources in the prediction of emotional exhaustion. However, as predicted by the TMP, the shape of this nonmatch interaction effect ran counter to standard stress theory. The graphical representations of the first two double-match interactions of extended kind are depicted in Figures 3 and 4. Both figures indicate that the combination of high physical stressors and low emotional resources led to higher feelings of exhaustion and physical symptoms over time. Conversely, Figures 3 and 4 show that at high levels of emotional resources (plus one standard deviation), the impact of physical stressors on both kinds of health complaints was reduced when physical stressors increased. Contrarily, the graphical representation regarding the nonmatch interaction shows an opposite effect of stressors and resources (Figure 5): The combination of high physical stressors and low cognitive resources predicted the lowest feelings of exhaustion over time. Furthermore, the figure shows that the combination of high physical stressors and high cognitive resources led to the highest feelings of exhaustion 2 years later. In summary, in Study 1 we found 1 significant effect out of 3 tested triple-match interaction effects, 1 out of 6 tested doublematch interaction effects of common kind, 2 out of 12 doublematch interaction effects of extended kind, and 0 (but 1 reversed) out of 6 tested nonmatch interaction effects. In the following, we present the results of Study 2 in an identical fashion. Study 2 Analysis 1 (triple match and stressor resource double match). Table 4 shows the results we obtained from simultaneously testing triple-match effects and stressor resource double-match (common kind) effects. For the lack of cognitive strain (active learning), none of the interaction effects reached significance, as shown at the bottom of Table 4. For the emotional strain (emotional exhaustion), there was one significant interaction. This was the expected triple-match interaction effect of emotional stressors and emotional resources. For the physical strain (physical symptoms), a double-match (common kind) interaction effect was significant (i.e., Cognitive Stressors Cognitive Resources). Again, to study the shape of the interactions, we created graphical representations. Figure 6 shows the regression lines representing the triple-match interaction between emotional stressors and resources in the prediction of emotional exhaustion. As depicted in Figure 6, the combination of high emotional stressors and low emotional resources led to higher feelings of emotional exhaustion over time. In addition, Figure 6 also shows that at high levels of emotional resources (plus one standard deviation), the impact of emotional stressors on emotional exhaustion became substantially mitigated. The plot representing the double-match interaction of common kind is shown in Figure 7. This figure indicates that the combination of high cognitive stressors and low cognitive resources predicted the highest feelings of physical symptoms over time. Added to this, the combination of high cognitive stressors and high cognitive resources led to the lowest health complaints 2 years later. Analysis 2 (nonmatch, stressor outcome double match, and resource outcome double match). The second analysis of Study 2 data investigated the remaining interaction effects not considered in the first analysis. It concerns all nonmatching interaction effects as well as double-match (extended kind) effects in which strains matched stressors or strains matched resources. Emot. Exhaustion 2 on Phys. Stressors Emot. Exhaustion Emot. Resources 1 SD Emot. Resources + 1 SD Phys. Stressors Figure 3. Double-match interaction of extended kind between physical stressors and emotional resources for emotional exhaustion (Study 1). Emot. emotional; Phys. physical.

9 RESEARCH REPORTS 1367 Phys. Symptoms 2 on Phys. Stressors Emot. Resources 1 SD Phys. Symptoms Emot. Resources + 1 SD Phys. Stressors Figure 4. Double-match interaction of extended kind between physical stressors and emotional resources for physical health symptoms (Study 1). Phys. physical; Emot. emotional. Emot. Exhaustion 2 on Phys. Stressors Emot. Exhaustion 2 - Cogn. Resources + 1 SD - - Phys. Stressors Cogn. Resources 1 SD Figure 5. Nonmatch interaction between physical stressors and cognitive resources for emotional exhaustion (Study 1). Emot. emotional; Phys. physical; Cogn. cognitive.

10 1368 RESEARCH REPORTS Table 4 Lagged Structural Equation Models of Active Learning Behavior, Emotional Exhaustion, and Physical Symptoms with Triple-Match and Double-Match (Common Kind) Interactions Dependent variable Active learning Time 2 Emotional exhaustion Time 2 Physical symptoms Time 2 Source B SE T B SE T B SE T Control variables Gender * Age ** Stressors and resources Cogn stressors Cogn resources * Phys stressors Phys resources Emo stressors Emo resources Time 1 outcome variables Cogn outcome (learning) Time ** Emo exhaustion Time ** Phys symptoms Time Interaction effects Cogn Stressors Cogn Resources T D *.11 D Emo Stressors Emo Resources D *.12 T D Phys Stressors Phys Resources D D T Note. Study 2, N 267. Cogn cognitive; Emo emotional; Phys physical; T triple match; D double match. * p.05. ** p.01 (two-tailed). Emot. Exhaustion 2 on Emot. Stressors Emot. Exhaustion Emot. Resources 1 SD Emot. Resources + 1 SD - Emot. Stressors Figure 6. Triple-match interaction between emotional stressors and emotional resources for emotional exhaustion (Study 2). Emot. emotional.

