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Objective: The aim was to investigate differences in the levels of work engagement across demographic and work- and organization-related factors, and their relative importance for work engagement. Methods: The study was based on a sample of 17 498 male and 17 897 female employees from the sixth European Working Conditions Survey collected in 2015. Linear regression models and dominance analysis were used. Results: Several significant differences between the levels of work engagement in different demographic and work- and organization-related groups. Employees working in human service occupations reported higher levels of work engagement than employees in other industries. Relatively, occupational group (68%) and industry (17%) contributed most to work engagement. Conclusions: It is important to focus on enhancing work engagement, particularly among less educated employees, among those with non-permanent contracts, and in certain occupations.
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Journal of Occupational and Environmental Medicine, Publish Ahead of Print
DOI : 10.1097/JOM.0000000000001528
Who is engaged at work?
A large-scale study in 30 European Countries
Jari J. Hakanen, PhD1
Annina Ropponen, PhD1
Wilmar B. Schaufeli PhD 2,3
Hans De Witte PhD2,4
1 Workability and Working Careers, Finnish Institute of Occupational Health,
Topeliuksenkatu 41 B, BOX 40, 00032 Finnish Institute of Occupational Health, Helsinki,
2 Research Unit Occupational & Organizational Psychology and Professional Learning, KU
Leuven, Dekenstraat 2 - box 3725, 3000 Leuven, Belgium
3Department of Social, Health & Organizational Psychology, Utrecht University,
Padualaan14, 3584 CH Utrecht, The Netherlands
4Optentia Research Focus Area, North-West University, Vaal Triangle Campus, PO Box
1174, Vanderbijlpark, 1900 South Africa
Revised manuscript for Journal of Occupational and Environmental Medicine
December 4, 2018
Words in the main text: 3961 (+ refs.)
Corresponding author: Jari Hakanen, Finnish Institute of Occupational Health, Workability
and Work Careers, Topeliuksenkatu 41 B, BOX 40, 00032 Finnish Institute of Occupational
Health, Helsinki, tel. +358 40 562 5433, email
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Conflict of Interest. None declared.
Acknowledgements. The authors gratefully acknowledge the European Foundation for the
Improvement and of Living and Working Conditions as the original data creators.
Sources of funding. This study was supported by SWiPE research consortium 303667,
which is funded by the Strategic Research Council of the Academy of Finland.
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Objective. The aim was to investigate differences in the levels of work engagement across
demographic and work- and organization-related factors, and their relative importance for
work engagement.
Methods. The study was based on a sample of 17 498 male and 17 897 female employees
from the sixth European Working Conditions Survey collected in 2015. Linear regression
models and dominance analysis were used.
Results. Several significant differences between the levels of work engagement in different
demographic and work- and organization-related groups. Employees working in human
service occupations reported higher levels of work engagement than employees in other
industries. Relatively, occupational group (68%) and industry (17%) contributed most to
work engagement.
Conclusions. It is important to focus on enhancing work engagement, particularly among less
educated employees, among those with non-permanent contracts, and in certain occupations.
Key words: Work engagement; employment arrangements; population study; employee
well-being; epidemiology; Europe
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Work engagement also called employee engagement is an important and popular
issue, in both academia and business. For instance, since its introduction at the turn of the
century, the number of scientific papers on the topic has increased steadily year by year and
currently amounts to over 7000 (Google Scholar). However, despite the overwhelming
number of scientific publications, valid and reliable information on the epidemiology of work
engagement is lacking. So far, scientific research has focused on specific occupational
samples or organizations rather than on the workforce as a whole. The current study fills this
gap by using epidemiological data on work engagement from the sixth European Working
Conditions Survey (EWCS) carried out in 2015.
Work engagement is defined as a positive, fulfilling state of mind that is characterized
by vigor, dedication, and absorption,and operationalized by the Utrecht Work Engagement
Scale (UWES).1 Vigor refers to high levels of energy and mental resilience while working,
the willingness to invest effort in ones work, and persistence in the face of difficulties.
Dedication refers to a sense of significance, enthusiasm, inspiration, pride, and challenge.
The third defining characteristic of engagement is absorption, which is characterized by being
fully concentrated on and happily engrossed in ones work, a sense that time passes quickly,
and possible difficulties in detaching oneself from ones work.
The antecedents and consequences of work engagement have been intensively
investigated using the Job Demands-Resources (JD-R) model. 2-5 This research has shown
that different job resources (e.g., skill variety, job control, learning opportunities) and
personal resources (e.g. self-efficacy, proactivity, optimism) are the main drivers of work
engagement, whereas job demands (e.g., workload, role conflicts, emotional demands) play a
minor role. 6,7
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Multiple studies suggest that work engagement is beneficial for both employees and
organizations. For instance, work engagement has been associated with better mental and
physical health among employees in terms of low levels of depression8 and anxiety,9 healthy
cardiac autonomic activity10, better workability11, better cortisol suppression in response to
dexamethasone12, lower systolic blood pressure,13, and better sleep quality. 14 In addition,
work engagement has been found to predict less from work to family conflicts and more
positive from work to home enrichment experiences. 15
Research also suggests that work engagement is beneficial for employee performance,
and hence also for organizations. For instance, work engagement is related to a low risk of
sickness absences, 16, 17 several indicators of job performance, 18-22 and workplace safety. 7
As work engagement seems to be valuable for both organizations and employees,
knowledge regarding the prevalence of work engagement in different occupational groups
would be important to be able to take specific, targeted measures to increase it. Identifying
(dis)engaged employees is not only important at the European and national governmental
level (e.g. Engaging for successin Britain, 23 but also for employers, trade unions, and non-
governmental organizations such as the ILO. However, very little is known about how
demographic and occupational factors (other than immediate working conditions) relate to
work engagement. According to the validation study of the Utrecht Work Engagement Scale,
which used samples from 10 countries, 24 work engagement was weakly positively related to
age, whereas the relation with gender varied across countries. Of occupational groups,
educators, managers, and police officers reported the highest levels of work engagement,
whereas groups such as blue-collar workers and social and health care workers reported the
lowest levels. 24 However, these results are based on non-representative studies conducted at
different times and in different occupational and organizational samples. In addition, the
present dataset showed that work engagement differs across countries, and that these
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differences are related to various economic, cultural and governance indicators. 25 Generally
speaking, the workforce is more likely to be engaged at work in well-governed,
individualistic countries with a strong democracy, low corruption and gender inequality.
The aim of this current, unique paper is to investigate the prevalence of work
engagement at the individual level across different socio-demographic, work (e.g. type of
contract, working hours) and organizational (e.g. size of the company, sector) groups in 30
European countries. Another aim is to compare the relative importance of these factors to the
level of work engagement to determine the most important contributors to feeling engaged at
Participants and study design
We used the data of the 6th European Working Conditions Survey (EWCS), collected in
2015. 26 EWCS is collected every five years from random samples of the workforce and
focuses on their occupation, working conditions and health. The target population for the
EWCS consists of all residents from EU countries aged 15 or above and in employment at the
time of the survey. A stratified (by region and degree of urbanization) multi-stage, random
sample is drawn in each country, using individual-, household- and address-level registers. In
each stratum, primary sampling units are randomly selected, in proportion to the size of the
country. Subsequently, a random sample of households is drawn in each of these units.
