on the relationship between job demands and emotional exhaustion Marije van Iersel ANR: Master s thesis Social Psychology

Transcription

1 The stress-buffering effect of job resources and the moderating role of proactive personality on the relationship between job demands and emotional exhaustion Marije van Iersel ANR: Master s thesis Social Psychology School of Social and Behavioral Sciences Tilburg University st supervisor: dr. M van den Tooren 2 nd supervisor: dr. ing. N. van de Ven 1

2 Abstract This study examined whether job resources moderate the assumed positive relationship between job demands and emotional exhaustion. Moreover, it was examined whether proactive personality moderates the assumed stress-buffering effect of job resources on the relationship between job demands and emotional exhaustion. This study made use of three key principles of the Demand-Induced Strain Compensation Model (DISC Model): multidimensionality of concepts (that distinguished between emotional-, cognitive- and physical- components for job demands, job resources, and job strain), the triple match principle (that distinguished between a triple match, a double match and no match), and the compensation principle (which assumed an interaction/stress-buffering effect of job resources on the relationship between job demands and job strain). A survey research was conducted in which online questionnaires were filled in by 155 employees of 16 different healthcare organizations. Regression analyses have supported that emotional demands are positively related to emotional exhaustion. Stress-buffering effects were found for cognitive resources and emotional resources in the relationship between cognitive demands and emotional exhaustion. Unexpectedly, no significant role was found for proactive personality in the stress-buffering effect of job resources. Limitations concern the use of a cross-sectional study and a small sample (n=155). It is recommended for future research to make use of larger samples and longitudinal designs. Other recommendations concern the examination of other moderators and other (positive) outcomes. Keywords: job demands, job resources, proactive personality, emotional exhaustion, DISC Model 2

3 Introduction Nowadays, a lot of modern organizations are changing the world of work, for example by means of work intensification, a need for flexibility, increased job insecurity, and poor work-life balance (de Jonge & Kompier, 1997; Green & McIntosh, 2001; EU-OSHA, 2007). This might result in greater demands on employees, for example in the form of a higher emotional, cognitive and/or physical workload. Recent research (CBS, 2012) has shown that 12.5% of all employees in the Netherlands suffered from burnout-complaints in The same research showed that 40% of all employees had to deal with a high workload in the same year. Of these employees, 22% suffered from burnout-complaints (compared to 7% for employees with low workload). These results give an alarm signal in that a lot of current Dutch employees are experiencing work overload and as a result experience burnout signals. Many researchers already found a positive relationship between workload and burnout (Greenglass, Burke, & Fiksenbaum, 2001; Schaufeli & Bakker, 2004; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Thus, a high workload is assumed to have detrimental effects on the health of employees. The easiest solution to this problem seems to limit the amount of demands that are imposed on employees. However, since it is for some organizations or employees not possible to reduce the workload, it might be interesting to look for other ways to minimize the detrimental effects that a high workload can have on the health of employees. This study wants to find out whether certain job characteristics (job resources) in combination with certain personality characteristics (proactive personality) are able to counteract the assumed detrimental effect that high job demands (e.g. workload) can have on job strain (e.g. burnout). So, the main focus of this study is on the relation between job characteristics, worker characteristics, and worker health and well-being. More specifically, this study aims to find out whether specific (matching) combinations of job resources (cognitive/emotional/physical) 3

4 and job demands (cognitive/emotional/physical) are more likely to counteract the detrimental effect that high job demands can have on emotional exhaustion. Furthermore, this research examines whether proactive personality moderates the stress-buffering effect of job resources on the relationship between job demands and emotional exhaustion, such that a stressbuffering effect is more likely for people who have a high proactive personality than for people with a low proactive personality. Early models about the relationship between job demands and job strain Over the last couple of years, a number of job stress models have been developed in which the relationships between job characteristics and employee health and well-being were described. Examples of these job stress models are the Demand-Control Model (Karasek, 1979), the Effort-Reward Imbalance Model (Siegrist, 1996), and the Job Demands-Resources Model (Bakker & Demerouti, 2006). All these models make use of the concept of job demands. Job demands can be described as job related aspects that require effort or skills and are associated with certain costs (Bakker & Demerouti, 2006; van den Tooren, 2010). A core assumption of the JD-R Model (Bakker & Demerouti, 2006), the D-C Model (Karasek, 1979) and the ERI Model (Siegrist, 1996) is that certain job demands (for example workload) can lead to job strain (for example emotional exhaustion). In following the reasoning of the JD-R model, for instance, it is assumed that extreme job demands can lead to constant overtaxing of (psychological/emotional/cognitive) personal resources and as a result this could lead to emotional exhaustion (Demerouti et al., 2001). This is the psychological process that is called the health impairment process (Demerouti et al., 2001). As mentioned before, there is nowadays a lot of empirical research that confirms the positive relation between certain job demands and certain types of job strain (Greenglass et al., 2001; Schaufeli & Bakker, 2004; 4

