job demands resources burnout performance

The job demands-resources (JD-R) model was used to examine the relationship between jobcharacteristics, burnout, and (other-ratings of) performance (N � 146). We hypothesized thatjob demands (e.g., work pressure and emotional demands) would be the most important an-tecedents of the exhaustion component of burnout, which, in turn, would predict in-role per-formance (hypothesis 1). In contrast, job resources (e.g., autonomy and social support) were hy-pothesized to be the most important predictors of extra-role performance, through theirrelationship with the disengagement component of burnout (hypothesis 2). In addition, we pre-dicted that job resources would buffer the relationship between job demands and exhaustion(hypothesis 3), and that exhaustion would be positively related to disengagement (hypothesis 4).The results of structural equation modeling analyses provided strong support for hypotheses 1,2, and 4, but rejected hypothesis 3. These findings support the JD-R model’s claim that job de-mands and job resources initiate two psychological processes, which eventually affect organiza-tional outcomes. © 2004 Wiley Periodicals, Inc.

Introduction

Although job burnout is known to negativelyaffect job satisfaction and organizationalcommitment, and creates such undesired be-haviors as personnel turnover and absen-teeism (see Lee & Ashforth, 1996, for anoverview), its relationship with an organiza-tion’s most important outcome—namely, jobperformance—has hardly received any re-search attention. In fact, in Lee and Ash-forth’s meta-analysis, the relationship be-tween burnout and performance was noteven mentioned. In addition, the few studies

reported in the literature thus far have showninconsistent relationships between burnoutand performance, with some studies showingthe expected negative relationships (e.g.,Bhagat, Allie, & Ford, 1995; Parker & Kulik,1995; Wright & Cropanzano, 1998), andothers showing zero or positive relationships(e.g., Keijsers, Schaufeli, Le Blanc, Zwerts, &Reis-Miranda, 1995; Lazaro, Shinn, &Robinson, 1985; Randall & Scott, 1988).One of the reasons for these mixed findingsmay be that several studies only examinedthe relationship between performance andone dimension of burnout—exhaustion (e.g.,

USING THE JOB DEMANDS-RESOURCESMODEL TO PREDICT BURNOUT ANDPERFORMANCE

Human Resource Management, Spring 2004, Vol. 43, No. 1, Pp. 83–104© 2004 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/hrm.20004

Arnold B. Bakker, Evangelia Demerouti, and Willem Verbeke

Correspondence to: Arnold B. Bakker, Utrecht University, Dept. of Social & Organizational Psychology, P.O.Box 80.14, 3508 TC Utrecht, The Netherlands, Tel: 31 30-253-4794, Fax: 31 30-253-4718, E-mail: [email protected]

84 • HUMAN RESOURCE MANAGEMENT, Spring 2004

Wright & Cropanzano, 1998). In addition,the majority of studies used self-reports anddid not distinguish between two types of per-formance: in-role and extra-role performance.

The aim of the current study is to inves-tigate how burnout may be related to other-ratings of performance by using a theoreticalmodel that incorporates the core dimensionsof burnout and by employing adequate mea-sures to capture in-role and extra-role perfor-mance. We built upon the job demands-re-sources model of burnout (Demerouti,Bakker, Nachreiner, & Schaufeli, 2001),which delineates how the core burnout di-mensions, exhaustion and disengagement,both have different etiologies in organiza-tional environments, and may subsequentlyhave different effects on in-role and extra-role performance.

Defining and Measuring Job Burnout

Burnout is a work-related stress syndromethat was originally observed among thosewho do “people work” (Maslach & Jackson,1986). However, research of the past decadehas shown that the core dimensions ofburnout—exhaustion and cynicism or disen-gagement from work—can be observed invirtually any occupational group (Bakker,Demerouti, & Schaufeli, 2002; Demerouti etal., 2001; Leiter & Schaufeli, 1996). Demer-outi, Bakker, Vardakou, and Kantas (2003)have defined exhaustion as an extreme formof fatigue as a consequence of prolonged andintense physical, affective, and cognitivestrain caused by prolonged exposure to spe-cific working conditions (or stressors; cf.Aronson, Pines, & Kafry, 1983; Lee & Ash-forth, 1993; Shirom, 1989). Disengagementrefers to distancing oneself from one’s work,work objects (e.g., computers, recipients), orwork content (e.g., software programming,providing services). It represents an extensiveand intensive reaction in terms of an emo-tional, cognitive, and behavioral rejection ofthe job and it delineates an occupational dis-illusionment (cf. Freudenberger, 1974).

The third classical burnout component,reduced personal accomplishment (Maslach& Jackson, 1986), is excluded from our defi-nition of burnout for several reasons. First,

there is accumulating empirical evidencethat exhaustion and cynicism (disengage-ment) constitute the core of burnout,whereas reduced personal accomplishmentplays a far less prominent role (Maslach,Schaufeli, & Leiter, 2001; Shirom, 2002).Personal accomplishment has weak relation-ships with the two other components ofburnout as well as with hypothesized an-tecedents and outcomes (for a meta-analysis,see Lee & Ashforth, 1996). This supports thenotion that emotional exhaustion and disen-gagement constitute a syndrome that is onlyloosely related to personal accomplishment(see also, Schaufeli, Bakker, Hoogduin,Schaap, & Kladler, 2001). Second, Leiter(1993) has argued and shown that emotionalexhaustion leads to cynicism/disengagement,whereas feelings of reduced personal accom-plishment develop independently. This is an-other indication of the exceptional status ofthis particular burnout dimension. Finally, ithas been suggested that personal accom-plishment reflects an individual differencecharacteristic similar to self-efficacy (Cordes& Dougherty, 1993).

Although the Maslach Burnout Inven-tory (MBI; Maslach, Jackson, & Leiter,1996) is the most often used instrument toassess burnout, in the present study, we ap-plied an alternative measure of burnout thatcan be used among occupations within andoutside human service professions. This in-strument—called the Oldenburg BurnoutInventory (OLBI; Demerouti et al., 2001,2003)—conceives burnout as a syndrome ofwork-related negative experiences, includingfeelings of exhaustion and disengagementfrom work. The reason why we chose to em-ploy this instrument is the distinctive featureof the OLBI compared to the MBI to includeboth negatively and positively framed items.Such a procedure is recommended by con-ventional psychometric standards and has ahigher probability to avoid artifacts due toacquiescence tendencies.

Burnout and Performance

Employees normally engage in two sorts ofperformances: they accomplish in-role andextra-role performance. In-role performance

... research ofthe past decadehas shown that the coredimensions ofburnout—exhaustion andcynicism ordisengagementfrom work—can be observedin virtually anyoccupationalgroup.

Using the Job Demands-Resources Model To Predict Burnout and Performance • 85

... burnoutentrapsemployees in anegative,vicious spiral inwhich they donot seek help orare not proneto strive forchanges in theirsituation, and,as a result, theycontinue toperformineffectively.

is defined as those officially required out-comes and behaviors that directly serve thegoals of the organization (Motowidlo & VanScotter, 1994). Among other things, in-roleperformance includes meeting organiza-tional objectives and effective functioning(Behrman & Perreault, 1984). In addition,employees display extra-role activities (Mor-rison, 1994). Extra-role performance is de-fined as discretionary behaviors on the partof an employee that are believed to directlypromote the effective functioning of an or-ganization, without necessarily directly influ-encing a person’s target productivity (Pod-sakoff & MacKenzie, 1994). Examples arethe willingness to help colleagues who haveheavy workloads or the avoidance of prob-lems with colleagues (this is also known as aspecific form of organizational citizenshipbehavior; Organ & Paine, 1999).

A generally accepted notion in work psy-chology is that job stressors tend to reduce theindividual’s capacity to exert control over theirwork environment, which, in turn, is supposedto adversely affect an individual’s ability tofunction in an efficient way (Fried, Ben-David,Tiegs, Avital, & Yeverechyahu, 1998; McGrath,1976). While plausible, this notion has re-ceived little empirical support. The apparentinconsistencies in the obtained associationsbetween stressors and job performance haveled researchers to search for moderators ofthese relationships (Fisher & Gitelson, 1983;Fried et al., 1998). For instance, several stud-ies have examined the potential impact of con-textual and personal variables as moderators ofthe role stressor-job performance relationship(Fisher & Gitelson, 1983). Another possibilityis to use mediators in the aforementioned rela-tionship that directly indicate the remained in-dividual capacity. Burnout may be such a me-diator since it represents an outcome of thecombined effect of several work characteristics(Demerouti, Bakker, Nachreiner, & Schaufeli,2000, 2001) and indicates the depletion of in-dividual coping and energy resources (Hobfoll& Freedy, 1993; Shirom, 1989).

Singh, Goolsby, and Rhoads (1994) pro-vide some explanations why burnout shouldaffect behavioral outcomes such as job per-formance. According to them, exhaustion di-minishes the available energy of employees

and leads to an impairment of the efforts putinto work. Moreover, burnout entraps em-ployees in a negative, vicious spiral in whichthey do not seek help or are not prone tostrive for changes in their situation, and, as aresult, they continue to perform ineffectively.Finally, the experience of burnout reducesemployees’ self-confidence in solving work-related problems (e.g., Bakker, Demerouti,Taris, Schaufeli, & Schreurs, 2003), andtherefore their performance diminishes.While these explanations are plausible, re-sults are somewhat less than convincing.

More specifically, Schaufeli and Enz-mann (1998) traced five studies in which therelationship between burnout components,as measured by the Maslach Burnout Inven-tory–Human Services Survey (MBI-HSS),and self-reported performance were exam-ined. They reanalyzed the data and foundthat, on average, self-reported performanceshared 5% of its variance with emotional ex-haustion, 4% with depersonalization, and 6%with reduced personal accomplishment (p.92). The studies that examined the relation-ship between exhaustion and others-rated orobjectively assessed performance resulted ineven lower explained variance (only a meager1%). In addition, several studies on burnoutand objective performance that included notonly exhaustion, but also depersonalizationand (reduced) personal accomplishment(Parker & Kulik, 1995; Wright & Bonett,1997) failed to find relationships between thelatter two MBI dimensions and performance.Even more surprising is the finding that whilesome studies showed the expected negativerelationships between burnout dimensionsand performance (e.g., Bhagat et al., 1995;Parker & Kulik, 1995; Wright & Cropanzano,1998), others have shown zero or positive re-lationships (e.g., Keijsers et al., 1995; Lazaroet al., 1985; Randall & Scott, 1988).

