Content uploaded by Roger Muñoz-Navarro
Author content
All content in this area was uploaded by Roger Muñoz-Navarro on Jan 29, 2019
Content may be subject to copyright.
73
Research on occupational well-being has focused particular
attention in recent years on two major topics, burnout and work
engagement. Burnout is a persistent mental state related to work,
predominated by negative affect such as stress, anxiety and worry,
and has a huge impact on people’s health (Grandey, 2015). This
syndrome is characterized by several dimensions: 1) exhaustion,
2) mental distance and 3) professional ineffi cacy. Since mental
distance includes cynicism and depersonalization as two separate
dimensions, a four-dimension model has more recently been
proposed for burnout (Salanova et al., 2005). Engagement has
been proposed as the opposite of the burnout state (Salanova,
Schaufeli, Llorens, Peiró, & Grau, 2000) and is defi ned as a
positive motivational construct related to vigor (high energy
levels while working), dedication (manifested by signifi cance,
pride, and goals related to the work done), and absorption (full
concentration levels at work). As indicated by Bakker & Albrecth
(2018), engaged employees tend to experience positive affect, such
as joy, enjoyment and enthusiasm, which expands the cognitive-
behavioral repertoire involved in their work. Moreover, engaged
employees can focus and devote all their resources and skills to
their work. They also tend to show greater collaboration with their
immediate environment, which allows them to effectively meet
the demands of their work and benefi t the whole organization
(S a l an o va , Ll o re n s, & Sc h au f el i , 2 0 11) . I t is t he r e fo r e h i g hl y re l ev a nt
to identify those factors that promote “healthy” employees who are
“engaged” and feel fulfi llment in their work, since these aspects
have a positive infl uence both on the workers themselves (Lent &
ISSN 0214 - 9915 CODEN PSOTEG
Copyright © 2019 Psicothema
www.psicothema.com
Cognitive processes of emotional regulation, burnout and work
engagement
Estanislao Castellano
1
, Roger Muñoz-Navarro
2
, María Sol Toledo
1
, Carlos Spontón
1
, and Leonardo Adrián Medrano
1,3
1 Universidad Siglo 21, Córdoba (Argentina), 2 Universitat de València (España), and 3 Universidad Nacional de Córdoba, Argentina
Abstract Resumen
Background: Workers constantly resort to cognit ive processes of emotion
regulation to deal with emotions they experience in the workplace.
These processes belong either to the “automatic” (preconscious and
fast) or the “elaborative” (conscious and slow) mode. This study aims to
determine the role of these variables in the work setting and to analyze
their relationship with positive and negative affect, engagement and
burnout. Method: 350 employees (54.8% men and 45.2% women) were
presented with several instruments measuring burnout, engagement,
affect and cognitive emotion regulation strategies in a prospective study.
An explanatory model was tested through structural equation modeling
analysis. Results: Acceptable fi t indices and a signifi cant explanatory
value both for burnout (61%) and engagement (58%) were obtained. The
use of “automatic” cognitive regulation strategies was associated with the
presence of negative affect and burnout whereas “elaborative” processes
were associated with positive affect and engagement. Conclusions: Our
fi ndings underscore the importance of the role of cognitive emotion
regulation in organizational settings.
Keywords: Cognitive processes, emotion regulation, affect, engagement,
burnout.
Procesos cognitivos de regulación emocional, burnout y engagement
en el trabajo. Antecedentes: los trabajadores apelan constantemente a
procesos cognitivos de regulación emo cional para lidiar con las emociones
que experimentan en el trabajo. Estos procesos se pueden distinguir en
dos modos de procesamiento, uno es preconsciente y rápido, llamado
“automático”; y otro es consciente y más lento, llamado “elaborativo”. El
objetivo de este trabajo fue determinar el papel de estas variables en el
entorno de trabajo y analizar su relación con el afecto positivo y negativo,
el burnout y el engagement. Método: 350 trabajadores (54,8% hombres
y 45,2% mujeres) completaron varios instrumentos que miden burnout,
engagement, afecto y estrategias cognitivas de regulación emocional
en un estudio prospectivo. Se testeó un modelo explicativo a través del
análisis de ecuaciones estructurales. Resultados: se obtuvieron valores
aceptables en los índices de ajuste y un valor explicativo signifi cativo
tanto para el burnout (61%) como para el engagement (58%). El uso
de estrategias cognitivas de regulación emocional “automáticas” se
relacionó con la presencia de afecto negativo y burnout, mientras que el
uso de procesos “elaborativos” se relacionó con la experiencia de afecto
y compromiso positivo. Conclusiones: estos resultados corroboran la
importancia del papel de la regulación cognitiva de las emociones en el
entorno organizacional.
Palabras clave: procesos cognitivos, regulación emocional, afecto,
engagement, burnout.
Psicothema 2019, Vol. 31, No. 1, 73-80
doi: 10.7334/psicothema2018.228
Received: August 24, 2018 • Accepted: December 10, 2018
Corresponding author: Roger Muñoz-Navarro
Facultad de Psicología
Universitat de València
46010 Valencia (Spain)
e-mail: roger.munoz@uv.es
Estanislao Castellano, Roger Muñoz-Navarro, María Sol Toledo, Carlos Spontón, and Leonardo Adrián Medrano
74
Brown, 2008) and on the well-being of the company as a whole
(Schaufeli, Bakker, & Van Rhenen, 2009). Furthermore, knowing
how to prevent the burnout syndrome, which has a negative impact
on productivity and people’s health, could help tackle one of the
biggest problems of the twenty-fi rst century (Prasad, 2016).
