engagement vs. burnout: an examination of the...

ESADE WORKING PAPER Nº 234 July 2012

Engagement vs. Burnout: An examination of the relationships between the two concepts within the framework of the JDR model

Scott Moodie

Simon L. Dolan

Ronald J. Burke

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Scott Moodie PhD Candidate in Management Science

ESADE Business School, Ramon Llull University, Spain

Simon L. Dolan Professor and Future of Work Chair

ESADE Business School, Ramon Llull University, Spain (corresponding author)

Ronald J. Burke Professor Emeritus, Schulich School of Business, York University (Canada)

and Associate Professor at the Future of Work Chair (ESADE Business School)

July 2012 Abstract

The precise relationship between the positive psychological state of work (i.e.

engagement) and the negative psychological state (i.e. burnout) has been receiving an

increased attention. Some view these as opposite states on the same or similar

continuum, while others take the position that they represent different biobehavioral

spheres. Both states exhibit significant correlations to job demands and resources,

elements of physical and mental wellbeing, and to each other. This study expands our

knowledge of the phenomena of engagement and burnout by analyzing their separate and

joint manifestations. Using a large sample of 2,094 nurses, respondents were segmented

into quadrants that represent a 50/50 (median split) of Engagement and Burnout. The four

resulting quadrants were examined in a series of analyses including logistic regression

and ANOVAs. This configurational approach allowed us to examine both inverse and

concurrent states of Engagement and Burnout. The findings suggested that engagement

and burnout were generally inversely related (67% of the sample) but could be manifested

concurrently at either extreme (33% of the sample). Burnout was chiefly driven by work

demands as both quadrants of low burnout had lower demands and both quadrants of

high burnout had higher demands. Engagement was primarily driven by resources and

affinity. Social support acted independently by aligning with states of burnout. Worker

health was primarily driven by burnout wherein both states of low burnout exhibited better

health and both states of high burnout exhibited poorer health.

Keywords: Engagement, Burnout, JD-R, Affinity, Health, Social Support

Short Title: Exploring the multiple linkages between Work Engagement and Burnout


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Notes and Acknowledgements

1. A version of this paper has been presented at the Academy of Management

meeting in Boston (August 2012).

2. This work has been supported in part by the MEC (Spanish Ministry of

Education and Science) SEJ2007-67618 and AGAUR - Generalitat de

Catalunya. We also wish to express our gratitude to the Col∙legi Oficial

d'Infermeres de Barcelona for their instrumental assistance.

Corresponding author contact: Simon L. Dolan

ESADE Business School,

Av. Torre Blanca, 59

Sant Cugat, Spain 08172

[email protected]

Tel. +34 93 4952052

mailto:[email protected]


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Introduction

Researchers in the fields of organizational psychology and organizational behavior

have examined ways in which positive or negative perceptions and attitudes are

linked to performance at work. The subjective or perceptual measurement of

indicators such as satisfaction and motivation have a long history of use in

organizational research but increasingly have been criticized for being insufficient

to predict performance outcomes on both conceptual or methodological grounds

(Brief & Weiss, 2002; Latham & Pinder, 2005; Wright, 2006; van Saane, Sluiter,

Verbeek, & Frings-Dresen, 2003). They have been replaced by measurements

attempting to capture more objective, work-related states of mind. Two of the latter

include the constructs of burnout and engagement (Bakker, Schaufeli, Leiter, &

Taris, 2008). Burnout is a negative construct contributing to decreased job

satisfaction and organizational commitment, and increasing undesired outcomes

such as turnover and absenteeism (Lee & Ashforth, 1996). Engagement is a

positive indicator characterized by vigor, dedication, and absorption (Schaufeli,

Salanova, González-Romá, & Bakker, 2002). Vigor is characterized by high levels

of energy and mental resilience at work. Dedication is a strong sense of

involvement with one’s work along with a sense of significance, enthusiasm, and

challenge. Absorption occurs when an individual is fully concentrated and happily

engrossed in their work to the extent that time passes quickly and they have

difficulty detaching themselves from their work (Bakker & Demerouti, 2008;

Schaufeli & Bakker, 2004).

