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Keywords:BurnoutWork engagJob DemandExploratory(ESEM)

1. Introd

The fierent concthe impo(i.e., ill-bDue to thpractitionill-being–on burnosuggestedrepresentoverall leSchaufelithis proptesting, wwe investunderlie

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http://dx.do2213-0586/4.0/).

Trépanier, S. G., Fernet, C.,CC BY-NC-ND https://doi.or

Burnout Research 2 (2015) 51–59

Contents lists available at ScienceDirect

Burnout Research

jo ur nal homepage: www.elsev ier .com/ locate /burn

iting the interplay between burnout and work engagement: Anratory Structural Equation Modeling (ESEM) approach�

eneviève Trépaniera,∗, Claude Fernetb, Stéphanie Austinb, Julie Ménarda

t of Psychology, Université du Québec à Montréal, Quebec, Canadat of Management, Université du Québec à Trois-Rivières, Quebec, Canada

l e i n f o

ry: October 2014

revised form 13 April 2015 April 2015

a b s t r a c t

This study aimed to investigate the interplay between burnout and work engagement. More specifically,we examined the energy and identification continua theorized to underlie the relationship betweenburnout and work engagement by simultaneously evaluating the factorial structure of the MaslachBurnout Inventory–General Survey (MBI–GS) and the Utrecht Work Engagement Scale (UWES). Resultsfrom Exploratory Structural Equation Modeling (ESEM) offered little support for these continua, suggest-

Austin, S. et Ménard, J. (2015) Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach. Burnout Research, 2 (2-3), 51-59. g/10.1016/j.burn.2015.04.002

ements-Resources (JD-R) model

Structural Equation Modeling

ing that burnout and work engagement are not diametrical counterparts. Moreover, ESEM significantlyaltered the relationships burnout and work engagement hold with job demands and resources (i.e., workoverload, job autonomy, and recognition), as well as health-related (i.e., psychological distress) and moti-vational (i.e., turnover intention) outcomes. These findings shed new light on the health-impairment andmotivational processes theorized by the JD-R model.

© 2015 The Authors. Published by Elsevier GmbH. This is an open access article under the CCNC-N

out Igemorato

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ld of positive psychology has greatly influenced our cur-eptualization of employee functioning by highlightingrtance of not only preventing negative manifestationseing) but also promoting positive ones (i.e., well-being).is conceptual shift, occupational health researchers anders investigating burnout–a key indicator of employeehave expanded their scope of interest and begun focusing

ut’s antipodal counterpart, work engagement. It has been that the dimensions of burnout and work engagement

opposite ends of two continua reflecting employees’vel of energy and identification with their work (Bakker,, Leiter, & Taris, 2008; Demerouti & Bakker, 2008). Becauseosition has not been subjected to an extensive empiricale attempted to investigate this issue. More specifically,

BurnEngaexplinedby thNachof bushipdimeand

1.1.

mea

E

igated the energy and identification continua proposed tothe relationship between burnout and work engagementaneously evaluating the factorial structure of the Maslach

rk was supported by a grant from the Fonds Québécois de la Rechercheté et la Culture awarded to Claude Fernet.onding author at: Department of Psychology, Université du Québec, C.P. 8888, Succ. Centre-Ville, Montréal, Québec, Canada H3C 3P8.

987 3000x5331.ddress: trepanier.sarah-genevieve@uqam.ca (S.-G. Trépanier).

more thaBurnout cing from2010; Maitself thrcynicismexhaustioof one’s micism is cregardingburnout t

i.org/10.1016/j.burn.2015.04.002© 2015 The Authors. Published by Elsevier GmbH. This is an open access article under the

D license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

nventory–General Survey (MBI–GS) and the Utrecht Workent Scale (UWES) using a novel statistical approach calledry structural equation modeling (ESEM). We also exam-

health-impairment and motivational processes proposedb Demands-Resources (JD-R) model (Demerouti, Bakker,er, & Schaufeli, 2001). Through SEM with ESEM factorst/work engagement, we evaluate the pattern of relation-

ween job characteristics (job demands and resources), thens of burnout/work engagement, as well as health-relatedvational outcomes.

out and work engagement: conceptualization andent

sive research conducted on burnout over the course ofn 30 years has improved our understanding of its nature.an be viewed as a negative psychological response result-

employees’ interaction with their job (Leiter & Bakker,slach, 1982). This negative reaction is said to manifest

ough two core dimensions: emotional exhaustion and (Bakker, Demerouti, & Sanz-Vergel, 2014). Emotionaln reflects feelings of being overextended and drained

ental, emotional and physical resources, whereas cyn-

haracterized by an overly negative and detached attitude one’s work. Of the instruments developed to measurehe Maslach Burnout Inventory–General Survey (MBI–GS;

CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/

52 rch 2 (

Schaufeliscale (Sch

More

psycholowork engitive, fulfiGonzálezexperiencvitality, avigor). Thasm regamost comScale (UW

1.1.1. TheTheor

direct optively (Scvigor arelabeled “as opposcation”. Iconsideredent consThis impla continuother endstudies (eet al., 200adequateDemerou(CFA) usiInventoryitems saition and

dedicatiosions (cynsecond oopposite

excessivea strong idiametricsions (exhsecond otended) awhile woexperiencgated intrengagemamong mto the cathe “stabcontinuu(2012) foexperiencindepend

The faempiricarelationshexplainedmost stuSchaufeli2002) hawork enggiven therequirem

notionsed, ssticaM; Aed teen

pot

. Inv

FA isholon, a

archelaten

eachross-mptintatirs (en bel., 20d co

cturacts (eworelingSEM

This all c

arch

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ing

) pr connterns becedes. Foents,s of

r in (r = .