Recent studies on Positive Organizational Psychology have
highlighted the relevance of emotional factors in the work
setting owing to their explanatory value in assessing burnout and
engagement as mediators of occupational well-being (Castellano,
Cifre, Spontón, Medrano, & Maffei, 2013; Lisbona, Palaci,
Salanova, & Frese, 2018; Rodríguez-Mantilla & Fernández-
Díaz, 2017). Workers continually appeal to different processes of
emotion regulation (ER) in order to take charge of the emotions
they experience in their work setting (Grandey, 2015). These
same ER processes can be of relevance in preventing burnout
(Arnold, Connelly, Walsh, & Martin Ginis, 2015), promoting work
e n g a g e m e n t o n t h e p a r t o f e m p l o y e e s ( S a l a n o v a e t a l . , 2 0 11 ), c r e a t i n g
more adaptive organizational behavior (Rodríguez García, López-
Pérez, Férreo Cruzado, Fernández Carrascoso, & Fernández,
2017) and are closely related to job satisfaction (Côté & Morgan,
2002). Indeed, based on the model of emotional dissonance, the
latter authors showed that the suppression of pleasant emotions
decreased work satisfaction, while their amplifi cation increased it.
These fi ndings are consistent with those reported by Grandey &
Melloy (2017) who, after a systematic review, concluded that ER
in the work setting has a direct impact on variables such as work
satisfaction, burnout, work performance and task abandonment.
The concept of ER has been defi ned as the processes that
infl uence the way in which people experience and express their
emotions (Gross, 1998). People are able to reroute the spontaneous
fl ow of their emotions, increasing, maintaining or decreasing them
(Gross, 2015). A recent meta-analysis of the most accepted ER
strategies in t he literature list s ten strategies: accepta nce, behavioral
avoidance, distraction, experiential avoidance, expressive
suppression, mindfulness, problem-solving, reappraisal, refl ection
and concern (Naragon-Gainey, McMahon, & Chacko, 2017). As
seen, there are many strategies for regulating emotions and the
cognitive processes involved during an emotional episode play a
key role (Garnefski, Kraaij, & Spinhoven, 2001). Along these lines,
the latter authors propose a model of cognitive emotion regulation
by selecting nine ER cognitive strategies: self-blaming, blaming
others, rumination, catastrophizing, putting into perspective,
positive focus, positive reappraisa l, acceptanc e, and planni ng. Such
strategies can be grouped into one of two systems: “automatic”
or “elaborative” processing (Clore & Ortony, 2000; Medrano,
Muñoz-Navarro, & Cano-Vindel, 2017). Automatic processing is
characterized by being fast, preconscious and diffi cult to control
whereas elaborative processing is voluntary, conscious, and slow.
Automatic ER processes contain functions such as rumination
or catastrophizing that enable situations of immediate threat to
be dealt with. However, they can in turn increase anxiety or alert
responses (Beck & Clark, 1997). Elaborative processes, such as
cognitive reappraisal, focusing on plans or acceptance, facilitate a
more rational and profi table interpretation of problems, decreasing
anxiety responses. These processes, as mentioned by Medrano et
al. (2016), could be explained from the perspective of evolutionary
psychology. Humans have developed different systems to detect
threats and react adaptively throughout evolutionary history, thus
increasing their chances of safety and survival. These processes
developed at earlier stages of evolution are automatic, simple, fast,
and without voluntary control. However, as our cognition system
evolved, we acquired more complex, rational and controlled
capabilities for conscious elaborative processes that can infl uence
our experienced emotions in a benefi cial manner.
Accordingly, this study aims to determine the role of ER in the
work setting and more specifi cally, to analyze its relationship with
workers’ positive and negative affect, engagement and burnout.
The following hypotheses are proposed: There is a direct, positive
relationship between ER elaborative processes and positive affect
(hypothesis 1) and a direct relationship between ER elaborative
processes and engagement (hypothesis 2). There is a direct, negative
relationship between ER elaborative processes and negative affect
(hypothesis 3). There is a direct, positive relationship between
ER automatic processes and negative affect (hypothesis 4) and a
direct relationship between ER automatic processes and burnout
(hypothesis 5). There is a direct, negative relationship between
ER automatic processes and positive affect (hypothesis 6). There
is a direct, negative relationship between negative affect and
engagement (hypothesis 7) and a direct, positive relationship
between negative affect and burnout (hypothesis 8). Finally, a
direct, negative relationship is hypothesized between positive
affect and bur nout (hypothesis 9) and a direct, positive relationship
between positive affect and engagement (hypothesis 10).
Method
Participants
This study involved the participation of 350 workers belonging
to different companies in the city of Córdoba, Argentina. The
sample comprises workers of both genders (45% are women)
aged between 20 and 60 years (M = 36.44; SD = 8.28), selected
through non-probability, accidental sampling. To ensure a greater
heterogeneity in the sample, workers from different sectors and
areas were included (Table 1).