Workers that scored high on engagement have been shown to exhibit high energy

and self-efficacy (Schaufeli et al., 2001). The positive nature of work engagement

leads workers to create their own positive feedback in terms of appreciation,

recognition, and success (Bakker & Demerouti, 2008). Bakker and Demerouti

(2008) found that engaged workers carried their enthusiasm and energy with them

outside of the organization and felt a sense of accomplishment at the end of the

work day. Schaufeli & van Rhenen, (2006) reported a strong connection between

positive emotions and engagement. Engagement has also been shown to be

positively related to health (Schaufeli, Taris, & van Rhenen, 2008). Furthermore,


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engaged workers reported fewer psychosomatic complaints than co-workers who

displayed low levels of engagement (Demerouti, Bakker, De Jonge, Janssen, &

Schaufeli, 2001). Schaufeli & Bakker, (2004) observed that engaged workers

suffered from fewer self-reported headaches, cardiovascular problems, and

stomach aches.

Different approaches to the conceptualization of burnout have at their core low

levels of physical and emotional energy (Shirom, 2005). Burnout represents a

chronic condition that remains stable over time (Taris et al., 2005; Kristensen et

al., 2005; Halbesleben & Demerouti, 2005). Several studies indicated that aspects

of the job environment were stronger predictors of burnout than were personality

factors (Lee & Ashforth, 1996; Schaufeli & Enzmann, 1998). However, research

also suggested that there was a link between emotional exhaustion, as

operationalized within a burnout framework, and family-related factors (Bakker et

al., 2005). This connection between low physical and emotional energy was

primarily dependent upon social aspects of the work environment and was distinct

from chronic states of depression that represented a propensity towards negative

affective states (Shirom, 2005; Schaufeli & Enzmann, 1998). Burnout has been

also linked to a variety of health complaints including circulatory and heart

problems, muscular pains, sleep disturbances, headaches, and gastro-intestinal

problems (Gorter et al., 2000; Kahill, 1988; Westman and Bakker, 2008)

The predominant formulations of burnout and engagement bear a number of

similarities which can create confusion concerning the manner in which they relate

to each other. At the most basic level, constructs evaluating engagement have

been linked to a set of positive emotions (Schuafeli & Van Rhenen, 2006) whereas

burnout constructs wwere closely linked to a set of corresponding negative

emotions (Schaufeli & Enzmann, 1998). Some researchers have positioned

burnout and engagement at opposite ends of a single dimension (Demourouti and

Bakker, n.d). For example, The Oldenburg Burnout Inventory (OLBI; Demerouti et

al., 2001, 2003) is a tool which measures burnout by categorizing respondent’s

levels of exhaustion and disengagement thereby effectively placing burnout and

engagement at opposite ends of the same scale.


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The Dutch UWES model connected with the pioneering work of Bakker and

Schaufeli conceptualizes engagement according to states of vigor, dedication, and

absorption. The UWES measures of vigor and dedication are regarded as polar

opposites of the most frequently used measure used in burnout research, the

Maslach Burnout Inventory -MBI (Maslach & Jackson 1981). The central

measurers of the MBI are exhaustion and cynicism. However, the UWES ignores

the MBI dimension of reduced personal efficacy and replaces it with the concept of

absorption (Schaufeli & Bakker, 2003). According to Schaufeli & Bakker, (2003),

this modification was made in part because studies had demonstrated that lack of

professional efficacy was less determinate of burnout than exhaustion and

cynicism (Maslach et al., 2001; Shirom, 2002). A second reason for the addition of

absorption to the UWES resulted from qualitative findings suggesting that

engagement was characterized by being immersed and happily engrossed in

one’s work (Schaufi & Bakker, 2003).

Shirom, (2003) argued that vigor (engagement) and burnout were obliquely related

and were thus not positioned at opposite ends of the same continuum. One of his

arguments ass that vigor and burnout are subjective components of different

biobehavioral systems (Watson et al., 1999). By adapting this theory, Shirom,

(2003) posited that burnout may be an antecedent of an internal withdrawal-

oriented behavioral inhibition system whereas vigor is a component of an

approach-oriented behavior facilitation system. This argument presented the

possibility that both burnout and vigor could be operationalized in the same

context since many job situations present both demands and opportunities for

rewards. In this way, demanding situations may evoke burnout, but the anticipated

rewards of completion may simultaneously evoke vigor (Dweck & Legget, 1988).

