Scholarly article on topic 'Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach'

Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach Academic research paper on "Psychology"

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{Burnout / "Work engagement" / "Job Demands-Resources (JD-R) model" / "Exploratory Structural Equation Modeling (ESEM)"}

Abstract of research paper on Psychology, author of scientific article — Sarah-Geneviève Trépanier, Claude Fernet, Stéphanie Austin, Julie Ménard

Abstract 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 between burnout and work engagement by simultaneously evaluating the factorial structure of the Maslach Burnout Inventory–General Survey (MBI–GS) and the Utrecht Work Engagement Scale (UWES). Results from Exploratory Structural Equation Modeling (ESEM) offered little support for these continua, suggesting that burnout and work engagement are not diametrical counterparts. Moreover, ESEM significantly altered the relationships burnout and work engagement hold with job demands and resources (i.e., work overload, job autonomy, and recognition), as well as health-related (i.e., psychological distress) and motivational (i.e., turnover intention) outcomes. These findings shed new light on the health-impairment and motivational processes theorized by the JD-R model.

Academic research paper on topic "Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach"


Burnout Researchxxx (2015) xxx-xxx


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Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach^

Q3 Sarah-Geneviève Trépanier2'*, Claude Fernetb, Stéphanie Austinb, Julie Ménarda

a Department of Psychology, Université du Québec à Montréal, Quebec, Canada b Department of Management, Université du Québec à Trois-Rivières, Quebec, Canada


Article history: Received 28 October 2014 Received in revised form 13 April 2015 Accepted 14 April 2015

Keywords: Burnout

Work engagement

Job Demands-Resources QD-R) model Exploratory Structural Equation Modeling (ESEM)


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 between burnout and work engagement by simultaneously evaluating the factorial structure of the Maslach Burnout Inventory-General Survey (MBI-GS) and the Utrecht Work Engagement Scale (UWES). Results from Exploratory Structural Equation Modeling (ESEM) offered little support for these continua, suggesting that burnout and work engagement are not diametrical counterparts. Moreover, ESEM significantly altered the relationships burnout and work engagement hold with job demands and resources (i.e., work overload, job autonomy, and recognition), as well as health-related (i.e., psychological distress) and motivational (i.e., turnover intention) outcomes. These findings shed new light on the health-impairment and motivational processes theorized by the JD-R model.

© 2 015 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license


1. Introduction

The field of positive psychology has greatly influenced our current conceptualization of employee functioning by highlighting the importance of not only preventing negative manifestations (i.e., ill-being) but also promoting positive ones (i.e., well-being). Due to this conceptual shift, occupational health researchers and practitioners investigating burnout-a key indicator of employee ill-being-have expanded their scope of interest and begun focusing on burnout's antipodal counterpart, work engagement. It has been suggested that the dimensions of burnout and work engagement represent opposite ends of two continua reflecting employees' overall level of energy and identification with their work (Bakker, Schaufeli, Leiter, &Taris, 2008; Demerouti&Bakker, 2008). Because this proposition has not been subjected to an extensive empirical testing, we attempted to investigate this issue. More specifically, we investigated the energy and identification continua proposed to underlie the relationship between burnout and work engagement by simultaneously evaluating the factorial structure of the Maslach

* This work was supported by a grant from the Fonds Québécois de la Recherche sur la Société et la Culture awarded to Claude Fernet.

* Corresponding author at: Department of Psychology, Université du Québec à Montréal, C.P. 8888, Succ. Centre-Ville, Montréal, Québec, Canada H3C 3P8.

Tel.: +1 514 987 3000x5331.

E-mail address: (S.-G. Trépanier).

Burnout Inventory-General Survey (MBI-GS) and the Utrecht Work Engagement Scale (UWES) using a novel statistical approach called exploratory structural equation modeling (ESEM). We also examined the health-impairment and motivational processes proposed by the Job Demands-Resources (JD-R) model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Through SEM with ESEM factors of burnout/work engagement, we evaluate the pattern of relationships between job characteristics (job demands and resources), the dimensions of burnout/work engagement, as well as health-related and motivational outcomes.

1.1. Burnout and work engagement: conceptualization and measurement

Extensive research conducted on burnout over the course of more than 30 years has improved our understanding of its nature. Burnout can be viewed as a negative psychological response resulting from employees' interaction with their job (Leiter & Bakker, 2010; Maslach, 1982). This negative reaction is said to manifest itself through two core dimensions: emotional exhaustion and cynicism (Bakker, Demerouti, & Sanz-Vergel, 2014). Emotional exhaustion reflects feelings of being overextended and drained of one's mental, emotional and physical resources, whereas cynicism is characterized by an overly negative and detached attitude regarding one's work. Of the instruments developed to measure burnout the Maslach Burnout Inventory-General Survey (MBI-GS;

60 61 62

2213-0586/© 2015 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

63 Schaufeli, Leiter, Maslach, & Jackson, 1996) is the most widely used

64 scale (Schaufeli & Taris, 2005).

65 More recently, researchers have begun to investigate employee

66 psychological functioning from a more positive perspective:

67 work engagement. Work engagement can be defined as "a pos-

68 itive, fulfilling, work-related state of mind" (Schaufeli, Salanova,

69 Gonzalez-Roma, & Bakker, 2002, p. 74). More specifically, when

70 experiencing work engagement, employees exhibit high levels of

71 vitality, and willingness to fully invest themselves in their tasks (i.e.,

72 vigor). They also have a strong sense of involvement and enthusi-

73 asm regarding their work (i.e., dedication). Work engagement is

74 most commonly measured using the Utrecht Work Engagement

75 Scale (UWES; Schaufeli et al., 2002).

76 1.1.1. The relationship between burnout and work engagement

77 Theoretically, vigor and dedication are considered to be the

78 direct opposites of emotional exhaustion and cynicism, respec-

79 tively (Schaufeli et al., 2002). As such, emotional exhaustion and

80 vigor are viewed as opposite ends of an underlying continuum

81 labeled "energy", whereas cynicism and dedication are viewed as

82 opposite ends of an underlying continuum labeled "identification".