gly

al an.58–

hesesuremionsciall

. Buacterhe JD) de

acterurnoairmee job

S.-G. Trépanier et al. / Burnout Resea

, Leiter, Maslach, & Jackson, 1996) is the most widely usedaufeli & Taris, 2005).

recently, researchers have begun to investigate employeegical functioning from a more positive perspective:agement. Work engagement can be defined as “a pos-lling, work-related state of mind” (Schaufeli, Salanova,

-Romá, & Bakker, 2002, p. 74). More specifically, whening work engagement, employees exhibit high levels of

nd willingness to fully invest themselves in their tasks (i.e.,ey also have a strong sense of involvement and enthusi-rding their work (i.e., dedication). Work engagement ismonly measured using the Utrecht Work EngagementES; Schaufeli et al., 2002).

relationship between burnout and work engagementetically, vigor and dedication are considered to be theposites of emotional exhaustion and cynicism, respec-haufeli et al., 2002). As such, emotional exhaustion and

viewed as opposite ends of an underlying continuumenergy”, whereas cynicism and dedication are viewedite ends of an underlying continuum labeled “identifi-n this perspective, burnout and work engagement ared as opposite sides of the same coin and not indepen-tructs (González-Romá, Schaufeli, Bakker, & Lloret, 2006).ies that employees who score high on one dimension ofum (e.g., dedication) would necessarily score low on the

of that continuum (e.g., cynicism). However, very few.g., Demerouti, Mostert, & Bakker, 2010; González-Romá6; Mäkikangas, Feldt, Kinnunen, & Tolvanen, 2012) havely investigated this proposition empirically. For example,ti et al. (2010) conducted confirmatory factor analysisng the MBI-GS, the UWES and the Oldenburg Burnout

(OLBI), which contains positively and negatively wordedd to reflect both ends of the energy (i.e., labeled exhaus-vigor) and identification (i.e., labeled disengagement andn) continua. They found that the identification dimen-icism/disengagement and dedication) represent identical

rder factors, suggesting that they can be considered asends of a single continuum. In this view, negative andly detached attitudes about one’s work (i.e., cynicism) andnvolvement in one’s work (i.e., dedication) would reflectally opposite attitudes. However, the energy dimen-austion and vigor) were found to represent independent

rder factors: exhaustion (i.e., feelings of being overex-nd vigor (i.e., high levels of energy and mental resiliencerking) appear to be distinct, albeit highly related (r = .87),es. More recently, Mäkikangas et al. (2012) investi-aindividual developmental patterns of burnout and workent (and their interplay) in a two-year follow-up studyanagers. Results showed that managers who belongedtegory “low cynicism” also predominantly belonged tole high dedication” category, supporting the identificationm. Much like Demerouti et al. (2010), Mäkikangas et al.und little support for the energy continuum: managers’es of emotional exhaustion and vigor appeared to evolveently.ct that past research has failed to provide unambiguousl support for the two continua assumed to underlie the

ip between burnout and work engagement can be partly from a statistical standpoint. Like Demerouti et al. (2010)

dies (e.g., Hakanen, Bakker, & Schaufeli, 2006; Hakanen,, & Ahola, 2008; Schaufeli & Bakker, 2004; Schaufeli et al.,

mayrelatrelatstati(ESEneedbetwtheir

1.1.2CFA

Cpsycvatioresethe

sentall cassuresefactolatioet amatestrustruand

modE

CFA.matereseMorMorcomallowESEMthanthe ilatioantetionstudtypelowetionstronretic(r = −all, tmearelatartifi

1.1.3char

T2004charof bimpof th

ve investigated the relationships between burnout andagement (and their dimensions) using CFA. However,

rigidity of some of its fundamental postulates (e.g., strictent of zero cross-loadings), CFA measurement models

deplete ethereforeTaris, 201tive healt

2015) 51–59

be the most suitable approach for investigating thehip between concepts that are theoretically very closelyuch as burnout and work engagement. A relatively newl tool called Exploratory Structural Equation Modelingsparouhov & Muthén, 2009) may provide the flexibility

o conduct a more thorough investigation of the interplay the dimensions of burnout and work engagement andential underlying continua.

estigating burnout and work engagement: ESEM versus

a statistical approach often used in occupational healthgy to assess latent constructs (e.g., job demands, moti-nd work engagement). In CFA measurement models,rs specify (1) the number of factors assumed to reflectt constructs and (2) which items (or indicators) repre-

factor. Items are specified to represent their factor only:links are fixed at zero. However, the no cross-loadingon is often too restrictive and may provide a biased rep-on of the relationship between theoretically related latent.g., dedication and cynicism) by overestimating the corre-tween these factors (Asparouhov & Muthén, 2009; Marsh09; Morin, Marsh, & Nagengast, 2013). These overesti-rrelations may result in a distorted representation of thel relationship between the latent factors and other con-.g., work-related antecedents and outcomes of burnout

k engagement) when integrated in structural equation (SEM; Asparouhov & Muthén, 2009).

may allow scholars to overcome the limits associated with modeling procedure enables researchers to freely esti-ross-loadings of indicators of latent factors. Much recenthas illustrated the merits of ESEM over CFA (e.g., Guay,

italien, Valois, & Vallerand, 2015; Marsh, Liem, Martin, Nagengast, 2011; Marsh, Nagengast, & Morin, 2012). The

denominator of these studies is that they reveal thatcross-loading between theoretically linked factors (viaovides a significantly better representation of the datastraining all cross-loadings at zero (via CFA). Moreover,-correlations between latent factors as well as the corre-tween these factors and other variables (i.e., theoretical

nts or outcomes) are considerably reduced in ESEM solu-r example, in a multi-sample study conducted among