Joint and Separate Determinants and consequences of Work Engagement and

Burnout.

The origins of both burnout and engagement are multi-factorial and multi-faced

(Shirom, 2005). Possible sources can include individual differences and

personalities, organizational demands and environmental factors, and cultural


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factors that interact in specific configurations (Diez-Pinol, Dolan, Sierra, &

Cannings, 2008). To develop a broader and more complete view of the

relationship between work engagement and burnout, it is important to explore

them together to determine the joint influence that antecedents have on separate

and concurrent states of Engagement and Burnout. Thus, this study employs

frequently used measures of Engagement and Burnout and uses a configurational

approach dividing respondents into quartiles based on the presence of Low

Engagement and Low Burnout (LELB), Low Engagement and High Burnout

(LEHB), High Engagement and Low Burnout (HELB), and High Engagement and

High Burnout (HEHB). These quartiles were then examined using personal and

organizational determines nested within the larger framework of the J-DR model

(Bakker & Demerouti, 2007; Demerouti et al., 2001) in an attempt to best predict

the quartiles along with the comparative states of physical and mental health of its

members.

---------------------------------------- (Table 1 about here)

------------------------------------------

Study objectives and research questions

This study expands on our knowledge of the phenomena of Engagement and

Burnout by analyzing their separate and joint manifestations. Thus, as explained

below, the respondents were segmented into quadrants that represented a

50/50, or median split, on both Engagement and Burnout.The four resulting

quadrants include those with A. Low Engagement and Low Burnout (LELB); B.

Low Engagement and High Burnout (LEHB); C. High Engagement and Low

Burnout (HELB); and D. High Engagement and High Burnout (HEHB). This

classification enabled the empirical testing that shows which combination of

antecedents can best predict each manifestation, and what consequences are

exhibited on individual health and wellbeing. This is the essence of what has been

referred to in this study as a configurational approach.


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More specifically, the following research questions represent the core for the

present study:

1. Are Engagement and Burnout inversely or concurrently related?

2. What is the configuration of individual and organizational variables that predict

states of

A. Low Engagement and Low Burnout?

B. Low Engagement and High Burnout?

C. High Engagement and Low Burnout?

D. High Engagement and High Burnout?

3. What is the effect of each joint configuration of Engagement and Burnout on

Physical and Mental Health?

To address these questions, the study was positioned into the broader J-DR

research model (Bakker & Demerouti, 2007; Demerouti et al., 2001).

Respondents were nurses employed in multiple regions and multiple hospitals in

Spain.. Nurses have been studied extensively due to the worsening of their

respective working conditions and the alarming rates of burnout reported

worldwide (Gilbert et al, 2010, Leiter et al, 2009; Leiter et al, 2010). Studying the

nursing population can be instrumental for comparative reasons and enhance the

external validity of the study.

Personal and organizational factors linked to engagement and burnout were

divided into four categories: Job Demands, Job Resources, Social Support, and

Affinity. Job demands and resources have a long established connection to both

engagement and burnout. The JD-R model proposes that burnout arises from

situations where many demands are made without the provision of sufficient

resources to meet those demands (Demerouti, Bakkar, Nachreiner, & Schaufeli,

2001). The conservation of resources theory suggests that burnout increases

when valued resources are lost, are unable to meet job requirements, or are

insufficient (Hobfoll & Freedy, 1993). Structural equation modeling in the

Schaufeli et al, (2009) study found that increases in demands and decreases in


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resources predicted burnout and that increases in resources predicted

engagement. This study also concluded that resources and engagement were

reciprocal in that work engagement lead to an increase in resources which

subsequently increased the level of engagement.

A meta-analysis by Crawford et. al (2010) found that demands and burnout were

positively associated and that resources and burnout were negatively associated.

With respect to engagement, they found that resources and engagement were

positively associated but the association between demands and engagement

varied according to the nature of the demand. Demands that were considered to

be hindrances were negatively associated with engagement whereas demands

considered to be challenges had a positive association with engagement. Another

meta-analysis conducted by Halbesleben et al, (2010) found that engagement and

burnout constructs were negatively associated albeit with a few exceptions.

Engagement was positively related to resources and negatively related to

demands with resources having a stronger relationship.