Finally, in each household, the selected respondent is the working person whose birthday is
next. More details on sampling can be found elsewhere. 26
The survey interviews were carried out face-to-face using computer-assisted personal
interviewing. The average duration of these interviews was 45 minutes. The minimum sample
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size per country was 1000. The overall response rate was 43%, ranging from 11% in Sweden
to 78% in Albania, and resulting in a total of 43 850 responses. All 28 EU Member States
were included, as well as Norway and Switzerland. EWCSquality assurance24 meant that
five EU-associated counties had to be excluded. The EWCS is representative of those aged
15 years and above (16 and above in Bulgaria, Norway, Spain and the UK) who are in
employment and are resident in the country that is being surveyed. We also excluded those
not working at the time of the data collection. Table 1 presents the demographic details of the
participants as well as the mean levels of work engagement in the different groups.
Work engagement was assessed using three items from the Utrecht Work Engagement
Scale (UWES) 24: At my work, I feel full of energy(vigour), I am enthusiastic about my
work(dedication), and Time flies when I am working(absorption). Recently, a similar
three-item version of the UWES was validated and shown to be psychometrically as sound as
the nine-item version (UWES-3). 27 The present survey uses two items that differ from
UWES-3. 27 However, using the third authors database, which includes 109 975 employees
from 25 countries, both the slightly different three-item UWES versions correlated at .88. 27
Across countries, the correlations varied between .68 and .92. In eight countries, the
correlations were .90 or higher (> 80% shared variance). In short, both the ultra-short three-
item versions of the UWES were similar. The items were rated on a five-point scale, ranging
from 1 (always) to 5 (never). We reversed the scale so that the higher value referred to higher
work engagement.
In addition, we utilized sociodemographic and work-related factors regarding gender,
age, highest level of education (ISCED) and country, and included design weights to adjust
for different selection probabilities, sectors of economic activity (NACE), occupational
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groups (ISCO), part-time vs. full-time work, being self-employed, type of labour contract,
public or private sector, size of workplace, length of tenure, restructuring during last three
years (yes/no), and number of work hours/week. These were categorized in accordance with
the EU working time directive into four classes: < 35 hours/week, 3540 hours/week, 4048
hours/week, and > 48 hours/week. The purpose of using weights was to ensure that the
samples were comparable and representative of each country.
Statistical analyses
The statistical analysis included three steps. First, we studied each demographic and
occupation-related factor separately, both unadjusted and adjusted for age and gender, in
linear regression models to predict work engagement. Second, we investigated all factors
were simultaneously as independent variables in the same model to predict work engagement.
As some of the sociodemographic categories included less than 500 individuals, which might
add bias or chance to the estimations, we chose to present only the categories with 500 or
more individuals, to add comparability and reliability. However, to avoid losing information
and to maximize the number of individuals in the models, we retained these categories in the
models but did not present the results. Consequently, we did not use them for making any
interpretations. Third, to assess the relative importance of different demographic and work-
related factors, we conducted dominance analysis (DA) to determine the most important
contributors to work engagement. 28 This analysis compares all independent variables in the
model to each other and ranks them by their relative importance in predicting work
engagement. 29
DA is used to overcome methodological difficulties, such as the multicollinearity
related to traditional regression models with several correlated predictors. Regression,
including stepwise and hierarchical approaches with several overlapping independent
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variables, may overestimate the importance of the strongest predictors, underestimate the
importance of the less important predictors, reverse the signs of predictors (i.e. suppressor
effect), and allow slight differences in inter-predictor correlations to change the pattern of
derived regression weights. 28 In addition to overcoming multicollinearity problems, DA is
particularly useful when the number of predictors is large. 29 We also reported the domin
value, which means the proportion of each factors explained variance (%) of the variance
explained (100%) by the whole model.
In all analyses, the reference category for demographics and work-related factors was
the largest category (i.e. including most employees). In addition, we weighted all the analyses
with design weights to adjust for different selection probabilities of EWCS to control for the
impact of the country. We used Stata 14.0 software (Stata Corporation) and its DOMIN
module to conduct the analyses. 30
Table 2 presents the crude and age- and gender-adjusted associations between
demographic and work-related factors and work engagement. Women and workers over 60
years of age were significantly more engaged than men and those in younger age groups,
respectively. After adjusting for age and gender, the results remained similar, indicating a
clear linear trend in education: the lower the educational attainment, the lower the level of
work engagement. For example, those with primary education were clearly less likely to feel
engaged at work [Exp(coef) = 0.89, 95%CI 0.83-0.96], whereas those with a doctorate or
equivalent education were more likely to feel engaged [Exp(coef) = 1.39, 95%CI 1.24-1.57].
Of the work-related factors, longer tenure in the same company and being self-
employed were positively related to work engagement. Working more than 48 hours/week
was also associated with work engagement [Exp(coef) = 1.12, 95%CI 1.08-1.16] both in the
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
crude and age- and gender-adjusted models. In regard to the type of employment contract, a
clear trend was observed, as contracts with a limited duration [Exp(coef) = 0.93, 95%CI 0.89-
0.97], temporary agency contracts [Exp(coef) = 0.83, 95%CI 0.73-0.94], no contract
[Exp(coef) = 0.92, 95%CI 0.86-0.98], or other type of contract [Exp(coef) = 0.80, 95%CI
0.70-0.91] were all associated with lower work engagement in comparison to permanent
work contracts.
The differences between industries indicated that working in health care and social
work [Exp(coef) = 1.15, 95%CI 1.09-1.20] and other service activities [Exp(coef) = 1.16,
95%CI 1.09-1.24], and in education [Exp(coef) = 1.25, 95%CI 1.19-1.32] were associated
with higher levels of work engagement. In contrast, working in manufacturing and transport,
storage and communications [Exp(coef) = 0.92, 95%CI 0.88-0.97] was associated with lower
levels of work engagement. Of the occupational groups, managers [Exp(coef) = 1.17, 95%CI
1.11-1.23] and professionals [Exp(coef) = 1.08, 95%CI, 1.04-1.13] reported higher levels of
work engagement, whereas plant and machine operators [Exp(coef) = 0.76, 95%CI 0.72-0.81]
and elementary occupations [Exp(coef) = 0.74, 95%CI 0.70-.0.78] reported lower work
engagement. Moreover, in the private sector, the likelihood of work engagement was
somewhat lower than in other sectors. We found no association between work engagement
and the size of the company, nor between work engagement and recent company
restructuring or reorganization.
In the multivariate analysis (Table 3), being a worker over 60 years of age [Exp(coef) =
1.18, 95%CI 1.06-1.18] was positively associated with work engagement. In addition,
working in health care and social work [Exp(coef) = 1.17, 95%CI 1.07-1.28], education
[Exp(coef) = 1.15, 95%CI 1.03-1.30], and agriculture, hunting or forestry [Exp(coef)=1.22,
95%CI 1.01-1.48] increased the likelihood of work engagement. In contrast, working in
manufacturing and transport, storage and communications [Exp(coef) = 0.92, 95%CI 0.88-
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
0.97] was associated with lower levels of work engagement. Of the different occupations,
plant and machine operators and assemblers [Exp(coef) = 0.80, 95%CI 0.72-0.89],
elementary occupations [Exp(coef) = 0.77, 95%CI 0.69-0.85], clerical support workers
[Exp(coef) = 0.91, 95%CI 0.84-0.99], and service and sales workers [Exp(coef) = 0.91,
95%CI 0.85-0.98] were likely to be less engaged, and managers [Exp(coef) = 1.16, 95%CI
1.06-1.26] were more likely to feel engaged at work. Finally, working in the public sector
was positively related to work engagement [Exp(coef) = 1.10, 95%CI 1.00-1.20], and
working in a company with 500 or more employees was negatively [Exp(coef)=0.91, 95%CI
0.83-0.99] related to work engagement. We found no other significant associations.