Guay et al. (2015) found the inter-relationships betweenmotivation (e.g., intrinsic, extrinsic) to be considerably

the ESEM solution (r = .24–.46) than in the CFA solu-56–.80). Moreover, these types of motivation were morerelated to perceived academic competence (i.e., a theo-tecedent) in the CFA measurement models of motivation.57), compared to the ESEM solutions (r = −.35–.32). Over-

findings highlight that, due to its restrictive nature, CFAent models may result in a biased representation of the

hip between strongly theoretically related concepts byy inflating these relationships.

rnout and work engagement: associations with jobistics and outcomes-R model (Demerouti et al., 2001; Schaufeli & Bakker,

scribes the psychological processes through which jobistics (i.e., demands and resources) act as key predictorsut and work engagement. Accordingly, in the health-nt process, job demands (i.e., negatively valued aspects

that require sustained effort; (Schaufeli & Taris, 2014)

mployees’ mental, emotional and physical resources and

lead to burnout (Bakker & Demerouti, 2007; Schaufeli &4). The prolonged experience of burnout results in nega-h consequences (Bakker & Demerouti, 2007; Bakker et al.,

rch 2 (

2014), intoms (HaHoonakkvational pjob resouinvestmetively valgoals, allelate persoSchaufeli(e.g., sociment (Ba2004) anorganizatet al., 200

The hposed to

although(Schaufelbe negatihave beeRich, 201found to

work engoutcomeset al., 200Bakker, 2substantiimpairmeSchaufelia two-samjob demaand .62) wengagemresourcesment (.47and burn

Howehave usedit is possijob charaas well aet al., 200in substaand workanalyses.the relatishould alship betwas well asfunctioniprocessesinterdepe

1.2. The p

The aiplay betwinto the

the factorusing ESEtiple factshould althat contabove me

ositiropo

othes-loa

crosnerg

othee de

s-loainuu

othe/vigoprov

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etho

Parti

he saof 39eceivil andrity

s (SDe on

% in s

Mea

ll mationble 1ficiendardels t

obsidere

. Buhe coal exh., 1996). Each of these subscales contains five statements per-ng to either emotional exhaustion (e.g., “I feel emotionally

S.-G. Trépanier et al. / Burnout Resea

cluding psychosomatic complaints and depressive symp-kanen et al., 2008, 2006; Korunka, Kubicek, Schaufeli, &er, 2009; Schaufeli & Bakker, 2004). Conversely, the moti-rocess is proposed to underlie the relationship betweenrces, work engagement and indicators of psychologicalnt at work (Bakker et al., 2014). Job resources are posi-ued aspects of the job that help employees achieve workviate the strain associated with job demands and stimu-nal development and growth (Bakker & Demerouti, 2007;

& Taris, 2014). Given these positive effects, job resourcesal support, performance feedback) boost work engage-kker et al., 2014; Halbesleben, 2010; Schaufeli & Bakker,d lead to various positive motivational outcomes such asional commitment and low turnover intention (Hakanen6; Schaufeli & Bakker, 2004).ealth-impairment and motivational processes are pro-be relatively independent (Bakker & Demerouti, 2007),

cumulative evidence suggests that they are interrelatedi & Taris, 2014). Indeed, job demands have been found tovely related to work engagement, whereas job resourcesn negatively linked to burnout (e.g., Crawford, LePine, &0; Hakanen et al., 2006). Furthermore, burnout has beenbe negatively related to motivational outcomes, whereasagement has been positively linked to health-related

(e.g., Bakker, Demerouti, & Schaufeli, 2003; Hakanen6; Richardsen, Burke, & Martinussen, 2006; Schaufeli &

004). Nevertheless, these cross-links do not appear to be asal or systematic as the direct links underlying the health-nt and motivational processes (Halbesleben, 2010; Hu,

, & Taris, 2011; Schaufeli & Bakker, 2004). For example, inple study, Hu et al. (2011) found that the links between

nds (e.g., workload, emotional demands) and burnout (.58ere stronger than those between job demands and work

ent (−.09 and −.05). Conversely, the links between job (i.e., job control, colleague support) and work engage-

and .53) were stronger than those between job resourcesout (−.18 and −.37).ver, because most studies investigating both processes

SEM with CFA factors of burnout and work engagement,ble that their representation of the relationships betweencteristics, burnout/work engagement, and health-relateds motivational outcomes is significantly biased (Marsh9). Indeed, given that CFA measurement models result

ntially inflated factor correlations (e.g., between burnout engagement), it is likely to distort subsequent structural

As such, because it provides a more exact estimate ofonship between burnout and work engagement, ESEMso provide a more accurate representation of the relation-een these two concepts and job demands, job resources,

health-related and motivational indicators of employeeng. Investigating the health-impairment and motivational

through ESEM would thus shed new light on the possiblendency of the two processes.

resent study

m of this study is to deepen our understanding of the inter-een burnout and work engagement by delving further

energy and identification continua. First, we investigateial structure of the MBI-GS and the UWES simultaneouslyM. This approach allows indicators to cross-load on mul-

propwe p

Hypcroscantthe e

Hypon thcroscont

Hyptionwill

tionrepr

Bmetrof (oburntheir

ShealJD-Rfactoof thESEMcharmot

2. M

2.1.

Trate

ers rdetamajoyearrienc34.7

2.2.