Within the JD-R model, the commonly used measure of social support measures

cooperation among colleagues. For the present study, we replaced this measure

with a measure of social support that assesses support at three levels: supervisor

support, colleague support, and spousal support. A meta-analysis by Halbesleben

(2011) found that work-related social support was more closely associated with the

exhaustion component of burnout than non-work sources of social support.

Shaufeli et al.,(2008) reported that co-worker support was negatively associated

with the reduced professional efficacy dimension of the MBI burnout measure and

positively associated with the dedication dimension of Engagement and that

supervisor support was negatively associated with the exhaustion and cynicism

dimensions of burnout.

Affinity refers to the long-term affective evaluation of an employee to their work

and includes the aspects of Affective Commitment and Job Involvement.

Organizational Commitment and Job Involvement have been found to be

discriminately different constructs from work engagement (Hallberg & Shaufelli,


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2006). Affective commitment is one of the dimensions of organizational

commitment as proposed by Allen & Meyer (1990). Affective organizational

commitment is more closely related to job characteristics than to personal factors

and therefore related to extrinsic circumstances of the work (Morrow, 1983,

Hallberg & Shaufeli, 2006). Organizational commitment has been found to be

inversely associated with the cynicism and reduced professional efficacy

dimensions of burnout and positively associated with the dedication and

absorption dimensions of work engagement (Schaufeli et al., 2008).

Job involvement has been defined as a ‘cognitive belief state of psychological

identification’ with one’s job (Kanungo, 1982, p. 80). This relatively stable attitude

(Kunel et al., 2009) is different from short-term states of engagement which can

change from day to day (Dalal, Brummel, Wee, & Thomas, 2008). Job Involvement

has been shown to have a direct positive effect on Engagement among nurses

(Kunel et al., 2009). Griffen et al, (2010) found that job involvement was positively

related to the emotional exhaustion dimension of burnout.

Present states of physical and mental health were evaluated by assessing general

physical health, anxiety and depression. As noted above, both engagement and

burnout are measures of employee wellbeing and both have been extensively

linked to health. It is common for people suffering from stress to engage in

behaviors such as smoking, alcohol abuse, or eating unhealthy food that may

alleviate short term effects, but have long-term negative health consequences

(Scwarzer & Fuchs, 1995). Several studies have confirmed that health is closely

correlated with burnout. (Kahill, 1988; Soderfeldt et al., 2000). Engagement is

linked to positive emotions and better health (Bakker et al., 2008). While there is a

need for further research on the specific paths between engagement and health, a

recent study found that patients who demonstrated positive affect had a reduction

in 10-year incidents of coronary heart disease (Davidson et al., 2010). A summary

of the general hypothesized relationships of these categories with regards to

Engagement and Burnout can be found in Table 2


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--------------------------------- (Table 2 about here)

---------------------------------

Sample Methods and procedures

Respondents

Nurses must be registered and qualified within a regional association to practice

nursing n Spain.. This study was carried out with the support of these associations

in selected regions in Spain. An online survey was prepared, pre-tested and pre-

validated, and sent to the regional associations for distribution among their

members. The total recipient number could not be determined but 2,115 surveys

were completed online. Of these, 21 surveys were removed from the dataset due

to spurious data or omissions leaving 2094 complete responses. The majority of

responses were received from the provinces of Catalunya and Gipuzkoa. The

respondents were 90.3% female, had a mean age of 39 years and had worked as

a nurse for an average number of 16 years. Of these, 72% reported living with a

partner and 85% of respondents worked full time as a nurse.

Measures

The central measures of the study were the shortened (Schaufeli et al., 2006)

Utrecht Work Engagement Scale (UWES) developed by Schaufeli and Bakker

(2003) and the Shirom-Melamed Burnout Measure (SMBM, Shirom et al., 2005).

The UWES is based upon a three dimension model of engagement which includes

vigor, dedication, and absorption (Schaufeli et al., 2002) whereas the SMBM

assesses the depletion of an individual’s energetic resources at work by

measuring the three dimensions of physical fatigue, emotional exhaustion, and

cognitive weariness.