11 RESEARCH REPORTS 1369 Phys. Symptoms 2 on Cogn. Stressors Phys. Symptoms 2 - Cogn. Resources 1 SD Cogn. Resources + 1 SD - - Cogn. Stressors Figure 7. Double-match interaction of common kind between cognitive stressors and cognitive resources for physical health symptoms (Study 2). Phys. physical; Cogn. cognitive. The six rows at the bottom of Table 5 show the effects of the multiplicative interaction variables; there were three significant effects. All of them involved a double match of extended kind. First, the interaction of cognitive stressors with emotional resources predicted emotional exhaustion. In particular, Figure 8 shows that the combination of high cognitive stressors and low emotional resources led to higher feelings of emotional exhaustion over time. Further, at high levels of emotional resources (plus one standard deviation), the impact of cognitive stressors on emotional exhaustion was reduced. Second, the abovementioned interaction of cognitive stressors with emotional resources predicted active learning as well. The regression lines in Figure 9 indicate that the combination of high cognitive stressors and high emotional resources led to the highest levels of active learning behavior 2 years later. In addition, Figure 9 shows that the combination of high cognitive stressors and low emotional resources predicted the lowest levels of active learning over time. Finally, the third significant interaction was between cognitive stressors and physical resources in the prediction of active learning behavior. However, the shape of this double-match interaction effect was against theoretical predictions: The plot shows a reversed effect of stressors and resources (Figure 10). In particular, Figure 10 shows that the combination of high cognitive stressors and high physical resources predicted lower levels of active learning over time. Further, the figure also shows that the combination of high cognitive stressors and low physical resources led to higher levels of active learning 2 years later. In summary, we found 1 significant out of 3 tested triple-match interactions, 1 out of 6 tested double-match interaction of common kind, 2 (and 1 reversed) out of 12 double-match interactions of extended kind, and 0 out of 6 tested nonmatch interactions. The combined pattern of interaction findings of Study 1 and 2 is summarized in Table 6. 2 The last column of this table shows that the valid percentage (i.e., interactions with a shape that conformed to stress theory in general) of significant interaction effects found was a perfect linear function of the degree of match. Discussion Inconsistencies in demonstrating interaction effects between stressors and resources represent one of the major threats to prominent models on organizational stress. The current article provides an integrative theoretical framework and empirical evidence to solve these inconsistencies. We argue that interactions between stressors and resources occur predominantly if stressors, resources, and strains address similar domains of human psychological functioning (i.e., cognitive, emotional, or physical). The evidence of the two longitudinal studies (see also Table 6) supports our hypothesis. 2 Comparison of the tables is not completely fair because of the different numbers of interactions tested. To ensure that the comparisons between Tables 2 and 3 and between Tables 4 and 5 were not biased, we conducted separate analyses for Tables 3 and 5. We tested three interactions in one analysis for each sample that is (a) Cognitive Emotional, Cognitive Physical, and Emotional Cognitive, and (b) Emotional Physical, Physical Cognitive, and Physical Emotional. These analyses led to the same pattern of significant double-match and nonmatch interactions as in the original analyses of Tables 3 and 5.

12 1370 RESEARCH REPORTS Table 5 Lagged Structural Equation Models of Active Learning Behavior, Emotional Exhaustion, and Physical Symptoms With Nonmatch or Double-Match (Extended Kind) Interactions Dependent variable Active learning Time 2 Emotional exhaustion Time 2 Physical symptoms Time 2 Source B SE T B SE T B SE T Control variables Gender * Age * Stressors and resources Cogn stressors Cogn resources * Phys stressors *.12 Phys resources Emo stressors Emo resources Time 1 outcome variables Cogn outcome (learning) Time ** Emo exhaustion Time ** Phys symptoms Time **.58 Interaction effects Cogn Stressors Emot Resources *.19 D *.19 D N Cogn Stressors Phys Resources *.22 D N D Emo Stressors Cogn Resources D D N Emo Stressors Phys Resources N D D Phys Stressors Cogn Resources D N D Phys Stressors Emo Resources N D D Note. Study 2, N 267. Cogn cognitive; Emo emotional; Phys physical; D double match; N nonmatch. * p.05. ** p.01 (two-tailed). Emot. Exhaustion 2 on Cogn. Stressors Emot. Exhaustion 2 - Emot. Resources 1 SD Emot. Resources + 1 SD - - Cogn. Stressors Figure 8. Double-match interaction of extended kind between cognitive stressors and emotional resources for emotional exhaustion (Study 2). Emot. emotional; Phys. physical.

13 RESEARCH REPORTS 1371 Active Learning 2 on Cogn. Stressors Emot. Resources + 1 SD Active Learning Emot. Resources 1 SD - Cogn. Stressors Figure 9. Double-match interaction of extended kind between cognitive stressors and emotional resources for active learning behavior (Study 2). Cogn. cognitive; Emot. emotional. Active Learning 2 on Cogn. Stressors Active Learning 2 - Phys. Resources 1 SD Phys. Resources + 1 SD - - Cogn. Stressors Figure 10. Double-match interaction of extended kind between cognitive stressors and physical resources for active learning behavior (Study 2). Cogn. cognitive; phys. physical.