Instruments
Burnout: To assess exhaustion, cynicism, and ineffi cacy, the
Spanish version of the Maslach Burnout Inventory-General
Survey (MBI-GS) (Salanova et al., 2000) was used. To assess
depersonalization, we used the Spanish version of the Maslach
Burnout Inventory Human Services-Survey (MBI-HSS; Gil-
Monte, 2005). A total of 17 items corresponding to the four
dimensions of burnout were administered: exhaustion (4 items),
cynicism (4 items), depersonalization (5 items) and ineffi cacy
(4 items). To respond to the above-mentioned items a response
scale was used ranging from 0 (‘never’) to 6 (‘always/every
day’). We used the version adapted to the worker population of
Argentina (Spontón, Trógolo, Castellano, & Medrano, in press),
which has satisfactory psychometric properties. Analyses using
the Cronbach alpha coeffi cient showed that the scales have an
acceptable internal consistency (exhaustion = .77, cynicism =
.84 depersonalization = .71 and ineffi cacy = .80). The MBI-GS
scores correlated signifi cantly and in the expected direction with
the levels of engagement, negative affect and positive affect, thus
providing external evidence of validity. The Cronbach alpha
coeffi cients found in the sample of this study for each dimension
were: .72 (exhaustion), .80 (cynicism), .87 (depersonalization) and
.64 (ineffi cacy).
Cognitive processes of emotional regulation, burnout and work engagement
75
Engagement: The Spanish version of the Utrecht Work
Engagement Scale (UWES; Salanova et al., 2000) was used,
enabling three dimensions of engagement to be evaluated: vigor (6
items), dedication (6 i t e ms ) an d absorption (5 items). All exam inees
used a seven-point scale (from 0 ‘never’ to 6 ‘always/every day’),
to respond to each item. Studies conducted in Argentina (Spontón,
Medrano, Maffei, Spontón, & Castellano, 2012) indicate that the
scale retains the same factorial structure as the original scale; the
reliability values calculated using the acceptable Cronbach alpha
coeffi cient were .69, .76 and .88 for the dimensions of absorption,
vigor and dedication, respect ively. In addition, Spontón et al. (2018)
p rov id e d ex te r na l e vi de nc e o f v al id it y b y c or r el at in g U W ES sc or es
with professional self-effi cacy. The values of internal reliability
for each dimension in this work were: .85 (dedication), .82 (vigor),
and .76 (absorption).
Affect: The Positive and Negative Affect Schedule (PANAS;
Watson, Clark, & Tellegen, 1988) was used, which consists of
20 words describing different feelings and positive emotions
(for example, active, strong, inspired) and negative emotions (for
example, irritated, fearful, nervous). The evaluated subject used a
fi ve-point scale (from 1 = ‘very little or nothing’ to 5 = ‘always or
almost always’) to show the extent to which he/she experienced
each of the mentioned emotions. The validated version for the
population of Córdoba, Argentina, was used (Moriondo, Palma,
Medrano, & Murillo, 2010; Medrano et al., 2015). The scale has
an acceptable level of internal consistency (α = .73 positive affect;
α = .82 negative affect). In order to achieve a greater delimitation
of the examined construct, the general scale prompt was slightly
modifi ed. Thus, workers were asked to indicate how often they
experienced positive and negative affect in their workplace. In this
study, PANAS’s internal consistency was good (α = .88 positive
affect; α = .82 negative affect).
Cognitive Emotion Regulation: The Cognitive Emotion
Regulation Questionnaire (CERQ: Garnefski & Kraaij, 2007) is
an instrument consisting of 36 items that examines the ability to
regulate personal emotions through the use of nine cognitive-type
strategies. A fi ve-point scale as adapted by Medrano, Moretti,
Ortiz, and Pereno (2013) was used, ranging from ‘never or almost
never’ (1) to ‘always or almost always’ (5). The adapted version
has nine underlying factors: 1) Self-blame (α = .69), 2) Other-
blame (α = .82), 3) Rumination (α = .70), 4) Catastrophizing (α
= .68), 5) Positive refocusing (α = .83), 6) Planning (α = .66), 7)
Positive reappraisal (α = .77), 8) Putting into perspective (α = .70),
and 9) Acceptance (α = .59). The scales can be grouped into two
factors, termed automatic processes (rumination, catastrophizing,
self-incriminating and blaming others) and elaborative processes
(positive reinterpretation, focus on plans, acceptance, positive
focus and putting into perspective). CERQ scores have been shown
to be valid for predicting emotional interference, positive affect
and negative affect (Medrano et al., 2013). The values of internal
reliability for each dimension in the present study were low: 1)
Self-blame (α = .54), 2) Other-blame (α = .62), 3) Rumination (α
= .65), 4) Catastrophizing (α = .60), 5) Positive refocusing (α =
.81), 6) Planning (α = .61), 7) Positive reappraisal (α = .67), 8)
Putting into perspective (α = .58), and 9) Acceptance (α = .41).
Since some of the sub-dimensions of the variables had low levels
of internal consistency (alpha values below .70), it was decided to
“collapse” the items of each dimension into a single score in order
to achieve more reliable measures: automatic processes (α = .75)
and elaborative processes (α = .80)”.
Procedure
A prospective, ex-post facto study was conducted, with no
manipulation of independent variables. A standardized procedure
was used to ensure all participants received the same instructions.