Adeviin Tacoefstanmodtiplecons

2.2.1T

tionet altaini

ors. As such, items representing one end of a continuumso load on the factor representing the opposite end ofinuum (albeit negatively). More specifically, based on thentioned JD-R-based empirical evidence and theoretical

drained bthe signifiasked to ion a scale

2015) 51–59 53

ons in support of the energy and identification continua,se the following hypotheses:

sis 1. Emotional exhaustion items will have significantdings on the vigor factor and vigor items will have signifi-s-loadings on the emotional exhaustion factor (supportingy continuum).

sis 2. Cynicism items will have significant cross-loadingsdication factor and dedication items will have significantdings on the cynicism factor (supporting the identificationm).

sis 3. A two-factor ESEM solution (i.e., emotional exhaus-r and cynicism/dedication) representing the two continuaide a better fit to the data than a four-factor ESEM solu-

emotional exhaustion, cynicism, vigor and dedication)ting the four separate dimensions.

loring whether burnout and work engagement are dia-counterparts, this study will evaluate the added valuedundancy in) investigating both work engagement ando assess employees’ level of energy and identification withk.d, we conduct exploratory ESEM analyses to examine thepairment and motivational processes proposed by theel. By comparing two structural models (one with CFA

f burnout/work engagement and one with ESEM factorsonstructs), this study ultimately aims to assess whethernificantly alters the pattern of relationship between jobistics, burnout/work engagement, and health-related andnal outcomes.

d

cipants and procedures

mple comprised school teachers (n = 1159, participation%) working in the province of Quebec, Canada. All teach-ed a letter at work describing the purpose of the study in

inviting them to complete an online questionnaire. Theof participants were women (85.8%). Mean age was 27.79

= 4.13), with an average of 3.29 (SD = 1.68) years of expe- the job. The majority taught in primary schools (60.3%),econdary schools and 5% in other school settings.

sures

easures were administered in French. Means, standards and latent correlations of these measures are presented. Reliability of the measures was established by Hancock’st (i.e., coefficient H; Hancock & Mueller, 2001), which uses

ized factor loadings obtained through CFA measuremento estimate the stability of latent constructs across mul-erved variables. Values equal to or greater than .70 ared satisfactory (Hancock & Mueller, 2001).

rnoutre dimensions of burnout were assessed using the emo-austion and cynicism subscales of the MBI-GS (Schaufeli

y my work”, coefficient H = .92) or cynicism (e.g., “I doubtcance of my work”, coefficient H = .87). Participants werendicate how often they experienced these feelings at work

from 1 (never) to 7 (every day).

54 S.-G. Trépanier et al. / Burnout Research 2 (2015) 51–59

Table 1Means, standard deviations and correlations between latent variables.

Mean SD Range Emotionalexhaustion

Cynicism Vigor Dedication Workoverload

Jobautonomy

Recognition Turnoverintention

Emotionalexhaustion

3.168 1.287 1–7 –

Cynicism 2.331 1.097 1–7 .467.740

–

Vigor 5.388 1.053 1–7 −.265−.565

−.356−.706

–

Dedication 5.675 1.001 1–7 −.170−.530

−.436−.764

.580

.924–

Work overload 3.078 .808 1–5 .753.743

.383

.494−.251−.381

−.263−.369

–

Job autonomy 3.452 .677 1–5 −.436−.506

−.422−.538

.443

.550.447.516

−.603−.604

–

Recognition 3.733 .832 1–5 −.255−.361

−.480−.534

.384

.492.456.520

−.321−.322

.544

.544–

Turnoverintention

1.871 1.372 1–7 .488.610

.757

.793−.432−.599

−.514−.662

.422

.426−.420−.420

−.447−.447

–

Psychologdistress

−.3−.4

Note. Correl ureme

2.2.2. WoThe co

the vigor2002). Saenergy” (about mypants wefeelings a

2.2.3. JobJob de

the Areasjob resoution substo do the

cient H =

3 items; ction; 4 itefrequencpoint scathe itemsrespectiv

2.2.4. PsyThe Fr

1992) of

used to aburnout aof anxietcoefficienficient H =experiencis: “I felt ewere scooften). In

used as in

2.2.5. TurTurno

of burnofrom O’Danother jscale ran

EM

truct

Ethic

pprod ofr exassoscript 30

k exp alsange

Stati

thethénvariastigamensuremctureS wrs w

andg an

s) wt fac

l testwithTuckroximate a). Foe co

r of a

esul

ical 1.688 .527 1–4 .667.713

.531

.622−.390−.503

ations of the ESEM solution are in bold. Means and SD obtained through CFA meas

rk engagementre dimensions of work engagement were assessed using

and dedication subscales of the UWES (Schaufeli et al.,mple items are: “At work I feel like I am bursting with

vigor; 6 items; coefficient H = .89) and “I am enthusiastic work” (dedication; 5 items; coefficient H = .93). Partici-re asked to indicate how often they experienced theset work on a scale from 1 (never) to 7 (every day).

characteristicsmands were assessed with the work overload subscale of

of Work Life Scale (AWS; Leiter & Maslach, 2004), whereasrces were assessed with the job autonomy and recogni-cales of the AWS. Sample items are: “I do not have timework that must be done” (work overload; 6 items; coeffi-.88), “I have control over how I do my work” (job autonomy;oefficient H = .58) and “My work is appreciated” (recogni-ms; coefficient H = .89). Participants were asked to rate they with which they experienced these situations on a five-le from 1 (never) to 5 (almost always). In the SEM analyses,

of the three subscales were used as indicators of theire latent factor.

chological distressench version (Préville, Boyer, Potvin, Perrault, & Légaré,the Psychiatric Symptom Index (PSI; Ilfeld, 1976) wasssess psychological distress, a health-related outcome ofnd work engagement. This scale measures the presence

y (3 items; coefficient H = .83) and depressive (5 items;t H = .82) symptoms, as well as irritability (4 items; coef-

.88) and cognitive problems (2 items; coefficient H = .88)ed during the previous week. A sample item of the scaleasily annoyed or irritated” (i.e., irritability problem). Items

red on a four-point scale ranging from 1 (never) to 4 (verythe SEM analyses, mean scores on the four subscales weredicators of the latent construct of psychological distress.

nover intentionver intention was evaluated as a motivational outcome

the Scons

2.3.