5 Demerouti et al., 2001; Bakker, Demerouti, & Verbeke, 2004; Schaufeli, Bakker, & van Rhenen, 2009; Hakanen, Schaufeli, & Ahola, 2008; Crawford, LePine, & Rich, 2010). The buffering effect of job resources Next to the suggested direct relationship between job demands and burnout complaints, the D- C Model, the ERI Model and the JD-R Model also predict an interactive role for job resources. Job resources are job aspects that might be used to deal with job demands and the associated costs, achieve work goals, or might stimulate growth, learning and development (Bakker & Demerouti, 2006; van den Tooren, 2010). These above mentioned models (e.g. the D-C Model, the ERI Model and the JD-R Model) are so called interactive effect models in which it is proposed that job resources moderate the relationship between job demands and job strain. This means that certain job resources (e.g. job decision latitude in the D-C Model, rewards in the ERI Model and different types of job resources in the JD-R Model) can counteract the detrimental effect that high job demands can have on the development of job strain (e.g. burnout). So the ERI Model (Siegrist, 1996) assumes that high rewards can counteract the positive relationship between effort (either intrinsic or extrinsic) and job strain. The D-C Model (Karasek, 1979) states that job decision latitude (a job resource) can mitigate the detrimental effect that high job demands have on mental strain. The JD-R Model elaborates on this idea by including many other job resources that can buffer the positive relationship between job demands and strain (Bakker & Demerouti, 2006). For example, autonomy, feedback, support (e.g. social support and/or support from supervisor) and job security are frequently mentioned job resources of the JD-R Model (Demerouti et al., 2001; Bakker & Demerouti, 2006, 2008, 2011). In sum, these three models predict that certain job resources can buffer the adverse effect of high job demands on worker health and well-being. This is also known as the stress- 5

6 buffering effect. However, empirical research does not provide a decisive answer on the question whether there exists a stress buffering effect of job resources on the relation between job demands and job strain. For example, de Lange, Taris, Kompier, Houtman, and Bongers (2003) found only modest support for the idea that the combination of high demands and low control results in job strain. Bakker, Demerouti, and Euwema (2005) confirmed that job resources played a role in buffering the impact of several job demands on burnout for eighteen out of 32 possible two-way interactions (between job demands and job resources). Finally, other researchers supported for 21 out of 32 (66%) of possible two-interactions that job resources buffered the relationship between job demands and burnout (Xanthopoulou et al., 2007). This lack of convergence about the existence of stress-buffering effects for job resources might be due to the fact that job demands and job resources are often seen and measured as global and unidimensional constructs, which might obscure the differential impact of specific components (de Jonge, Dormann, & van den Tooren, 2008; de Jonge & Dormann, 2003). Based on the ideas of earlier job stress models about the stress-buffering effect and as a reaction on the aforementioned critique, researchers have developed a new job stress model, namely the Demand-Induced Strain Compensation Model (DISC Model) (de Jonge & Dormann, 2003). The DISC Model consists of five basic principles about the relationships between job demands, job resources and job strain. In describing the assumed buffering effect of job resources on the relationship between job demands and emotional exhaustion, three of these five basic ideas will be further outlined (e.g. multidimensionality of concepts, the triple match principle, and the compensation principle). The DISC Model: multidimensionality of concepts, the triple match principle and the compensation principle 6

7 Multidimensionality of concepts The first basic principle of the DISC model is multidimensionality of concepts (de Jonge, Dorman, & van Veghel, 2004; de Jonge et al., 2008; van den Tooren, 2010). This concept originated at least partly from the critique around the often too global and unidimensional measurement for the constructs of job demands, job resources and job strain mentioned above. The principle of multidimensionality of concepts assumes that job demands, job resources and job strain can all be subdivided into physical, emotional and cognitive dimensions (van den Tooren, 2010). The physical component of job demands describes how physically demanding the job is in terms of movements and actions that need to be made in the job and do have a negative influence on the musculoskeletal system (e.g. lifting, bending, making the same movement over and over, adopting uncomfortable or awkward positions, pushing, pulling, turning controlling, and activating) (van den Tooren, 2010; van Velthoven & Meijman, 1994; Hart & Staveland, 1988). Emotional job demands describe how emotionally demanding the job is in terms of delivering effort to deal with job-inherent emotions (e.g. feel personally threatened by a client because of personal attacks) and/or organizationally desired emotions during transactions with other people (e.g. stay friendly when experiencing troubles with clients at work and trying to convince others) (van den Tooren, 2010; van Velthoven & Meijman, 1994). Finally, the cognitive component of job demands describes how cognitively demanding the job is in terms of mental processes that need to be done at work (e.g. concentrating, doing precise work, taking into account a lot of things at the same time during work, thinking and remembering a lot, deciding, calculating, and searching) (van den Tooren, 2010; van Velthoven & Meijman, 1994; Hart & Staveland, 1988). The same subdivision can be applied to job resources such that there are physical job 7