Should we conclude that burnout is notsystematically related to objective perfor-mance? We believe this conclusion is prema-ture, since there are at least two reasons whyit is difficult to reveal a relationship betweenburnout and objective performance. First,the fact that researchers can explain only alimited amount of variance in objective per-formance may partly be attributed to the use


of different sources of information, whichleads to an underestimation of the strengthof relationships (cf. Zapf, Dormann, & Frese,1996). It may be expected that two sourcesof information (e.g., a supervisor who as-sesses performance and an employee who in-dicates his/her burnout) have their ownunique causes of (statistically independent)error variance. If one uses only one method,the sources of error variance are the same.The consequence of this is inflated correla-tions. Second, and particularly relevant tothe present study, most researchers have notmade a distinction between in-role and extra-role performance. For example, Wright andBonett (1997) used a one-item measure ofglobal performance as assessed by the super-visors of 44 human service workers, andWright and Cropanzano (1998) used thesame measure of global performance in theirstudy among 52 social workers. Supervisors’global performance ratings may be based onboth in-role and extra-role behaviors shownby their subordinates and unrelated to any ofthe burnout dimensions if in-role and extra-role performance have different predictors.

Regarding extra-role performance, wecould only locate the study of Klein and Ver-beke (1999), who reported that depersonaliza-tion and reduced personal accomplishmentbut not emotional exhaustion had substantialnegative correlations with extra-role perfor-mance. It can be hypothesized that it might bedifficult for employees who experienceburnout to reduce their output or quality ofperformance because of organizational sanc-tions and reward systems. Instead, they mightchoose to withhold behaviors that are discre-tionary, such as organizational citizenship be-havior, since this would not result in directconsequences for themselves (Schnake,1991). Indeed, as Schaufeli and Enzmann(1998, p. 26) note, burned-out professionalslose their concern for the organization and be-come hypercritical, distrusting management,peers, and colleagues, which corresponds, inother words, to low extra-role performance.

The Job Demands-Resources Model

In the present study, we use the job demands-resources (JD-R) model of burnout (Bakker,

Demerouti, De Boer, & Schaufeli, 2003; De-merouti et al., 2000, 2001) to examine howjob characteristics and burnout contribute toexplaining variance in in-role and extra-roleperformance. One central assumption of theJD-R model is that although every occupationmay have its own specific work characteris-tics associated with burnout, it is still possibleto model these characteristics in two broadcategories—namely, job demands and job re-sources. Job demands refer to those physical,psychological, social, or organizational as-pects of the job that require sustained physi-cal and/or psychological (cognitive and emo-tional) effort and are therefore associatedwith certain physiological and/or psychologi-cal costs. Examples are a high work pressure,role overload, emotional demands, and poorenvironmental conditions.

Job resources refer to those physical, psy-chological, social, or organizational aspectsof the job that are (1) functional in achievingwork goals; (2) reduce job demands and theassociated physiological and psychologicalcosts; or (3) stimulate personal growth anddevelopment. Resources may be located atthe level of the organization (e.g., salary, ca-reer opportunities, job security), interper-sonal and social relations (e.g., supervisorand coworker support, team climate), the or-ganization of work (e.g., role clarity, partici-pation in decision making), and the level ofthe task (e.g., performance feedback, skill va-riety, task significance, task identity, auton-omy). In fact, these latter working character-istics are the classical job characteristics inHackman and Oldham’s (1976) model. Ingeneral, job demands and resources are neg-atively related, since job demands such as ahigh work pressure and emotionally demand-ing interactions with clients may precludethe mobilization of job resources (seeBakker, Demerouti, De Boer et al., 2003,Bakker, Demerouti, & Euwema, 2003b; De-merouti et al., 2000, 2001). In a similar vein,high job resources such as social support andfeedback may reduce job demands.

A second assumption in the JD-R modelis that working characteristics may evoketwo psychologically different processes (seeFigure 1; the numbers in the figure corre-spond with the hypotheses). In the first

… burned-outprofessionalslose theirconcern for theorganizationand becomehypercritical,distrustingmanagement,peers, andcolleagues,whichcorresponds, inother words, tolow extra-roleperformance.


process, demanding aspects of work (i.e.,work overload) lead to constant overtaxingand, in the long run, exhaustion (e.g., Lee &Ashforth, 1996; Leiter, 1993; Wright &Cropanzano, 1998). The literature on men-tal fatigue may be used to explain thisprocess. Mental fatigue is a response of themind and body to the reduction in re-sources due to mental task execution. Itwarns of the increasing risk of performancefailure (Veldhuizen, Gaillard, & de Vries,2003). Under normal circumstances, peo-ple become tired by their everyday work ac-tivities, but their energetical resources aresufficient to meet the task demands. How-ever, when a person is working under highlevels of (mental) workload and is alreadyfatigued (e.g., at the end of a workday),extra energy to compensate fatigue has tobe mobilized through mental effort in orderto maintain task performance (Gaillard,2001; Hockey, 1997; Hockey, Coles, &Gaillard, 1986). The mobilization of extraenergy may result in (feelings of) acute fa-tigue. A subsequent return to physiologicaland emotional baseline levels is crucial. In-complete recovery from workload demandsdisrupts the energetic homeostasis, whichin turn may lead to chronic effects on

health and well-being (Frankenhaeuser,1979; Frankenhaeuser & Johansson, 1986).When incomplete recovery takes place, theeffects of high workload demands can accu-mulate gradually, carrying over from oneday to the next (Craig & Cooper, 1992;Frankenhaeuser, 1980; Frankenhaeuser &Johansson, 1986; Gaillard, 2001; Ursin,1980). Veldhuizen et al. (2003), using of-fice tasks in order to simulate a working day,found that exhausted participants (assessedwith the emotional exhaustion subscale ofthe Maslach Burnout Inventory; Maslach &Jackson, 1986) had problems investing suf-ficient energy in their tasks. Moreover, theirperformance results decreased since theyreacted more slowly and produced a smallernumber of correct responses. Exhaustedsubjects seemed to be unable to performparticularly well in the evening, althoughthey tried to invest more effort than theirnonexhausted counterparts.

This implies that when people becomeexhausted under the influence of environ-mental demands, they will not be able toperform well because their energetical re-sources are diminished. Thus, the impactof job demands on job performance shouldbe mediated by the feelings of (enhanced)

Figure 1. The Job Demands-Resources Model Applied to Burnout and Performance.


exhaustion (cf. Hockey, 1993). Therefore,we formulated the following hypothesis:

Hypothesis 1: Job demands (and not job re-sources) will be the most important an-tecedents of in-role performance, throughthe experience of exhaustion. Thus, we ex-pect that exhaustion will play a mediatingrole in the relationship between job de-mands and in-role performance.

In the second process proposed by theJD-R model, a lack of job resources precludesactual goal accomplishment, which causesfailure and frustration (Bakker, Demerouti,De Boer et al., 2003). Usually, when employ-ees do possess resources (such as supportfrom colleagues or having the ability to or-ganize one’s own work) they tend to go be-yond actual goal accomplishment (job craft-ing; Wrzesniewski & Dutton, 2001). Forinstance, Wayne, Shore, and Liden (1997)showed that perceived organizational supportpredicted citizenship behavior, which in-cluded helping one another and helping newcolleagues to orient and so take over for themanager. In addition, as Goodman andSvyantek (1999) claim, contextual or extra-role performance derives from the psycholog-ical contract between employees and organi-zation. The psychological contract prescribesthe manner in which organizations rewardemployees for extra effort above their taskperformance (Makin, Cooper, & Cox, 1996).It establishes reciprocity rules showing howemployees’ extra effort is rewarded by the or-ganization (Goodman & Svyantek, 1999).However, when organizations do not provideor reward employees with job resources, thelong-term consequence is withdrawal fromwork, and reduced motivation and commit-ment (Bakker, Demerouti, De Boer et al.,2003; Demerouti et al., 2001), and this takesaway one of the primary mechanisms bywhich extra-role performance is supported bythe organization (Goodman & Svyantek,1999). We believe that in such a situation areduction of motivation or withdrawal fromwork can be an important self-protectionmechanism that may prevent the future frus-tration of not obtaining work-related goals(cf. Antonovsky, 1987; Hackman & Oldham,

1976) or of not being rewarded for extra ef-fort above task performance (cf. Goodman &Svyantek, 1999).

When the external environment lacks re-sources, individuals cannot reduce the poten-tially negative influence of high job demandsand they cannot achieve their work goals. Ad-ditionally, they cannot develop themselvesfurther in their job and organization. Conser-vation of resources (COR) theory predictsthat in such a situation, employees will expe-rience a loss of resources or failure to gain aninvestment (Hobfoll, 1989; Hobfoll & Freedy,1993). Moreover, in order to reduce this dis-comfort or job stress, employees will attemptto minimize losses. With the intention ofachieving equity without having further neg-ative, personal consequences (like, for in-stance, when they lower their in-role perfor-mance) they will most probably reduce theirdiscretionary inputs (Schnake, 1991). Inother words, we expect that in order to attaintheir main resources (or equity between in-puts and outputs), employees will engage inloss-based selection (Freund, Li, & Baltes,1999), specifically seeking to reduce extra-role performance and focusing more on in-role performance. In short, it can be hypoth-esized that when employees lack jobresources, extra-role performance will sufferaccordingly (see also Figure 1).

Hypothesis 2: Job resources (and not jobdemands) will be the most important pre-dictors of extra-role performance, throughtheir influence on disengagement. Thus,we expect that disengagement will play amediating role in the relationship betweenjob resources and extra-role performance.