Aboarlettetors

a deabouworwereexch

2.4.

In(Muical

invesuremeastruUWEfactotwo-usinMplulatenof alible

the

Appindic1999of therro

3. R

ut and work engagement, using three items adaptedriscoll and Beehr’s scale (1994; e.g., “I plan on looking forob within the next 12 months”). Items were scored on aging from 1 (strongly disagree) to 7 (strongly agree). In

3.1. Mea

Two Ctested: (1

3778

.538

.538−.449−.449

−.372−.372

.554

.554

nt models.

analyses, each item was used as an indicator of the latent of turnover intention (coefficient H = .97).

al considerations

val for the study was obtained from the research ethics the researchers’ institution. All participants received aplaining the purpose (i.e., investigating workplace fac-ciated with well-being in the teaching profession) andtion of what their participation consisted of (i.e., taking

min to complete an online questionnaire regarding theireriences). The confidentiality and anonymity of responseso emphasized in the letter. No incentive was given in

for participation.

stical analyses

present study, all analyses were performed using Mplus & Muthén, 2012) with the WLSMV estimator for categor-bles. CFA and ESEM measurement models were tested tote the factorial structure of the MBI-GS and UWES (mea-t analyses). For each analysis (CFA and ESEM), two types of

ent models were tested: a two-factor and a four-factor. In the CFA solution, each indicator of the MBI-GS andas allowed to load on its respective factor only. Latentere allowed to correlate. In the two ESEM solutions (a

a four-factor structure) all loadings were freely estimatedoblique Geomin rotation (the default rotation solution inith an epsilon value of 0.5 (Marsh et al., 2009, 2012). Thetors were also allowed to correlate. The goodness-of-fited models was evaluated using three fit indices compat-

the WLSMV estimator: the Comparative Fit Index (CFI),er–Lewis Index (TLI) and the Root Mean Square Error of

ation (RMSEA). Values higher than .95 for the CFI and TLI good fit (Hooper, Coughlan, & Mullen, 2008; Hu & Bentler,r the RMSEA, values lower than .07 (with the upper limitnfidence interval [CI] less than .08) represent reasonablepproximation (Hooper et al., 2008; Steiger, 2007).

ts

surement analyses

FA measurement models of the MBI-GS and UWES were) a four-factor (M1) structure (i.e., emotional exhaustion,

S.-G. Trépanier et al. / Burnout Research 2 (2015) 51–59 55

Table 2Fit indices for the tested models.

Model description �2 df CFI TLI RMSEA and 90% CI MC ��2 �df

CFA measurement modelsM1: Four factors 1934.131 183 .962 .957 .099 (.095–.103) M1 vs M2 4672.316** 5M2: Two factors (energy and identification continua) 6606.447 188 .862 .846 .189 (.183–.191) – –

ESEM measurement modelsM3: Four factors 939.235 132 .983 .972 .079 (.075–.084) M3 vs M1 994.896** 51M4: Two factors 2566.335 169 .948 .936 .121 (.117–.125) M3 vs M4 1627.100** 37

Structural AnalysesM5: SEM with ESEM factors of burnout/work engagement 3066.010 698 .963 .956 .057 (.055–.060) M5 vs M6 1102.506** 51M6: CFA with ESEM factors of burnout/work engagement 4168.516 749 .946 .941 .067 (.065–.069)

Note. CFI = comparative fit index; TLI = Tuckey–Lewis index; RMSEA = root mean square error of approximation; CI = confidence interval; SRMR = standardized root meansquare; MC: model comparison; ��2 = chi-square difference; ** p < .001.

Table 3Measurement model: correlations between the dimensions of burnout and work engagement (CFA and ESEM solutions).

Emotional exhaustion Cynicism Vigor Dedication

Emotional exhaustion –Cynicism .473/.740 –Vigor −.280/−.561 −.352/−.704 –Dedicatio 73/−.

Note. ESEM

cynicismcomprisevigor itemthe cynicM1 proviRMSEA wthresholdto the datCorrelatioing from

.924 (betwNext,

(M4) factnot fit ththeir cut-Althoughclose to .0also showM4. Morenificantlythese reslatent facicantly co−.188 to

between

tion betwlowest cotion (r = −(r = −.188support fshowed tare best rtwo undethat the cnot stronemotionamoderateCohen, 19

All facfour-factoenable a m

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xplothe

ovgem

psyc, two models were compared: M5 including ESEM-factors ofout/work engagement, and M6, including CFA-factors of thesetructs. In both models, all links between job characteristics

iven that the short version of the UWES (UWES-9; Schaufeli, Bakker, &ova, 2006) is often used to assess work engagement, CFA and ESEM measure-

analyses were subsequently conducted using the MBI-GS and the UWES-9. Thes revealed a pattern similar to the one obtained with the complete version ofork engagement scale. More specifically, no significant (i.e., above .30) cross-

n −.188/−.530 −.4

correlations are in bold above the dashed line.