8 resources, emotional job resources, and cognitive job resources. Physical job resources are physical job aspects (such as physical support from others (employees/supervisors) or from technical equipment and ergonomic aids) that are useful to deal with job demands and achieve work goals (Bakker & Demerouti, 2006; van den Tooren, 2010). Emotional job resources can be described as emotional aspects of the job (such as emotional support) that might be used to deal with job demands and achieve work goals (Bakker & Demerouti, 2006; van den Tooren, 2010). Cognitive job resources can be defined as cognitive aspects of the job (such as job control and information provision by experts or books) that are useful to deal with job demands and achieve work goals (Bakker & Demerouti, 2006; van den Tooren, 2010). The same subdivision can also be made for job strain outcomes such that these can be subdivided in both positive and negative physical, emotional, and cognitive job outcomes (van den Tooren, 2010). Physical job outcomes can be positive (such as bodily fitness) but can also be negative (such as coronary heart disease and musculoskeletal problems). Positive affectivity, affective job satisfaction and emotional parts of work engagement are examples of positive emotional job outcomes while emotional exhaustion can be seen as a negative outcome. Cognitive job outcomes can also be negative (e.g. problems with memory) or positive (e.g. cognitive job satisfaction, cognitive skills). The triple match principle Based on the earlier mentioned principle of multidimensionality, the DISC Model developed another principle, namely the the triple match principle. This principle originated from the match hypothesis of Cohen and Wills (1985) and the extended match hypothesis of Frese (1999). In the match hypothesis of Cohen and Wills (1985), it was stated that a buffering effect is more likely to be found when there is a reasonable match between the coping requirements/stressful event (job demands) and the available support/coping resource (job 8

9 resources). Thus, they suggested that a buffering effect of job resources (moderator) on the relationship between job demands (independent variable) and job strain (dependent variable) is more likely when there is a match between the type of job demands and the type of job resources. Later, Frese (1999) introduced the extended match hypothesis, in which it was argued that the ideas of the match hypothesis can also be applied to dependent variables, such that a match between the moderating variable (e.g. job resources) and the dependent variable (e.g. job strain) would lead to a greater buffering effect than when there is no match between moderating-, and dependent variable (Frese, 1999). The triple match principle integrates both ideas and assumes that a match between the independent variable (e.g. job demands), the moderating variable (e.g. job resources) and the dependent variable (e.g. job strain) would lead to a greater buffering effect than when there is no (full) match between independent-, moderating- and dependent variable (de Jonge & Dormann, 2003). Next to the triple match principle, research has also distinguished between a double match of common kind (in which a match exists between job demands and job resources while there is no match with the job outcome), a double match of extended kind (there is a match between job resources and job outcomes while there is no match with job demands or there is a match between job demands and job outcomes while there is no match with job resources) and a non-match (in which no match exists between job demands, job resources and job outcomes) (van den Tooren, 2010). The triple match principle assumes that the stress-buffering effect of job resources on the relationship between job demands and job strain will be strongest for triple matches (e.g. a combination of emotional job demands, emotional job resources and emotional job strain outcomes) and less strong for double matches of common kind (e.g. cognitive job demands, cognitive job resources and emotional job strain outcomes) and double matches of extended kind (e.g. emotional job demands, cognitive job resources and emotional job strain outcomes / 9

10 cognitive job demands, emotional job resources and emotional job strain outcomes). Furthermore, it is assumed that the buffering effect of job resources is least strong for nonmatches (e.g. cognitive job demands, physical job resources and emotional job strain outcomes) (van den Tooren, 2010). The compensation principle The third basic principle of the DISC Model is equivalent to the earlier mentioned interactive/stress-buffering effects of job resources on the relationship between job demands and job strain. But, in the DISC Model this is called the compensation principle. This principle assumes that job resources can compensate the detrimental effect that high job demands can have on job strain (de Jonge & Dormann, 2003). For example, the assumed detrimental effect that frequent contact with difficult clients or patients at work (an emotional job demand) can have on emotional exhaustion can be compensated by (matching) job resources, such as emotional support from others (an emotional job resource). Emotional exhaustion and the DISC Model Based on the DISC Model, it can be concluded that job demands, job resources and job outcomes can all be subdivided into emotional, cognitive and physical components ( multidimensionality of concepts ). Furthermore, it is assumed that a possible stressbuffering effect of job resources on the relationship between job demands and job strain outcomes will be most likely in case of a triple match ( triple match principle and compensation principle ). The focus of this study is on a specific negative outcome that refers to the emotional component of job (strain) outcomes: emotional exhaustion (van den Tooren, 2010). Most researchers agree that emotional exhaustion is the primary component of burnout (Maslach & 10