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Job Demands and Resources were investigated by employing measures used in

the Job Demands – Resources (JD-R) model of Bakker et al. (2003) which has

been employed in many other studies on engagement (i.e. Xanthopoulou et al.,

2007a, b) and burnout (Bakker et al., 2004). While the model seems to be dynamic

and in a continued state of evolution ( Demerouti & Bakker, 2011), some core

components were present across various versions of it. For example, job demands

refer to physical, psychological, social, or organizational aspects of the job that

require sustained psychological or physical effort (Bakker et al, 2004). This model

examines job demands according to three categories: workload, emotional

demands, and work-home conflict.

Job Resources refer to physical, psychological, social, or organizational aspects of

the job that are necessary to achieve goals, reduce job demands, and stimulate

personal growth and development (Bakker et al., 2004). Of the original JD-R

measures, the scales for Autonomy and Self Development Opportunities were

used. The JD-R measure of Social Support that measures a single factor of social

support was replaced with the scale of Dolan et al (1992a, 1992b) which assesses

support along the axes of Supervisor Support, Colleague Support, and Spousal

Support.

Affinity towards work was assessed using measures of Affective Commitment and

Job Involvement. The inclusion of these variables permits an assessment of

perceived affiliation and identification with the work and workplace. Affective

Commitment was assessed using the measures of Meyer, Allen and Smith, 1993.

Job Involvement was assessed using a 4 item version from Frone and Rice (1987)

which was based on the original measure by Kanungo (1982).

Employee health and wellbeing was measured by assessing General Health,

Anxiety, and Depression. General health was assessed through a single item

which asked respondents to evaluate their general state of health varying from

very poor and very good (Benyamini & Idler, 1999). The single-item construct has

been found to be a valid predictor of all-cause morbidity and mortality (DeSalvo et

al., 2006a) and has demonstrated comparable reliability and validity when


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compared to other multi-item measures used by health professionals (DeSalvo et

al., 2006b). Mental health was measured via the anxiety and depression scales

developed by Dolan and Arsenault (1983). The variables used in the study,

including their respective psychometric properties are presented in Table 3.

-------------------------------- (Table 3 about here)

-------------------------------

Analysis Strategy

The data was analyzed in stages which included zero-item correlations and

ANOVAs to determine their independence and suitability for predicting states of

Engagement and Burnout. The Engagement and Burnout scores were binned

using a 50/50 median split and then merged to reflect four distinct groups

representing Low Engagement and Low Burnout, Low Engagement and High

Burnout, High Engagement and Low Burnout, and High Engagement and High

Burnout. A series of Binary Logistic Regression analyses were undertaken to

determine the relative influence of the independent variables as predictors of each

state of Engagement and Burnout after controlling for Age, Gender, and Part

Time/Full Time work status. Explained variance was measured with the

Nagelkerke Pseudo R2 score. A series of ANOVA analyses were then conducted

to determine whether the independent variables demonstrated a significant

difference within each group when compared to the remaining population.

Results

The purpose of this study was to identify whether the states of Engagement and

Burnout are manifested inversely or can be manifested concurrently. An effort

was also made to illustrate these conditions by identifying the primary antecedents

of each condition. In addition, levels of physical and mental health found among

each configuration were analyzed.


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Segmentation of respondents according to levels of Engagement and Burnout

revealed that 67% of the population experienced states of either Low

Engagement and High Burnout or High Engagement and Low Burnout. The

remaining 33% of the population experienced states of either Low Engagement

and Low Burnout or High Engagement and High Burnout. Thus while the majority

of workers exhibited “one or the other” dominant states, a sizable percentage of

workers were functioning in one of the extremes.

------------------------------------------ (Tables 4 and 5 about here)

------------------------------------------

The group defined by states of Low Engagement and Low Burnout was best

predicted by all three Demands and Job Involvement (see results of Logistic

Regression in Table 4). Results of the ANOVA analysis (see Table 5) indicated

that when compared to the remaining population, these nurses exhibited

significantly lower Demands and Affinity and had higher levels of Colleague

Support. These nurses demonstrated better overall health on all measures. This

group has been labeled ‘Loafers’ to indicate their relative lack of demands, normal

levels of resources and support and their apathy towards the work.

The group defineded by states of Low Engagement and High Burnout was best

predicted by Emotional Demands, Work Interfering with Home, Self Development

Opportunities, Colleague and Spousal Support, and the two measures of Affinity.

Results of the ANOVA analysis indicated that this group of nurses demonstrated

significantly higher Demands, lower Resources, lower Social Support, and lower

Affinity. They reported a significantly poorer level of health on all measures. This

group has been labeled ‘Slaves’, to indicate their increased demands and lack of

resources, support or personal affinity to the work.