, vigor, and dedication) and (2) a two-factor (M2) structured of one factor including the emotional exhaustion ands (i.e., energy continuum) and a second factor including

ism and dedication items (i.e., identification continuum).ded an adequate fit to the data with the exception of thehich was above the .07 (and upper CI limit above the .08)

(see Table 2). M1 also provided a significantly better fita than M2, which provided a poor fit to the data (Table 2).ns between the four latent factors in M1 were high, rang-

−.530 (between emotional exhaustion and dedication) toeen vigor and dedication; see Table 3).

two ESEM measurement models with four (M3) and twoors were tested (see Table 2). Results show that M4 dide data particularly well (none of the fit indices respectedoff thresholds). M3 provided a satisfactory fit to the data.

the RMSEA was above .07, the upper CI limit was very8, suggesting a reasonable error of approximation. Results

that M3 provided a significantly better fit to the data thanover, M3 (i.e., ESEM four-factor solution) provided a sig-

better fit than M1 (i.e., CFA four-factor solution). Overall,ults infirm Hypothesis 3. Correlations between the fourtors of M3 (ESEM four-factor solution) decreased signif-mpared to M1 (CFA four-factor solution), ranging from.597 (see Table 3). The strongest correlation was foundvigor and dedication (r = .597), followed by the correla-een emotional exhaustion and cynicism (r = .473). Therrelations were between vigor and emotional exhaus-.280) and between emotional exhaustion and dedication). Taken together, these results provide weak preliminaryor the energy and identification continua. Indeed, resultshat the four dimensions of burnout and work engagementepresented as distinct factors as opposed to components ofrlying factors. Moreover, the pattern of correlations showsomponents of the energy and identification continua are

gly negatively related. There is a small correlation betweenl exhaustion and vigor (r = −.280; Cohen, 1988) and a

correlation between cynicism and dedication (r = −.473;

contfor ethe

itemresu(infino cr2007A difof wloadsix vlaten

Oand

worksuggcorequat

3.2.

Etest

workenga(i.e.,callyburncons

1 GSalanmentresultthe w

88).tor loadings (primary and cross-loadings) of the ESEMr solution are presented in Table 4. These factor loadingsore rigorous evaluation of the energy and identification

loadings weTwo dedicalatent factoon the dedcontaining

764 .597/.924 –

. In order to support these continua, strong cross-loadingsatent factor should be found from indicators representingsite dimension of the same continuum (e.g., dedicationuld have strong cross-links on the cynicism factor). Theown in Table 3 offer little support for any of the continuag Hypotheses 1 and 2). For the two dimensions of burnout,loadings reached the threshold of .30 (Tabachnick & Fidell,is suggests that both latent factors are relatively distinct.t pattern of results was obtained for the two dimensionsngagement. Three out of five dedication items had cross-of .30 or higher on the vigor latent factor, and two out ofitems had cross-loadings of .30 or higher on the dedicationtor.1

ll, these results offer little empirical support for the energytification continua and reveal that the two dimensions ofagement are highly intertwined. Moreover, these resultshat ESEM, which considers cross-loadings between theensions of burnout and work engagement, more ade-eflects the interplay between these dimensions than CFA.

tural analyses

ratory SEM analyses were conducted subsequently tostructural relationships between job characteristic (i.e.,erload, job autonomy, and recognition), burnout/workent, and health-related as well as motivational outcomeshological distress and turnover intention). More specifi-

re observed for the latent factors of emotional exhaustion and cynicism.tion items (out of three) had cross-loadings of .30 or higher on the vigorr and one vigor item (out of three) had cross-loadings of .30 or higherication latent factor. Detailed results of the ESEM measurement modelthe UWES-9 can be obtained from the first author.

56 S.-G. Trépanier et al. / Burnout Research 2 (2015) 51–59

Table 4Measurement Model: CFA and ESEM solutions for the MBI-GS and UWES.

Factor loadings

Factor 1 Factor 2 Factor3 Factor 4

Emotional exhaustion Item 1 .752/.843 .147 .013 −.083Item 2 .932/.855 −.029 .032 −.084Item 3 .728/.852 .080 −.256 .064Item 4 .615/.885 .248 −.240 .057Item 5 .731/.881 .214 .002 −.082

Cynicism Item 1 .153 .702/.868 −.153 −.039Item 2 .150 .704/.919 .032 −.056Item 3 .034 .497/.497 −.256 −.063Item 4 .097 .465/.637 .006 −.211Item 5 .113 .582/.745 .071 −.270

Vigor Item 1 −.146 −.205 .420/.924 .369Item 2 −.114 −.137 .738/.879 .084Item 3 .099 .155 .402/.517 .392Item 4 .143 .098 .358/.361 .250Item 5 −.058 .204 .357/.313 .147Item 6 −.086 −.116 .754/.898 .128

Dedication Item 1 −.060 −.274 .395 472/.948Item 2 −.014 −.255 .428 471/.875Item 3 −.080 −.188 .405 448/.893Item 4 −.080 −.041 .129 733/.836

Note. CFA lo e theare underlin

and the fbetween

Althoughto the .95nificantlysolutions

trucrnou

Fig. 1. ResuResults of t

Item 5 −.029

adings are in italics below the dashed line. Factor loadings of the items reflecting thed.

our dimensions of burnout/work engagement as well asthese dimensions and the two outcomes were assessed.

the sof bu

M6 fits the data reasonably well (CFI and TLI were close threshold) the results indicate that M5 provided a sig-

better fit to the data (see Table 3). The results of both (ESEM and CFA) are depicted in Fig. 1. Results show that

stronger

Interestinjob autonsolution.

lts comparing the structural relationships between job characteristics, the dimensions ofhe ESEM solution are in bold above the dashed line.

−.092 .062 .857/.819

oretical counterpart of each factor are in bold. Significant cross-loadings

tural links between job characteristics and the dimensionst and work engagement in the CFA solution are generally

(7 out of 10) than those obtained in the ESEM solution.gly, the CFA solution reveals a significant link betweenomy and cynicism that was not significant in the ESEM

Moreover, the “emotional exhaustion-turnover intention”

burnout/work engagement and outcomes through CFA and ESEM. Note.

rch 2 (

and “dedto be nonsolution.

unstandaduct to m(CFA soludifferencically, M6work oveication (zrecogniti(z = 4.37).relationshwell as be

4. Discu

The pbetween

gating whspecificalstructureto examification (support fsions arewith eacover, intengagemhealth-retern of rehealth-imR model.

below.