DA (Table 3) revealed that occupational group and industry were relatively the most
important factors associated with work engagement, explaining 68.1% and 17.1% of all the
explained variance of the model, respectively. Far less important were sector (4.3%) and
employment contract (3.0%), followed by education (2.5%) and gender (2.1%). Individual
factors such as age, job tenure, employment contract (part-time/full-time and self-
employed/salaried employee), and similarly organization-related factors such as size of the
company, and restructuring or reorganization during the last 12 months played almost no role
in explaining the variance of work engagement.
The purpose of this large-scale study in 30 European countries among over 35 000
employees was twofold: to examine the prevalence of work engagement in different socio-
demographic and work- and organizational-related groups, and to investigate the relative
importance of these factors for work engagement. To the best of our knowledge, this is the
first prevalence study on work engagement to be based on national representative data from
various countries.
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Our study found that work engagement, a positive affective-motivational state at work,
was related to many sociodemographic and work-related factors, particularly to educational
attainment, employment contract, occupation, industry, and sector. In addition, the results of
DA indicated that occupation and industry made the strongest contribution to work
We found a clear social gradient related to the prevalence of work engagement: those
who had better educational attainment were also more likely to be engaged at work, whereas
less educated employees were less engaged. Research on social inequalities in health which
indicates that poor socioeconomic status (education, social class) is associated with mortality
and morbidity is abundant. Already two decades ago, Marmot et al. 31 suggested that the
inequality gap in health and health promotive mechanisms may be much more profound and
persistent than was assumed on the basis of mere mortality and register-based disease studies.
Previously it was found that the origins of job burnout may lie in childhood socio-economic
status. 32 Our study suggests that a positive state of employee well-being, i.e., work
engagement may also indicate an inequality gap. Previous research has also found a positive
relation between work engagement and physical and mental health8-14 and work-family
balance15, and thus (lack of) work engagement may be one mechanism linking
socioeconomic status differences to poorer health in the long term.
Similarly, employment contract was related to work engagement, so that those with
permanent employment contracts were more likely to report higher levels of work
engagement than those with other types of contract or no contract at all. Those with no
permanent contract may experience work stress due to several reasons. For instance, they
may be considered peripheral workers, and employers may not be willing to invest in them
by, for example providing training. They also may lack job control and social supportnd have
monotonous jobs with less challenges33-35 all these job characteristics are known to
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
influence work engagement. 36 More research is needed that both compares stable, permanent
and increasingly untypical, precarious employment contracts, and their impacts on work
Interestingly, we also found curvilinear relationship between working hours and work
engagement, meaning that particularly those working more than 48 hours per week but also
those working less than 35 hours per week were more likely to be engaged than those
working between 35-48 hours per week. Previously, work engagement has been found to
correlate positively with working hours. 37 As engaged employees have high levels of energy
and are enthusiastic about their work, they also often work voluntarily more hours than
required by their organizations. In the present study, among those who worked more than 48
hours per week there were relatively more managers (12%) than in the whole sample (4%)
and also more self-employed (42% vs. 14%) both occupational groups scored high in work
engagement in the present study. The higher likelihood of feeling engaged among those
working less than 35 hours is likely to be explained by the fact that it was more typical to
work shorter hours in highly engaged industries, i.e., in social and health care (16% in the
group working less than 35 hours per week vs. 11% in the whole sample) and in education
(14% vs. 8%). Indeed, the (curvilinear) association between working hours and work
engagement disappeared after controlling the impact of other factors, such as occupational
group and industry.
The DA results showed that the most important contributors to work engagement were
occupation and industry, explaining 68% and 17% of the variance of work engagement,
respectively. This left only 15% for all remaining factors, such as economic sector (4.3%),
employment contract (3%) and education (2.5%). As regards occupations, not surprisingly,
managers and professionals were clearly more engaged than, for example, plant and machine
operators, assemblers, other elementary occupations, and clerical support workers. These
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
differences are likely due to variances in the job resources available in these jobs. Several
studies3-5 have confirmed the assumptions of the Job Demands-Resources model2: that job
demands are the primary antecedents of job burnout, and that job resources (e.g., job
autonomy, skill variety, feedback, social support) are the main drivers of work engagement.
Managers, professionals and many other well-educated employees are more likely to be able
to draw upon these resources, whereas other jobs may lack resources.
Employees in human service jobs such as health and social care and education, as well
as those in agriculture, hunting and forestry reported more work engagement than employees
in other types of industries such as manufacturing, transport, storage, and communication.
Interestingly, chronic job burnout was long considered typical only among human service
professionals because of the emotional and interpersonal stressors in these jobs. 38 According
to this study, those employed in human services may feel particularly highly engaged in their
work. Working with and for people, and helping them, is often experienced as meaningful
and many may consider these jobs a calling. These jobs often also include many job resources
such as skill variety, professional development, receiving immediate feedback, good climate,
and colleague support, which all positively impact work engagement. 3,39,40
A possible explanation for the likelihood of higher work engagement in agriculture,
hunting and forestry is that these fields have a high ratio of self-employed people (60% in the
present sample). Self-employed individuals have been found to be more engaged in their
work than salaried employees. 41 This may be because individuals in entrepreneurial jobs are
usually proactive and have achievement-related personality characteristics, and/or because
the self-employed by definition have more autonomy.
Finally, being over 60 years of age was positively related to work engagement. The
healthy worker effect may have impacted this result, in that the more engaged aged
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
employees stay longer in the labour force, whereas those who are less engaged and less
healthy drop out at an earlier age. Interestingly, women reported slightly more work
engagement than men. This is surprising, as research usually shows that although women live
longer, they report more symptoms and suffer from more diseases than men. 42 However,
positive and negative feelings and states of ill-being and well-being are not necessarily
opposites. Research on employee well-being (work engagement vs. job burnout) and on
general mental well-being (emotional, social and psychological well-being vs. depression)
has shown that positive and negative well-being constitute separate although correlated
unipolar dimensions. 4,43 More research is needed on the mechanisms that explain why
employed women may be more engaged than men.
Many large private companies have human resource policies to assess and boost work
engagement. However, not all organizations have such policies. As work engagement is
beneficial for all types of organizations and for employees themselves, it is important that
particular attention is paid to developing more resourceful working conditions (e.g. more job
autonomy, skill variety and job security), especially among those who lack higher education,
have blue-collar jobs, and no permanent contract.
Strengths and limitations
The major strength of this study was its large and representative sample size, which
consisted of employees from 30 European countries. In addition, the response rate was
satisfactory (43%). Although work engagement is currently widely investigated all over the
globe, very little is known about its prevalence. This unique dataset enabled us to investigate
this prevalence and to assess the relative importance of various individual, work-related and
organizational factors for work engagement after controlling for the impact of country.