11 Leiter, 1997; Maslach, Schaufeli, & Leiter, 2001; Sulsky & Smith, 2005; Schirom, 1989). Research into burnout seems important, since it could lead to negative effects in personal-, organizational- and social domains (Yaniv, 1995; Bakker & Demerouti, 2006). An example of a negative effect of burnout is sickness absenteeism (Schaufeli, Bakker, & van Rhenen, 2009). Although researchers have shown that burnout is multidimensional and consists of three components (Maslach & Leiter, 1997; Maslach, Schaufeli, & Leiter, 2001; Sulsky & Smith, 2005; Schirom, 1989), the focus of this research is on emotional exhaustion because it has a clear focus on the emotional component of job strain. The other two burnout components (cynicism and lack of professional efficacy) have a less clear focus on one of the three components (e.g. emotional/cognitive/physical job strain). The emotional exhaustion state of burnout refers to the feeling of emotional resource depletion and being emotionally overextended (Maslach & Leiter, 1997; Maslach, Schaufeli, & Leiter, 2001). Emotionally exhausted people feel drained, used up, are unable to face a day s work and are unable to unwind and recover (Maslach & Leiter, 1997; Sulsky & Smith, 2005). Emotional exhausted employees are characterized by a lack of energy (Bakker, Schaufeli, Leiter, Taris, 2008; Wright & Bonnet, 1997). Emotional exhaustion might be the result of facing too high job demands (Zohar, 1997; Wright & Cropanzano, 1998). Employees with emotional exhaustion lack the energy to deal with another project or person and are just as tired when they wake up than when they are going to bed (Maslach & Leiter, 1997). When the ideas of the DISC Model are applied on the variables of job demands, job resources and emotional exhaustion, a couple of assumptions can be made. First, it is expected that there is a positive relationship between job demands and emotional exhaustion, such that the higher the job demands, the higher the amount of emotional exhaustion experienced by employees. Second, it is expected that job resources will moderate the relationship between 11

12 job demands and emotional exhaustion, such that job resources will protect the employees from the detrimental effect that high job demands can have on emotional exhaustion. More specifically, based on the compensation principle and the triple match principle it is expected that the stress-buffering effect of job resources on the relationship between job demands and emotional exhaustion will be strongest when there is a triple match: a combination of emotional job demands (e.g. having to deal with personally threatening experiences), emotional job resources (e.g. social support) and emotional exhaustion. It is expected that this relationship is less strong when there is a double-match of common kind (e.g. cognitive demands, cognitive resources and emotional exhaustion) or a double-match of extended kind (e.g. physical demands, emotional job resources and emotional exhaustion) and least strong when there is no-match (e.g. physical demands, cognitive job resources and emotional exhaustion). H1: Job demands will have a positive relationship with emotional exhaustion, such that the higher the amount of job demands, the higher the amount of emotional exhaustion an employee experiences. H2:Job resources (cognitive, emotional, physical) act as a moderator on the positive relationship between job demands (cognitive, emotional, physical) and emotional exhaustion, such that job resources will counteract the assumed detrimental effect that high job demands have on emotional exhaustion. H2A: Stress-buffering effects of job resources are most likely to occur in case of triple matches, less likely to occur in case of double matches, and least likely to occur in case of non-matches. The moderating role of proactive personality in the stress-buffering effect 12

13 The DISC Model is based on the idea that matching job resources can best be used to counteract the negative effect of high (matching) job demands on emotional exhaustion. However, there are at least two important situational conditions that need to be fulfilled to make sure that employees make use of these matching resources. The first is that matching job resources need to be available. Second, employees must be aware of the availability of the matching job resources in the workplace (van den Tooren, 2010). However, even when these conditions are fulfilled, it is still possible that employees opt for less functional non-matching job resources or use no job resources at all. The research of van den Tooren (2010) provides two different personal reasons for why people might be inclined not to choose matching job resources. The first is that employees do not perceive the use of matching job resources as the best way to deal with high job demands. The other is that certain personal characteristics can hinder employees from the activation of (matching) job resources. Both points suggest that it might be the case that certain personal characteristics (e.g. personality traits) play a role in the stress-buffering effect of job resources on the relation between job demands and emotional exhaustion. For example, it might be the case that individuals with certain personal characteristics are more likely to perceive matching job resources as the best way to deal with high job demands. Moreover, it might be the case that employees with certain personal characteristics are also more likely to activate and use these (matching) job resources. So in line with the model of van den Tooren (2010) it is suggested that personal characteristics can moderate the stress-buffering effect of job resources on the relationship between job demands and emotional exhaustion. Empirical research by van den Tooren (2010) has already tested the moderating role of two different personal characteristics, namely matching active coping styles (van den Tooren, de Jonge, Vlerick, Daniels, & van de Ven, 2011) and regulatory focus (van den Tooren & de Jonge, 2011). It was hypothesized that the moderating effect of job resources on the relation between job demands and job strain 13