83 In this perspective, burnout and work engagement are considered

84 as opposite sides of the same coin and not independent constructs

85 (Gonzalez-Roma, Schaufeli, Bakker, & Lloret, 2006). This implies

86 that employees who score high on one dimension of a contin-

87 uum (e.g., dedication) would necessarily score low on the other

88 end of that continuum (e.g., cynicism). However, very few studies

89 (e.g., Demerouti, Mostert, & Bakker, 2010; Gonzalez-Roma et al.,

90 2006; Makikangas et al., 2012) have adequately investigated this 9iQ5 proposition empirically. For example, Demerouti et al. (2010) con-

92 ducted confirmatory factor analysis (CFA) using the MBI-GS, the

93 UWES and the Oldenburg Burnout Inventory (OLBI), which con-

94 tains positively and negatively worded items said to reflect both

95 ends of the energy (i.e., labeled exhaustion and vigor) and identifi-

96 cation (i.e., labeled disengagement and dedication) continua. They

97 found that the identification dimensions (cynicism/disengagement

98 and dedication) represent identical second order factors, suggest-

99 ing that they can be considered as opposite ends of a single

100 continuum. In this view, negative and excessively detached atti-

101 tudes about one's work (i.e., cynicism) and a strong involvement

102 in one's work (i.e., dedication) would reflect diametrically oppo-

103 site attitudes. However, the energy dimensions (exhaustion and

104 vigor) were found to represent independent second order fac-

105 tors: exhaustion (i.e., feelings of being overextended) and vigor

106 (i.e., high levels of energy and mental resilience while working)

107 appear to be distinct, albeit highly related (r =.87), experiences.

108 More recently, Makikangas et al. (2012) investigated intraindivid-

109 ual developmental patterns of burnout and work engagement (and

110 their interplay) in a two-year follow-up study among managers.

111 Results showed that managers who belonged to the category "low

112 cynicism" also predominantly belonged to the "stable high dedica-

113 tion" category, supporting the identification continuum. Much like

114 Demerouti et al. (2010), Makikangas et al. (2012) found little sup-

115 port forthe energy continuum: managers'experiences of emotional

116 exhaustion and vigor appeared to evolve independently.

117 The fact that past research has failed to provide unambiguous

118 empirical support for the two continua assumed to underlie the

119 relationship between burnout and work engagement can be partly

120 explained from a statistical standpoint. Like Demerouti et al. (2010)