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Our study also has some limitations. First, as it was cross-sectional, it was not possible
to investigate how different factors predicted future work engagement, or how changes in
these factors (e.g. in job status and employment contract) would influence engagement over
time. Second, it was based on self-reports. However, we assume that common method bias
did not considerably influence the results, as work engagement was the only variable based
on subjective experiences; all the other variables (e.g. age, employment contract and sector)
were more factual by nature. Third, despite the large sample size, some of the
sociodemographic categories included less than 500 employees, so we were restricted in
making any interpretations based on these if we wished to maintain comparability with the
categories with up to 4000 employees. Hence, an even larger and representative sample
would be needed to confirm findings across all sociodemographic categories with sufficient
statistical confidence. Finally, although the study included 30 countries, they were all
European, so it is uncertain how far our results can be generalized to other parts of the world.
According to this first large-scale prevalence study on work engagement, an
employees level of work engagement depends on individual (e.g. education), work-related
(e.g. contract) and contextual (e.g. occupation, industry) factors. Job-related and contractual
factors seem to be the most significant determinants of work engagement. In the changing
world of work it is important to strive for enhancing work engagement, especially among
those who lack higher education, have blue-collar jobs, and have no permanent contract.
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
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Table 1. Work engagement by sociodemographic and work-related factors (including only
those reporting being at work and limited to EU 28 countries, excluding candidate countries
but Switzerland and Norway included)
Work engagement
n Mean SD
All 35395 3.95 0.70
Men 17498 3.94 0.70
Women 17897 3.96 0.70
Age groups
< 30 years 5221 3.91 0.72
30-40 years 8240 3.94 0.71
40-50 years 9645 3.95 0.70
50-60 years 8924 3.96 0.70
> 60 years 3080 4.03 0.68
Early childhood education 164 3.76 0.84
Primary education 1083 3.76 0.84
Lower secondary education 4443 3.87 0.77
Upper secondary education 14542 3.91 0.72
Post-secondary non-tertiary education 2903 3.94 0.70
Short-cycle tertiary education 3511 4.04 0.63
Bachelor or equivalent 4393 4.05 0.64
Master or equivalent 3706 4.06 0.61
Doctorate or equivalent 372 4.14 0.59
Years worked in the company
1 year 1986 3.96 0.75
2-5 years 9196 3.92 0.71
5-10 years 7037 3.96 0.69
10-15 years 4282 3.96 0.66
15-20 years 2964 3.97 0.69
over 20 years 5557 4.02 0.67
Employment contract (main job)
Contract of unlimited duration 24181 3.94 0.69
Contract of limited duration 3366 3.85 0.78
A temporary agency contract 380 3.79 0.86
An apprenticeship or other training schedule 141 3.98 0.76
No contract 1721 3.82 0.72
Other 228 3.83 0.81
Working hours (limits by EU Working time directive)
< 35 hours/week 8021 3.98 0.72
35-40 hours/week 17727 3.94 0.69
40-48 hours/week 4172 3.90 0.69
>48 hours/week 4451 4.00 0.71
Part-time work 7178 3.95 0.73
Full-time work 27965 3.95 0.70
Self-employed 5294 4.11 0.66
Employee 29839 3.92 0.71
Occupational group
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Plant and machine operators, and assemblers 2445 3.74 0.79
Elementary occupations 3365 3.76 0.82
Clerical support workers 3228 3.88 0.69
Service and sales workers 7411 3.91 0.73
Craft and related trades workers 4248 3.94 0.70
Skilled agricultural, forestry and fishery workers 958 3.97 0.73
Technicians and associate professionals 4267 4.03 0.63
Professionals 6760 4.09 0.60
Managers 2353 4.15 0.61
Manufacturing 4982 3.85 0.74
Transport, storage and communication 2372 3.88 0.72
Wholesale and retail trade 5267 3.89 0.72
Hotels and restaurants 1823 3.93 0.74
Agriculture, hunting and forestry 1380 3.94 0.73
Real estate activities 4126 3.94 0.71
Public administration and defense 2058 3.94 0.69
Construction 2220 3.97 0.67
Financial intermediation 1011 4.00 0.63
Health and social work 3778 4.04 0.64
Other service activities 2011 4.09 0.69
Education 2825 4.13 0.60
Private sector 24276 3.93 0.72
Public sector 8509 4.00 0.67
A joint private-public organization or company 1254 4.00 0.68
Other 630 4.01 0.69
Not-for-profit sector or NGO 385 4.03 0.65
Size of the company
1 (participant works alone) 552 4.05 0.72
2-4 1149 3.99 0.70
5-9 1480 3.95 0.69
10-49 4172 3.94 0.69
50-99 1802 3.90 0.70
100-249 1753 3.91 0.71
250-499 1002 3.90 0.71
500 and over 1672 3.91 0.70
During last year, restructuring or reorganizing (yes) 8053 3.93 0.70
no 26067 3.96 0.70
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Table 2. Crude and age- and gender-adjusted regression coefficients with 95% Confidence
intervals (CI) for demographic and work-related factors predicting work engagement
Work engagement
Crude Age- and gender-adjusted
95%CI Regression
Gender (men as reference) 1.03 1.01, 1.06 1.03 1.01, 1.06
Age groups (40-50 years as
< 30 years 0.99 0.95, 1.03 1.05 0.97, 1.15
30-40 years 0.99 0.96, 1.03 1.03 0.97, 1.08
50-60 years 1.01 0.98, 1.05 0.98 0.93, 1.03
> 60 years 1.09 1.04, 1.14 1.02 0.94, 1.12
Education (Upper secondary
education as reference)
Early childhood education 0.86 0.70, 1.05 0.86 0.70, 1.05
Primary education 0.89 0.83, 0.96 0.89 0.83, 0.96
Lower secondary education 1.01 0.96, 1.05 1.01 0.96, 1.05
Post-secondary non-tertiary education 1.04 1.00, 1.09 1.04 0.99. 1.09
Short-cycle tertiary education 1.14 1.04, 1.19 1.09 1.10, 1.19
Bachelor or equivalent 1.15 1.10, 1.20 1.15 1.10, 1.19
Master or equivalent 1.20 1.15, 1.24 1.19 1.15, 1.24
Doctorate or equivalent 1.39 1.24, 1.57 1.39 1.24, 1.57
Years worked in the company (2-5
years as reference)
1 year 1.01 0.95, 1.09 1.02 0.95, 1.09
5-10 years 1.04 1.00, 1.08 1.04 1.00, 1.08
10-15 years 1.02 0.98, 1.06 1.02 0.98, 1.07
15-20 years 1.05 1.00, 1.10 1.05 1.00, 1.10
over 20 years 1.10 1.06, 1.15 1.11 1.06, 1.15
Working hours (limits by EU
Working time directive, 35-40
hours/week as reference)
< 35 hours/week 1.03 1.00, 1.06 1.02 0.99, 1.05
40-48 hours/week 0.99 0.96, 1.03 1.00 0.96, 1.03
>48 hours/week 1.11 1.07, 1.15 1.12 1.08, 1.16