The group defined by states of High Engagement and Low Burnout was predicted

using all variables except for Autonomy. The ANOVA results revealed that,

compared to the remaining population, this group exhibited significantly lower

Demands, higher Resources, higher Social Support, and higher Affinity. They also

demonstrated significantly better health on all measures. This group has been


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labeled ‘Apprentices’ to indicate their lower demands and higher levels of

resources, support, and affinity to the work.

Finally, the group defined by states of High Engagement and High Burnout was

best predicted by all measures of Demands, Self Development, Supervisor

Support and Job Involvement. The results of the ANOVA analysis indicate that

these workers experience higher Demands, higher Self Development

Opportunities, lower Social Support, and higher Affinity than their colleagues. They

were also marked by comparatively lower health on all measure when compared

to their colleagues. We have termed as ‘Lone Rangers’ to reflect their higher

demands, resources, and affinity to the work but lack of social support.

Discussion

The findings suggested that engagement and burnout were generally inversely

related but could be manifested concurrently at either extreme. The explained

variance of the dominant quadrants is very satisfactory assuming individual level

of analysis (LEHB R2=.36, HELB R2=.35), but the extreme quadrants are lower

(LELB R2=.17, HEHB R2=.12). This suggests that the primary determinants of

these states are external to the working conditions. This may reflect personality

types or social and cultural factors that influence their participation in the work

environment or their ability to manage resources, demands, or social relationships.

Results show that burnout was chiefly driven by work demands as both quadrants

of low burnout had lower demands and both quadrants of high burnout had higher

demands. This supports the findings of other studies (Xanthopoulou et al, 2007).

Engagement was found to be primarily driven by resources and affinity. The

relationship between resources and engagement has been amply documented in

other studies (Bakker et al, 2007, Crawford et al, 2010). While both job

involvement and organizational commitment have been independently examined

along with burnout and engagement, this is the first time that they have been

combined as dimensions of a single construct we called affinity. This provides


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insight into the long-term attitude toward the work and the identification that

workers feel with their job which may be a useful construct in future studies. The

relationship of affinity to engagement can be partially explained in the conceptual

similarity of these attitudinal states with the UWES construct of dedication which

asks respondents several questions related to their identification with their work.

Findings pertaining to the influence of social support on burnout and engagement

provided an interesting insight. Social support has historically been considered a

resource as evidenced by its inclusion as a resource in the JD-R model.

Nonetheless, findings in this study do not confirm this assertion. Social support

did not behave like the other resources which were associated with states of

engagement. Instead, social support acted independently by aligning with states of

burnout. Both states of low burnout evidenced higher social support whereas both

states of high burnout evidenced lower social support. This suggests that social

support should perhaps be regarded as a distinct construct not to be considered

as a traditional resource.

Worker health was primarily influenced by burnout wherein both states of low

burnout exhibited better health and both states of high burnout exhibited poorer

health. While there has been an abundance of research linking engagement to

positive health (Kanste 2011) and burnout to negative health (Ahola, 2007,

Shirom, 2009), the findings of this study suggest that burnout is the greater

determinant of health. However, our measures of burnout, anxiety, and depression

were highly correlated which may be indicative of an inherent susceptibility of this

type of psychometric research to common method variance. The difficulty of

effectively isolating these phenomena has been noted by Shirom (2003).

Practical Implications

There are a number of practical implications that follow from this study. There has

been a recent debate about the relative merits of focusing on positive states of

engagement or negative states of burnout. Our study suggests that these states


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are not mutually exclusive and that focusing too strongly on either state may

distract from a more fundamental need to create a positive and balanced work

environment. The strongest predictors in this study were demands and self

development opportunities. If management would focus on balancing demands

and resources and follow that by fostering social support and encouraging affinity

through training and recruitment, the result should be positive regardless of

whether it is measured according to burnout or engagement. We are not

suggesting that measuring either engagement or burnout is irrelevant. To the

contrary, they are both useful measures for assessing an employee’s current state

as reflective of the work environment. However, if the objective is to improve

working conditions and enhance employee wellbeing, it would at times be more

appropriate to directly assess demands and resources and find ways to optimize

their balance. Beyond that, it is in the best interest of companies and managers to

continually seek ways to foster social support and feelings of affinity to the work

and workplace.