Interactions between Job Demands andResources

One of the assumptions in the JD-R modelthat has received little research attention sofar is that job resources may buffer the im-pact of job demands on stress reactions, in-cluding burnout. This assumption is consis-tent with the demand-control model (DCM;Karasek, 1979) and the effort-reward imbal-ance (ERI) model (Siegrist, 1996), but ex-pands these models by claiming that several

... when organi-zations do notprovide orrewardemployees with jobresources, the long-termconsequence iswithdrawalfrom work, and reducedmotivation andcommitment ... and thistakes away oneof the primarymechanisms by which extra-roleperformance is supported by theorganization.


Social supportis probably themost well-knownsituationalvariable thathas beenproposed as apotential bufferagainst jobstress.

different job resources can play the role ofbuffer for several different job demands.Which job demands and resources play a rolein a certain organization depends upon thespecific job characteristics that prevail.Thus, whereas the DCM states that controlover the execution of tasks (autonomy) maybuffer the impact of work pressure on jobstress, the JD-R model expands this view andstates that many different types of job de-mands and job resources may interact inpredicting job stress.

In their study among four home-care or-ganizations, Bakker, Demerouti, Taris et al.(2003) found evidence for the buffering roleof job resources. More specifically, theyfound that the impact of job demands (e.g.,workload, physical demands, and patient ha-rassment) on feelings of exhaustion was par-ticularly strong if home-care professionalspossessed few resources (e.g., autonomy,possibilities for professional development,performance feedback). In a similar vein, intheir study among over 1,000 employees of alarge institute for higher education, Bakker,Demerouti, & Euwema (2003) showed thatseveral job demands only influenced burnoutif employees possessed few job resources(autonomy, social support, supervisorycoaching, and feedback).

The buffer hypothesis is consistent withKahn and Byosiere (1992), who argue thatthe buffering or interaction effect can occurbetween any pair of variables in the stress-strain sequence. They claim that properties ofthe work situation, as well as characteristicsof the individual, can buffer the effects of astressor. The buffering variable can reducethe tendency of organizational properties togenerate specific stressors, alter the percep-tions and cognitions evoked by such stressors,moderate responses that follow the appraisalprocess, or reduce the health-damaging con-sequences of such responses (Kahn &Byosiere, 1992, p. 622). Social support isprobably the most well-known situationalvariable that has been proposed as a potentialbuffer against job stress (e.g., Haines, Hurl-bert, & Zimmer, 1991; Johnson & Hall, 1988;see Van der Doef & Maes, 1999, for a re-view). Other characteristics of the work situ-ation that may act as moderators are (a) the

extent to which the onset of a stressor is pre-dictable (e.g., role ambiguity and feedback),(b) the extent to which the reasons for thepresence of a stressor are understandable(e.g., through information provided by super-visors), and (c) the extent to which aspects ofthe stressor are controllable by the personwho must experience it (e.g., job autonomy;Kahn & Byosiere, 1992). This leads to ourthird hypothesis (see also Figure 1).

Hypothesis 3: Job resources buffer the re-lationship between job demands and ex-haustion. More specifically, the relation-ship between job demands and exhaustionwill be stronger for employees with few(versus many) resources.

Finally, we explore whether Leiter’s(1993) process model of burnout may beadded to the JD-R model. Leiter’s modelstates that feelings of exhaustion evoke cyni-cal attitudes toward work as employees at-tempt to gain emotional distance from theirjob as a way of coping with stress (cf. es-cape/emotion-focused coping; Latack &Havlovic, 1992). This model has been sup-ported by several studies (e.g., Bakker,Schaufeli, Sixma, Bosveld, & Van Dieren-donck, 2000; Cordes, Dougherty, & Blum,1997). Thus, we assume that exhaustion fos-tered by job demands may evoke psychologi-cal withdrawal (cf. Bakker, Demerouti, Taris,et al., 2003). Or, in terms of our fourth andfinal hypothesis:

Hypothesis 4: Exhaustion has a positive re-lationship with disengagement.

Note that this latter hypothesis implies thatthe two basic processes proposed by the JD-Rmodel are not totally independent. Morespecifically, inclusion of Leiter’s processmodel in the JD-R model means that job de-mands are not only related to (reduced) in-role performance (through exhaustion), butalso to (reduced) extra-role performance. Thecombination of hypotheses 1, 2, and 4 im-plies the following sequence: job demands →exhaustion → disengagement → extra-roleperformance (see also Figure 1). Thus, thecompensatory strategy used to deal with high


demands will eventually lead to exhaustion,and exhaustion will lead to withdrawal fromwork (disengagement). This disengagement,in turn, will lead to decreased extra-role per-formance. Nevertheless, we expect that jobdemands will be the most important an-tecedents of in-role performance, through theexperience of exhaustion, whereas job re-sources will be the most important predictorsof extra-role performance, through their in-fluence on disengagement.

Method

Participants and Procedure

The participants in the present study wereemployed in several different sectors and jobpositions. Warr (1990) has advised to includesuch a broad range of job positions for the testof relationships between job characteristicsand outcomes because this would increase theprobability to find variation in job characteris-tics. After a first phone call, supervisors fromseveral small and large companies received aletter explaining the goal of the study. Threedays later, they were contacted by telephoneagain to ask how many questionnaires couldbe sent. In total, 274 questionnaires were dis-tributed to a total of 11 companies. In the ac-companying letter, the anonymity and confi-dentiality of the data were emphasized. Inaddition, participants were instructed to askone colleague to fill out one separate page in-cluding questions regarding in-role and extra-role performance. The dyads were latermatched by using a unique code for each setof questionnaires. Participants and their col-leagues could send back the questionnairesseparately with stamped envelopes. Two andfour weeks after the distribution of the ques-tionnaires, reminder phone calls were made.

A total of 146 employees and their col-leagues filled out and returned the question-naire (response rate was 53%). The sampleincludes 65 males (45%) and 81 females(55%). Their ages ranged from 21 to 62 yearswith an average of 38 years (sd � 10.66). Themajority of the sample had a university de-gree (36%) or higher vocational training(32%). Organizational tenure was 14 years(sd � 11.20), and 86% of the sample were

salaried. Since we did not ask participants atwhich company or institute they worked, wedo not know how many different organiza-tions were included in the study. However,what we do know is that most participantsworked with people (72%); 23% worked pri-marily with information and 5% worked pri-marily with things. They were employed inthe following sectors: industrial work (6.9%),construction (.7%), trade (5.6%), pubs andrestaurants (1.4%), transportation (8.3%), fi-nancial institutions (1.4%), business services(28.5%), communications (6.9%), govern-ment (13.2%), education (1.4%), health care(12.5%), culture and recreation services(8.3%), or other (4.9%).

Measures

Job Demands. Three job demands poten-tially related to burnout were included inthe questionnaire—workload, emotionaldemands, and work-home conflict. Work-load was based on a Dutch version (Furda,1995) of Karasek’s (1985) job content in-strument. The scale includes five itemsthat refer to quantitative, demanding as-pects of the job. Examples are “Do youhave too much work to do?” “Do you haveto work very fast?” and “How often does itoccur that you have to work extra hard tofinish your work?” Responses could bemade on a five-point scale (1 � never, 5 �always). Emotional demands were based ona scale developed by Van Veldhoven andMeijman (1994) and included four items.Examples are “Does your work put you inemotional situations?” and “Do the peoplewho you meet through your work intimi-date you?” (1 � never, 5 � always). Finally,work-home conflict was assessed with threeitems, based on Geurts (2000). For exam-ple: “How often does it happen that yourwork schedule makes it difficult for you tofulfill your domestic obligations?” and“How often do you find it difficult to fulfillyour domestic obligations because you areconstantly thinking about work?” (1 �never, 5 � always).

Job Resources. Three job resources were in-cluded in the questionnaire—autonomy, pos-

Three jobdemandspotentiallyrelated toburnout were includedin thequestionnaire—workload,emotionaldemands, andwork-homeconflict.


sibilities for professional development, andsocial support from colleagues. Autonomywas assessed with a three-item scale, basedon Karasek’s (1985) job content instrument.Example items are “I can decide myself howI execute my work” and “On my job, I havefreedom to decide how I do my work” (1 �never, 5 � always). Possibilities for profes-sional development were measured with thethree-item scale of Bakker, Demerouti, Tariset al. (2003), including “My work offers methe opportunity to learn new things” and “Ihave sufficient possibilities to develop myselfat work” (1 � totally disagree, 5 � totallyagree). Social support was measured withthree items of the scale developed by VanVeldhoven and Meijman (1994). Exampleitems are “Can you ask your colleagues forhelp if necessary?” and “Can you count onyour colleagues when you face difficulties atwork?” (1 � never, 5 � always). All responseswere coded such that higher scores referredto higher job demands and more job re-sources, respectively.

Burnout. The Oldenburg Burnout Inventory(OLBI; Demerouti et al., 2001, 2003) mea-sures the two core dimensions of burnout: ex-haustion and disengagement. The eight itemsof the exhaustion subscale are generic andrefer to general feelings of emptiness, overtax-ing from work, a strong need for rest, and astate of physical exhaustion. Example itemsare “After my work, I usually feel worn outand weary” and “After working, I have enoughenergy for my leisure activities” (reversed) (1� totally disagree, 4 � totally agree). Fouritems were positively worded and four nega-tively. Disengagement refers to distancing one-self from the object and the content of one’swork and to negative, cynical attitudes andbehaviors toward one’s work in general. Thissubscale also comprises eight items, including“It happens more and more often that I talkabout my work in a negative way” and “I feelmore and more engaged in my work” (re-versed). Similar answering categories to theones employed for exhaustion were used.Again, four items were positively worded andfour negatively.1 The positive and negative ex-haustion and disengagement items were pre-sented in mixed form. A recent study among

232 Greek employees from different occupa-tional groups (e.g., banking and insurance,chemical industry) examined the factorial andconvergent validity of the OLBI and theMaslach Burnout Inventory–General Survey(MBI-GS; Demerouti et al., 2003). Results ofconfirmatory factor analyses supported theproposed factor structure for both instru-ments. In addition, the convergent and dis-criminant validity of the OLBI vis-à-vis theMBI-GS was supported by the results of mul-titrait-multimethod analyses.

