AN ANALYSIS OF WORK ENGAGEMENT BASEDON THE JOB DEMAND-RESOURCES (JD-R)
MODEL: EMPIRICAL FINDINGS UNDER WORKFROM-HOME ENVIRONMENTS
Tomohiro HIGUCHI1 and Kiyoshi TAKAHASHI2

1Member of SGB, Doctoral student, Graduate school of Human Science, Ritsumeikan University(Address 567-8570, Japan)
E-mail: tomhigu@gmail.com
2Professor, College of Comprehensive Psychology, Ritsumeikan University(Address 567-8570, Japan)
E-mail: ktakahas@fc.ritsumei.ac.jp

The significance role of work engagement has generated considerable interest within the academic andpractitioner communities. This study examined the job demand-resources (JD-R) model under work fromhome environment during the COVID-19. The author hypothesized that (Hypothesis 1) job resources
increase work engagement both directly and indirectly mediated by personal resources in the work-fromhome environments, (Hypothesis 2) job demands strengthen exhaustion. Hypotheses were tested among
648 remote workers who utilized on a regular basis compared with total 10,000 workers ages from 15 to
69 years old in Japan, utilizing the data survey conducted by Persol Research and Consulting (2020). The
annual survey has been conducted since 2017. Results of a series of structured equation model analysis
showed that the most increased observed variable of job resources (i.e., autonomy, social support,
supervision coaching, and professional development) was autonomy and job resources increased work
engagement (i.e., vigor, dedication, and absorption). Hypothesis 1 was partially supported. Job demands(i.e., workload, emotional demands, emotional dissonance, and organizational changes) strengthened
exhaustion (i.e., anxiety, sadness, anger, fatigue, and confusion) under work from home environment
compared to office workers. Hypothesis 2 was supported. Implications from theoretical and practical
viewpoints for verification of JD-R model, big data of 10,000 samples, data source in Japan, and work fromhome environment were discussed.
Key Words : work engagement, job demand-resources model, work-from-home, exhaustion, autonomy

1. INTRODUCTION

the model proposes that high job demands lead tostrain and health impairment (the health impairment
process), and that high resources lead to increased
motivation and higher productivity (the motivational
process) (Schaufeli et al., 2014) 3). The practical use
of JD-R model in combination with its broad scope
and flexibility presumably accounts for the
proliferation in both research and practice. Empiricalfindings on the JD-R model have been accumulated
worldwide, yet it is not the case for Japaneseemployment settings. Thus far, scholars in Japan
have not made enough effort to investigate thevalidity of the theory in the national and regional
work environments.
Despite that practitioners all over the world havegiven positive aspiration to work engagement, thisenthusiasm was interrupted by the COVID-19
pandemic. It forced the practice of work-from-home(WFH) to reduce the risk of infection among
employees. According to the Japan Institute forWork engagement has called significant interestsfrom academic and business communities. For
instance, the Ministry of Health, Labor and Welfareof Japan (MHLW) issued the labor economics white
paper (2018) 1) that introduced the concept of work
engagement to the Japanese readers for the first time.
The next white paper (2019) 2) devoted the volume of
82 pages, one-third of the total paper, as the special
issue of work engagement. In these two white papers,
the MHLW placed the job demand-resources (JD-R)
model as the framework for administering work
engagement and posited that under labor shortages
and high demands of work, it is the key to utilize
work resources such as decent work, willingness towork, and work-life balance in full.
Since its first appearance in the literature, the JDR model (Demerouti et al., 2001) has gained
popularity among researchers. The current version of1