121 most studies (e.g., Hakanen, Bakker, & Schaufeli, 2006; Hakanen,

122 Schaufeli, & Ahola, 2008; Schaufeli & Bakker, 2004; Schaufeli et al.,

123 2002) have investigated the relationships between burnout and

124 work engagement (and their dimensions) using CFA. However,

125 given the rigidity of some of its fundamental postulates (e.g.,

126 strict requirement of zero cross-loadings), CFA measurement mod-

127 els may not be the most suitable approach for investigating the

relationship between concepts that are theoretically very closely 128

related, such as burnout and work engagement. A relatively new 129

statistical tool called Exploratory Structural Equation Modeling 130

(ESEM; Asparouhov & Muthen, 2009) may provide the flexibility 131

needed to conduct a more thorough investigation of the interplay 132

between the dimensions of burnout and work engagement and 133

their potential underlying continua. 134

1.1.2. Investigating burnout and work engagement: ESEM versus 135 CFA 136

CFA is a statistical approach often used in occupational health 137

psychology to assess latent constructs (e.g., job demands, moti- 138

vation, and work engagement). In CFA measurement models, 139

researchers specify (1) the number of factors assumed to reflect 140

the latent constructs and (2) which items (or indicators) repre- 141

sent each factor. Items are specified to represent their factor only: 142

all cross-links are fixed at zero. However, the no cross-loading 143

assumption is often too restrictive and may provide a biased rep- 144

resentation of the relationship between theoretically related latent 145

factors (e.g., dedication and cynicism) by overestimating the corre- 146

lation between these factors (Asparouhov & Muthen, 2009; Marsh 147

et al., 2009; Morin, Marsh, & Nagengast, 2013). These overesti- 148

mated correlations may result in a distorted representation of the 149

structural relationship between the latent factors and other con- 150

structs (e.g., work-related antecedents and outcomes of burnout 151

and work engagement) when integrated in structural equation 152

modeling (SEM; Asparouhov & Muthen, 2009). 153

ESEM may allow scholars to overcome the limits associated with 154

CFA. This modeling procedure enables researchers to freely esti- 155

mate all cross-loadings of indicators of latent factors Much recent 156

research has illustrated the merits of ESEM over CFA (e.g., Guay, 157

Morin, Litalien, Valois, & Vallerand, 2015; Marsh, Liem, Martin, 158

Morin, & Nagengast, 2011; Marsh, Nagengast, & Morin, 2012). The 159

common denominator of these studies is that they reveal that 160

allowing cross-loading between theoretically linked factors (via 161

ESEM) provides a significantly better representation of the data 162

than constraining all cross-loadings at zero (via CFA). Moreover, 163

the inter-correlations between latent factors as well as the corre- 164

lations between these factors and other variables (i.e., theoretical 165

antecedents or outcomes) are considerably reduced in ESEM solu- 166

tions. For example, in a multi-sample study conducted among 167

students, Guay et al. (2015) found the inter-relationships between 168

types of motivation (e.g., intrinsic, extrinsic) to be considerably 169

lower in the ESEM solution (r =.24-.46) than in the CFA solu- 170

tion (r = .56-.80). Moreover, these types of motivation were more 171

strongly related to perceived academic competence (i.e., a theo- 172

retical antecedent) in the CFA measurement models of motivation 173

(r = -.58-.57), compared to the ESEM solutions (r = -.35-.32). Over- 174

all, these findings highlight that, due to its restrictive nature, CFA 175

measurement models may result in a biased representation of the 176

relationship between strongly theoretically related concepts by 177

artificially inflating these relationships. 178

1.1.3. Burnout and work engagement: associations with job 179 characteristics and outcomes 180

The JD-R model (Demerouti et al., 2001; Schaufeli & Bakker, 181

2004) describes the psychological processes through which job 182

characteristics (i.e., demands and resources) act as key predictors 183

of burnout and work engagement. Accordingly, in the health- 184

impairment process, job demands (i.e., negatively valued aspects 185

of the job that require sustained effort; Schaufeli & Taris, 2014) 186

deplete employees' mental, emotional and physical resources and 187

therefore lead to burnout (Bakker & Demerouti, 2007; Schaufeli 188 & Taris, 2014). The prolonged experience of burnout results in Q6 189

negative health consequences (Bakker & Demerouti, 2007; Bakker 190

et al., 2014), including psychosomatic complaints and depressive 191


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

192 symptoms (Hakanen et al., 2008,2006; Korunka, Kubicek, Schaufeli,

193 & Hoonakker, 2009; Schaufeli & Bakker, 2004). Conversely, the

194 motivational process is proposed to underlie the relationship

195 between job resources, work engagement and indicators of psycho-

196 logical investment at work (Bakker et al., 2014). Job resources are

197 positively valued aspects of the job that help employees achieve

198 work goals, alleviate the strain associated with job demands and

199 stimulate personal development and growth (Bakker & Demerouti,

200 2007; Schaufeli and Taris, 2014). Given these positive effects, job

201 resources (e.g., social support, performance feedback) boost work

202 engagement (Bakker et al., 2014; Halbesleben, 2010; Schaufeli &

203 Bakker, 2004) and lead to various positive motivational outcomes

204 such as organizational commitment and low turnover intention 2oQ7 (Hakanen etal., 2006; Korunaetal., 2008; Schaufeli & Bakker, 2004).

206 The health-impairment and motivational processes are pro-

207 posed to be relatively independent (Bakker & Demerouti, 2007),

208 although cumulative evidence suggests that they are interrelated

209 (Schaufeli and Taris, 2014). Indeed, job demands have been found to

210 be negatively related to work engagement, whereas job resources

211 have been negatively linked to burnout (e.g., Crawford, LePine, &

212 Rich, 2010; Hakanen et al., 2006). Furthermore, burnout has been

213 found to be negatively related to motivational outcomes, whereas

214 work engagement has been positively linked to health-related

215 outcomes (e.g., Bakker, Demerouti, & Schaufeli, 2003; Hakanen

216 et al., 2006; Richardsen, Burke, & Martinussen, 2006; Schaufeli &

217 Bakker, 2004). Nevertheless, these cross-links do not appear to be as

218 substantial or systematic as the direct links underlying the health-

219 impairment and motivational processes (Halbesleben, 2010; Hu,

220 Schaufeli, & Taris, 2011; Schaufeli & Bakker, 2004). For example, in

221 a two-sample study, Hu et al. (2011) found that the links between

222 job demands (e.g., workload, emotional demands) and burnout (.58

223 and .62) were stronger than those between job demands and work

224 engagement (-.09 and -.05). Conversely, the links between job

225 resources (i.e., job control, colleague support) and work engage-

226 ment (.47 and .53) were stronger than those between job resources

227 and burnout (-.18 and -.37).

228 However, because most studies investigating both processes

229 have used SEM with CFA factors of burnout and work engagement,

230 it is possible that their representation of the relationships between

231 job characteristics, burnout/work engagement, and health-related

232 as well as motivational outcomes is significantly biased (Marsh

233 et al., 2009). Indeed, given that CFA measurement models result

234 in substantially inflated factor correlations (e.g., between burnout

235 and work engagement), it is likely to distort subsequent structural

236 analyses. As such, because it provides a more exact estimate of

237 the relationship between burnout and work engagement, ESEM

238 should also provide a more accurate representation of the relation-

239 ship between these two concepts and job demands, job resources,

240 as well as health-related and motivational indicators of employee

241 functioning. Investigating the health-impairment and motivational

242 processes through ESEM would thus shed new light on the possible

243 interdependency of the two processes.