Employment contract (main job,
Contract of unlimited duration as
Contract of limited duration 0.94 0.90, 0.98 0.93 0.89, 0.97
A temporary agency contract 0.83 0.73, 0.94 0.83 0.73, 0.94
An apprenticeship or other training
1.05 0.81, 1.35 1.05 0.81, 1.36
No contract 0.92 0.87, 0.98 0.92 0.86, 0.98
Other 0.81 0.71, 0.92 0.80 0.70, 0.91
Part-time work (Full-time work as
0.97 0.94, 1.00 0.95 0.92, 0.98
Self-employed (Employee as 1.21 1.17, 1.25 1.21 1.17, 1.25
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Occupational group (Technicians
and associate professionals as
Plant and machine operators, and
0.75 0.71, 0.80 0.76 0.72, 0.81
Elementary occupations 0.74 0.70, 0.79 0.74 0.70, 0.78
Skilled agricultural, forestry and
fishery workers
1.05 0.97, 1.14 1.06 0.98, 1.14
Clerical support workers 0.87 0.82, 0.91 0.86 0.82, 0.91
Service and sales workers 0.91 0.87, 0.95 0.91 0.87, 0.95
Craft and related trades workers 0.95 0.91, 1.00 0.97 0.92, 1.01
Professionals 1.09 1.04, 1.13 1.08 1.04, 1.13
Managers 1.16 1.10, 1.23 1.17 1.11, 1.23
Industry (Wholesale and retail trade
as reference)
Agriculture, hunting and forestry 1.12 1.04, 1.20 1.12 1.04, 1.20
Activities of households 0.92 0.83, 1.02 0.92 0.83, 1.03
Manufacturing 0.92 0.88, 0.97 0.92 0.88, 0.97
Transport, storage and communication 0.93 0.87, 0.98 0.92 0.87, 0.98
Hotels and restaurants 1.07 1.00, 1.15 1.07 1.00, 1.15
Electricity, gas, and water supply 1.02 0.90, 1.16 1.02 0.90, 1.15
Real estate activities 0.99 0.94, 1.04 0.99 0.94, 1.04
Public administration and defense 1.00 0.94, 1.06 1.00 0.94, 1.06
Construction 1.05 0.99, 1.12 1.05 0.98, 1.12
Financial intermediation 1.11 1.03, 1.20 1.11 1.02, 1.20
Health and social work 1.14 1.09, 1.24 1.15 1.09, 1.20
Other service activities 1.16 1.09, 1.24 1.16 1.09, 1.24
Education 1.25 1.19, 1.32 1.25 1.19, 1.32
Sector (Private sector as reference)
Public sector 1.07 1.04, 1.10 1.06 1.00, 1.13
A joint private-public organization or
1.06 1.00, 1.13 1.05 0.96, 1.14
Other 1.14 1.03, 1.26 1.14 1.03, 1.26
Not-for-profit sector or NGO 1.05 0.96, 1.15 1.07 1.04, 1.10
Size of the company (10-49 as
1 (participant works alone) 1.16 1.05, 1.29 1.10 0.98, 1.23
2-4 1.08 0.99, 1.17 1.02 0.93, 1.12
5-9 1.06 0.98, 1.14 0.95 0.88, 1.02
50-99 1.00 0.94, 1.06 0.95 0.88, 1.02
100-249 0.99 0.92, 1.06 0.94 0.86, 1.02
250-499 0.97 0.88, 1.06 0.92 0.83, 1.02
500 and over 0.98 0.92, 1.05 0.94 0.86, 1.01
During last year, restructuring or
reorganizing (no as reference)
0.97 0.94, 1.00 0.97 0.94, 1.00
*Statistically significant regression coefficients and 95%CIs in boldface
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Table 3. Multivariate regression coefficients with 95% Confidence intervals (CI) for
demographic and work-related factors predicting work engagement and standardized
dominance estimates (Domin = % R2explained) and the position in the ranking of dominance
analysis (Domin rank).
Work engagement
Multivariate model Domin Domi
Gender (men as reference) 1.04 0.95, 1.13 2.1% 6
Age groups (40-50 years as reference) 0.4% 10
< 30 years 1.04 0.97, 1.12
30-40 years 1.03 0.97, 1.09
50-60 years 1.05 0.99, 1.11
> 60 years 1.18 1.06, 1.29
Education (Upper secondary education
as reference)
2.5% 5
Early childhood education 1.27 0.85, 1.89
Primary education 1.12 0.97, 1.29
Lower secondary education 1.04 0.96, 1.13
Post-secondary non-tertiary education 0.98 0.91, 1.05
Short-cycle tertiary education 1.06 1.00, 1.13
Bachelor or equivalent 1.05 0.98, 1.12
Master or equivalent 1.04 0.97, 1.12
Doctorate or equivalent 1.06 0.88, 1.26
Years worked in the company (2-5
years as reference)
0.4% 9
1 year 1.06 0.94, 1.20
5-10 years 1.03 0.97, 1.10
10-15 years 1.03 0.96, 1.10
15-20 years 0.98 0.90, 1.06
over 20 years 1.03 0.95, 1.11
Working hours (limits by EU
Working time directive, 35-40
hours/week as reference)
1.2% 7
< 35 hours/week 1.02 0.93, 1.11
40-48 hours/week 0.98 0.93, 1.04
>48 hours/week 1.05 0.99, 1.12
Employment contract (main job,
Contract of unlimited duration as
3.0% 4
Contract of limited duration 0.95 0.88, 1.02
A temporary agency contract 0.89 0.73, 1.09
An apprenticeship or other training
1.08 0.85, 1.37
No contract 0.90 0.77, 1.04
Other 0.86 0.68, 1.08
Part-time work (Full-time work as 1.04 0.95, 1.14 0.7% 8
⇐°↓ƒ↑∂÷•↔ ϖ  ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Self-employed (Employee as reference) 0.99 0.92, 1.05 0.0% 13
Industry (Wholesale and retail trade as
17.1% 2
Agriculture, hunting and forestry 1.22 1.01, 1.48
Activities of households 1.23 0.88, 1.73
Manufacturing 0.94 0.86, 1.03
Transport, storage and communication 1.01 0.91, 1.11
Hotels and restaurants 1.04 0.92, 1.18
Electricity, gas, and water supply 1.07 0.91, 1.25
Real estate activities 0.96 0.87, 1.05
Public administration and defense 1.02 0.92, 1.14
Construction 1.00 0.89, 1.12
Financial intermediation 1.10 0.99, 1.22
Health and social work 1.17 1.07, 1.28
Other service activities 1.08 0.94, 1.25
Education 1.15 1.03, 1.30
Occupational group (Technicians and
associate professionals as reference)
68.0% 1
Plant and machine operators, and
0.80 0.72, 0.89
Elementary occupations 0.77 0.69, 0.85
Skilled agricultural, forestry and fishery
0.89 0.69, 1.16
Clerical support workers 0.91 0.84, 0.99
Service and sales workers 0.91 0.85, 0.98
Craft and related trades workers 1.04 0.94, 1.14
Professionals 1.01 0.94, 1.09
Managers 1.16 1.06, 1.26
Sector (Private sector as reference) 4.3% 3
Not-for-profit sector or NGO 0.99 0.92, 1.07
Public sector 1.10 1.00, 1.20
A joint private-public organization or
0.88 0.78, 1.00
Other 1.15 0.94, 1.38
Size of the company (10-49 as
0.1% 12
1 (participant works alone) 0.98 0.84, 1.14
2-4 0.98 0.90, 1.08
5-9 0.94 0.87, 1.01
50-99 0.94 0.87, 1.01
100-249 0.92 0.85, 1.01
250-499 0.93 0.84, 1.03
500 and over 0.91 0.83, 0.99
During last year, restructuring or
reorganizing (no as reference)
0.95 0.91, 0.99 0.2% 11
*Statistically significant regression coefficients and 95% CIs in boldface
... Previous studies have found that teachers experience high levels of work engagement (Hakanen et al., 2018), while also demonstrating increasing symptoms of burnout Salmela-Aro et al., 2019;Schaufeli et al., 2009). The combination of engagement and burnout has been extensively investigated in recent years (e.g., Innanen et al., 2014;Salmela-Aro et al., 2019;, and the rather complex relationship between these two concepts has been discussed. ...