In-role performance was assessed withnine items, based on Goodman and Svyantek(1999). Example items are “Demonstrates ex-pertise in all job-related tasks” and “Achievesthe objectives of the job.” Colleagues of theparticipants were asked to indicate the extentto which they found each statement charac-teristic of the participant (0 � not at all char-acteristic, 6 � totally characteristic).

Extra-role performance is defined as ac-tions that go beyond what is stated in formaljob descriptions and that increase organiza-tional effectiveness (McKenzie, Podsakoff, &Fetter, 1991). The instrument utilized in thepresent research was Goodman and Svyan-tek’s (1999) measure and included sevenitems. They based their instrument onSmith, Organ, and Near’s (1983) organiza-tional citizenship behavior measure, and la-beled their measure “altruism,” characterizedas citizenship behavior toward individuals.Example items used by the observers (col-leagues) are “Willingly attends functions notrequired by the organization, but helps in itsoverall image,” and “Takes initiative to orientnew employees to the department eventhough not part of his/her job description.”The same answer categories as for in-roleperformance were used.

Strategy of Analysis

In order to test the JD-R model, we performedstructural equation modeling (SEM) analysesusing the AMOS software package (Arbuckle,1997). The fit of the model to the data was ex-amined with the adjusted-goodness-of-fitindex (AGFI) and the root mean square errorof approximation (RMSEA). Further, the non-normed fit index (NNFI), the comparative fit

Extra-roleperformance isdefined asactions that gobeyond what isstated in formaljob descriptionsand thatincreaseorganizationaleffectiveness.


index (CFI), and the incremental fit index(IFI) are utilized. In general, models with fitindices � .90 and an RMSEA � .08 indicate aclose fit between the model and the data(Browne & Cudeck, 1989; Hoyle, 1995). Thesix job characteristics were modelled in two la-tent factors, one representing job demands(three indicators) and the other job resources(three indicators), which were treated as ex-ogenous variables in the model. The twoburnout dimensions—exhaustion and disen-gagement—were included as endogenous,mediating variables. The two multi-itemscales were treated as single indicators of eachconstruct. We corrected for measurementerror by setting the random error variance as-sociated with each construct equal to theproduct of its variance and the quantity oneminus the estimated reliability (Bollen, 1989).This approach has been used in several previ-ous studies (e.g., Frone, Russell, & Cooper,1992; Schaubroeck, Cotton, & Jennings,1989), and the utility of this approach hasbeen supported by a study by Netemeyer,Johnston, and Burton (1990). Finally, in-roleand extra-role performance were each indi-cated by the multi-item scales introduced be-fore and included as endogenous outcomevariables. Correction for measurement errorwas realized similar to the procedure followed

for the two burnout scales. Finally, the latentfactors of job demands and resources were al-lowed to correlate, and the hypothesized rela-tionships were included in the model.

Results

Descriptive Statistics

Table I shows the means, intercorrelations,and the internal consistencies (Cronbach’salpha) of the scales included in the analyses.As can be seen from this table, most scalesshow reasonable to good reliabilities. Note,however, that the reliability coefficients forwork pressure and autonomy are somewhatlow (Cronbach’s alpha is .69 and .68, respec-tively). Preliminary analyses revealed that de-mographic variables were not substantiallyrelated to the model components, and thatinclusion of these variables in the structuralequation model did not significantly affectthe results. They were therefore omittedfrom further analyses.

Several relationships from Table I areworth noting. First, the raw scores of the threejob demands are only marginally (not signifi-cantly) related to the other ratings of in-roleperformance, while among the job resources“possibilities for professional development” is

Means, Standard Deviations, Internal Consistencies (Standardized Alphas—on the Diagonal) and Correlationsbetween the Variables (N � 146)

Range M sd 1 2 3 4 5 6 7 8 9 10

1. Work pressure (1–5) 2.96 .80 (.69)2. Emotional demands (1–5) 1.98 .67 .32** (.80)3. Work-home interference (1–5) 1.82 .65 .47** .33** (.72)4. Autonomy (1–5) 3.78 .73 .17* .20* .24** (.68)5. Possibilities development (1–5) 3.81 .77 .27** .18* .19* .48** (.86)6. Social support (1–5) 4.14 .63 –.10 –.14 –.29** .21* .31** (.81)7. Exhaustion (1–4) 2.09 .44 .29** .24** .38** –.05 –.13 –.32** (.75)8. Disengagement (1–4) 2.16 .50 –.12 –.16 –.04 –.43** –.70** –.35** .35** (.81)9. In-role performance

(other) (0–6) 4.22 .94 .15 .00 .08 .03 .11 .10 –.23* –.16* (.90)10. Extra-role

performance (other) (0–6) 4.17 .95 .20* .08 .14 .10 .22** .08 –.18* –.24** .73** (.88)

* p � .05, ** p � .01.

TABLE I


particularly related to other-ratings of extra-role performance. Second, both exhaustionand disengagement are significantly and nega-tively related to in-role and extra-role perfor-mance. Third, several job demands and re-sources show unexpected positive relationshipswith each other. These relationships will be ad-dressed in the discussion section.

Dimensionality of Job Demands andResources

The next step in the analyses was to test thedimensionality of the job demands-resourcesmeasurement model. The observed variableswere, in this case, all items measuring theworking conditions. These were used as theindicators of the first-order latent factors(i.e., the specific working conditions). Thespecific working conditions were the indica-tors of two second-order latent factors: jobdemands and job resources. This measure-ment model showed a reasonable fit to thedata: �2 (202) � 306.07, AGFI � .81,RMSEA � .06, NNFI � .90, CFI � .92, IFI� .92. All items had significant loadings onthe intended working conditions, and allworking conditions had significant loadingson the intended second-order latent factors.

Probably more important, the proposedmeasurement model was significantly betterthan a model including only one second-order latent factor (i.e., the general workingenvironment), ��2 (1) � 21.75, p � .001.

Test of the Job Demands-Resources Model

SEM analyses were used to test the JD-Rmodel and hypotheses 1, 2, and 4 (see Fig-ure 1). The results showed that the pro-posed model did not fit adequately to thedata (see first row in Table II). Inspection ofthe modification indices showed that thiswas particularly due to the path betweenjob resources and exhaustion. Therefore,this path was included in a second model(M2). This revised model fit closely to thedata and was significantly better than theinitial model, ��2 (1) � 18.58, p � .001.With the exception of the AGFI, which issensitive to sample size, all fit indices havevalues higher than .90, and the RMSEA is.08 (see second row in Table II).

Importantly, all indicators loaded signifi-cantly on the intended latent factors, and theproposed relationships in the JD-R were sig-nificant, and in the expected direction. Thecoefficient of the path from job demands to

Results of Structural Equation Modeling: Fit Indices of the Job Demands-Resources Modeland the Alternative Models, Standardized Maximum Likelihood Estimates (N � 146)

Model �2 df GFI RMSEA NNFI CFI IFI

M1. JD-R model 75.79 32 .85 .09 .85 .90 .90 M2. JD-R model, revised 57.21 31 .88 .08 .91 .94 .94 M3. Alternative model 56.96 30 .88 .08 .90 .94 .94 M4. Alternative model 55.34 29 .88 .08 .90 .94 .94 M5. Direct effects model 53.42 29 .88 .08 .91 .94 .94 M6. Direct effects model 55.25 29 .88 .08 .90 .94 .94 M7. Final, revised JD-R model 53.93 30 .88 .07 .91 .94 .95 M0. Null model 664.73 45 .50 .25

Note. �2 � chi-square; df � degrees of freedom; GFI � goodness-of-fit index; RMSEA � root mean square error of approx-imation; NNFI � non-normed fit index; CFI � comparative fit index; IFI � incremental fit index; M2 � Revised JD-Rmodel, including the path from job resources to exhaustion; M3 � Alternative model, including the path from job de-mands to disengagement; M4 � Alternative model, including the paths from exhaustion to extra-role performance, andfrom disengagement to in-role performance; M5 � direct effects model, including the paths from job demands to in-roleperformance, and from job resources to extra-role performance; M6 � direct effects model, including the paths from jobdemands to extra-role performance, and from job resources to in-role performance; M7 � Final, revised JD-R model, in-cluding the path from job resources to exhaustion, and the path from job demands to in-role performance.

TABLE II


exhaustion was positive and highly signifi-cant (� � .77; t � 4.76, p � .001), whereasthe coefficient of the path from job resourcesto disengagement was negative and highlysignificant (� � –.82; t � –8.75, p � .001). Inaddition, the coefficient of the additionalpath from job resources to exhaustion wasnegative (� � –.52; t � –4,10, p � .001).However, consistent with the JD-R model,job resources were more strongly related todisengagement than to exhaustion (criticalratio for differences between parameters �3.42, p � .01), and job demands were morestrongly related to exhaustion than job re-sources (critical ratio for differences �–5.23, p � .001).

Furthermore, the coefficient of thepath from exhaustion to in-role perfor-mance was � � –.18 (t � –2.32, p � .05),whereas the coefficient of the path fromdisengagement to extra-role performancewas � � –.21 (t � –3.05, p � .01). Finally,the covariation between job demands andjob resources was � � .38 (t � 3.06, p �.01). The revised JD-R model explained56% of the variance in exhaustion, 86% ofthe variance in disengagement, 3% of thevariance in in-role performance, and 4% ofthe variance in extra-role performance.