The instr uments were administere d collectively and dur ing regular
working hours with prior authorization from the administration
of each company. The questionnaires were administered in paper
format, in a quiet physical space away from the place where the
workers habitually carry out their tasks. The approximate time
taken to complete the quest ionnaires was 20 m inutes per part icipant.
The investigators were present during the administration of the
tests in order to clear up any doubts that arose and to verify the
independent administration by the participants.
This research was evaluated and approved by the Institutional
Ethics Committee of Siglo 21 University, Córdoba, Argentina.
All participants gave informed consent, and the workers’
data confi dentiality and the anonymity of their responses was
guaranteed. Once the data were analyzed, brief and anonymous
reports were offered to the participating companies.
Data analysis
To perform the statistical a nalysis the collected data were loaded
into the IBM SPSS 17 version. An initial exploratory data analysis
was conducted to evaluate the statistical assumptions required
for the use of Structural Equation Modeling (SEM). In this way,
univariate and multivariate normality were examined, descriptive
Table 1
Work and demographic characteristics of sample
Total sample
(n = 350)
N%
Gender
Female
Male
161
189
46
54
Type of work
Company
Independent
Government
Other
175
77
35
63
50
22
10
18
Sector
Public
Private
84
266
24
76
Size of the organization
Small (0-50 employees)
Medium (50 to 250 employees)
Large (more than 205 employees)
161
154
35
46
44
10
Type of Company
Trade
Services
Industry
Other
77
231
21
21
22
66
6
6
Income
Less than $15,000
Between $15,000 and $35,000
More than $35000
126
168
56
36
48
16
Estanislao Castellano, Roger Muñoz-Navarro, María Sol Toledo, Carlos Spontón, and Leonardo Adrián Medrano
76
statistics were calculated, and the existence of multicollinearity
was analyzed considering the bivariate relationships between
variables.
As the current study relies exclusively on self-report data, we
tested for possible bias due to common method variance using
Harman’s single factor test (Podsakoff, MacKenzie, Lee, &
Podsakoff, 2003). Different models were specifi ed to examine the
hypotheses. First, an orthogonal contribution model of cognitive
processes, in which automatic processes only infl uence negative
affect and elaborative processes only infl uence positive affect
(model 1), was compared with a cross-contribution model (model
2), in which automatic and elaborative processes infl uence both
negative and positive affect. We proceeded in a similar manner
to contrast the hypotheses referring to the infl uence of affect
on burnout and engagement. An orthogonal contribution model
of affect, where positive affect only infl uences engagement, and
negative affect only infl uences burnout (model 3) was compared
with a cross-contribution model, where positive and negative
affect infl uence both burnout and engagement (model 4). All
models were specifi ed in the AMOS program, 17 version.
To e valuat e t h e fi t of the models, several fi t indicators were
used: the absolute fi t index (χ2), the goodness of fi t index (GFI),
the Tucker-Lewis index (TLI), the comparative fi t index (CFI),
and the root mean square error of approximation (RMSEA). GFI,
TLI, and CFI values greater than .90 and RMSEA values smaller
than .08 indicate acceptable model fi t, while values greater than
.95 (for GFI, TLI and CFI) and smaller than .05 (for RMSEA)
are indicative of excellent fi t (Hu & Bentler, 1999). The division
of the coeffi cient χ² by the degrees of freedom (χ²/df) was also
considered; according to the literature, values lower than 3
indicate a good adjustment (Medrano & Muñoz-Navarro, 2017).
Finally, we calculated the Akaike Information Criterion (AIC;
Akaike, 1987) in order to compare competitive models, since it
is convenient to compare the suitability of non-tested models that
fi t into the same correlation matrix. The lower the AIC index, the
better the fi t (Byrne, 2001).
Results
An initial exploration and descriptive analysis was carried out
to assess the pattern of missing cases, to identify univariate and
multivariate atypical cases, to check the assumptions of normality
and to determ ine the behavior of the variables. The “missing values
analysis” of the SPSS revealed a random pattern of missing cases,
so a method of case allocation was used to replace the missing
values (mean in the series). Only 12 univariate atypical cases and
3 multivariate atypical cases were observed; these were retained in
the base considering their low ratio. Table 2 shows the descriptive
statistics of mean, standard deviation, skewness, and kurtosis
of the variables in the study. As can be seen, all variables have
levels of skewness and kurtosis lower than ±2, so the univariate
normality assumption is corroborated (George & Mallery, 2010).
Using the Pearson correlation coeffi cient, the bivariate
relationships (Table 2) were examined. These are theoretically
coherent and no r values higher than .90 were observed, thus
ruling out the existence of multicollinearity among variables
(Tabachnick & Fidell, 2001).
Since some of the sub-dimensions of the variables had low
levels of internal consistency (alpha values below .70), it was
decided to “collapse” the items of each dimension into a single
score, thus providing more reliable measures.