244 1.2. The present study

245 The aim of this study is to deepen our understanding of the inter-

246 play between burnout and work engagement by delving further

247 into the energy and identification continua. First, we investigate

248 the factorial structure of the MBI-GS and the UWES simultaneously

249 using ESEM. This approach allows indicators to cross-load on mul-

250 tiple factors. As such, items representing one end of a continuum

251 should also load on the factor representing the opposite end of

252 that continuum (albeit negatively). More specifically, based on the

253 above mentioned JD-R-based empirical evidence and theoretical

254 propositions in support of the energy and identification continua,

255 we propose the following hypotheses:

Hypothesis 1. Emotional exhaustion items will have significant 256

cross-loadings on the vigor factor and vigor items will have signifi- 257

cant cross-loadings on the emotional exhaustion factor (supporting 258

the energy continuum). 259

Hypothesis 2. Cynicism items will have significant cross-loadings 260

on the dedication factor and dedication items will have significant 261

cross-loadings on the cynicism factor (supporting the identification 262

continuum). 263

Hypothesis3. Atwo-factorESEMsolution(i.e.,emotionalexhaus- 264

tion/vigor and cynicism/dedication) representing the two continua 265

will provide a better fit to the data than a four-factor ESEM solu- 266

tion (i.e., emotional exhaustion, cynicism, vigor and dedication) 267

representing the four separate dimensions. 268

By exploring whether burnout and work engagement are dia- 269

metrical counterparts, this study will evaluate the added value 270

of (or redundancy in) investigating both work engagement and 271

burnout to assess employees' level of energy and identification with 272

their work. 273

Second, we conduct exploratory ESEM analyses to examine the 274

health-impairment and motivational processes proposed by the 275

JD-R model. By comparing two structural models (one with CFA 276

factors of burnout/work engagement and one with ESEM factors 277

of these constructs), this study ultimately aims to assess whether 278

ESEM significantly alters the pattern of relationship between job 279

characteristics, burnout/work engagement, and health-related and 280

motivational outcomes. 281

2. Method 282

2.1. Participants and procedures 283

The sample comprised school teachers (n = 1159, participation 284

rate of 39%) working in the province of Quebec, Canada. All teach- 285

ers received a letter at work describing the purpose of the study in 286

detail and inviting them to complete an online questionnaire. The 287

majority of participants were women (85.8%). Mean age was 27.79 288

years (SD = 4.13), with an average of3.29 (SD = 1.68) years of expe- 289

rience on the job. The majority taught in primary schools (60.3%), 290

34.7% in secondary schools and 5% in other school settings. 291

2.2. Measures 292

All measures were administered in French. Means, standard 293

deviations and latent correlations of these measures are presented 294

in Table 1. Reliability of the measures was established by Hancock's 295

coefficient (i.e., coefficient H; Hancock & Mueller, 2001), which uses 296

standardized factor loadings obtained through CFA measurement 297

models to estimate the stability of latent constructs across mul- 298

tiple observed variables. Values equal to or greater than .70 are 299

considered satisfactory (Hancock & Mueller, 2001). 300

2.2.1. Burnout 301 The core dimensions of burnout were assessed using the emo- 302

tional exhaustion and cynicism subscales of the MBI-GS (Schaufeli 303

et al., 1996). Each of these subscales contains five statements per- 304

taining to either emotional exhaustion (e.g., "I feel emotionally 305

drained by my work", coefficient H = .92) or cynicism (e.g., "I doubt 306

the significance of my work", coefficient H = .87). Participants were 307

asked to indicate how often they experienced these feelings at work 308

on a scale from 1 (never) to 7 (every day). 309

2.2.2. Work engagement 310 The core dimensions of work engagement were assessed using 311

the vigor and dedication subscales of the UWES (Schaufeli et al., 312

fli^^^M AllIil.E IN PRESS

4 S.-G. Trepanier et al. / Burnout Research xxx (2015) xxx-xxx

Table 1

Means, standard deviations and correlations between latent variables.

Mean SD Range Emotional Cynicism Vigor Dedication Work Job Recognition Turnover

exhaustion overload autonomy intention

Emotional 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 -.480 .384 .456 -.321 .544 -

-.361 -.534 .492 .520 -.322 .544

Turnover 1.871 1.372 1-7 .488 .757 -.432 -.514 .422 -.420 -.447

intention .610 .793 -.599 -.662 .426 -.420 -.447

Psychological 1.688 .527 1-4 .667 .531 -.390 -.337 .538 -.449 -.372

distress .713 .622 -.503 -.478 .538 -.449 -.372

Note. Correlations of the ESEM solution are in bold. Means and SD obtained through CFA measurement models.

2002). Sample items are: "At work I feel like I am bursting with energy" (vigor; 6 items; coefficient H =.89) and "I am enthusiastic about my work" (dedication; 5 items; coefficient H =.93). Participants were asked to indicate how often they experienced these feelings at work on a scale from 1 (never) to 7 (every day).

2.2.3. Job characteristics

Job demands were assessed with the work overload subscale of the Areas of Work Life Scale (AWS; Leiter & Maslach, 2004), whereas job resources were assessed with the job autonomy and recognition subscales of the AWS. Sample items are: "I do not have time to do the work that must be done" (work overload; 6 items; coefficient H = .88), "I have control over how I do my work" (job autonomy; 3 items; coefficient H = .58) and "My work is appreciated" (recognition; 4 items; coefficient H = .89). Participants were asked to rate the frequency with which they experienced these situations on a five-point scale from 1 (never) to 5 (almost always). In the SEM analyses, the items of the three subscales were used as indicators of their respective latent factor.

2.2.4. Psychological distress

The French version (Préville, Boyer, Potvin, Perrault, & Légaré, 1992) of the Psychiatric Symptom Index (PSI; Ilfeld, 1976) was used to assess psychological distress, a health-related outcome of burnout and work engagement. This scale measures the presence of anxiety (3 items; coefficient H =.83) and depressive (5 items; coefficient H = .82) symptoms, as well as irritability (4 items; coefficient H= .88) and cognitive problems (2 items; coefficient H= .88) experienced during the previous week. A sample item of the scale is: "I felt easily annoyed or irritated" (i.e., irritability problem). Items were scored on a four-point scale ranging from 1 (never) to 4 (very often). In the SEM analyses, mean scores on the four subscales were used as indicators of the latent construct of psychological distress.