... As described above, teachers are often engaged in their work (Hakanen et al., 2018;Salmela-Aro et al., 2019;Salmela-Aro et al., 2020) but, at the same time, work-related stress and even burnout among teachers seem to have increased in recent years (e.g., Pyh€ alt€ o et al., 2021; Salmela-Aro et al., 2019; Upadyaya & Salmela-Aro, 2020). Despite this rather negative development, it is important to note that not all teachers are at risk to burn out (see e.g., Hascher & Waber, 2021). ...
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This study provides new insights into the work-related well-being of teachers, defined here as engagement and burnout, by investigating their associations with the teachers' sense of efficacy and interprofessional collaboration in school. Using a person-oriented approach and latent profile analysis, a sample of Finnish comprehensive school teachers (N = 355) were classified based on their work engagement and burnout. Three profiles were identified: engaged, engaged-exhausted, and burned-out. Teachers with distinct profiles differed from each other in terms of their sense of efficacy and experiences of interprofessional collaboration, suggesting that both might have an important role in enhancing work engagement and preventing burnout.
... Está demonstrado que o engagement se relaciona com resultados positivos (maior satisfação com o trabalho, maior compromisso com a organização, menor absentismo, menor desejo de procurar outros trabalhos, maior capacidade para responder adequadamente às mudanças, maior proatividade, melhor desempenho, satisfação e lealdade por parte do cliente) e deste modo é inquestionável a importância de promovê-lo como um recurso fundamental das organizações (Hakanen, Ropponen, Schaufeli, De Witte, 2019). Pelo anteriormente referido, o engagement representa um objetivo válido em qualquer organização, contudo, criar condições e ambientes de trabalho que promovam positivamente este estado afetivo-motivacional constitui-se como um grande desafio no panorama atual. ...
... A nível organizacional, o engagement tem sido relacionado com vários indicadores e resultados positivos como: inovação e criatividade; menores níveis de absentismo por doença; menores índices de turnover e custos associados; menor taxa de erro; segurança no local de trabalho (menor frequência de acidentes de trabalho; maior comprometimento com comportamentos de segurança); melhoria nos níveis de desempenho e consequente aumento na produtividade. A partir destes indicadores, é clara a relação positiva entre o engagement e o sucesso da organização, traduzido na qualidade dos serviços prestados, na satisfação e confiança dos clientes, na retenção de profissionais, no retorno financeiro e num nível superior de crescimento e desenvolvimento do core business da organização (Hakanen et al., 2019;Shimazu, Schaufeli, Kubota, Watanabe & Kawakami, 2018). ...
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A investigação científica tem manifestado um grande interesse pelo engagement no trabalho dos enfermeiros, procurando determinar os seus níveis de expressão e os fatores que contribuem para a sua manifestação. Por ser um tema de incontornável relevância para as organizações de saúde, objetivamos: I) conhecer os níveis de engagement dos Enfermeiros Especialistas em Enfermagem de Reabilitação (EEER); II) descrever a sua perceção relativamente à favorabilidade do ambiente de prática de enfermagem (APE) e III) analisar a relação entre as características do APE e o engagement. À uma amostra de 113 EEER foram aplicadas as escalas Utrecht Work Engagement Scale (UWES) e Practice Environment Scale of the Nursing Work Index (PES-NWI). Os resultados revelaram níveis moderados a elevados de vigor, absorção e dedicação que variaram significativamente em função da idade e do tempo de profissão. O ambiente de prática de enfermagem foi percecionado como misto, com três dimensões avaliadas favoravelmente e duas avaliadas desfavoravelmente. Foram encontradas correlações significativas entre o APE e o engagement. O estudo reforça a importância de assegurar ambientes favoráveis à prestação de cuidados de enfermagem e, com isso, contribuir para o reforçar do vínculo à profissão e elevar os níveis de dedicação, foco e energia no trabalho.
... This limitation indicates that our analysis strictly explains the differences between very high work engagement and work engagement lower than the midpoint (i.e., 3) of the scale. One explanation is that healthcare employees are generally high in work engagement, so the limited generalizability of our results may be more pronounced in sectors with lower work engagement, like manufacturing (Hakanen et al., 2019). Nevertheless, future research may explicitly include employees with low work engagement by, for example, targeting employees who intend to quit their jobs (e.g., in exit interviews). ...
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We introduce text mining to study work engagement by using this method to classify employees' survey-based self-narratives into high or low work engagement and analyzing the text features that contribute to the classification. We used two samples, representing the 2020 and 2021 waves of an annual survey among healthcare employees. In the first study, we used exploratory sample 1 (N = 5591) to explore which text features explain work engagement (unigrams, bigrams, psychological, or linguistic). In the second study, we confirmed whether features persisted over time between exploratory sample 1 and confirmatory sample 2 (N = 4470). We find that psychological features classify employees across two samples with 60% accuracy. These features partly validate the literature: High-engaged employees refer more to affiliation and positive emotions, and low-engaged employees refer more to negative emotions and power. We extend the literature by studying linguistics: High-engaged employees use more first-person plural (“we”) than low-engaged employees. Finally, some results question the literature, like the finding that low-engaged employees refer more to their managers. This study shows text mining can contribute by confirming, extending, or questioning the literature on work engagement and explores how future research could build on our findings with survey-based or in vivo applications.
... This condition implies that the engaged employees have lower risk of being absent in the working process due to personal matters. The employees are also more cautious in the working process needing full concentration, thus capable of improving occupational safety rate [10]. The concept of work engagement is a strategic orientation to the employees in organization. ...
This research aims to develop an instrument of measuring work engagement in the company employees. The concept of work engagement used in developing a measuring instrument consists of three aspects: vigor, dedication and absorption. Work engagement scale was trialed online with 152 employees consisting of 72 males and 80 females. Data analysis was conducted using Aiken’s V with ten (10) raters. The result of Aikens’ V validity analysis resulted in value ranging between 0.6 and 0.95. The development of measuring instrument undertook reliability test using Cronbach’s Alpha technique with score of 0.885. The item with discriminating power ranging between 0.249 and 0.642 was maintained as it did not affect the reliability of scale. The concept of work engagement used in this measuring instrument development was unidimensional. The scale developing process in this research used factor analysis with Exploratory Factor Analysis (EFA) approach. The result of factor analysis shows Barlett Test of Sphericity value of 1181,992 with p<0.01 meaning that there is no significant correlation between variables. The result of calculation with KMO of 0.864 indicates that the factor analysis can be continued. Based on the result of factor analysis obtained, the cumulative percentage is 50.385%. The scale’s loading factor obtained from factor analysis using EFA ranges between 0.428 and 0.867. Eighteen (18) out of 39 items are said to be valid.