In order to test the alternative possibilitythat the latent factor job demands is relatedto disengagement, this path was included inthe model (M3). As can be seen from TableII, this modification hardly affected the fitindices, ��2 (1) � .25, n.s. In addition, out-put inspection revealed that the relationshipbetween job demands and disengagementwas not significant (� � –.09; t � –.51, n.s.).A second alternative model to be tested in-cluded the paths from exhaustion to extra-role performance, and from disengagementto in-role performance (M4). This model wasalso not better than the revised JD-R model(M2), ��2 (2) � 1.87, n.s., and the two addi-tional paths were not significant (exhaustion→ extra-role performance, � � –.02, t � 1,n.s.; disengagement → in-role performance,� � –.13, t � –1.21, n.s.).

In a next step, two direct effects modelswere tested. The first model (M5) includeddirect relationships between demands andin-role performance, and between job re-

sources and extra-role performance. Table IIshows that this alternative model signifi-cantly improved on M2, ��2 (2) � 3.79, p �.05. However, output inspection revealedthat only the direct relationship between jobdemands and in-role performance was signif-icant, � � .24, t � 1.86, p � .05 (job re-sources → extra-role performance, � � .17, t� 1, n.s.). The second direct effects model(M6) included the direct paths from job de-mands to extra-role performance, and fromjob resources to in-role performance. Consis-tent with the JD-R model, these additionsdid not improve the fit between the modeland the data, ��2 (2) � 1.96, n.s., and theadditional paths were not significant (job de-mands → extra-role performance: � � .06, t� 1, n.s.; job resources → in-role perfor-mance, � � .13, t � 1.39, n.s.).

Taken together, these findings suggestthat the proposed model should be modi-fied by including the path from job re-sources to exhaustion, and the direct pathfrom job demands to in-role performance.Table II shows that this model (M7) fitsclosely to the data, with all fit-indicesabove .90 (except the AGFI, which is .88),and an RMSEA of .06. All parameters inthis final model are significant, p’s � .05.The final model is displayed in Figure 2. Insum, job demands are the most importantantecedents of in-role performance, prima-rily through the experience of exhaustion(cf. hypothesis 1). Job resources, on theother hand, are the most important predic-tors of extra-role performance, throughtheir influence on disengagement (cf. hy-pothesis 2). This final model explained 8%of the variance in in-role performance and8% of the variance in extra-role perfor-mance. Note that job resources are alsonegatively related to exhaustion. In addi-tion, our findings are consistent with hy-pothesis 4: Exhaustion has a positive rela-tionship with disengagement.

Interactions between Job Demands andResources

According to hypothesis 3, job resourcesbuffer the relationship between job demandsand exhaustion. In order to test this hypoth-

Job resources… are the mostimportantpredictors ofextra-roleperformance,through theirinfluence ondisengagement.


esis, a new model was built, retaining allvariables and relationships included in thefinal, revised JD-R model (see Figure 2), ex-cept job resources. This model treats “job de-mands” as the only exogenous latent variablein the model. The model was tested withmultigroup SEM analyses for employeeswith few versus many job resources (cf. Agui-nis, 2001; Schumacker & Lomax, 1996).These subgroups were formed following amedian-split procedure, where employeeswho scored lower than the median on thefactor including each of the three specific re-sources were considered as the “few job re-sources” group. The others were consideredas the group with many job resources.

The hypothesis would be confirmed ifthe model where the coefficient of the pathfrom job demands to exhaustion is con-strained to be equal in both groups wouldshow a worse fit to the data than the uncon-strained model, and if the job demands–ex-haustion parameter would be higher for the

“few job resources” group. The results ofmultigroup analyses showed that the uncon-strained model did not fit better to the datathan the constrained model, ��2 (1) � 1, n.s.This means that hypothesis 3 was rejected;job resources did not buffer the impact of jobdemands on exhaustion. However, throughthe analyses to test prior hypotheses 1, 2,and 4, job resources were found to have amain effect on exhaustion.

Discussion

Although most scholars and managers wouldagree that employee performance is of utmostimportance for organizations’ effectiveness,thus far, research on the relationship betweenjob burnout and performance has been scarceand produced mixed findings. We argued thatthe main reason for these mixed findings is alack of a sound theoretical basis. This studytherefore sought to investigate the relation-ship between burnout and performance using

Figure 2. Maximum Likelihood Estimates of the Revised JD-R Model of Burnout and Performance (N � 146).


the job demands-resources model (Bakker,Demerouti, De Boer et al., 2003; Demeroutiet al., 2000, 2001). Two main hypotheseswere formulated, namely that job demandswould be the most important predictors of in-role performance, through their relationshipwith the exhaustion component of burnout(i.e., chronic feelings of physical, cognitive,and emotional fatigue), whereas job re-sources would be the most important predic-tors of extra-role performance, through theirrelationship with the disengagement dimen-sion of burnout (distancing oneself fromone’s work, work objects, or work content).The results were greatly supportive of bothhypotheses, with some qualifications.

The starting point for our analysis wasthe OLBI, which includes feelings of ex-haustion and disengagement from work asthe core dimensions of burnout. Exhaustionas defined by the OLBI not only implies af-fective exhaustion but also physical andcognitive exhaustion, whereas disengage-ment includes an extensive and intensivereaction in terms of an emotional, cogni-tive, and behavioral rejection of the job.Next, we introduced two central dimensionsof performance: in-role performance, whichgauges the activities strictly required on thejob, and extra-role performance, whichrefers to those activities that enhance thecollective character of the organization. Inorder to avoid common-method varianceproblems, we asked participants’ colleaguesto evaluate participants’ in-role and extra-role performance.

Differential Effects of Job Demands andResources

The central idea in this article is that the de-mands and resources that exist within em-ployees’ working environments both have dif-ferential effects on in-role and extra-roleperformance. The JD-R model was indeedcapable of explaining variance in other-rat-ings of performance in the predicted way.These findings are consistent with Bakker,Demerouti, De Boer et al.’s (2003) study,which showed that job demands were themost important predictors of exhaustion, andindirectly of absence duration during the

one-year follow-up (an indicator of healthproblems), whereas job resources were themost important predictor of reduced com-mitment (a form of disengagement), and in-directly of Time 2 absence frequency (an in-dicator of reduced motivation).

First, when demands are high—specifi-cally when workload, emotional demands, andwork-home conflicts are elevated—it becomesdifficult for employees to allocate their atten-tion and energy efficiently because they haveto engage in greater activation and/or effortand this, in turn, negatively affects their per-formance. Moreover, our findings are consis-tent with and expand findings of previousstudies on performance. For instance, Wrightand Bonett (1997) found that, among theburnout dimensions, only exhaustion was neg-atively related to in-role performance. Theirlongitudinal study revealed nonsignificant re-lationships between depersonalization (ahuman-service-related form of disengage-ment) and performance (as rated by supervi-sors). Also, Cropanzano, Rupp, and Byrne(2003) found that emotional exhaustion wassignificantly negatively related to in-role per-formance as rated by supervisors, while the ef-fect of emotional exhaustion on organizationalcitizenship behavior was fully mediated by or-ganizational commitment (a measure that cor-responds in a way to our disengagement di-mension). Additionally, the finding that thetwo burnout dimensions were strongly relatedto (in-role or extra-role) performance whileperceptions of work characteristics were unre-lated to it has also been reported in the litera-ture. Accordingly, role problems (conflict, am-biguity, overload; Schaubroeck & Fink, 1998;Singh et al., 1994), workload (Schaubroeck &Fink, 1998), skill utilization (Schaubroeck &Fink, 1998), job control, and social support(Sargent & Terry, 2000; Schaubroeck & Fink,1998) were not related to overall measures ofperformance.

Extra-role performance, on the otherhand, is a reflection of people’s availability ofresources within the organization—specifi-cally when autonomy, social support, andpossibilities for professional development arehigh. In exchange for the availability of re-sources, employees are willing to go beyondtheir personal roles and engage in activities

The centralidea in thisarticle is thatthe demandsand resourcesthat existwithinemployees’workingenvironmentsboth havedifferentialeffects on in-role and extra-roleperformance.


that benefit the organization as a whole.These findings on extra-role performance arefairly unique, since previous studies havemainly related this aspect of performance tovolitional variables associated with individualdifferences in motivational characteristics,predispositions, or person-environment fit(Goodman & Svyantek, 1999). While taskautonomy was related to contextual perfor-mance, another term for extra-role perfor-mance (Gellalty & Irving, 2001), organiza-tional support did not foster organizationalcitizenship behavior in the study of Lambert(2000). Similar to our findings, Munene(1995) found that job involvement (a moti-vational variable that comes close to our[dis]engagement measure) was positively re-lated to organizational citizenship behavior.

What makes our findings noteworthy isthat they link specific aspects of organizationsto two specific psychological mechanisms,which, in turn, explain two different dimen-sions of performance. In fact, the JD-R modelmakes the burnout syndrome straightforwardand assessable to management intervention.High demands in an employee’s job generatedecrements in primary task performance, be-cause they diminish people’s ability to performwell (Wright & Cropanzano, 1998). Put differ-ently, if management is capable of reducingthe demands—for instance, by means of pro-viding employees a better focus or by requiringthem to have a proper workload—(burned-out)employees’ performance should increase. TheJD-R model suggests that extra-role perfor-mance is also a reflection of the organizationalenvironment but more specifically a reflectionof the available resources, and, once again, re-sources imply such job characteristics like au-tonomy, social support, and possibilities forself-growth. When employees notice that theyhave resources available and are not presentlyoverwhelmed by job demands, they, in ex-change for those resources, tend to engage inpro-organizational actions.

This research project on the relationshipbetween the JD-R model and extra-role per-formance is in line with Organ and Paine’s(1999) argument that researchers ought tobetter articulate what it is in the organiza-tional environment that kindles employees’investment in extra-role behaviors. As we are

moving toward an era in which people workin teams and must be able to form cross-func-tional teams, the employee’s ability to engagein extra-role performance has become key,but management now is better able to stimu-late those crucial behaviors. Building uponOrgan and Paine’s (1999) suggestion, the or-ganizational resources identified in this studyare probably a better starting point for inter-ventions and concrete measures than the ear-lier positive affective states to which extra-role behavior has been linked.