14 would be more likely for employees with a high specific active coping style (or people with a promotion focus) than for workers with a low specific active coping style (or a prevention focus) (van den Tooren et al., 2011; van den Tooren & de Jonge, 2011). However, their results showed no support for these hypotheses. It is possible that others personal characteristics have an influence on this stress-buffering effect. For example, it might be the case that employees with a proactive personality (when compared to less proactive employees) are more likely to anticipate increases in job demands and also think that action is needed to deal with the detrimental effects that high job demands can have. One possible way in which these proactive employees engage in action might be by activating and using certain job resources. Therefore, the focus of this study is on the influence of proactive personality on the stress-buffering effect of job resources. A proactive personality can be described as the relatively stable behavioral tendency to effect environmental change (Bateman & Crant, 1993; p.103). People with a proactive personality are likely to engage in proactive behavior (Erdogan & Bauer, 2005; Tims & Bakker, 2011), are relatively unconstrained by situational forces and are characterized by an inclination to look for opportunities, show initiative, take action and will keep trying to find and solve problems and change things (Bateman & Crant, 1993). In contrast to relatively passive people (who show little initiative, are relatively constrained by situational forces and rely on others for change), proactive people believe that they are themselves responsible and are able to have an influence on the world around them (Bateman & Crant, 1993). Thus, proactive people do not passively receive environmental pressures but they can actively determine which situations to avoid and which situation to enter (Buss, 1987). There exists some research which suggests a link between proactive personality and job resources. Fuller, Marler, and Hester (2006) suggested that proactive personality is related to access to resources. Others have linked an important characteristic of proactive individuals, 14

15 namely personal initiative taking, to job resources (Hakanen et al., 2008). Fay and Frese (2001) argue that employees with a proactive personality are likely to anticipate future demands and prepare themselves for these demands. It is possible that proactive people prepare themselves for future demands by activating and using certain job resources. There also exists a positive relationship between proactive personality and job crafting (Tims & Bakker, 2011; Bakker, Tims, & Derks, 2012). Job crafting refers to the cognitive and physical changes that employees make to shape their jobs (Wrzesniewski & Dutton, 2001). This can be by means of changing the tasks or relationships at work (physical changes) or by changing the way you perceive things at work (cognitive change) (Wresniewski & Dutton, 2001). Bakker, Tims, and Derks (2012) defined job crafting in terms of changes an employee can make in job demands and job resources. Their research described three different ways in which employees can craft their jobs: by increasing social job resources (e.g. feedback from others), increasing structural job resources (e.g. autonomy, skill variety, training) and increasing job demands. Based on this research it seems likely that employees with a proactive personality, when facing high job demands, are more likely than passive employees to proactively influence their work environment. This can be done by means of increasing both social and structural job resources (Bakker, Tims, & Derks, 2012). Thus, research has shown that people with a proactive personality are likely to engage in proactive behavior, such as problem solving behavior (Erdogan & Bauer, 2005; Tims & Bakker, 2011; Bateman & Crant, 1993). It seems likely that employees might perceive (too) high job demands as a problem. Because employees with a proactive personality are more likely than passive employees to engage in problem solving behavior and prepare themselves for demands (Bateman & Crant, 1993; Fay & Frese, 2001), it might be assumed that proactive employees start looking for job resources, and also use them, as a way to deal with high job demands. This is in contrast to more passive employees who show little initiative and 15

16 are relatively constrained by situational forces, such as increasing job demands. Figure 1 provides an overview of the expected relationships between the variables of interest. This study assumes that the stress-buffering effect of job resources on the relationship between job demands and emotional exhaustion will be more likely to occur for employees who have a proactive personality than for employees with a more passive personality. H3: Proactive personality acts as a moderator of the stress-buffering effect of job resources on the relation between job demands and emotional exhaustion, such that the stress-buffering effect of job resources will be stronger for people with a proactive personality than for people who do not have a proactive personality. Figure 1 Chart of the Relation Between Job Demands and Emotional Exhaustion, Moderated by Job Resources and Proactive Personality 16

17 Method Procedure An online cross-sectional survey was conducted to test the hypotheses that are stated in this study. The data were collected between March 2013 and April 2013 from employees who volunteered for this study. Employees from different healthcare organizations received an in which they were asked to participate voluntarily in this study. A link to the questionnaire was available in that . When participants clicked on this link they confirmed that they would liked to participate and they were given access to the questionnaire. At the first page of the questionnaire the participants received some information about how to fill out the questionnaire. After that, the participant was asked to provide answers on general questions (e.g. age, gender, educational level), questions regarding their employment and organization (e.g. name of organization, organizational tenure, amount of working hours in contract and amount of real working hours), and questions regarding their function (e.g. name of function, job tenure, and type of shifts). Subsequently, the employee filled out other questions directly related towards the variables under study (e.g. cognitive, emotional, and physical job demands and job resources, proactive personality and emotional exhaustion). Anonymity and confidentiality were guaranteed. An overview of the complete questionnaire can be found in appendix A (A1 for the English version of the questionnaire and A2 for the Dutch version of the questionnaire). Sample The sample consisted of 155 employees working in healthcare. The largest part of the sample (72.3%) worked at one healthcare organization (which is one that is specialized in helping people who are in trouble due to addictions and other kinds of problems). Of the 587 employees employed in this organization and that received an with the link to the 17