2.3. Ethical considerations

Approval for the study was obtained from the research ethics board of the researchers' institution. All participants received a letter explaining the purpose (i.e., investigating workplace factors associated with well-being in the teaching profession) and a description of what their participation consisted of (i.e., taking about 30 min to complete an online questionnaire regarding their work experiences). The confidentiality and anonymity of responses were also emphasized in the letter. No incentive was given in exchange for participation.

2.4. Statistical analyses

In the present study, all analyses were performed using Mplus (Muthen & Muthen, 2012) with the WLSMV estimator for categorical variables. CFA and ESEM measurement models were tested to investigate the factorial structure of the MBI-GS and UWES (measurement analyses). For each analysis (CFA and ESEM), two types of measurement models were tested: a two-factor and a four-factor structure. In the CFA solution, each indicator of the MBI-GS and UWES was allowed to load on its respective factor only. Latent factors were allowed to correlate. In the two ESEM solutions (a two- and a four-factor structure) all loadings were freely estimated using an oblique Geomin rotation (the default rotation solution in Mplus) with an epsilon value of 0.5 (Marsh et al., 2009, 2012). The latent factors were also allowed to correlate. The goodness-of-fit of all tested models was evaluated using three fit indices compatible with the WLSMV estimator: the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI) and the Root Mean Square Error of Approximation (RMSEA). Values higher than .95 for the CFI and TLI indicate a good fit (Hooper, Coughlan, & Mullen, 2008; Hu & Bentler, 1999). For the RMSEA, values lower than .07 (with the upper limit of the confidence interval [CI] less than .08) represent reasonable error of approximation (Hooper et al., 2008; Steiger, 2007).

2.2.5. Turnover intention

Turnover intention was evaluated as a motivational outcome of burnout and work engagement, using three items adapted from O'Driscoll and Beehr's scale (1994; e.g., "I plan on looking for another job within the next 12 months"). Items were scored on a scale ranging from 1 (strongly disagree) to 7 (strongly agree). In the SEM analyses, each item was used as an indicator of the latent construct of turnover intention (coefficient H= .97).

3. Results

3.1. Measurement analyses

Two CFA measurement models of the MBI-GS and UWES were tested: (1) a four-factor (M1) structure (i.e., emotional exhaustion, cynicism, vigor, and dedication) and (2) a two-factor (M2) structure comprised of one factor including the emotional exhaustion and


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

Table 2

Fit indices for the tested models.

Model description

RMSEA and 90% CI

Ml vs M2

M3 vs Ml M3 vs M4

M5 vs M6


994.896*' 1627.100*'


CFA measurement models

M1: Four factors 1934.131 183 .962 .957 .099 (.095-.103)

M2: Two factors (energy and identification continua) 6606.447 188 .862 .846 .189 (.183-.191)

ESEM measurement models

M3: Four factors 939.235 132 .983 .972 .079 (.075-.084)

M4: Two factors 2566.335 169 .948 .936 .121 (.117-.125)

Structural Analyses

M5:SEM with ESEM factors of burnout/work engagement 3066.010 698 .963 .956 .057 (.055-.060)

M6: 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 mean square; MC: model comparison; Ax2 = chi-square difference; ** p<.001.

Table 3

Measurement 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

Dedication -.188/-.530 -.473/-.764 .597/.924

Note. ESEM correlations are in bold above the dashed line.

391 vigor items (i.e., energy continuum) and a second factor including

392 the cynicism and dedication items (i.e., identification continuum).

393 Ml provided an adequate fit to the data with the exception of the

394 RMSEA which was above the .07 (and upper CI limit above the .08)

395 threshold (see Table 2). Ml also provided a significantly better fit

396 to the data than M2, which provided a poor fit to the data (Table 2).

397 Correlations between the four latent factors in Ml were high, rang-

398 ing from -.530 (between emotional exhaustion and dedication) to

399 .924 (between vigor and dedication; see Table 3).

400 Next, two ESEM measurement models with four (M3) and two

401 (M4) factors were tested (see Table 2). Results show that M4 did

402 not fit the data particularly well (none of the fit indices respected

403 their cut-off thresholds). M3 provided a satisfactory fit to the data.

404 Although the RMSEA was above .07, the upper CI limit was very

405 close to .08, suggesting a reasonable error of approximation. Results

406 also show that M3 provided a significantly better fit to the data than

407 M4. Moreover, M3 (i.e., ESEM four-factor solution) provided a sig-

408 nificantly better fit than Ml (i.e., CFA four-factor solution). Overall,

409 these results infirm Hypothesis 3. Correlations between the four

410 latent factors of M3 (ESEM four-factor solution) decreased signif-

411 icantly compared to Ml (CFA four-factor solution), ranging from

412 -.l88 to .597 (see Table 3). The strongest correlation was found

413 between vigor and dedication (r =.597), followed by the correla-

414 tion between emotional exhaustion and cynicism (r =.473). The

415 lowest correlations were between vigor and emotional exhaus-

416 tion (r= -.280) and between emotional exhaustion and dedication

417 (r= -.l88). Taken together, these results provide weak preliminary

418 support for the energy and identification continua. Indeed, results

419 showed that the four dimensions of burnout and work engagement

420 are best represented as distinct factors as opposed to components of

421 two underlying factors. Moreover, the pattern of correlations shows

422 that the components of the energy and identification continua are

423 not strongly negatively related. There is a small correlation between

424 emotional exhaustion and vigor (r = -.280; Cohen, l988) and a

425 moderate correlation between cynicism and dedication (r = -.473;