Our findings are also consistent withMoore’s (2000) causal attribution approach towork exhaustion consequences. Accordingly,individuals experiencing feelings of exhaustionwill be motivated to find out what the causesof their feelings are. How individuals perceivethe cause of their exhaustion and attribute theblame has enormous consequences for action(cf. Pines & Aronson, 1988). An individual’s at-tribution for the cause of their exhaustionforms the basis for decisions about how to actin order to bring about a discontinuance ofsuch feelings. Thus, attribution theory is usedto model reactions to exhaustion. Although at-tributions were not directly investigated in thepresent study, our findings are in keeping withMoore’s (2000) approach. Results showed thatfeelings of exhaustion were positively related todisengagement. This suggests that employeespsychologically withdrew from their work incases where they felt exhausted (see alsoBakker et al., 2000; Leiter & Maslach, 1988).In addition, results showed that job demandswere the most important predictors of exhaus-tion, which, in turn, contributed to explainingin-role performance. Thus, exhausted individ-uals were most likely to follow the strategy ofreducing their effort, thereby lowering the im-pact of external demands on their feelings offatigue. In a similar vein, results showed that(lack of) job resources were the most impor-tant predictors of disengagement, which, inturn, was a unique predictor of extra-role per-formance. This suggests that those employeeswho were disengaged limited their organiza-tional citizenship behaviors, thereby recipro-cating the lack of job resources.

In general, our model could explain 8% ofboth in-role and extra-role performance. Thesepercentages are clearly higher than those re-

... the JD-Rmodel makesthe burnoutsyndromestraightforwardand assessableto managementintervention.


ported in previous studies using other-ratingsof performance (about 1%; Schaufeli & Enz-mann, 1998). Still, it seems that burnout, incontrast to the prevailing view, is not stronglylinked to (low levels of) actual performance.What can we conclude from that? Eitherburnout or diminished well-being in general isindeed not a good predictor of work-relatedperformance, or the instruments used for mea-suring job performance (including the onesused in our study) are not adequate. As Arveyand Murphy (1998) proposed, we need to eval-uate a wide variety of organizational activitiesusing a larger number of predictor vehicles inorder to have a greater likelihood that perfor-mance will be more predictable.

Other Relationships in the JD-R Model

Job resources did not have a buffering ef-fect on the relationship between job de-mands and exhaustion (hypothesis 3). Thisresult is inconsistent with the findings ofBakker, Demerouti, & Euwema (2003) andBakker, Demerouti, Taris et al. (2003), whofound that several job resources were capa-ble of diminishing the impact of job de-mands on exhaustion. The nonsignificantinteraction effect may be attributable to thespecific demands and resources included inthe current study, as well as the type of jobpositions that were investigated. Note, how-ever, that many studies on the interactionsproposed by Karasek’s (1979) DCM and theextended demand-control-support model(DCS; Johnson & Hall, 1988) have similarlyfailed to produce significant results (cf. DeJonge & Kompier, 1997; Schreurs & Taris,1998; Van der Doef & Maes, 1999). Thismeans that resources such as autonomy andsocial support from colleagues only havelimited capability of buffering the undesiredimpact of job demands (e.g., workload,emotional demands) on work-related strain(including exhaustion).

In contrast, job resources were directlyand negatively related to exhaustion, al-though the strength of this relationship wassignificantly weaker than the predicted rela-tionship between job demands and exhaus-tion. This finding is not in line with earlierfindings (Bakker, Demerouti, De Boer et al.,

2003; Demerouti et al., 2000, 2001), butindicates that a lack of resources can pro-duce feelings of fatigue as well. We can onlyspeculate about the reason for this finding,but evident in the present study was thattwo of the three job resources (autonomyand possibilities for self-growth) werehigher when job demands were higher. Thissuggests that those employees who couldcount on resources in their working envi-ronment were also the ones who were ex-posed to the most job demands. Neverthe-less, our findings suggest that thedevelopment of each burnout component isinfluenced by a specific constellation ofwork conditions. When job demands arehigh, employees experience primarily ele-vated levels of exhaustion, whereas disen-gagement is affected to a lesser extent (andthat through the experience of exhaustion).When job resources are lacking, employeesprimarily show high levels of disengage-ment, whereas exhaustion is affected to alesser extent. It follows that in jobs withhigh job demands and limited job resources,employees develop exhaustion and disen-gagement, that is, burnout. Generallyspeaking, there seem to be two mainprocesses that take place in the working en-vironment. The first process is a stressprocess that initiates from job demands andresults in exhaustion. The second process ismotivational in nature and is driven by theavailability of resources and resulting feel-ings of dedication. When resources arelacking, individuals experience cynicism to-ward their jobs.

Finally, the present study also substanti-ated the proposed relationship between thetwo burnout components, namely, exhaus-tion and disengagement (hypothesis 4). Con-sistent with Leiter’s (1993) process model ofburnout, feelings of exhaustion were posi-tively related to disengagement. The burnoutliterature seems to systematically find evi-dence for this relationship (e.g., Bakker etal., 2000; Cordes et al., 1997) and, conse-quently, this relationship should be added tothe JD-R model. Apparently, employees at-tempt to gain emotional distance from theirjob as a way of coping with their work-relatedfeelings of exhaustion.

It follows thatin jobs withhigh jobdemands andlimited jobresources,employeesdevelopexhaustion anddisengagement,that is,burnout.


Strengths and Weaknesses

Some weaknesses of this study should bementioned as well. First, we used cross-sec-tional data to examine presumed causal rela-tionships between the variables in the JD-Rmodel, and the response rate was 53%.Therefore, the present findings are tentativeuntil replicated in studies with a higher re-sponse rate and longitudinal designs. For ex-ample, one may argue that in-role perfor-mance is also an antecedent of job demands,since working hard and doing well in one’s jobmay positively influence the perception of jobdemands. Indeed, evidence for such reversedcausal effects has been found in some previ-ous longitudinal studies (see Zapf et al.,1996, for an overview). On the positive side,however, our findings were basically in linewith our hypotheses and consistent with pre-vious research (Bakker, Demerouti, De Boeret al., 2003; Demerouti et al., 2001). Al-though some of our measures were highlycorrelated (e.g., exhaustion and disengage-ment, in-role and extra-role performance), wefound strong evidence for two processes: astress process and a motivational process. Inaddition, we used a heterogeneous sample ofemployees from several organizations. Thus,our findings seem generalizable over differentcompanies and working environments. An-other positive feature of our study is that weused peer ratings of performance, which maybe less subject to common method variancethan self-reports. A second weakness of thisstudy is that we could only incorporate a fewjob demands and resources in our question-naire. Future studies may include more jobcharacteristics in order to test the full poten-tial of the JD-R model in predicting burnoutand performance. Finally, we restricted ouranalysis to only one type of extra-role perfor-mance: altruism. Future studies are neededto examine whether similar results are foundwhen using other organizational citizenshipbehavior dimensions, such as conscientious-ness, courtesy, and sportsmanship.

Practical Implications

The JD-R model assumes that whereas everyoccupation may have its own specific risk

factors associated with burnout, these factorscan be classified in two general categories(i.e., job demands and job resources), thusconstituting an overarching model that maybe applied to various occupational settings,irrespective of the particular demands and re-sources involved. The central assumption ofthe JD-R model is that burnout develops—ir-respective of the type of job or occupation—when (certain) job demands are high andwhen (certain) job resources are limited.

This implies that the JD-R model can beused as a tool for human resource manage-ment. In close collaboration with human re-source managers and consultants, the modelhas now been applied in over 130 different or-ganizations in the Netherlands. Because everyoccupation may have its own unique risk fac-tors of burnout, we have started to use a two-stage procedure in our organizational researchwith the JD-R model. The first qualitativephase of the research includes exploratory in-terviews with job incumbents from differentlayers of an organization (e.g., representativesfrom management, staff, shop floor). The in-terviews, which last approximately 45 min-utes, include open questions about the jobs ofthe interviewees and refer to positive and neg-ative aspects. The incorporation of a qualita-tive phase in the research is valuable becauseit can generate knowledge about unexpected,organization-specific job demands and job re-sources that will be overlooked by highly stan-dardized approaches. For example, it is con-ceivable that in one organization (e.g., aproduction company), employees are exposedto high physical job demands, whereas in an-other organization (e.g., an insurance com-pany), employees are not exposed to such de-mands at all. In addition, in certaincompanies, employees are confronted withmergers, which may cause job insecurity androle ambiguity. Such organization-specific jobdemands can be traced in the exploratoryqualitative phase.

In the second phase of the research, thejob demands and job resources potentially as-sociated with burnout are operationalized initems and scales and incorporated in a tailor-made questionnaire. All employees from anorganization are then invited to fill out thisquestionnaire. This enables a quantitative

Theincorporation of a qualitativephase in theresearch isvaluablebecause it cangenerateknowledgeaboutunexpected,organization-specific jobdemands andjob resourcesthat will beoverlooked byhighlystandardizedapproaches.


analysis of the job demands and job re-sources that have been identified qualita-tively and that potentially play a role in thedevelopment of burnout. The analysis usu-ally concentrates on differences betweendepartments and job positions in terms ofjob demands, resources, and burnout andits consequences. In some projects, man-agers participate in JD-R workshops beforethe start of the study so they can learn howto use the information that will becomeavailable. The subgroup analyses can pro-vide clear indications for interventions,

since they highlight the strengths and theweaknesses of departments and job posi-tions. Tailor-made interventions are thenpossible, aimed at reducing the identifiedjob demands and increasing the most im-portant job resources, which, in turn, maydecrease the risk for burnout and conse-quently improve performance at the taskand contextual level.

We wish to thank Wiesje Monster andMyrthe Boswinkel for their help with datacollection.

Arnold B. Bakker, PhD, is an associate professor in the Department of Social andOrganizational Psychology, Utrecht University, Netherlands. He graduated fromGroningen University, and has published several articles on the job demands- re-sources model, attitudes, and burnout. His research interests include work engage-ment, crossover of stress and happiness, and organizational performance.