The multivariate normality assumption was examined using
the Mardia index (Mardia= 6.36; Z =1.54; p = 0.06). Considering
Table 2
Descriptive statistic of Mean (M), Standard deviation (DT), Asymmetry (A) y
Kurtosis (K)
Variables M DT A K
Exhaustion 2.43 1.34 .12 -.72
Depersonalization .92 .85 1.03 .99
Cynicism 1.18 1.13 1.06 .72
Ineffi cacy .80 .83 1.01 .36
Vigor 4.80 .91 -1.15 1.85
Dedication 4.61 1.14 -1.28 1.57
Absorption 4.24 1.02 -.45 -.37
Positive affect 3.86 .73 -.76 .03
Negative affect 1.54 .54 1.15 .83
Elaborative processes of emotion regulation* 55.40 8.28 -.21 -.21
Automatic processes of emotion regulation** 25.93 6.85 -.03 -.48
Note: * Values between 30 and 73; ** Values between 12 and 42
Table 3
Bivariate cor relations between affect regulation, emotions, burnout and engagement
Variables 12345678910
1 Elaborative processes
2 Automatic processes ,11
3 Positive affect ,46** -,10
4 Negative affect -,07 ,44** -,22*
5 Vigor ,43* -,13* ,75* -,21*
6 Dedication ,36* -,09 ,74* -,19* ,74**
7 Absorption ,21* ,01 ,45* -,18* ,58* ,59*
8 Exhaustion -,08 ,42* -,31* ,52* -,30* -,24* -,03
9 Depersonalization -,01 ,31* -,15* ,41* -,19* -,19* -,14* ,48*
10 Cynicism -,19* ,18* -,57* ,32* -,57* -,68* -,46* ,34* ,26*
11 Ineffi cacy -,17* ,23* -,28* ,19* -,29* -,13 -,07 ,20* ,17* ,26*
Note: * p<0,01
Cognitive processes of emotional regulation, burnout and work engagement
77
that the Mardia values are below the critical value of .70 suggested
by Rodríguez Ayán and Ruiz (2008), the “Maximum Likelihood”
(ML) was used as the calculation method. This method is
recommended in the literature as being the most effi cient in cases
of a wide sample of normally distributed data (Medrano & Muñoz-
Navarro, 2017). Firstly, we conducted Harman’s single factor test.
The results revealed a poor fi t to the data (χ2(9) = 95.62, GFI = 0.81,
CFI = 0.72, TLI = 0.53, RMSEA = 0.25). Consequently, common
method variance is not a serious defi ciency in our dataset.
A series of four models was specifi ed, tested and compared (see
Figure 1). Table 4 presents the fi t indices for these models. Model
4 is the only one of the four models to meet Hu and Bentler’s
(1999) criteria for a good fi t; models 1, 2, and 3 showed a much
worse fi t. From the analysis of each model it is clear that some of
the postulated hypotheses are not verifi ed. The direct relationship
between elaborative processes, engagement and negative affect is
not statistically signifi cant. It can also be observed that negative
affect does not contribute signifi cantly to engagement. In other
words, engagement is explained fundamentally by positive affect
and not by levels of negative affect.
Elaborative processes of
emotional regulation
Engagement
Positive
affect
Negative
affect
Burnout
Automatic processes of
emotional regulation
.05
.72
.46
.11
.24
.44
.44
R2 = .55
R2 = .34
R2 = .22
R2 = .20
Model 1
Elaborative processes of
emotional regulation
Engagement
Positive
affect
Negative
affect
Burnout
Automatic processes of
emotional regulation
.05
.72
.48
.11
.24
.44
.46
R2 = .55
R2 = .34
R2 = .24
R2 = .21
Model 2
Elaborative processes of
emotional regulation
Engagement
Positive
affect
Negative
affect
Burnout
Automatic processes of
emotional regulation
.74
.48
.11
.24
.44
-.16
R2 = .55
R2 = .34
R2 = .24
R2 = .20
Model 3
.44
Elaborative processes of
emotional regulation
Engagement
Positive
affect
Negative
affect
Burnout
Automatic processes of
emotional regulation
.73
.48
.11
.24
.35
-.16
R2 = .55
R2 = .46
R2 = .24
R2 = .20
Model 4
.44
.06
-.41
-.13
-.16
Figure 1. The four variations of models of cognitive emotion regulation, affect, burnout and engagement
Table 4
Adjustment indices
χ² df CFI GFI TLI RMSEA (90% IC) χ²dif AIC
Model 1 60.54* 8 .83 .90 .68 .20 (.16 - .27) 7.56 86.54
Model 2 51.86* 6 .85 .92 .63 .22 (.17 - .28) 8.64 81.86
Model 3 55.74* 8 .85 .91 .71 .19 (.15 - .24) 6.96 81.74
Model 4 12.69* 6 .98 .98 .95 .08 (.01 - .15) 2.11 42.69
Final Model 13.80* 7 .97 .98 .95 .08 (.00 - .14) 1.97 41.78
Note: * p<0,01
Estanislao Castellano, Roger Muñoz-Navarro, María Sol Toledo, Carlos Spontón, and Leonardo Adrián Medrano
78
When these parameters were removed (Table 4), the fi t of
the model was improved in a Final Model (see Figure 2). The
standardized regression coeffi cients shown in Figure 2 are all
statistically signifi cant. It should be mentioned that the model
has a high explanatory value, explaining 55% of the engagement
variance and 45% of the burnout variance. When considering
the total effects, it can be observed that elaborative process (β
total=.36 ) an d po sitive a ffe ct ( β total=.74) have a higher pre dictive
value for workers’ engagement. Automatic process (β total=.46)
and positive affect (β total= -.41) have a higher predictive value
for workers’ burnout.