426 Cohen, l988).

427 All factor loadings (primary and cross-loadings) of the ESEM

428 four-factor solution are presented in Table 4. These factor loadings

429 enable a more rigorous evaluation of the energy and identification

430 continua. In order to support these continua, strong cross-loadings

431 for each latent factor should be found from indicators representing

the opposite dimension of the same continuum (e.g., dedication 432

items should have strong cross-links on the cynicism factor). The 433

results shown in Table 3 offer little support for any of the continua 434

(infirming Hypotheses l and 2). For the two dimensions of burnout, 435

no cross-loadings reached the threshold of .30 (Tabachnick & Fidell, 436

2007). This suggests that both latent factors are relatively distinct. 437

A different pattern of results was obtained for the two dimensions 438

of work engagement. Three out of five dedication items had cross- 439

loadings of .30 or higher on the vigor latent factor, and two out of 440

six vigor items had cross-loadings of .30 or higher on the dedication 441

latent factor.l 442

Overall, these results offer little empirical support for the energy 443

and identification continua and reveal that the two dimensions of 444

work engagement are highly intertwined. Moreover, these results 445

suggest that ESEM, which considers cross-loadings between the 446

core dimensions of burnout and work engagement, more ade- 447

quately reflects the interplay between these dimensions than CFA. 448

3.2. Structural analyses 449

Exploratory SEM analyses were conducted subsequently to 450

test the structural relationships between job characteristic (i.e., 451

work overload, job autonomy, and recognition), burnout/work 452

engagement, and health-related as well as motivational outcomes 453

(i.e., psychological distress and turnover intention). More specifi- 454

cally, two models were compared: M5 including ESEM-factors of 455

burnout/work engagement, and M6, including CFA-factors of these 456

constructs. In both models, all links between job characteristics 457

and the four dimensions of burnout/work engagement as well as 458

between these dimensions and the two outcomes were assessed. 459

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

ment analyses were subsequently conducted using the MBI-GS and the UWES-9. The results revealed a pattern similar to the one obtained with the complete version of the work engagement scale. More specifically, no significant (i.e., above .30) cross-loadings were observed for the latent factors of emotional exhaustion and cynicism. Two dedication items (out of three) had cross-loadings of .30 or higher on the vigor latent factor and one vigor item (out of three) had cross-loadings of .30 or higher on the dedication latent factor. Detailed results of the ESEM measurement model containing the UWES-9 can be obtained from the first author.


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

Table 4

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

Factor loadings

Factor 1



Factor 4

Emotional exhaustion Item 1 .752/.843

Item 2 .932/.855

Item 3 .728/.852

Item 4 .615/.885

Item 5 .731/.88Î

Cynicism Item 1 .153

Item 2 .150

Item 3 .034

Item 4 .097

Item 5 .113

Vigor Item 1 -.146

Item 2 -.114

Item 3 .099

Item 4 .143

Item 5 -.058

Item 6 -.086

Dedication Item 1 -.060

Item 2 -.014

Item 3 -.080

Item 4 -.080

Item 5 -.029

-.029 .080 .248 .214

.702/.868 .704/.9Î9 .497/.497 .465/.637 .582/.745

-.205 -.137 .155 .098 .204 -.116

-.274 -.255 -.188 -.041 -.092

.013 .032 -.256 -.240 .002

-.153 .032 -.256 .006 .071

.420/.924 .738/.879 .402/.5Î7 .358/.36Î .357/.3Î3 .754/.898

.395 .428 .405 .129 .062

-.083 -.084 .064 .057 -.082

-.039 -.056 -.063 -.211 -.270

.369 .084 .392 .250 .147 .128

472/.948 471/.875 448/.893 733/.836 .857/.8Î9

Note. CFA loadings are in italics below the dashed line. Factor loadings of the items reflecting the theoretical counterpart of each factor are in bold. Significant cross-loadings are underlined.

460 Although M6 fits the data reasonably well (CFI and TLI were close

461 to the .95 threshold) the results indicate that M5 provided a sig-

462 nificantly better fit to the data (see Table 3). The results of both

463 solutions (ESEM and CFA) are depicted in Fig. 1. Results show that

464 the structural links between job characteristics and the dimensions

465 of burnout and work engagement in the CFA solution are generally

stronger (7 out of 10) than those obtained in the ESEM solution. 466

Interestingly, the CFA solution reveals a significant link between 467

job autonomy and cynicism that was not significant in the ESEM 468

solution. Moreover, the "emotional exhaustion-turnover intention" 469

and "dedication-psychological distress" relationships were found 470

to be non-significant in the CFA solution but significant in the ESEM 471

/ Emotional ^-"""TV exhaustion

.743/ ^ /

Fig. 1. Results comparing the structural relationships between job characteristics, the dimensions of burnout/work engagement and outcomes through CFA and ESEM. Note. Results of the ESEM solution are in bold above the dashed line.


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

472 solution. Subsequently, regression coefficient comparison (using

473 unstandardized coefficient estimates and standard errors) was con-

474 duct to more rigorously compare M5 (ESEM solution) and M6

475 (CFA solution). Results reveal several significant differences. These

476 differences are presented in bold dotted lines in Fig. 1. More specif-

477 ically, M6 revealed significantly stronger relationships between (1)

478 work overload and cynicism (z = 2 . 2 7), (2) work overload and ded-

479 ication (z = 2 .06), (3) job autonomy and dedication (z =2.0 2), (4)

480 recognition and vigor (z = 2 .54) and (5) recognition and dedication

481 (z = 4.37). On the other hand, M5 revealed significantly stronger

482 relationships between job autonomy and dedication (z = 2.16) as

483 well as between dedication and psychological distress (z =2.41).