Evangelia Demerouti, PhD, works as an assistant professor in the Department ofSocial and Organizational psychology at Utrecht University, Netherlands. She gradu-ated from Oldenburg University (Germany) and wrote her doctoral thesis on the JobDemands-Resources model. Her main research interests concern topics from the fieldof work and health including burnout, work-family interface, crossover of strain, flex-ible working times, and shift-work, as well as job design.

Willem Verbeke is the chaired professor in sales and account management withinthe Department of Marketing and Organization (School of Economics), research starmember at the Erasmus Research Institute of Management (ERIM), and director ofthe Institute for Sales and Account Management (ISAM) at Erasmus University, Rot-terdam. His research interests are sales and account management networks and therole of emotions within sales and account management. He has published in journalssuch as the Journal of Marketing and the Journal of Applied Psychology.

NOTE

1. The original OLBI (Demerouti et al., 2001,2003) included fifteen items: seven exhaus-tion items (four negatively and three positivelyworded items) and eight disengagement items(five negatively and three positively wordeditems). The OLBI was slightly adjusted byadding one positively framed exhaustion itemand rephrasing one negatively framed disen-gagement item. Thus, we used a balanced in-strument including eight negatively framedand eight positively framed items.

REFERENCES

Aguinis, H. (2001, May). Pitfalls in estimatingmoderating effects: Implications for W&Opsychology research and practice. Paper pre-sented at the 10th European Congress onWork and Organizational Psychology, Prague,Czech Republic.

Antonovsky, A. (1987). Unraveling the mystery ofhealth: How people manage stress and staywell. San Francisco, CA: Jossey-Bass.

Arbuckle, J. L. (1997). Amos users’ guide. Version3.6. Chicago: Smallwaters.


Aronson, E., Pines, A. M., & Kafry, D. (1983). Aus-gebrannt. Vom Überdruß zur Selbstentfaltung.[Burnout: From tedium to personal growth].Stuttgart, Germany: Klett-Cotta.

Arvey, R. D., & Murphy, K. R. (1998). Performanceevaluation in work settings. Annual Review ofPsychology, 49, 141–168.

Bakker, A. B., Demerouti, E., De Boer, E., &Schaufeli, W. B. (2003). Job demands and jobresources as predictors of absence duration andfrequency. Journal of Vocational Behavior, 62,341–356.

Bakker, A. B., Demerouti, E., & Euwema, M.(2003). Which job resources buffer the impactof job demands on burnout? Manuscript sub-mitted for publication.

Bakker, A. B., Demerouti, E., & Schaufeli, W. B.(2002). The validity of the Maslach Burnout In-ventory—General survey: An Internet study.Anxiety, Stress, and Coping, 15, 245–260.

Bakker, A. B., Demerouti, E., Taris, T., Schaufeli, W.B., & Schreurs, P. (2003). A multi-group analy-sis of the job demands-resources model in fourhome care organizations. International Journalof Stress Management. 10, 16–38.

Bakker, A. B., Schaufeli, W. B., Sixma, H., Bosveld,W., & Van Dierendonck, D. (2000). Patient de-mands, lack of reciprocity, and burnout: A five-year longitudinal study among general practi-tioners. Journal of Organizational Behavior, 21,425–441.

Behrman, D. N., & Perreault, W. D. J. (1984). A rolestress model of the performance and satisfac-tion of industrial salespersons. Journal of Mar-keting, 48, 9–21.

Bhagat, R. S., Allie, S. M., & Ford, D. L. (1995).Coping with stressful events: An empiricalanalysis. In R. Crandall & P. L. Perrewé(Eds.), Occupational stress: A handbook. Se-ries in health psychology and behavioral med-icine (pp. 93–112). Philadelphia, PA: Taylorand Francis.

Bollen, K. A. (1989). Structural equations with la-tent variables. New York: Wiley.

Browne, M. W., & Cudeck, R. (1989). Single samplecross-validation indices for covariance structures.Multivariate Behavioral Research, 24, 445–455.

Cordes, C. L., & Dougherty, T. W. (1993). A reviewand integration of research on job burnout.Academy of Management Review, 18, 621–656.

Cordes, C. L., Dougherty, T. W., & Blum, M. (1997).Patterns of burnout among managers and pro-

fessionals: A comparison of models. Journal ofOrganizational Behavior, 18, 685–701.

Craig, A., & Cooper, R. E. (1992). Symptoms of acuteand chronic fatigue. In A. P. Smith & D. A. Jones(Eds.), Handbook of human performance (pp.289–340). San Diego: Academic Press.

Cropanzano, R., Rupp, D. E., & Byrne, Z. S. (2003).The relationship of emotional exhaustion towork attitudes, job performance, and organiza-tional citizenship behaviors. Journal of AppliedPsychology, 88, 160–169.

De Jonge, J., & Kompier, M. A. J. (1997). A criticalexamination of the demand-control-supportmodel from a work psychological perspective.International Journal of Stress Management, 4,235–258.

Demerouti, E., Bakker, A. B., Nachreiner, F. &Schaufeli, W. B. (2000). A model of burnoutand life satisfaction among nurses. Journal ofAdvanced Nursing, 32, 454–464.

Demerouti, E., Bakker, A. B., Nachreiner, F., &Schaufeli, W. B. (2001). The job demands-re-sources model of burnout. Journal of AppliedPsychology, 86, 499–512.

Demerouti, E., Bakker, A.B., Vardakou, I., & Kantas,A. (2003). The convergent validity of twoburnout instruments: A multitrait-multimethodanalysis. European Journal of Psychological As-sessment, 19, 12–23.

Fisher, C. D., & Gitelson, R. (1983). A meta-analy-sis of the correlates of role conflict and ambi-guity. Journal of Applied Psychology, 68,320–333.

Frankenhaeuser, M. (1979). Psychoneuroendocrineapproaches to the study of emotion as related tostress and coping. In H. E. Howe & R. A. Dien-stbier (Eds.), Nebraska symposium on motiva-tion 1978 (pp. 123–161). Lincoln: University ofNebraska Press.

Frankenhaeuser, M. (1980). Psychoneuroendocrineapproaches to the study of stressful person-en-vironment transactions. In H. Selye (Ed.),Selye’s guide to stress research (Vol. I; pp.46–70). New York: Van Nostrand Reinhold.

Frankenhaeuser, M., & Johansson, G. (1986). Stressat work: Psychobiological and psychosocial as-pects. International Review of Applied Psychol-ogy, 35, 287–299.

Freudenberger, H. J. (1974). Staff burn-out. Journalof Social Issues, 30, 159–166.

Freund, A., Li, K. Z., & Baltes, H. P. B. (1999).Successful development and aging. In J.


Brandtstädter & R. M. Lerner (Eds.), Actionand development: Theory and researchthrough the life span (pp. 401–434). Thou-sand Oaks, CA: Sage Publications.

Fried, Y., Ben-David, H. A., Tiegs, R. B., Avital, N.,& Yeverechyahu, U. (1998). The interactive ef-fect of role conflict and role ambiguity on jobperformance. Journal of Occupational and Or-ganizational Psychology, 71, 19–27.

Frone, M. R., Russell, M., & Cooper, M. L. (1992).Antecedents and outcomes of work-family con-flict: Testing a model of the work-family inter-face. Journal of Applied Psychology, 77, 65–78.

Furda, J. (1995). Werk, persoon en welzijn: Eentoets van het JD-C model [Work, personality,and well-being: A test of the JD-C model]. Un-published doctoral dissertation, Utrecht Uni-versity, Netherlands.

Gaillard, A. W. K. (2001). Stress, workload, and fa-tigue as three biobehavioral states: A generaloverview. In P. A. Hancock & P. A. Desmond(Eds.), Stress, workload, and fatigue (pp.623–640). Mahwah, NJ: Lawrence Erlbaum.

Gellalty, I. R., & Irving, P. G. (2001). Personality, au-tonomy and contextual performance of man-agers. Human Performance, 14, 231–245.

Geurts, S. A. E. (2000). SWING: Survey work-homeinterference Nijmegen. Internal research report.Nijmegen, Netherlands: University of Nijmegen.

Goodman, S. A., & Svyantek, D. J. (1999). Person-organization fit and contextual performance:Do shared values matter? Journal of VocationalBehavior, 55, 254–275.

Hackman, J. R., & Oldham, G. R. (1976). Motiva-tion through the design of work: Test of a the-ory. Organizational Behavior and Human Per-formance, 16, 250–279.

Haines, V. A., Hurlbert, J. S., & Zimmer, C. (1991).Occupational stress, social support, and thebuffer hypothesis. Work and Occupations, 18,212–235.

Hobfoll, S. E. (1989). Conservation of resources: Anew approach at conceptualizing stress. Ameri-can Psychologist, 44, 513–524.

Hobfoll, S. E., & Freedy, J. (1993). Conservation ofresources: A general stress theory applied toburnout. In W. B. Schaufeli, C. Maslach, & T.Marek (Eds.), Professional burnout: Recent de-velopments in theory and research (pp.115–129). Washington, DC: Taylor and Francis.

Hockey, G. R. J. (1993). Cognitive-energetical con-trol mechanisms in the management of work

demands and psychological health. In A. Bad-dely & L. Weiskrantz (Eds.), Attention: Selec-tion, awareness, and control (pp. 328–345).Oxford, UK: Clarendon Press.

Hockey, G. R. J. (1997). Compensatory control inthe regulation of human performance understress and high workload: A cognitive-energeti-cal framework. Biological Psychology, 45,73–93.

Hockey, G. R. J., Coles, M. G. H., & Gaillard, A. W.K., (1986). Energetical issues in research onhuman information processing. In G. R. J.Hockey & A. W. K. Gaillard (Eds.), Energeticsin human information processing (pp. 3–21).Dordrecht: Nijhoff.

Hoyle, R. H. (1995). The structural equation mod-eling approach: Basic concepts and funda-mental issues. In R. H. Hoyle (Ed.), Struc-tural equation modeling, concepts, issues,and applications (pp. 1–15). Thousand Oaks,CA: Sage.