with detaching oneself from work (p.74-75). The
concept of engagement has been contrasted toburnout in academic and practical sense. Burnout
refers to a state of exhaustion and cynicism toward
work, while engagement is defined as a positive
motivational state of vigor, dedication, andabsorption (Bakker et al., 2014) 9 ). Engaged
employees are enthusiastically involved in their workand show high levels of energy (Bakker et al.,2008) 10).
Measuring work engagement, Schaufeli et al.
(2003) 11) developed the Utrecht Work Engagement
Scale (UWES). UWES includes three subscales
representing vigor, dedication, and absorption by atotal of 17 items. The shortened version (UWES-9,
Schaufeli et al., 2006) 12) and international versions(Japanese version, Shimazu et al., 2008, Dutch
version, 2003, English version, 2003, German
version, 2003, French version, 2003, Norwegianversion, 2003, Swedish version, 2003, Finnish
version, 2003, Spanish version, 2003, Greek version,
2003, Russian version, 2003, Portuguese student
version, 2003, Chinese version, 2003) have beendeveloped. UWES became one of the most popular
scalesformeasuringwork
engagement
internationally.
As the UWES has been widely spread, the pros andcons has been argued among the scholars. Questions
have arisen over the factorial validity of the scale;some studies suggest that a three-factor model issuperior to a single-factor, unidimensional model(e.g., Bakker et al., 2008 13); Schaufeli et al., 2006) 14),and others criticize that the three-factor structure isambiguous (e.g., Sonnentag, 2003) 15 ), or that themodels are equivalent (Hallberg et al., 2006) 16). The
ﬁeld is clearly divided over the meaning of
engagement and how best to measure it.
Nevertheless, Schaufeli et al.’s (2002) 17) perspective
appears to be the most popular and well researched(Hakanan et al., 2010) 18), and to underlie engagement
interventions to date.
Studies of work engagement have reported its
positive effects on diverse outcomes in work settings:
job satisfaction (Hoppock, 1935) 19 ), organizational
commitment (Meyer et al., 1991) 20), organizational
citizenship behavior (OCB: Organ, 1988) 21 ), job
performance (Austin et al., 1992 22 ); Campbell
1990 23 ); Murphy et al., 1995 24 ); Schmidt et al.,1992 25)), employee health (Beehr et al., 1978 26); Frese
1985 27 ); Ivancevich, 1986 28 ); Ivancevich et al.,1980 29); Warr, 1987 30); Weitz, 1970 31)), and wellbeing (Diener, 1984 32 )). Among others, work
engagement functions as positive actions andopposites to burnout, thus, leading to productive
behaviors.

Labour Policy and Training (JILPT) panel survey
regarding the impact of COVID-19 on work and daily
life (3rd wave, 2020) 4), 29.0% of workers in Japan
have experienced WFH, of which 60.9% continue it
as of February, 2020. Looking at the starting periodof WFH, 19.0% of WFH workers experienced thispractice before pandemic, prior to the declaration of
emergency state in April, 2020. While, 72.0%
experienced it for the first time during the first stateof emergency, indicating that many employees
started WFH unwillingly by the spread of infection.
The present study tries to examine the JD-R model
under WFH environment during the COVID-19.
Specifically, the objective of this study is to examine
the validity of job demand-resources (JD-R) model(Demerouti, Bakker, Nachreiner, & Schaufeli, 2001)
by the data of employees working in the new styleaway from office in the period of COVID-19
pandemic.
The next two sections of this paper provide aliterature review and hypotheses development, while
the fourth section justifies the methodology
employed and test the hypotheses. We present resultsin the fifth section, while the last three sections
discuss implications of the study, limitation, andconclusion.