Discussion
The role of emotions has been considered a predictor of burnout
(Castellano et al., 2013) and engagement (Lisbona et al., 2018) in
the workplace. However, there are still few studies that inquire into
the role of ER strategies. In this paper, an explanatory model of
burnout and engagement in response to worker ER was evaluated.
The fi t of the model was acceptable and many hypotheses were
corroborated with the exception of hypothesis 2 (direct, positive
relationship between ER elaborative processes and engagement),
hypothesis 3 (direct, negative relationship between ER elaborative
processes and negative affect) and hypothesis 7 (direct, negative
relationship between negative affect and engagement).
Basically, it can be seen that workers who activate elaborative
processes of ER (for example by positively reinterpret ing a negative
fact, or putting it into perspective) exp erience more positive affect,
greater engagement and less bur nout. On the contrary, workers who
tend to use mostly automatic ER processes (such as rumination or
catastrophizing) tend to have more negative affect, more burnout
and less positive affect. These fi ndings have a releva nt impact both
at the theoretical and practical levels.
As in previous studies (Garnefski et al., 2001; Gross, 2015),
our study shows a higher predictive value of positive affect and
elaborative processes of ER (for example, positive reappraisal).
Moreover, it can be observed that negative affect is not a signifi cant
predictor of engagement in this model. These results point to the
importance of considering the “positive” factors of organizational
behavior. As emphasized by Positive Organizational Psychology
(Salanova et al., 2000; 2011), focusing on the dysfunctional factors
is not enough. To achieve a comprehensive approach to occupational
health it is necessary to pay due attention to those factors that
promote the optimal psychosocial functioning of employees. The
fi ndings of the present study therefore highlight the role played by
elaborative processes in the regulation of positive affect.
Our fi ndings are consistent with previous theoretical proposals.
Thus, Côté, Gyurak, & Levenson (2010) noted that the use of
elaborative processes of ER (such as positive reappraisal or focus
on pla ns) in creased wor kers’ feelings of sat isfact ion and de creased
their intention to quit. The opposite happened when automatic
processes of ER (such as catastroph izing) predominated: i ntentions
to quite increased and the levels of job satisfaction decreased.
The main contribution of ER elaborative processes in relation to
workers’ engagement may lie in the fact that they enable pleasant
emotions to be amplifi ed. As Fredrickson & Joiner (2018) point
out, positive emotions, in addition to generating a pleasant feeling,
are a means to expand and develop a persons’ resources. This
coincides with the “resources/demands” theory (Schaufeli et al.,
2009), whereby workers with greater resources will experience
higher levels of engagement and those with fewer resources will
experience higher levels of burnout.
On a practical level, the results of this study provide a body
of evidence to support organizational intervention focused on
training to enable ER elaborative processes in workers. Programs
of this nature not only increase workers’ health, but also enhance
their degree of job satisfaction and engagement; this ultimately
affects their job performance, of considerable importance to the
organization as a whole (Côté et al., 2010). In this sense, it would
be benefi cial for all concerned to design and implement training
programs in ER techniques (Braunstein, Gross, & Ochsner, 2017)
so that workers can boost the impact of positive affect and inhibit
or decrease their negative affect. Such programs will strengthen
workers’ self-regulatory capacity, providing them with resources
to lower the levels of negative affect and bur nout (in the di mensions
of cynicism, exhaustion, depersonalization and ineffi cacy) while
increasing positive affect and engagement (in the dimensions of
vigor, dedication and absorption).
Some limitations mer it consideration. The workers participating
in the present study were selected from a non-probability sample,
so its representativeness may be biased. In fact, the sample had
a higher proportion of private sector workers (76%). It would
be convenient to carry out studies with samples of public sector
workers in order to examine the invariance of the models.
Unfortunately, the sample size of this study does not allow such
analyses to be carried out (Medrano & Muñoz-Navarro, 2017).
Beyond this limitation, it is important to emphasize that the
sample is suffi ciently heterogeneous in relation to income level,
type of work and size of the organization.
According to Salanova et al. (2011), the relationship between
engagement and positive affect is better conceptualized when it is
considered as a sequence of psychological experiences rather than
as a temporarily isolated episode. This aspect may constitute the
main limitation of this study, since the different instruments were
applied simultaneously, without leaving temporal intervals between
admin istrations. For this reason, the study should b e replicated in the
future, developing a plan for data collection in different phases, with
time intervals between administrations. This may provide greater
assurance that the independent variables precede one another and,
as a whole, of the effect of those on the dependent variable.
Elaborative processes of
emotional regulation
Automatic processes of
emotional regulation
Positive
affect
Negative
affect
Engagement
Burnout
R2 = .24
R2 = .20
R2 = .55
R2 = .46
.11
-.16
.44
.48 .73
-.41
.35
.24
Figure 2. Final model of cognitive, affect, burnout and engagement. Note:
All the path of the model was signifi cant at level p<0.05
Cognitive processes of emotional regulation, burnout and work engagement
79
Another useful line of research would be to assess the effectiveness
of an ER training program considering as a dependent variable the levels
of positive and negative affect, as well as workers’ levels of engagement
and burnout. This type of training program would be aimed at increasing
the use of elaborative processes in ER, thus contributing to improved
levels of occupational health in organizations and companies.
References
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317-
332. https://doi.org/10.1007/BF02294359
Arnold, K. A., Connelly, C. E., Walsh, M. M., & Martin Ginis, K. A.