484 4. Discussion

485 The present study aimed to shed new light on the interplay

486 between burnout and work engagement by empirically investi-

487 gating whether both concepts are diametrical counterparts. More

488 specifically, this study simultaneously investigated the factorial

489 structure of the MBI-GS and UWES using ESEM, which allowed us

490 to examine the energy (emotional exhaustion-vigor) and identi-

491 fication (cynicism-dedication) continua. Our results offered little

492 support for both continua, revealing that work engagement dimen-

493 sions are highly intertwined and have stronger relationships

494 with each other than with their burnout counterpart. More-

495 over, integrating ESEM measurement models of burnout/work

496 engagement within a SEM that includes job characteristics and

497 health-related/motivational outcomes significantly alters the pat-

498 tern of results. As such, our results extend the understanding of the

499 health-impairment and motivational processes proposed by theJD-

500 R model. The theoretical implications of these results are discussed

501 below.

502 4.1. Theoretical contributions

503 4.1.1. The energy and identification continua

504 The energy and identification continua said to connect the core

505 dimensions of burnout and work engagement were investigated

506 by comparing two-factor and four-factor measurement models

507 (CFA and ESEM). Results offered little support for these continua

508 as the four-factor solutions fit the data significantly better than

509 the two-factor solutions. Furthermore, ESEM - which takes cross-

510 loadings of all indicators on all factors into account - allowed for

511 an in-depth examination of the energy and identification continua.

512 More specifically, we investigated whether strong cross-loadings

513 for the four dimensions were found from their theoretical coun-

514 terpart. The results did not follow such a pattern, suggesting that

515 burnout and work engagement are not conceptual opposites. With

516 regard to the two dimensions of burnout, results revealed no signif-

517 icant cross-loadings (.30 or higher) for any other items, suggesting

518 that both dimensions of burnout are distinct and tap into unique

519 work-related psychological experiences. These results support past

520 research validating the factorial structure of MBI-GS through CFA

521 (e.g., Hu & Schaufeli, 2011; Schutte, Toppinen, Kalimo, & Schaufeli,

522 2000), which showed that emotional exhaustion and cynicism are

523 best represented as separate factors (as opposed to a single fac-

524 tor). Overall, the results of the present study, in conjunction with

525 past CFA studies, highlight the relevance of investigating emotional

526 exhaustion and cynicism separately given that they reflect distinct

527 energetic and attitudinal experiences.

528 A different pattern of results was obtained for work engage-

529 ment. Results revealed several cross-loadings between the two

530 dimensions (using both the long and short versions of the UWES)

531 and none from items reflecting their burnout counterparts. The

532 vigor and dedication subscales of the UWES (and UWES-9) thus

represent similar psychological experiences that are difficult to 533

distinguish. This overlap is supported by the particularly strong 534

correlation found in this study between the two dimensions of 535

work engagement (.924 in the CFA measurement model and .597 in 536

the ESEM solution). This also concurs with past research showing 537

a strong interrelation between these dimensions, with correla- 538

tion coefficients usually exceeding .60 (e.g., Hallberg & Schaufeli, 539 2008; Schaufeli et al., 2002; Shimazu et al., 2008). Moreover, our Q8 540

results support those obtained in several validation studies (e.g., 541

Hallberg & Schaufeli, 2006; Shimazu et al., 2008) that found a 542

one-dimensional representation of work engagement to be equiv- 543

alent (or even superior) to a multi-dimensional representation (i.e., 544

vigor and dedication as distinct concepts). Overall, our results, like 545

previous CFA studies, highlight the considerable overlap between 546

vigor and dedication, suggesting that work engagement may be 547

best investigated as a one-dimensional construct. Specifically, our 548

results suggest that, because both dimensions are intertwined, 549

investigating work engagement globally (i.e., vigor and dedication 550

combined) as opposed to its two dimensions separately, may be 551

most parsimonious and appropriate. Further research is required 552

to pursue investigation of the uniqueness of vigor and dedication 553

from a conceptual standpoint (i.e., the fundamental nature of both 554

constructs). Researchers could also revisit the relationship between 555

vigor and dedication from a methodological standpoint by assess- 556

ing the adequacy of the UWES for assessing work engagement 557 (Schaufeli & Salanova, 2011). Q9 558