18 questionnaire, 112 people completed the whole questionnaire (response rate 19.1%). People who did not finished the complete questionnaire were eliminated (47 people). Due to the use of snowball sampling for the other part of the sample (27.7%), the response rates are unknown. This part of the sample consisted of employees from fifteen other different types of health care organizations (e.g. home health care services, palliative care, hospitals etc.) The largest part of the sample was female (70.3% = 109 females). Ages ranged from 20 to 70 years for the 138 people that filled in their age (M = 43.81, SD = 11.97). For highest achieved education, one employee had an elementary education (0.6%), 11 employees had a basic education (7.1%), 43 employees had a secondary education (27.7%), 82 employees had a higher education (52.9%), and 18 employees had an academic education (11.6%). Average organizational tenure was 8.75 years (SD = 8.22) and average job tenure was 6.29 years (SD = 7.33). On average, employees had a contract of hours a week (SD = 10.84) while in reality they worked more, namely hours a week (SD = 9.66). Most people worked in daily shifts (63.9%), two people worked in evening shifts (1.3%), only one worked in nightshifts (0.6%), 32 people combined day-, and evening shifts (20.6%), only one person combined evening and nightshifts (0.6%) and 20 people combined day-, evening-, and nightshifts (12.9%). Measures Independent variables included in this study were cognitive, emotional, and physical job demands, corresponding types of job resources and proactive personality. The dependent variable was emotional exhaustion. Job demands Three different types of job demands were measured with three different scales of the VBBA (Vragenlijst Beleving en Beoordeling van de Arbeid; Questionnaire of the 18

19 experience and assessment of work. QEEW) of van Veldhoven and Meijman (1994). In total, these scales contain 21 items. Cognitive job demands were measured with the seven item scale cognitive demands. An example item is: Does your work demand a lot of concentration?. Emotional job demands were measured with the seven item scale emotional demands. An example item is: Does your work demand a lot from you emotionally? Physical job demands were measured with the seven item scale physical effort. An example item is Do you find your work physically strenuous?. Answers on the three scales could be given on a four point Likert scale. Response categories ranged from 0 = never to 3 = always 1. The Cronbach s alpha was α =.82 for the cognitive demands scale, it was α =.67 for the emotional demands scale and it was α =.88 for the physical demands scale. The Cronbach s alpha of the emotional demands scale is thus somewhat lower than the preferred coefficient of.70 or higher (Evers, van Vliet-Mulder, & Groot, 2000). 2 The underlying factor structure of job demands was checked by the use of principal component analysis (PCA). The Kaiser-Meyer-Olkin Index (KMO index) appeared to be.80 and the Bartlett s Test of sphericity was significant (p <.001). Because of that, it was allowed to continue with the exploratory factor analysis. Correlations between the factors were expected and because of that it was decided to make use of Oblimin rotation. The number of factors to extract was specified as three. An overview of the factor analyses for job demands can be found in Table B1 of appendix B. In general, the factor analysis satisfied the idea of a three factor structure in which job demands consist of three underlying factors (emotional, cognitive, and physical 1 In its original version, the response categories ranged from 0 = always to 3 = never. These categories have been reversed, because all other response categories in the questionnaire ranged from negative (e.g. never) to positive (e.g. always). Therefore, the response categories from this scale were reversed in an attempt to avoid any confusion of the respondents. 2 The norm for less important decisions at the individual level was used. 19

20 demands). This is because all items loaded higher than.3 3 on their matching factor (although some items loaded on two factors). Job resources Three different types of job resources were measured with an adapted version of the DISQ 2.1 of de Jonge et al. (2009). In total, these three scales contain 16 items. All items were reformulated such that each question concerns the employee that fills out the questionnaire. This is in contrast to the original questionnaire of de Jonge et al. (2009) in which all items referred to another employee (e.g. employee X who has one year of experience in the same function as you have ). To make the items more directly applicable to the respondent his/herself, it was decided to reformulate each item. Cognitive job resources were measured with a subscale that consisted of six items. An example item is Do you have the opportunity to take a mental break when tasks require a lot of concentration?. Emotional job resources were measured with a subscale that consisted of five items. An example item is: To what extent are you able to stop emotionally-laden interactions with others for a while whenever you want to?. Physical job resources were measured with a five item subscale. An example item includes To what extent are you able to plan your work so that physical tasks require no more physical exertion than you can manage?. Answers on these three scales could be given on a 5-point Likert scale. Response categories ranged from 1 = (almost) never to 5 = (almost) always. The Cronbach s alpha of the cognitive resources scale was α =.72, of the emotional resources scale it was α =.79 and for the physical resources scale the Cronbach s alpha was α =.84. Again, PCA was used to check the underlying factor structure of the whole job resources scale. The Kaiser-Meyer-Olkin Index (KMO index) appeared to be 0.81 and the Bartlett s Test of sphericity was significant (p <.001). An overview of this factor 3 In line with Pallant (2007), a loading of.30 or higher was seen as an indication that a specific item loads (high) on that factor. 20