(2015). Leadership styles, emotion regulation, and burnout. Journal
of Occupational Health Psychology, 20(4), 481-490. https://doi.
org/10.1037/a0039045
Bakker, A. B., & Albrecht, S. (2018). Work engagement: Current
trends. Career Development International, 23(1), 4-11. https://doi.
org/10.1108/CDI-11-2017-0207
Beck, A. T., & Clark, D. A. (1997). An information processing model of
anxiety: Automatic and strategic processes. Behaviour Research and
Therapy, 35(1), 49-58. https://doi.org/10.1016/S0005-7967(96)00069-1
Braunstein, L. M., Gross, J. J., & Ochsner, K. N. (2017). Explicit and
implicit emotion regu lation: A multi-level framework. Social Cognitive
and Af fective Neuroscience, 12(10), 1545-1557. https://doi.org/10.1093/
scan/nsx096
Byrne, B. M. (2001). Multivariate applications book series. Struct ural
equation modeling with AMOS: Basic concepts, applications, and
programming. Mahwah, NJ, US: Lawrence Erlbaum Associates
Publishers.
Castellano, E., Cifre, E., Spontón, C., Medrano, L. A., & Maffei, L. (2013).
Positive and negative emotions on engagement and burnout prediction.
Revista Perua na de Psicología y Trabajo Social, 2(1), 75 -88.
Clore, G. L., & Ortony, A. (2000). Cognition in emotion: Always,
sometimes, or never? In R. D. Lane & L. Nadel (Eds.), Series in
affective science. Cognitive neuroscience of emotion (pp. 24-61). New
York, NY, US: Oxford University Press.
Côté, S., & Morgan, L. M. (2002). A longitudinal ana lysis of the association
between emotion regulation, job satisfaction, and intentions to quit.
Journal of Organizational Behavior, 23(8), 947-962. htt ps://doi.
org/10.1002/job.174
Côté, S., Gyurak, A., & Levenson, R. W. (2010). The ability t o regulate emotion
is associated with greater well-being, income, and socioeconomic status.
Emotion, 10(6), 923-933. htt ps://doi.org/10.1037/a0021156
Fredrickson, B. L., & Joiner, T. (2018). Refl ections on Positive Emotions
and Upward Spirals. Perspectives on Psychological S cience, 13(2),
194 -199. https://doi.org/10.1177/174569161769210 6
Garnefski, N., & Kraaij, V. (2007). The cognitive emotion regulation
questionnaire: Psychometric features and prospective relationships
with depression and anxiety in adults. European Journal of
Psych ologi cal A sses smen t, 23(3), 141-149. ht t ps :// do i. or g/ 10.1 02 7/10 15 -
5759.23.3.141
Garnefski, N., Kraaij, V., & Spinhoven, P. (2001). Negative life events,
cognitive emotion regulation and emotional problems. Person alit y
and Individual Dif ferences, 30(8), 1311-1327. https://doi.org/10.1016/
S0191-8869(00)00113-6
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple
guide and reference. 11.0 update (4th ed.). Boston: Allyn & Bacon.
Gil-Monte, P. R. (2005). Factorial validity of the Maslach Burnout Inventory
(MBI-HSS) among Spanish professionals. Revista de Saúde Pública,
39(1), 1-8. http://dx.doi.org/10.1590/S0034-89102005000100001
Grandey, A. A. (2015). Smiling for a Wage: What emotional labor teaches
us about emotion regulation. Psychological Inquiry, 26(1), 54 -60.
https://doi.org/10.1080/1047840X.2015.962444
Grandey, A. A., & Melloy, R. C. (2017). The state of the heart: Emotional
labor as emotion regulation reviewed and revised. Journal of
Occupational Health Psychology, 22(3), 407- 422. https://doi.
org/10.1037/ocp0000067
Gross, J. J. (1998). The emerging fi eld of emotion regulation: An
integrative review. Review of General Psychology, 2(3), 271. https://
doi.org/10.1037/1089-2680.2.3.271
Gross, J. J. (2015). Emotion regulation: Cur rent status and fut ure prospects.
Psychological Inquir y, 26(1), 1-26. https://doi.org/10.1080/1047840X.
2014.940781
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fi t indexes in covariance
structure analysis: Conventional criteria versus new alternatives.
Structural Equation Modeling: A Multidisciplinary Journal, 6(1),
1-55. https://doi.org/10.1080/10705519909540118
Lent, R. W., & Brown, S. D. (2008). Social cognitive career theory and
subjective well-being in the context of work. Journal of Career
Assessment, 16(1), 6-21. https://doi.org/10.1177/1069072707305769
Lisbona, A., Palaci, F., Salanova, M., & Frese, M. (2018). The effects
of work engagement and self-effi cacy on personal initiative and
performance. Psicothema, 30(1), 89-96. https://doi.org/10.7334/
psicothema2016.245
Medrano, L. A., & Muñoz-Navarro, R. (2017). Conceptual and
practical approach to structural equations modeling. Revista Digital
de Investigación en Docencia Universitaria, 11(1). htt ps://doi.
org/10.19083/ridu.11.486
Medrano, L. A., Kanter, E. F., Trógolo, M., Ríos, M., Curerello, A., &
González, J. (2015). Adaptation of the Positive and Negative Affect
Scale (PANAS) to student university popu lation from Córdoba. Anuario
de Investigaciones de la Facultad de Psicología, 2(1), 22-36.