2. LITERTURE REVIEW(1) Work engagement
The term ‘engagement’ is characterized by energy,
involvement, and efficacy which are considered the
direct opposites of the three burnout dimensions, i.e.,
exhaustion, cynicism, and lack of professional
efficacy (Maslach et al.,1997) 5). By the influence of
positive psychology that focuses on human strengths
and optimal functioning rather than weaknesses andmalfunctioning (Seligman & Csikszentmihalyi,2000) 6 ), the concept of work engagement has
emerged from the research in burnout, a field of
traditional negative psychology. It is to cover theentire spectrum running from employee ill-being(burnout) to employee well-being (Maslach et al.,2001) 7).
According to Schaufeli et al. (2002) 8 ), work
engagement is defined as ‘a positive, fulfilling, workrelated state of mind that is characterized by vigor,
dedication, and absorption (p.74).’ Vigor ischaracterized by high levels of energy and mental
resilience while working, the willingness to invest
effort in one’s work, and persistence even in the faceof difficulties. Dedication indicates a sense ofsignificance, enthusiasm, inspiration, pride, andchallenge. Absorption is portrayed by being fully
concentrated and deeply engrossed in one’s work,whereby time passes quickly, and one has difficulties2(2) Job demand-resources (JD-R) model
The present study employs the JD-R model as atheoretical framework. Karasek (1979) 33) originallydeveloped the job demand-control model that can
explain the relationship between working conditions
and mental ill-being (i.e., stress and psychologicalstrain). This model postulates that both demanding
jobs and individual decisions interactively bringabout mental strain. It means that no matter how
strong the mental stress, it is a product of pressure
from job and the decisions individual workers madein the process. The major implication of thisinteractive relationship is that allowing wide range ofworker discretion can reduce mental strain without
affecting job demands and organizational
performance.
Demerouti et al. (2001) 34 ) expanded Karasek’s(1979)33) job demand-control model and the demandcontrol-support model (Johnson & Hall, 1988) 35) toscrutinize more complex relationship between workrelated stress and burnout. According to Demerouti
et al. (2001)34), job demands bear physical and mental
exhaustion when available resources on the job are
scarce. Both job demands and limited job resources
lead to disengagement and turnover interactively,
contributing to diminished job performance.
Looking at the personal resources belonging toindividuals, e.g., self-efficacy (Bandura, 1977) 36 ),self-esteem (Haltiwanger, 1989 37); Harter, 1982 38)),
and optimism (Scheier & Carver,1985 39); 1993 40)),
Xanthopoulou et al. (2007) 41) proposed that personal
resources, together with job demands and job
resources, can not only decrease physical and mental
exhaustion but increase work engagement
effectively.
Further development shifted our sights to thepositive side of worker attitudes. The early JD-R
model (Demerouti et al., 2001) 42 ) considered only
demands and resources in job side to explain
employee burnout. Bakker and Demerouti (2008)13)
have expanded a theoretical scope to work
engagement to look at the positive organizational
behavior like work engagement and job performance.
Taking job demands and resources into consideration
as the condition of successful job performance,
together with personal resources available toindividual workers, the model suggests that the
combination of work conditions and employee
factors affect the level of work engagement andperformance at work.
Consequently, the JD-R theory has been a leading
theory that can summarize the precedents of mental
ill-being as well as positive behavior in work settingscomprehensively (Lesener, Gusy & Wolter, 2019 43);
Schaufeli & Taris, 2014) 44).
Job demands in this model includes any kind ofphysical, psychological, social, and organizational
demands required for completing the job, e.g., workpressure, mental and emotional demands, andphysical strains. While job resources refer to any kindof physical, psychological, social, or organizational
assets and supports available to employees, such as
autonomy, performance feedback, social support,
supervisory coaching, etc. In addition, employee
skills and abilities usable for controlling and giving
impacts on working environment have been
introduced in the model as personal resources. Suchpersonal attributes like optimism, self-efficacy, selfesteem, and resilience (Holling, 1973) 45 ) areclassified in this category.
Bakker et al. (2008)13) diagrammatically depicted
the well-known model of work engagement. Twogroups of resources, i.e., job resources and personal
resources, work together to determine the level ofwork engagement, moderated by the level of job
demand. In other words, job and personal resources
have positive impacts on being engaged only when
job demands are high. Facing easy tasks with low job
demands, no extra-resources in job and personal sides
are needed, then employees experience noengagement in work.
The model also hypothesizes that two groups ofresources exert influence on job performance by amediation of work engagement. It means that
resources are generally a precondition of successful
performance such that engaged employees who havesufficient resources can achieve high performance,
while resources are used in vain when employees are
disengaged. Finally, the model postulated a loop to
be fed back to resources. Employees who are engaged
and performing well can create their own resources,
which foster engagement over time and create apositive spiral.
(3) Work-from-home
The dictionary of human resources management(Oxford University Press, Heery & Noon, 2017) 46)
defines working-from-home (WFH) as a style where
employees do not commute to an office but do theirjob from home. It’s a form of flexible working thatenables employees to perform their regular worktasks from home, using information andtelecommunication technology that allows instantcommunication anywhere at any time.
The Productivity Commission of AustralianGovernment (2021) published a public report
regarding WFH 47 ). According to the paper, mostworkers want to WFH, at least some of the time,
avoiding commuting time and efforts. Corporate
preferences to WHF over office work are largely
dependent on the balance between productivity andcosts of WFH.
3

+

Job

Exhaustion

Demands-+

Personal
Resources

Work

Job
Resources+

Engagement+

Fig. 1 Analytical model based on Xanthopoulou et al. (2007)41) with the hypotheses.

4. METHOD

Nonetheless, the COVID-19 pandemic forced
many workers and companies to experiment with
WFH. Pros and cons of WFH were recognized byboth workers and organizations.
Considering the Conservation of Resources (COR)
theory (Hobfoll, 1989 48), 2002) 49), Pulido-Martos etal. (2021) 50 )found that changing of working
conditions from face-to-face to full or hybrid
telework relocated the roles of job resources, such as
peer support, and personal resources, such as vigor atwork, to be fit to the pandemic situations.
WFH creates a new stress related to the digital andremote environment, which makes it difficult to berelaxed at home in non-working hours. The perceived
mingling of work and home domains can result in
unfavorable emotional responses, which in turndeplete available resources (Beal et al., 2005) 51).