21 analysis can be found in Table B2 of appendix B. In general, the factor analysis satisfied the idea of a three factor structure in which job resources consist of three underlying factors (emotional, cognitive, and physical resources). This is because all items (with the exception of one emotional resource item) loaded higher than.30 on their matching factor (although one item loaded on two factors). Emotional exhaustion Emotional exhaustion was measured with the eight item emotional exhaustion scale of the UBOS (Utrechtse Burnout Schaal; Utrecht Burnout Scale) of Schaufeli and van Dierendonck (2000). Due to the use of a sample of employees with contactual jobs, there was chose for the UBOS-C (contactual scale). An example item is I feel mentally exhausted because of my work. Respondents could indicate their answer on a seven point Likert scale, varying from 0 = never to 6 = always. The Cronbach s alpha of the emotional exhaustion scale was α =.90. Proactive personality The 17 item proactive personality scale of Bateman and Crant (1993) was used to measure proactive personality. An example item is If I see something I don t like, I fix it. Answers could be given on a seven-point Likert scale. Response categories ranged from 1 = strongly disagree to 7 = strongly agree. The Cronbach s alpha of the proactive personality scale was α =.85. Statistical analyses The analysis of all variables was done in a couple of steps. First, it was examined whether the control variables (age, gender, highest achieved education, job tenure, organizational tenure, hours according to contract, real working hours, type of shifts) were significantly related to the outcome variable (emotional exhaustion). Therefore, a preliminary regression analysis (see Table C1 in appendix C) was executed in which the control variables were regressed on 21

22 the dependent variable. Because none of the control variables turned out to have a significant relationship with emotional exhaustion, it was decided to omit the control variables from further analyses. This was done to make the analyses as compact as possible. The first hypothesis was tested by means of a linear regression analysis. The dependent variable was emotional exhaustion and the three unstandardized variables of cognitive-, emotional-, and physical demands were entered together in one model. The second hypothesis was tested by means of two different hierarchical multiple regression analyses. In line with earlier research (de Jonge & Dormann, 2006; van den Tooren & de Jonge, 2008), this research conducted one analysis in which the triple match and double matches of common kind were included and another analysis in which double matches of extended kind and non-matches were included. For both analyses, emotional exhaustion was the dependent variable and the standardized main effects of all types of job demands and job resources were the independent variables that were entered in the first step. In the second step, the interactions between job demands and job resources were entered. Thus, in the first analysis the triple match and two double matches of common kind were included here while in the second analysis four double matches of extended kind and two non-matches were included. To test Hypothesis 2A, the number of valid moderating effects was checked and compared with the number of tested moderating terms for each type of match (e.g. triple match, double matches and non-matches). Finally, the percentages of valid moderating effects were compared. The third hypothesis was also tested by means of two different hierarchical multiple regression analyses. Again, a distinction was made between the triple match and double matches of common kind on the one hand (first analysis) and the double matches of extended kind and non-matches on the other hand (second analysis). In both analyses, all standardized 22

23 main effects (cognitive-, emotional-, and physical job demands, cognitive-, emotional-, and physical job resources, and proactive personality) were entered in the first model. In the second model, the two-way interactions were entered. Thus, for the first analysis the triple match, double matches of common kind, and the two-way interactions with proactive personality were entered here. For the second analysis, the double matches of extended kind, the non matches, and the two-way interactions with proactive personality were entered here. In the third model, the three-way interactions with proactive personality were added. Thus, for the first analysis the following three way interactions were entered: the triple match in interaction with proactive personality, and the double matches in interaction with proactive personality. For the second analysis, the double matches of extended kind in interaction with proactive personality, and the non-matches in interaction with proactive personality were entered here. Results Correlation analysis Table 1 shows the means, standard deviations, number of items, Cronbach s alphas and Pearson zero-order correlations of all variables. As can be seen in Table 1, cognitive demands show a small positive correlation with emotional exhaustion (r =.22,p <.01) and emotional demands show a medium positive correlation with emotional exhaustion (r =.41,p <.01). Emotional resources show a medium negative correlation with emotional exhaustion (r = -.44,p <.01), and physical resources show a small negative correlation with emotional exhaustion (r = -.18,p <.05). 23

24 Table 1 Means, Standard Deviations, Number of Items, Cronbach s Alphas and Pearson Zero-Order Correlations (n = 155) Measure M SD Items α Cognitive demands 2. Emotional * - demands 3. Physical demands 4. Cognitive ** -.23 ** - resources 5. Emotional ** - resources 6. Physical * -.30 **.39 **.30 ** - resources 7. Emotional **.41 ** ** -.18 * - exhaustion 8. Proactive personality * Note. M = mean; SD = standard deviation; α = Cronbach s alpha. *p <.05, two-tailed. **p <.01, two-tailed. 24

25 Table 2 shows the results from the first linear regression analysis, that was conducted to test the hypothesis that job demands are positively related to emotional exhaustion (Hypothesis 1). The results show that only emotional demands were significantly related to emotional exhaustion (β =.38,p <.01) such that the higher the amount of emotional demands, the higher the amount of emotional exhaustion experienced by employees. Although in the predicted direction, neither cognitive demands (β =.13,p =.09) nor physical demands (β =.08,p =.27) had a significant relationship with emotional exhaustion. The total variance explained by the model as a whole was 19%, F(3, 151) = 12.15,p <.001. Thus, Hypothesis 1 was rejected for cognitive demands and physical demands but supported for emotional demands. Table 2 Simple Regression Analysis Predicting Emotional Exhaustion From Cognitive-, Emotional-, and Physical Demands (n = 155). Emotional exhaustion Variable β SE Cognitive demands Emotional demands 0.38***.18 Physical demands R 2.19 F 12.15*** Note. β = standardized coefficient; SE = standard error. ***p <.001, two-tailed. Table 3 shows the first hierarchical regression analysis that was used to test the second hypothesis (job resources act as a moderator of the positive relationship between job demands and emotional exhaustion) for the triple match and two double matches of common kind. The total variance explained by the first model (which included the standardized main effects) was 25