... The findings are consistent with a study by Rožman et al. (2021), who identified that work-from-home requests and a shortage of childcare resources contributed to a gender gap in work productivity, job satisfaction, and engagement during emergencies like the COVID-19 epidemic. However, this result contrasts with the typical circumstances before COVID-19, where women generally reported slightly higher levels of work engagement than men, according to a study by Hakanen et al. (2019). Moreover, there are age disparities between men and women in the grit variable. ...
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This research was conducted to identify the influence of job insecurity and grit toward work engagement on 276 hospitality employees during the COVID-19 pandemic. It consisted of 81 male respondents and 195 female respondents. The sampling technique used was the purposive sampling technique, which has specific criteria. Those criteria are hospitality workers in hotels, the food and beverage industry, administration, marketing, travel, transportation, and housekeeping companies in Jakarta. This research used three measurement tools, namely, the Utrecht Work Engagement Scale (17 items; a = .856), the Job Insecurity Scale (9 items; a = .887), and the Grit Scale (12 items; a = .797). Multiple linear regression was used as a data analysis method. The findings indicated that during the COVID-19 pandemic, job insecurity and grit had an impact on the level of work engagement among hospitality employees. Job insecurity and grit both significantly affect how motivated people are at work in the hospitality industry. Employees’ job insecurity and grit have a positive relationship that can raise the value of work engagement. Therefore, decreasing job insecurity calls for high levels of grit and work engagement to keep employee positions within the organization. The strongest correlation was found between the dimensions of grit (perseverance of effort) and work engagement (dedication). Unlike this research, which was conducted on hospitality workers, the previous research was conducted on general workers. They expected the best performance, including maintaining a friendly smile and offering the best service in all circumstances while the pandemic was ongoing.
... Control variables of gender, age, tenure, team size, department size, managerial position, and education were included as covariates in the moderated-mediation analyses based on prior research showing significant relationships with the variables in this study. For recent examples, please see Douglas and Roberts (2020) for age and engagement, Hakanen et al. (2018) for gender, age, education, tenure, and engagement, Martin et al. (2018) for managerial position and LMX, and Sui et al. (2016) for LMX and department size. ...
... As mentioned in earlier sections, transfer failures were largely attributed to personal motivations (Elliott et al., 2009). Given the specification of EE as a positive, fulfilling, and mindful state of motivation focused on work Hakanen et al., 2019), this study seeks to examine the influence of motivation on TOT through the construct of EE. Furthermore, Bailey et al. (2017) suggested that extant literature on engagement holds minimal practicality for managerial practitioners due to its weak establishment in human resource management (HRM) disciplines. ...
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Industrial Revolution 4.0 (IR4.0) offers vast potential in sustainable development through social progress and economic growth. Youths ought to take advantage of the sustainable development opportunities offered by IR4.0 in pursuit of the national agenda for sustainability. This study seeks to examine the relationships between psychological capital (PsyCap), employee engagement (EE), transfer of training (TOT), and organizational citizenship behaviour (OCB) among youth participants of IR4.0 in Sarawak. Data were collected from 251 working youths who were trained under Industrial Revolution 4.0 initiatives. Results revealed that PsyCap was positively related to EE, TOT, and OCB. EE and TOT were also found to be positively influence OCB. Additionally, TOT was found to mediate the relationship between PsyCap and OCB. This study contributes through the incorporation of digital competency into the TOT construct to examine the transfer of digital competencies. Besides, the model reflects the interplay between personal resource (PsyCap) and job resource (TOT) to influence effective organisational functioning (represented by OCB) through a motivational process. Findings suggest practical implications whereby organisations should engage pre-training psychological capital intervention to increase rate of training transfer whilst developing digital competencies of the workforce. Additionally, policymakers should formulate policies through training subsidies and tax exemptions for organisations to help strengthen youth conviction and resilience to facilitate their career progression.
Purpose The purpose of this paper is to improve the understanding of the applicability of employee engagement theories in a South Asian country, Sri Lanka, and determine whether engagement theories are universally applicable beyond the Western countries in which they have been developed and tested. Design/methodology/approach A heterogeneous sample of 451 private-sector employees in Sri Lanka was used. A mixed-method design was adopted; quantitative findings were compared with previous studies conducted in Western countries, and qualitative findings enabled a more nuanced understanding of employee engagement in the Sri Lankan context. Findings Despite cultural differences between Sri Lanka and Western countries, the antecedents of engagement did not manifest differently in a consistent way. Combined results suggest that the different manifestations of engagement in Sri Lanka cannot be attributed solely to cultural variance. Research limitations/implications The authors used cross-sectional data and tested only four antecedents of engagement. Practical implications This study highlights the importance of multinational organisations' awareness of how employee engagement manifests across different contexts and going beyond cultural adaptation when developing context-specific engagement strategies. Originality/value This is among the first studies on an Asian country to examine whether cultural differences impact the antecedents of engagement to empirically test Kahn's (1990) theory of engagement and the motivational process of the job demands-resources theory in a single study and to use a heterogeneous sample and mixed-methods design. The authors challenge the centrality of national culture as a determinant of employee engagement and highlight the importance of considering other contextual factors when examining employee engagement in different countries.
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Background: The importance of work engagement has been emphasised due to the increasing demand for health- and social care and the shortage of skilled labour. Improving organisational and managerial factors is important when enhancing professionals' work engagement. The association between management and work engagement has only been established in previous studies at a general level, but the association between appreciative management and work engagement has not received equivalent research interest. Aim: This study aimed to describe the association between appreciative management and work engagement among health- and social care professionals. Methods: The study used a cross-sectional survey design. The data were collected in five health and social services centres in one city in Finland from September to October 2022 using the Appreciative Management Scale 2.0 and the Utrecht Work Engagement Scale-9. A total of 182 health- and social care professionals participated. The data were analysed using correlations, linear regression analyses, independent samples t-tests and two-way analyses of variance (ANOVAs). Results: A moderate association was found between appreciative management and work engagement and its dimensions of vigor, dedication and absorption. Systematic management had the strongest association and equality had the weakest association with work engagement. Among the dimensions of work engagement, appreciative management had the strongest association with vigour and the weakest association with absorption. Appreciative management and work type predicted 18% of the variance in work engagement. Full-time employees reported higher levels of work engagement and all its dimensions than did part-time employees. Conclusion: The results indicate that appreciative management and full-time work predict work engagement among health- and social care professionals. Due to this positive association, it is important to promote managers' appreciative management skills by educating them to understand how appreciative management enables and supports professionals' vigour, dedication and absorption in health- and social care.