(1) Data collection
This study utilized data surveyed by Persol
Research and Consulting (2020) 52). This consulting
firm has conducted the fixed-point annual survey of
10,000 workers since 2017. The data were collectedin February 2020 from 10,000 Japanese workers
ranging 15 to 69 years of age. It includes 648 workers
who are working remotely on a regular basis. The
survey participants represent the national populationin terms of sex and ages.
The survey covers such topics as skill
development, working styles, employee mindsets,
and working conditions. Specifically, questions
asked in the questionnaire include basic attribute,current job, organizational culture, nature of job,
work engagement, career change experience,capability, values, skill development, learning
environment, job stress, anxiety for nursing care, andso forth. The data was archived by the Institute ofSocial Science, University of Tokyo. The authors
borrowed it for a research purpose.

3. HYPOTHESESThis study focuses on work engagement in the
WFH environment with reference to the JD-R model.
Based on the previous theoretical analyses, wehypothesized:(2) Measures
This study employed the framework of JD-R
model developed by Xanthopoulou et al. (2007)41).
The previous study specified the JD-R model withthree groups of predictors: 1) job demands (i.e.,
workload,emotional
demands,
emotional
dissonance, and organizational changes), 2) personal
resources (i.e., organizational-based self-esteem,
optimism, self-efficacy), and 3) job resources (i.e.,autonomy, social support, supervision coaching, andprofessional development). The model identified twogroups of outcomes: 1) exhaustion (i.e., anxiety,sadness, anger, fatigue, and confusion), and 2) work
engagement (i.e., vigor, dedication, and absorption).
This study tries to investigate the relationships
between predictors and outcomes by structural
equation modeling (SEM) analyses.

Hypothesis 1: Job resources increase work
engagement both directly and indirectly mediated bypersonal resources in the WFH environment.
Hypothesis 2: Job demands strengthen exhaustionin the WFH environment.
Fig.1 shows the analytical model drawn from
Xanthopoulou et al. (2007)41). Two hypotheses wereshown in the bold lines. In this model, the job
demands and job resources were antecedent factorsof work engagement and exhaustion. The personal
resources were set as a mediator functioning in
between job resources and work engagement.

4

Table 1 Mean values, standard deviations, and correlations for each variable (N = 10,000)
1 Workload
2 Emotional demands
3 Emotional dissonance
4 Organizational changes
5 Autonomy
6 Social support
7 Supervisory coaching
8 Professional development
9 OBSE
10 Optimism
11 Self efficacy
12 Anxiety
13 Sadness
14 Anger
15 Fatigue
16 Confusion
17 Vigor
18 Dedication
19 Absorption

1 Workload
2 Emotional demands
3 Emotional dissonance
4 Organizational changes
5 Autonomy
6 Social support
7 Supervisory coaching
8 Professional development
9 OBSE
10 Optimism
11 Self efficacy
12 Anxiety
13 Sadness
14 Anger
15 Fatigue
16 Confusion
17 Vigor
18 Dedication
19 Absorption

1

2

3

4

5

6

7

8

.042**.005.158**.074**.050**-.003.166**.011-.063**.045*.150**.127**.184**.155**.127**-.051**-.006.010.608**-.108**-.507**-.496**-.555**-.217**-.522**-.500**-.319**.109**.141**.126**.188**.025**-.437**
-.321
-.343

-.219**-.455**-.513**-.458**-.282**-.405**-.394*-.262**-.018**.010**.037**.076**-.075**-.381**-.284**-.320**.287**.406**.231**.347**.217**.132**.261**.199**.150**.162**.167**.204**.144**.155**.194**.510**.685**.437**.727**.532**.516**-.038**-.132**-.066**-.110**-.008.450**.425**.430**.581**.387**.521**.454**.369**.045**-.029**-.055**-.035**.077**.394**.329**.347**.256**.848**.559**.362**-.024*-.107**-.124**-.115**.027**.429**.351**.355**.282**.268**.370**.093**-.013.015.028**.056**.304**.328**.331**

10

11

12

13

14

15

16

17

18

.550**-.152**-.275**-.230**-.223**-.111**.473**.461**.427**-.082**-.204**-.079**-.091**-.116**.485**.587**.529**.718**.576**.632**.653**-.071**-.061**-.019.663**.625**.663**-.150**-.180**-.126**.581**.532**-.117**-.113**-.082**.643**-.204**-.150**-.126**-.049**-.085**.025*.669**.701**.715**

M

SD

1.86
2.39
2.02
3.09
3.14
3.10
2.96
3.34
3.13
3.31
3.42
2.54
2.13
2.24
2.66
2.31
2.99
3.30
3.20

.834
.695
.878
.719
.817
.686
.895
1.01
.925
.818
.765
1.05
.999
.973
1.08
.958
1.05
1.02
1.04