26 36%, F(6, 148) = 13.76,p <.001. Of the three interaction effects, only one moderating term reached significance (p <.05): the moderating effect of cognitive demands and cognitive resources in the prediction of emotional exhaustion (b = -.12,p <.05). This interaction term explained another 4% of the variance in emotional exhaustion, after controlling for the standardized main effects, ΔR² =.04, ΔF(3, 145) = 2.79,p <.05. Table 3 Hierarchical Regression Analysis Predicting Emotional Exhaustion From One Triple Match and Two Double Matches of Common Kind (n = 155) Emotional exhaustion Model 1 Model 2 Variable b SE B SE Constant Cognitive demands 0.13* Emotional demands 0.31*** *** 0.06 Physical demands Cognitive resources Emotional resources -0.40*** *** 0.07 Physical resources Emotional resources x emotional demands Cognitive resources x cognitive demands -0.12* 0.05 Physical resources x physical demands R R 2.04 F *** 10.44*** F 2.79* 26

27 Note. b = unstandardized coefficient; SE = standard error. *p <.05, two-tailed. ***p <.001, two-tailed. In line with the theory of Aiken and West (1991) it was decided to graphically present the statistically significant interaction. Therefore, two simple regression lines were plotted based on the values of one standard deviation below and one standard deviation above the standardized mean of the predictor variable that were entered in the regression equation (van den Tooren & de Jonge, 2008). Next to this, it was decided to execute simple slope tests since only plotting the interaction does not allow statistical inferences about whether each individual slope is a significant predictor of the dependent variable (Dawson & Richter, 2006). With this, it was investigated whether each slope differed statistically significant from zero (van den Tooren & de Jonge, 2008). Figure 2 shows the match of common kind in which cognitive resources moderated the relation between cognitive demands and emotional exhaustion. The figure shows that if cognitive resources were low (-1SD), an increase in cognitive demands was associated with an increase in emotional exhaustion (slope test: t = 2.90;p =.00). In case of high cognitive resources (+1SD), cognitive demands were not significantly related to emotional exhaustion (slope-test: t = -0.04;p =.97). This shows that the combination of high cognitive demands and low cognitive resources has an adverse effect on emotional exhaustion. However, when employees have high cognitive resources, an increase in cognitive demands in not associated with an increase in emotional exhaustion. Therefore, this supports the idea that cognitive resources can have a stress-buffer effect on the relation between cognitive demands and emotional exhaustion. 27

28 Figure 2 Double Match of Common Kind: Cognitive Resources Moderate the Relationship Between Cognitive Cemands and Emotional Exhaustion (n=155). Table 4 shows the second hierarchical regression analysis that was also used to test the second hypothesis (job resources act as a moderator of the positive relationship between job demands and emotional exhaustion) but this time it included four double matches of extended kind and two non-matches. Of the six interaction effects, again only one moderating term reached significance (p <.05): the moderating effect of cognitive demands and emotional resources in the prediction of emotional exhaustion (b = -.15,p <.05). 28

29 Table 4 Hierarchical Regression Analysis Predicting Emotional Exhaustion from Four Matches of Extended Kind and Two Non-matches (n = 155) Emotional exhaustion Model 1 Model 2 Variable b SE b SE Constant Cognitive demands 0.13* * 0.06 Emotional demands 0.31*** *** 0.07 Physical demands Cognitive resources Emotional resources -0.40*** *** 0.07 Physical resources Emotional resources x cognitive demands -0.15* 0.06 Emotional resources x physical demands Cognitive resources x emotional demands Cognitive resources x physical demands Physical resources x emotional demands Physical resources x cognitive demands R R 2.05 F 13.76*** 8.07*** F 1.89 Note. b = unstandardized coefficient; SE = standard error. *p <.05, two-tailed. **p <.01, two-tailed. ***p <.001, two-tailed. The significant interaction effect between cognitive demands and emotional resources was plotted in Figure 3. The figure shows that if emotional resources were low (-1SD), an increase in cognitive demands was associated with an increase in emotional exhaustion (slope test: t = 3.22;p =.00). In case of high emotional resources (+1SD), cognitive demands were not significantly related to emotional exhaustion (slope-test: t = -0.09;p =.93). This shows that high cognitive demands can have an adverse effect on emotional exhaustion in case of low emotional resources. However, when employees have high emotional resources, an increase in cognitive demands in not associated with an increase in emotional exhaustion. Therefore, this supports the idea that emotional resources can also have a stress-buffer effect in the 29