4.1.2. Health-impairment and motivational processes 559

Our study provides new insight into the relationships between 560

job characteristics (demands and resources), burnout/work 561

engagement and health-related as well as motivational outcomes. 562

Results reveal that SEM with CFA factors of burnout/work engage- 563

ment predominantly resulted in stronger relationships between 564

these factors and job characteristics. More specifically, half of the 565

links (five out of ten) were significantly stronger in the CFA solution 566

than in the ESEM solution. Of these five links, three were between 567

job resources (job autonomy, recognition) and work engage- 568

ment (the other significant links were between job demands and 569

cynicism/dedication). With regard to the relationships between 570

burnout/work engagement and indicators of employee functioning, 571

the ESEM solution revealed two cross-links (emotional exhaustion- 572

turnover intention and dedication-psychological distress) that 573

were not significant in the CFA solution. 574

Taken together, these findings reiterates the importance of 575

ESEM in structural solutions when investigating concepts that are 576

theoretically very closely related (Asparouhov & Muthen, 2009), 577

as it is the case for burnout and work engagement. Allowing 578

cross-loadings between these closely related concepts results in 579

a more adequate assessment of their interrelationship as well as 580

the association they have with their work-related antecedents 581

and outcomes (Asparouhov & Muthen, 2009). Our results show 582

that investigating the relationship between job characteristics and 583

burnout/work engagement through SEM with CFA factors may 584

result in an inflated representation ofthese relationships, especially 585

those involving job resources and work engagement (motivational 586

process). Moreover, these findings suggest that burnout and work 587

engagement may have more similar effects on employee func- 588

tioning than initially proposed by the JD-R model. That is, both 589

burnout and work engagement dimensions were found to pre- 590

dict health-related (i.e., psychological distress) and motivational 591

(i.e., turnover intention) outcomes. This corroborates findings of 592

past studies showing that the health-impairment and motivational 593

processes are interrelated (e.g., Bakker et al., 2003; Hakanen et al., 594

2008; Trepanier, Fernet, Austin, Forest, & Vallerand, 2014; Van den 595

Broeck, Vansteenkiste, De Witte, & Lens, 2008), as opposed to rela- 596

tively independent processes (Bakker & Demerouti, 2007). This also 597


S.-G. Trépanier et al. / Burnout Research xxx (2015) xxx-xxx

598 underlines the relevance of investigating burnout/work engage-

599 ment simultaneously and of systematically examining cross-links

600 in order to more adequately capture the interplay between job

601 demands, job resources, burnout, work engagement, as well as

602 health-related and motivational outcomes (Schaufeli and Taris,

603 2014). This would result in a clearer representation of the poten-

604 tial motivational effects of job demands as well as the energetic

605 impact of job resources on employee functioning through burnout

606 and work engagement. Unfortunately, research to date has often

607 investigated both processes in isolation and has usually omitted

608 to evaluate all cross-links between these processes (e.g., Hakanen 60Q10 et al., 2006; Kounka et al., 2009; Schaufeli & Bakker, 2004).

610 4.2. Methodological implications

611 From a measurement standpoint, the results of this study offer

612 valuable insight to researchers and practitioners assessing burnout

613 and work engagement. By offering little support for the energy

614 and identification continua proposed to underlie the relationship

615 between the dimensions of burnout and work engagement, our

616 results show that these two concepts are distinct psychological

617 experiences and should be evaluated as such. Indeed, our findings

618 nuance past propositions suggesting that adding the scores of vigor

619 (dedication) items to the reversed scores of emotional exhaustion

620 (cynicism) items adequately represent employees' overall level of

621 "energy" and "identification" (e.g., Gonzalez-Roma et al., 2006).

622 Our results also call into question the assessment of burnout and

623 work engagement with the same instrument, as it is the case

624 with the OLBI (Demerouti & Bakker, 2008). The OLBI contains both

625 negatively and positively worded items said to reflect both ends

626 of the energy and identification continua. It has been proposed

627 that recoding positively framed items (reflecting vigor and dedi-

628 cation) results in the measurement of burnout whereas recoding

629 negatively framed items (reflecting exhaustion and disengage-

630 ment) results in the measurement of work engagement (Demerouti

631 & Bakker, 2008). However, our results indicate that high vigor

632 (dedication) does not necessarily imply low emotional exhaustion

633 (cynicism) and low cynicism (emotional exhaustion) does not nec-

634 essarily imply high dedication (vigor). Taken together, our results

635 suggest that both researchers and practitioners would benefit from

636 assessing burnout and work engagement (as well as their dimen-

637 sions) independently and through different instruments as they

638 reflect distinct concepts.

639 4.3. Limitations and conclusion

640 The present study has some limitations that should be

641 addressed. First, the fact that the study was conducted among

642 school teachers only raises concerns regarding the generalizabil-

643 ity of the findings to other working populations. Future research is

644 encouraged to validate our findings by investigating burnout and

645 work engagement, from both measurement and structural stand-

646 points, using ESEM in other occupations. Second, it is important to

647 note that in all tested models, particularly for the CFA measurement

648 models, the RMSEA fit value did not indicate a particularly good fit

649 (see Table 2), which hints at model misspecification (Hu & Bentler,

650 1999). Future research is needed in order to validate our measure-

651 ment and structural findings in other samples. Third, the structural

652 model tested in the present study focused on a limited num-

653 ber of job characteristics (i.e., work overload, job autonomy and

654 recognition) and only on negative employee functioning outcomes

655 (i.e., psychological distress and turnover intention). Moreover, it is

656 worth mentioning that as in previous studies (e.g., Fernet, Austin,

657 & Vallerand, 2012) the measure of job autonomy did not meet

658 the benchmark for reliability (coefficient H = .57). The revised AWS

659 (Leiter & Maslach, 2011), which comprises an additional item

to capture job autonomy, would certainly help represent more 660

adequately this construct. Future studies are encourage to repli- 661

cate the results using this job autonomy measure and other job 662

demands (e.g., role ambiguity, physical demands), job resources 663

(e.g., social support, skill utilization) as well as both positive and 664

negative indicators of employee functioning (e.g., in-role per- 665

formance, psychosomatic complaints, commitment). This should 666

provide additional support for the relevance of using ESEM analysis 667

when investigating the health-impairment and motivational pro- 668

cesses. Including objective and multi-source health-related (e.g., 669

sickness absence records) and motivational (e.g., supervisor ratings 670

of employee extra-role performance) indicators of employee func- 671

tioning would also strengthen the results obtained in the present 672

study, which relied solely on self-reported data. 673

In summary, although it has been proposed that burnout and 674

work engagement are conceptual counterparts, the results of this 675

study offer little support of this proposition. Our results also illus- 676

trate that ESEM represents a promising avenue for future burnout 677

and work engagement research as it may more adequately capture 678

the interplay between these concepts as well as their specific rela- 679

tionships with job characteristics and employee functioning (i.e., 680

health-impairment and motivational processes). 681

Conflict of interest Q11682

The authors declare that there are no conflicts of interest. 683

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