Strengths and Limitations

The purpose of this study was to use a straightforward configurational approach to

classify the inverse and concurrent manifestations of engagement and burnout.

The recent growth in popularity of the concept of work engagement and the JD-R

model testify to the importance of these constructs and their relationships. Much of

the current research on this topic considers engagement and burnout to be linear

dimensions and focuses on building structural models of the precise relationships

between variables. That approach is to be encouraged, but there is also a need to

jointly deconstruct dimensions and relationships in a tactile manner that can inform

future structural models. While the majority of respondents fell in the expected

categories of HELB and LEHB, the 33% that exhibited one of the concurrent states

strongly suggests that new approaches to the study of these variables should be

considered. The secondary benefit of this approach is that these findings (Table 5)

can be submitted directly to managers to provide an easily understood approach

for assessments and interventions.


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This quantitative simplification is useful in understanding the underlying nature of

the dimensions, but it is also prone to numerous susceptibilities. The study was

conducted at a single point in time using purely psychometric sources. It is

therefore prone to common method variance (Podsakoff et al., 2003). Further, the

variables of burnout, anxiety and depression have been previously noted to exhibit

multicolinearity (Shirom, 2003). While the sample size was rather large for this

type of studies (N=2094), the findings may not be generalizable beyond nurses

working in Spain.

Future Research Direction

This study presents the first step towards the classification of specific joint states

of work engagement and burnout. By demonstrating that the clusters have unique

compositions of antecedents, it would be useful to study whether the

contemporary structural models of variables interact differently in each of the four

quadrants. It would also be useful to examine whether the extreme quadrants

(HEHB, LELB) are influenced by other variables which could include individual

personality types, social functioning ability, values, clear instructions, job control or

compensation, among others. One of the primary goals of future research should

be the identification of paths between the quadrants, or more specifically, to

identify paths to HELB from the other three quadrants. If we can increase the

accuracy of the worker classification and the paths between groups, we will be

able to provide managers with specific tools for maximizing the well-being and

productivity of their workers.


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Table 1

Configurational segmentation of sample (N = 2094)

Engagement

Low High B

urn

ou

t

Lo

w (LELB)

N = 342 (16.3%)

(HELB)

N = 731 (34.9%) H

igh

(LEHB)

N = 666 (31.6%)

(HEHB)

N = 355 (17.0%)


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Table 2

Summary of expected relationships of Engagement and Burnout with study

constructs

Engagement Burnout

Demands - +

Resources + -

Affinity + -

Mental Health + -

Physical Health + -

Note: + = positive relationship; - = negative relationship

Table 3

Means (M), Standard Deviations (SD), Internal Consistencies (Cronbach's α) and Zero-Order Correlations of the Study

Variables (N = 2094)

Variable M SD Items Range α 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Engagement 5.00 1.22 9 1-7 .91 -