4.1. Theo

4.1.1. TheThe en

dimensioby comp(CFA andas the fothe two-floadings

an in-depMore spefor the foterpart. Tburnout aregard toicant crosthat bothwork-relaresearch

(e.g., Hu &2000), whbest reprtor). Overpast CFA sexhaustioenergetic

A diffement. Redimensio

noner andesennguielatio

engSEMong

coef; Sch

lts suberg

dimet (or

r andious

r and invelts sstigabinedt parrsue

a cotructr andthe aaufel

. Heur stchargemlts ret pree fac

(five in thresout (thcismout/SEMover

notakenructuicallythe cings

uate the

arouhe regemesenesou, the

have progemholo

S.-G. Trépanier et al. / Burnout Resea

ication-psychological distress” relationships were found-significant in the CFA solution but significant in the ESEMSubsequently, regression coefficient comparison (usingrdized coefficient estimates and standard errors) was con-

ore rigorously compare M5 (ESEM solution) and M6tion). Results reveal several significant differences. Thesees are presented in bold dotted lines in Fig. 1. More specif-

revealed significantly stronger relationships between (1)rload and cynicism (z = 2.27), (2) work overload and ded-

= 2.06), (3) job autonomy and dedication (z = 2.02), (4)on and vigor (z = 2.54) and (5) recognition and dedication

On the other hand, M5 revealed significantly strongerips between job autonomy and dedication (z = 2.16) astween dedication and psychological distress (z = 2.41).

ssion

resent study aimed to shed new light on the interplayburnout and work engagement by empirically investi-ether both concepts are diametrical counterparts. More

ly, this study simultaneously investigated the factorial of the MBI-GS and UWES using ESEM, which allowed usne the energy (emotional exhaustion-vigor) and identi-cynicism-dedication) continua. Our results offered littleor both continua, revealing that work engagement dimen-

highly intertwined and have stronger relationshipsh other than with their burnout counterpart. More-egrating ESEM measurement models of burnout/workent within a SEM that includes job characteristics andlated/motivational outcomes significantly alters the pat-sults. As such, our results extend the understanding of thepairment and motivational processes proposed by the JD-

The theoretical implications of these results are discussed

retical contributions

energy and identification continuaergy and identification continua said to connect the corens of burnout and work engagement were investigatedaring two-factor and four-factor measurement models

ESEM). Results offered little support for these continuaur-factor solutions fit the data significantly better thanactor solutions. Furthermore, ESEM – which takes cross-of all indicators on all factors into account – allowed forth examination of the energy and identification continua.cifically, we investigated whether strong cross-loadingsur dimensions were found from their theoretical coun-he results did not follow such a pattern, suggesting thatnd work engagement are not conceptual opposites. With

the two dimensions of burnout, results revealed no signif-s-loadings (.30 or higher) for any other items, suggesting

dimensions of burnout are distinct and tap into uniqueted psychological experiences. These results support past

validating the factorial structure of MBI-GS through CFA Schaufeli, 2011; Schutte, Toppinen, Kalimo, & Schaufeli,ich showed that emotional exhaustion and cynicism are

esented as separate factors (as opposed to a single fac-all, the results of the present study, in conjunction withtudies, highlight the relevance of investigating emotionaln and cynicism separately given that they reflect distinct

and

vigoreprdisticorrworkthe Ea strtion2006resuHallone-alenvigoprevvigobestresuinvecommosto pufromconsvigoing

(Sch

4.1.2O

job

engaResumentheslinksthanjob

mencyniburnthe Eturnwere

Tin storetit is

loadadeqation(Asping tengareprjob rovermaytiallyengapsyc

and attitudinal experiences.rent pattern of results was obtained for work engage-

sults revealed several cross-loadings between the twons (using both the long and short versions of the UWES)

outcomethe health(e.g., BakAustin, F

2015) 51–59 57

from items reflecting their burnout counterparts. The dedication subscales of the UWES (and UWES-9) thus

t similar psychological experiences that are difficult tosh. This overlap is supported by the particularly strongn found in this study between the two dimensions ofagement (.924 in the CFA measurement model and .597 in

solution). This also concurs with past research showinginterrelation between these dimensions, with correla-

ficients usually exceeding .60 (e.g., Hallberg & Schaufeli,aufeli et al., 2002; Shimazu et al., 2008). Moreover, ourpport those obtained in several validation studies (e.g.,& Schaufeli, 2006; Shimazu et al., 2008) that found ansional representation of work engagement to be equiv-

even superior) to a multi-dimensional representation (i.e., dedication as distinct concepts). Overall, our results, likeCFA studies, highlight the considerable overlap between

dedication, suggesting that work engagement may bestigated as a one-dimensional construct. Specifically, ouruggest that, because both dimensions are intertwined,ting work engagement globally (i.e., vigor and dedication) as opposed to its two dimensions separately, may be

simonious and appropriate. Further research is required investigation of the uniqueness of vigor and dedicationnceptual standpoint (i.e., the fundamental nature of boths). Researchers could also revisit the relationship between

dedication from a methodological standpoint by assess-dequacy of the UWES for assessing work engagementi & Salanova, 2011).