Medrano, L. A., Moretti, L., Ortiz, Á., & Pereno, G. (2013). Validation
of the Cognitive Emotion Regulation Questionnaire in University
Students of Córdoba, Argentina. Psykhe, 22(1), 83-9 6. http://dx.doi.
org/10.7764/psykhe.22.1.473
Medrano, L. A., Muñoz-Navarro, R., & Cano-Vindel, A. (2016). Cognitive
processes and emotion regulation: Contributions from a psycho-
evolutionary approach. Ansiedad y Estrés, 22(2-3), 47-54. https://doi.
org/10.1016/j.anyes.2016.11.001
Moriondo, M., De Palma, P., Medrano, L. A., & Murillo, P. (2012).
Adaptation of Positive and Negative Affectivity Scale (PANAS) to
adults in Cordoba cit y: Preliminar y psychometric analysis. Universitas
Psych ologica, 11(1), 187-196.
Naragon-Gainey, K., McMahon, T. P., & Chacko, T. P. (2017). The
structure of common emotion regulation strategies: A meta-analytic
examination. Psychological Bulletin, 14 3(4), 38 4-427. https://doi.
org/10.1037/bul0000093
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003).
Common method biases in behavioral research: A critical review
of the literature and recommended remedies. Journal of A pplied
Psych ology, 88(5), 879. doi:10.1037/0 021-9010. 88.5. 879
Prasad, A. B. (2016). Stress and burnout: Solutions to a global
problem. Adhyayan: A Journal of Management Sciences, 2(2). https://
doi.org/10.21567/adhyayan.v2i2.10248
Rodríguez Ayán, M. N., & Ruiz Díaz, M. Á. (2008). Atenuación de la
asimetría y de la curtosis de las puntuaciones observadas mediante
transformaciones de variables: incidencia sobre la estructura factorial
[Attenuation of the asymmetry and kurtosis of the observed scores
through transformations of variables: Incidence on the factorial
structure]. Psicológica, 29(2), 205-227.
Rodríguez Ga rcía, G. A., López-Pérez, B., Férreo Cru zado, M. A., Ferná ndez
Carr ascoso, M. E., & Fernández, J. (2017). Impact of t he Intensive Progra m
of Emotional Intelligence (IPEI) on work supervisors. Psicothema,
29(4), 508-513. https://doi.org/10.7334/psicothema2016.396
Rodríguez-Mantilla, J. M., & Fernández-Díaz, M. (2017). The effect
of interpersonal relationships on burnout syndrome in Secondary
Education teachers. Psicothema, 29(3), 370-377. https://doi.
org/10.7334/psicothema2016.309
Salanova, M., Llorens, S., & Schaufeli, W. B. (2011). “Yes, I can, I feel
good, and I just do it!” On gain cycles and spirals of effi cacy beliefs,
Estanislao Castellano, Roger Muñoz-Navarro, María Sol Toledo, Carlos Spontón, and Leonardo Adrián Medrano
80
affect, and engagement. Applied Psychology, 60(2), 255-285. https://
doi.org/10.1111/j.14 64-0597.2010.00435. x
Salanova, M., Llorens, S., García-Renedo, M., Burriel, R., Bresó, E., &
Schaufeli, W. B. (2005). Toward a four-dimensional model of burnout:
A multigroup factor-analytic study including depersonalization and
cynicism. Educational and Psychological Measurement, 65, 901-913.
https://doi.org/10.1177/0013164405275662
Salanova, M., Schaufeli, W., Llorens, S., Peiró, J., & Grau, R. (2000).
Desde el “burnout” al “Engagement”: ¿una nueva perspectiva?
[From “burnout” to “Engagement”: A new perspective?]. Revista de
Psicología del Trabajo y las Organizaciones, 16(2), 117-134.
Schaufeli, W. B., Bakker, A. B., & van Rhenen, W. (2009). How changes
in job demands and resources predict burnout, work engagement, and
sickness absenteeism. Journal of Organizational Behavior, 30(7),
893-917. https://doi.org/10.1002/job.595
Spontón, C., Castellano, E., Salanova, M., Llorens, S., Maffei, L., & Medrano, L.
(2018). Evaluation of a socio-cognit ive model of self-effi cacy, burnout and
work engagement: Invariance analysis between Argentina and Spain.
Psych ologia, 12(1), 89-101. doi: 10.21500/19002386.3226
Spontón, C., Medrano, L. A., Maffei, L., Spontón, M., & Castellano, E.
(2012). Validation of the engagement Questionnaire UWES for the
population of workers of Córdoba, Argentina. Liberabit, 18(2), 147-154.
Spontón, C., Trógolo, M., Castellano, E., & Medrano, L. (in press).
Psychometric properties of the Maslach Burnout Inventory- General
Survey in a sample of Argentinian workers. Interdisciplinaria: Revista
de Psicología y Ciencias Afi nes
Tabachnick, B. G., & Fidell, L. S. (2007). Using Multiva riat e Sta tist ics.
Pearson Education, Inc. https://doi.org/10.1037/022267
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and
Validation of Brief Measures of Positive and Negative Affect: The
PANAS Scales. Journal of Personality and Social Psychology, 54(6),
1063-1070. https://doi.org/10.1037/0022-3514.54.6.1063