2. Burnout 3.35 1.41 12 1-7 .95 -.50** -

3. Work Overload 3.69 .88 4 1-5 .86 -.03 .35** -

4. Emotional Demands 3.09 .82 6 1-5 .85 -.19** .50

** .51

** -

5. Work Int. w/ Home 2.21 .81 3 1-5 .77 -.13** .47

** .31

** .40

** -

6. Self Dev. Opp. 3.69 .72 3 1-5 .67 .48** -.39

** -.12

** -.20

** -.19

** -

7. Autonomy 3.35 .95 3 1-5 .86 .32** -.31

** -.21

** -.22

** -.19

** .50

** -

8. Supervisor Support 2.56 .83 4 1-4 .92 .29** -.34

** -.19

** -.23

** -.17

** .36

** .39

** -

9. Colleague Support 2.86 .67 4 1-4 .87 .25** -.32

** -.13

** -.23

** -.19

** .30

** .29

** .36

** -

10. Spousal Support 3.34 .68 4 1-4 .88 .14** -.17

** -.04

* -.11

** -.12

** .11

** .10

** .17

** .21

** -

11. Affective Commitment 4.12 1.34 6 1-7 .84 .43** -.30

** -.12

** -.16

** -.10

** .38

** .30

** .34

** .21

** .05

* -

12. Job Involvement 3.99 1.17 4 1-7 .70 .37** -.03 .07

** .06

* .23

** .19

** .08

** .11

** .02 -.05

* .39

** -

13. Anxiety .54 .38 4 0-1 .77 -.26** .58

** .27

** .39

** .42

** -.25

** -.25

** -.19

** -.20

** -.13

** -.12

** .09

** -

14. Depression .41 .33 4 0-1 .67 -.33** .65

** .21

** .37

** .37

** -.32

** -.25

** -.22

** -.22

** -.14

** -.19

** .03 .63

** -

15. Physical Health 3.10 .79 1 1-4 n.a. .24** -.50

** -.13

** -.23

** -.25

** .18

** .17

** .15

** .18

** .18

** .07

** -.08

** -.37

** -.40

**

Note: * p < .05; ** p < .01; *** p <.001.


23

Table 4

Binary Logistic Regression of Engagement and Burnout Quartiles on the

Study Variables (β-values) (N= 2094)

LELB LEHB HELB HEHB

Demands

1. Work Overload -.43*** .00 .17* .20*

2. Emotional Demands -.37*** .56*** -.52*** .35***

3. Work Int. w/ Home -.48*** .57*** -.61*** .23**

Resources

4. Self Development -.17 -.75*** .72*** .39***

5. Autonomy -.06 -.08 .10 - .02

Social Support

6. Supervisor Support -.04 -.04 .23** - .21*

7. Colleague Support .14 -.26** .21* -.03

8. Spousal Support -.12 -.20* .30** -.06

Affinity

9. Affective Commitment -.08 -.29*** .32*** .03

10. Job Involvement -.33*** -.30*** .29*** .32***

Explained Variance (R2) .17 .36 .35 .12

Note: * p < .05; ** p < .01; *** p < .001. Explained Variance is measured with the Nagelkerke Pseudo R

2


24

Table 5

Synoptic ANOVA analysis for each quadrant compared to the remaining

population (N= 2094)

Low Engagement High Engagement

Lo

w B

urn

ou

t

‘Loafers’ N = 342 (16.3%)

↓ Work Overload *** Demands: ↓

↓ Emotional Demands ***

↓ Work Int. w/ Home ***

Resources: Avg.

Social Support: ~↑

↑ Colleague Support *

↓ Affective Commitment ** Affinity: ↓

↓ Job Involvement ***

↓ Anxiety ***

Wellbeing: ↑ ↓ Depression ***

↑ Health ***

‘Apprentices’ N = 731 (34.9%)

↓ Work Overload *** Demands: ↓

↓ Emotional Demands ***

↓ Work Int. w/ Home ***

↑ Self Development *** Resources: ↑

↑ Autonomy ***

↑ Supervisor Support *** Social Support: ↑

↑ Colleague Support ***

↑ Spousal Support ***

↑ Affective Commitment *** Affinity: ↑

↑ Job Involvement ***

↓ Anxiety ***

Wellbeing: ↑ ↓ Depression ***

↑ Health ***

Hig

h B

urn

ou

t

‘Slaves’ N = 666 (31.6%)

↑ Work Overload *** Demands: ↑

↑Emotional Demands ***

↑Work Int. Home ***

↓ Self Development *** Resources: ↓

↓ Autonomy ***

↓ Supervisor Support *** Social Support: ↓

↓ Colleague Support ***

↓ Spousal Support ***

↓ Affective Commitment *** Affinity: ↓

↓ Job Involvement ***

↑ Anxiety ***

Wellbeing: ↓ ↑ Depression ***

↓ Health ***

‘Lone Rangers’ N = 355 (17.0%)

↑ Work Overload *** Demands: ↑

↑ Emotional Demands ***

↑ Work Int. w/ Home ***

↑ Self Development * Resources: ~↑

↓ Supervisor Support ** Social Support: ↓

↓ Colleague Support *

↓ Spousal Support *

↑ Affective Commitment * Affinity: ↑

↑ Job Involvement ***

↑ Anxiety ***

Wellbeing: ↓ ↑ Depression ***

↓ Health ***

Notes:

(A) Levels of Significance: * p < .05; ** p < .01; *** p < .001

(B) Arrows: ↑ indicates significantly greater levels than the remaining population; ↓indicates

significantly lesser levels than remaining population


25

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