Objective: Internationally, about 40 percent of midwives report symptoms of burnout, with young and inexperienced midwives being most vulnerable. There is a lack of recent research on burnout among Dutch midwives. The aim of this study was to examine the occupational wellbeing and its determinants of newly qualified and inexperienced midwives in the Netherlands. The majority of practicing Dutch midwives are aged under 40, which could lead to premature turnover. Design: A cross-sectional study was conducted using an online questionnaire that consisted of validated scales measuring job demands, job and personal resources, burnout symptoms and work engagement. The Job Demands-Resources model was used as a theoretical model. Setting and participants: We recruited Dutch midwives who were actually working in midwifery practice. A total of N=896 midwives participated in this study, representing 28 percent of practicing Dutch midwives. Measurements and findings: Data were analysed using regression analysis. Seven percent of Dutch midwives reported burnout symptoms and 19 percent scored high on exhaustion. Determinants of burnout were all measured job demands, except for experience level. Almost 40 percent of midwives showed high work engagement; newly qualified midwives had the highest odds of high work engagement. Master's or PhD-level qualifications and employment status were associated with high work engagement. All measured resources were associated with high work engagement. Key conclusions: A relatively small percentage of Dutch midwives reported burnout symptoms, the work engagement of Dutch midwives was very high. However, a relatively large number reported symptoms of exhaustion, which is concerning because of the risk of increasing cynicism levels leading to burnout. In contrast to previous international research findings, being young and having less working experience was not related to burnout symptoms of Dutch newly qualified midwives. Implications for practice: The recognition of job and personal resources for midwives' occupational wellbeing must be considered for a sustainable midwifery workforce. Midwifery Academies need to develop personal resources of their students that will help them in future practice.
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Objective: The aim of this study was to gain insight in the importance of job demands and resources and the validity of the Job Demands Resources Model across sectors. Methods: We used one-way analyses of variance to examine mean differences, and multi-group Structural Equation Modeling analyses to test the strength of the relationships among job demands, resources, burnout, and work engagement across the health care, industry, service, and public sector. Results: The four sectors differed in the experience of job demands, resources, burnout, and work engagement, but they did not vary in how (strongly) job demands and resources associated with burnout and work engagement. Conclusion: More attention is needed to decrease burnout and increase work engagement, particularly in industry, service, and the public sector. The Job Demands-Resources model may be helpful in this regard, as it is valid across sectors.
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We theorize that engagement, conceptualized as the investment of an individual's complete self into a role, provides a more comprehensive explanation of relationships with performance than do well-known concepts that reflect narrower aspects of the individual's self. Results of a study of 245 firefighters and their supervisors supported our hypotheses that engagement mediates relationships between value congruence, perceived organizational support, and core self-evaluations, and two job performance dimensions: task performance and organizational citizenship behavior. Job involvement, job satisfaction, and intrinsic motivation were included as mediators but did not exceed engagement in explaining relationships among the antecedents and performance outcomes.
A substantial proportion of the total working population, ranging from 8% in the United States to 14% in Europe, is temporarily employed. Temporary employment refers to dependent employment of limited duration, either directly by the organization or by an agency. The use of temporary employment is mostly inspired by employers' motives related to cost reduction and flexibility. This has led to concerns about the impact of temporary employment for the worker. These concerns are rooted in the observation of poorer quality of temporary jobs compared with permanent jobs. Surprisingly, however, this poorer quality does not lead to an overall negative experience in terms of attitude, health and well-being among the temporary workforce. Possibly, temporary workers see their job as a transitory stage to permanent employment.
The job demands-resources (JD-R) model was introduced in the international literature 15 years ago (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). The model has been applied in thousands of organizations and has inspired hundreds of empirical articles, including 1 of the most downloaded articles of the Journal of Occupational Health Psychology (Bakker, Demerouti, & Euwema, 2005). This article provides evidence for the buffering role of various job resources on the impact of various job demands on burnout. In the present article, we look back on the first 10 years of the JD-R model (2001-2010), and discuss how the model matured into JD-R theory (2011-2016). Moreover, we look at the future of the theory and outline which new issues in JD-R theory are worthwhile of investigation. We also discuss practical applications. It is our hope that JD-R theory will continue to inspire researchers and practitioners who want to promote employee well-being and effective organizational functioning. (PsycINFO Database Record
Background: Work engagement in professional nursing practice is critically important to consider when addressing key challenges of health systems, including the global nursing shortage, pressures to reduce health care spending, and increasing demands for quality care and positive outcomes for patients. However, research on work engagement in professional nursing practice has not yet been synthesized and therefore, does not provide a sufficient foundation of knowledge to guide practice and further research. Objectives: The overall aim of this systematic review is to determine what is currently known about the antecedents and outcomes of work engagement in professional nursing practice. Design: Systematic review. Data sources: The search strategy included eight electronic databases: CINAHL, MEDLINE, PsycINFO, PROQUEST, SCOPUS, Web of Science, EMBASE, and Business Source Complete. The search was conducted in October 2013. Quantitative and qualitative research that examined relationships between work engagement and antecedent or outcome factors was included. Review methods: Quality assessment, data extractions, and analysis were completed on all included studies. Data extracted from included studies were synthesized through descriptive and narrative synthesis. Content analysis was used to categorize factors into themes and categories. Results: 3621 titles and abstracts were screened and yielded 113 manuscripts for full text review. Full text review resulted in 18 included studies. All factors examined were grouped into either influences or outcomes of work engagement. A total of 77 influencing factors were categorized into 6 themes: organizational climate, job resources, professional resources, personal resources, job demands, and demographic variables. A total of 17 outcomes of work engagement were categorized into 3 themes: performance and care outcomes, professional outcomes, and personal outcomes. Based on the results, we adapted the Job Demands-Resources (JD-R) model and developed the Nursing Job Demands-Resources (NJD-R) model for work engagement in professional nursing practice, which reflects key adaptations related to organizational climate and professional resources. Conclusions: Our findings indicate that a wide range of antecedents, at multiple levels, are related to registered nurses' work engagement. Positive outcomes of work engagement are valuable to both performance and the individual nurse. The NJD-R model offers nursing science a valuable beginning framework to understand the current evidence, further direct nursing research, and begin to guide practice and policy. The results offer opportunities for nurse leaders to promote work engagement in professional nurses through action on organizational level resources.
To investigate the long-term relationships between work engagement, workaholism, work-to-family enrichment, and work-to-family conflict (WFC). We used structural equation modeling and the three-wave 7-year follow-up data of 1580 Finnish dentists to test our hypotheses. Work engagement and work-to-family enrichment mutually predicted each other, and work engagement also negatively predicted WFC. Workaholism predicted WFC, but not vice versa. Work engagement and workaholism were unrelated over time. The results indicate that beyond its suggested benefits for organizations, work engagement may boost the positive interaction between work and family, whereas workaholism is likely to lead to WFC over time. It is valuable for organizations to distinguish work engagement from workaholism and to enhance the former while preventing the latter to have sustainably hardworking working employees with happy home lives.
Objective: To investigate whether work engagement influences self-perceived health, work ability, and sickness absence beyond health behaviors and work-related characteristics. Methods: Employees of two organizations participated in a 6-month longitudinal study (n = 733). Using questionnaires, information was collected on health behaviors, work-related characteristics, and work engagement at baseline, and self-perceived health, work ability, and sickness absence at 6-month follow-up. Associations between baseline and follow-up variables were studied using multivariate and multinomial logistic regression analyses and changes in R2 were calculated. Results: Low work engagement was related with low work ability (odds ratio: 3.68; 95% confidence interval: 2.15 to 6.30) and long-term sickness absence (odds ratio: 1.84; 95% confidence interval: 1.04 to 3.27). Work engagement increased the explained variance in work ability and sickness absence with 4.1% and 0.5%, respectively. Conclusions: Work engagement contributes to work ability beyond known health behaviors and work-related characteristics.