Johnson, J. V., & Hall, E. M. (1988). Job strain, workplace social support and cardiovascular disease:A cross-sectional study of a random sample ofthe Swedish working population. AmericanJournal of Public Health, 78, 1336–1342.

Kahn, R. L., & Byosiere, P. (1992). Stress in organi-zations. In M. D. Dunnette & L. M. Hough(Eds.), Handbook of industrial and organiza-tional psychology (Vol. 3; pp. 571–650). PaloAlto, CA: Consulting Psychologists Press.

Karasek, R. A. (1979). Job demands, job decisionlatitude and mental strain: Implications for jobredesign. Administrative Science Quarterly,24, 285–308.

Karasek, R. (1985). Job content instrument: Ques-tionnaire and user’s guide, revision 1.1. Los An-geles: University of Southern California.

Keijsers, G. J., Schaufeli, W. B., Le Blanc, P. M.,Zwerts, C., & Reis-Miranda, D. (1995). Perfor-mance and burnout in intensive care units.Work and Stress, 9, 513–527.

Klein, D. J., & Verbeke, W. (1999). Autonomic feed-back in stressful environments: How do individ-ual differences in autonomic feedback relate toburnout, job performance, and job attitudes insalespeople? Journal of Applied Psychology, 84,911–924.

Lambert, S. J. (2000). Added benefits: The link be-tween work-life benefits and organizational cit-izenship behavior. Academy of ManagementJournal, 43, 801–815.


Latack, J. C., & Havlovic, S. J. (1992). Coping withjob stress: a conceptual evaluation frameworkfor coping measures. Journal of OrganizationalBehavior, 13, 479–508.

Lazaro, L., Shinn, M., & Robinson, P. E. (1985).Burnout, performance and job withdrawal be-havior. Journal of Health and Human Re-sources Administration, 7, 213–234.

Lee, R. T., & Ashforth, B. E. (1993). A further ex-amination of managerial burnout: Toward anintegrated model. Journal of Organizational Be-havior, 14, 3–20.

Lee, R. T., & Ashforth, B. E. (1996). A meta-analyticexamination of the correlates of the three di-mensions of job burnout. Journal of AppliedPsychology, 81, 123–133.

Leiter, M. P. (1993). Burnout as a developmentalprocess: Consideration of models. In W. B.Schaufeli, C. Maslach, & T. Marek (Eds.), Pro-fessional burnout: Recent developments in the-ory and research (pp. 237–250). Washington,DC: Taylor & Francis.

Leiter, M. P., & Maslach, C. (1988). The impact ofinterpersonal environment of burnout and orga-nizational commitment. Journal of Organiza-tional Behavior, 9, 297–308.

Leiter, M. P., & Schaufeli, W. B. (1996). Consis-tency of the burnout construct across occupa-tions. Anxiety, Stress and Coping, 9, 229–243.

Makin, P., Cooper, C., & Cox, C. (1996). Organiza-tions and the psychological contract. Westport,CT: Praeger.

Maslach, C., & Jackson, S. E. (1986). MaslachBurnout Inventory (2nd ed.). Palo Alto, CA:Consulting Psychologists Press.

Maslach, C., Jackson, S. E., & Leiter, M.P. (1996).Maslach Burnout Inventory. Manual (3rded.). Palo Alto, CA: Consulting PsychologistsPress.

Maslach, C., Schaufeli, W. B., & Leiter, M. P.(2001). Job burnout. Annual Review of Psy-chology, 52, 397–422.

McGrath, J. E. (1976). Stress and behavior in or-ganizations. In M. D. Dunnette (Ed.), Hand-book of industrial and organizational psychol-ogy (pp. 1351–1396). Chicago: Rand-McNally.

McKenzie, S. B., Podsakoff, P. M., & Fetter, R.(1991). Organizational citizenship behavior andobjective productivity as determinants of mana-gerial evaluations of salespersons’ performance.Organizational Behavior and Human DecisionProcesses, 50, 123–150.

Moore, J. E. (2000). Why is this happening? Acausal attribution approach to work exhaustionconsequences. Academy of Management Re-view, 25, 335–349.

Morrison, E. W. (1994). Role definitions and orga-nizational citizenship behavior: The importanceof employee’s perspective. Academy of Manage-ment Journal, 37, 1543–1567.

Motowidlo, S. J., & Van Scotter, J. R. (1994). Evi-dence that task performance should be distin-guished from contextual performance. Journalof Applied Psychology, 79, 475–480.

Munene, J. C. (1995). “Not-on-seat”: An investi-gation of some correlates of organizational cit-izenship behaviour in Nigeria. Applied Psy-chology: An International Review, 44,111–122.

Netemeyer, R. G., Johnston, M. W., & Burton, S.(1990). Analysis of role conflict and role ambi-guity in a structural equations framework. Jour-nal of Applied Psychology, 75, 148–157.

Organ, D. W., & Paine, J. B. (1999). A new kindof performance for industrial and organiza-tional psychology: Recent contributions tothe study of organizational citizenship behav-ior. In C. L. Cooper & I. T. Robertson (Eds.),International review of industrial and organi-zational psychology (Vol. 14; pp. 337–368).West Sussex: Wiley.

Parker, P. A., & Kulik, J. A. (1995). Burnout, self-and supervisor-related job performance, and ab-senteeism among nurses. Journal of BehavioralMedicine, 18, 581–599.

Pines, A., & Aronson, E. (1988). Career burnout:Causes and cures. New York: Free Press.

Podsakoff, P. M., & MacKenzie, S. B. (1994). Orga-nizational citizenship behaviors and sales uniteffectiveness. Journal of Marketing Research,31, 351–363.

Randall, M., & Scott, W. A. (1988). Burnout, jobsatisfaction, and job performance. AustralianPsychologist, 23, 335–347.

Sargent, L. D., & Terry, D. J. (2000). The moderat-ing role of social support in Karasek’s job strainmodel. Work & Stress, 14, 245–261.

Schaubroeck, J., Cotton, J. L., & Jennings, K. R.(1989). Antecedents and consequences of rolestress: A covariance structure analysis. Journalof Organizational Behavior, 10, 35–58.

Schaubroeck, J., & Fink, L. S. (1998). Facilitatingand inhibiting effects of job control and socialsupport on stress outcomes and role behavior: A


contingency model. Journal of OrganizationalBehavior, 19, 167–195.

Schaufeli, W. B., Bakker, A. B., Hoogduin, K.,Schaap, C., & Kladler, A. (2001). On the clini-cal validity of the Maslach Burnout Inventoryand the Burnout Measure. Psychology &Health, 16, 565–582.

Schaufeli, W. B., & Enzmann, D. (1998). Theburnout companion to study and practice: Acritical analysis. London: Taylor and Francis.

Schnake, M. (1991). Organizational citizenship: Areview, proposed model, and research agenda.Human Relations, 44, 735–759.

Schreurs, P. J. G., & Taris, T. W. (1998). Constructvalidity of the demand-control model: A dou-ble cross-validation approach. Work & Stress,12, 66–84.

Schumacker, R. E., & Lomax, R. G. (1996). A be-ginner’s guide to structural equation modeling.Mahwah, NJ: Erlbaum.

Shirom, A. (1989). Burnout in work organizations.In C. L. Cooper & I. Robertson (Eds.), Interna-tional review of industrial and organizationalpsychology (pp. 25–48). Chichester: Wiley.

Shirom, A. (2002). Job related burnout: A review.In J. C. Quick & L. E. Tetrick (Eds.), Hand-book of occupational health psychology (pp.245–264). Washington, DC: American Psy-chological Association.

Siegrist, J. (1996). Adverse health effects of high ef-fort-low reward conditions. Journal of Occupa-tional Health Psychology, 1, 27–41.

Singh, J., Goolsby, J. R., & Rhoads, G. K. (1994).Behavioral and psychological consequences ofboundary spanning burnout for customer ser-vice representatives. Journal of Marketing Re-search, 31, 558–569.

Smith, C.A., Organ, D.W., & Near, J.P. (1983). Or-ganizational citizenship behavior: Its nature andantecedents. Journal of Applied Psychology, 68,653–663.

Ursin, H. (1980). Personality, activation and somatic

health: A new psychosomatic theory. In S.Levine & H. Ursin (Eds.), Coping and health(pp. 259–279). New York: Plenum Press.

Van der Doef, M., & Maes, S. (1999). The job de-mand-control(-support) model and psychologi-cal well-being: A review of 20 years of empiricalresearch. Work and Stress, 13, 87–114.

Van Veldhoven, M., & Meijman, T. F. (1994). Hetmeten van psychosociale arbeidsbelasting meteen vragenlijst: De vragenlijst beleving en beo-ordeling van de arbeid [The measurement ofpsychosocial strain at work: The questionnaireexperience and evaluation of work]. Amster-dam: NIA.

Veldhuizen, I. J. T., Gaillard, A. W. K., & de Vries J.(2003) The influence of mental fatigue on fa-cial EMG activity during a simulated workday.Biological Psychology, 63, 59–78.

Warr, P. B. (1990). Decision latitude, job demands,and employee well-being. Work and Stress, 4,285–294.

Wayne, S. J., Shore, L. M., & Liden, R. C. (1997).Perceived organizational support and leader-member exchange: A social exchange perspec-tive. Academy of Management Journal, 40,82–111.

Wrzesniewski, A., & Dutton, J. (2001). Crafting ajob: Employees as active crafters of theirwork. Academy of Management Review, 26,179–201.

Wright, T. A., & Bonett, D. G. (1997). The contri-bution of burnout to work performance. Journalof Organizational Behavior, 18, 491–499.

Wright, T. A., & Cropanzano, R. (1998). Emotionalexhaustion as a predictor of job performanceand voluntary turnover. Journal of Applied Psy-chology, 83, 486–493.

Zapf, D., Dormann, C., & Frese, M. (1996). Longi-tudinal studies in organizational stress research:A review of the literature with reference tomethodological issues. Journal of OccupationalHealth Psychology, 1, 145–169.

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