alth-impairment and motivational processesudy provides new insight into the relationships betweenacteristics (demands and resources), burnout/workent and health-related as well as motivational outcomes.veal that SEM with CFA factors of burnout/work engage-dominantly resulted in stronger relationships between

tors and job characteristics. More specifically, half of the out of ten) were significantly stronger in the CFA solutione ESEM solution. Of these five links, three were betweenrces (job autonomy, recognition) and work engage-

e other significant links were between job demands and/dedication). With regard to the relationships betweenwork engagement and indicators of employee functioning,

solution revealed two cross-links (emotional exhaustion- intention and dedication-psychological distress) that

significant in the CFA solution. together, these findings reiterate the importance of ESEMral solutions when investigating concepts that are the-

very closely related (Asparouhov & Muthén, 2009), asase for burnout and work engagement. Allowing cross-

between these closely related concepts results in a more assessment of their interrelationship as well as the associ-y have with their work-related antecedents and outcomeshov & Muthén, 2009). Our results show that investigat-lationship between job characteristics and burnout/workent through SEM with CFA factors may result in an inflatedtation of these relationships, especially those involvingrces and work engagement (motivational process). More-se findings suggest that burnout and work engagement

more similar effects on employee functioning than ini-posed by the JD-R model. That is, both burnout and workent dimensions were found to predict health-related (i.e.,gical distress) and motivational (i.e., turnover intention)

s. This corroborates findings of past studies showing that

-impairment and motivational processes are interrelatedker et al., 2003; Hakanen et al., 2008; Trépanier, Fernet,orest, & Vallerand, 2014; Van den Broeck, Vansteenkiste,

58 rch 2 (

De Witteprocessesrelevanceously anmore aderesourcesand motiresult in

effects oresourcesengagemboth proall cross-Schaufeli

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valuable

and workand idenbetween

results shexperiencnuance p(dedicati(cynicism“energy”

Our resulwork engwith the

negativelof the enthat recocation) renegativelment) res& Bakker(dedicati(cynicismessarily isuggest thassessingsions) indreflect di

4.3. Limi

The paddresseschool teity of theencouragwork engpoints, usnote thatmodels, t(see Tabl1999). Fument andmodel teber of jobrecogniti(i.e., psycworth me& Vallera

encher &apturuate

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ands, socitive

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ent: A2, 187n, J. (1rsey:

ford, Employst. Jourouti,

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S.-G. Trépanier et al. / Burnout Resea

, & Lens, 2008), as opposed to relatively independent (Bakker & Demerouti, 2007). This also underlines the

of investigating burnout/work engagement simultane-d of systematically examining cross-links in order toquately capture the interplay between job demands, job, burnout, work engagement, as well as health-relatedvational outcomes (Schaufeli & Taris, 2014). This woulda clearer representation of the potential motivational

f job demands as well as the energetic impact of job on employee functioning through burnout and workent. Unfortunately, research to date has often investigatedcesses in isolation and has usually omitted to evaluatelinks between these processes (e.g., Hakanen et al., 2006;

& Bakker, 2004).

odological implications

a measurement standpoint, the results of this study offerinsight to researchers and practitioners assessing burnout

engagement. By offering little support for the energytification continua proposed to underlie the relationshipthe dimensions of burnout and work engagement, ourow that these two concepts are distinct psychologicales and should be evaluated as such. Indeed, our findings

ast propositions suggesting that adding the scores of vigoron) items to the reversed scores of emotional exhaustion) items adequately represent employees’ overall level ofand “identification” (e.g., González-Romá et al., 2006).ts also call into question the assessment of burnout andagement with the same instrument, as it is the case

OLBI (Demerouti & Bakker, 2008). The OLBI contains bothy and positively worded items said to reflect both endsergy and identification continua. It has been proposedding positively framed items (reflecting vigor and dedi-sults in the measurement of burnout whereas recodingy framed items (reflecting exhaustion and disengage-ults in the measurement of work engagement (Demerouti, 2008). However, our results indicate that high vigor

on) does not necessarily imply low emotional exhaustion) and low cynicism (emotional exhaustion) does not nec-

mply high dedication (vigor). Taken together, our resultsat both researchers and practitioners would benefit from

burnout and work engagement (as well as their dimen-ependently and through different instruments as they

stinct concepts.

tations and conclusion

resent study has some limitations that should bed. First, the fact that the study was conducted amongachers only raises concerns regarding the generalizabil-

findings to other working populations. Future research ised to validate our findings by investigating burnout andagement, from both measurement and structural stand-ing ESEM in other occupations. Second, it is important to

in all tested models, particularly for the CFA measurementhe RMSEA fit value did not indicate a particularly good fite 2), which hints at model misspecification (Hu & Bentler,ture research is needed in order to validate our measure-

structural findings in other samples. Third, the structuralsted in the present study focused on a limited num-

characteristics (i.e., work overload, job autonomy and

the b(Leitto cadeqcatedem(e.g.negaformprovwhecesssicknof emtionstud

Inworstudtrateand

the itionheal

Con

T

Refe

AsparS

Bakketh

BakkecW

BakkemO

Bakkem2

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on) and only on negative employee functioning outcomeshological distress and turnover intention). Moreover, it isntioning that as in previous studies (e.g., Fernet, Austin,nd, 2012) the measure of job autonomy did not meet

engageHalbeslebe

with buLeiter (102–11

2015) 51–59

mark for reliability (coefficient H = .57). The revised AWS Maslach, 2011), which comprises an additional iteme job autonomy, would certainly help represent morely this construct. Future studies are encourage to repli-results using this job autonomy measure and other job

(e.g., role ambiguity, physical demands), job resourcesal support, skill utilization) as well as both positive andindicators of employee functioning (e.g., in-role per-, psychosomatic complaints, commitment). This shoulddditional support for the relevance of using ESEM analysisestigating the health-impairment and motivational pro-cluding objective and multi-source health-related (e.g.,bsence records) and motivational (e.g., supervisor ratings

yee extra-role performance) indicators of employee func-ould also strengthen the results obtained in the presentich relied solely on self-reported data.mary, although it has been proposed that burnout and

agement are conceptual counterparts, the results of thiser little support of this proposition. Our results also illus-

ESEM represents a promising avenue for future burnout engagement research as it may more adequately capturelay between these concepts as well as their specific rela-

with job characteristics and employee functioning (i.e.,pairment and motivational processes).

f interest

thors declare that there are no conflicts of interest.

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