9

.577**.454**-.078**-.187**-.144**-.145**-.038**.403**.371**.362**

Note. OBSE=Organizational-Based Self-Esteem. Range of scale is in parentheses. Cronbach’s alphas are on diagonal. **p < .01, *p< .05.
Table 2 Results of SEM
Model
df
P
GFI
AGFI
CFI
RMSEA
χ2
Model 1: Xanthopoulou et al. (2007)
16,350.8
146
.000
.834
.784
.847
.105
model Workers (N = 10,000)
Model 2: Workers (N = 10,000)
6,861.7
84
.000
.911
.873
.926
.090
Model 3: Remote workers (N = 648)
598.6
84
.000
.883
.833
.922
.097
Note. EFA=exploratory factor analysis; GFI=goodness-of fit index; AGFI=adjusted goodness-of fit index; CFI=comparative fit index;
RMSEA=root means square error of approximation.

5

Fig.2 the expanded JD-R model, N=10,000 workers.

Fig.3 the expanded JD-R model, N=648 remote workers.

5. RESULTS

resources to work engagement decreased from 1.59
to 1.47 standardized estimates. This is largely due tosmall changes of OBSE and optimism. Therefore,
Hypothesis 1 was partially supported. Similarly, job
demands (i.e., workload, emotional demands,
emotional dissonance, and organizational changes)
strengthened exhaustion (i.e., anxiety, sadness,anger, fatigue, and tangle). The latent variable of job
demands to exhaustion gained from .09 to .26
standardized estimates under WFH environment ascompared to office workers. This is presented bydecreasing of emotional demands from 8.6 to 8.3
standardized estimates. Hypothesis 2 was supported.

(1) Descriptive statistics
Means, standard deviations, and correlations
between variables, as well as the internalconsistencies of the scales are presented in Table 1.
(2) Results of SEM analyses
Results of SEM analysis regarding Xanthopoulou
et al.’ (2007)41) model (Model 1, N = 10,000) with 19
variables showed that the model did not fit well to thedata (χ2 =16,350.8, df = 146, GFI = .834, AGFI
= .784, CFI = .847, RMSEA = .105), as presented in
the Table 2. By an exploratory factor analysis, 15 out
of 19 variables were screened in the second model.
This Model 2 (N = 10,000, Fig.2) fit well to the dataof office workers (χ2 =6,861.7, df = 84, GFI = .911,
AGFI = .873, CFI = .926, RMSEA = .090). For
remote workers, Model 3 (N = 648, Fig.3) also fit
well (χ2 =598.6, df = 84, GFI = .883, AGFI = .833,
CFI = .922, RMSEA = .097).
Comparing the Models 2 and 3, results found thatamong job resources (i.e., autonomy, social support,
supervisioncoaching,
andprofessional
development), autonomy was the most important
observed variable and that job resources strengthened
work engagement (i.e., vigor, dedication, andabsorption) directly. The latent variable of job
resources to work engagement increased from .00
to .20 standardized estimates. This is mainly caused
by increase of autonomy observed variable from .63
to .70 standardized estimates.On the other hand,indirect effect from job resources through personal

6. DISCUSSION(1) Theoretical implications
The theoretical implications of this study were
threefold. First, the JD-R model presented and tested
by Xanthopoulou et al. (2007)41) was verified again
through empirical findings of this study. Results ofModel 2 showed that factors were all related in thesame directions as the original model.
Second, the JD-R model was tested with the large
data of 10,000 workers in Japan. The composition ofsex and ages were followed by the national census,
indicating that the data represented the reality oflabor force in Japan.
Lastly, this study focused on the comparison
between working-from-home workers and all types
of workers. For both groups of workers, Hypothesis
1 was partially supported and Hypothesis 2 was
supported under WFH environment. Therefore, this6

study made a significant contribution to thetheoretical development of JD-R model withexpansion.

This study has certain limitations that need to beacknowledged. First, because this study used asurvey data secondly, measures did not match
variables of Xanthopoulou et al.’ (2007) 41) model
exactly. Consequently, the Model 1 did not find agood fit. Modification to Models 2 and 3 by
exploratory factor analyses were required to fit wellto the data. Such modification may damage thevalidity and usefulness of the original theory.
Secondly, although this study compared between
WFH workers and workers, data did not cover fullperiod of COVID-19 pandemic. Data was collectedin February 2020 when COVID-19 had justoutspread.
Consequently,
respondents
who
experienced WFH were only 648 out of 10,000
workers. Potential remote works may not be allowed
to WFH owing to insufficient network and digital
environment, and to inadequate home office. The
next survey administered in 2021 may provide more
realistic information regarding the effects ofpandemic.

(2) Practical implications
The practical significance of this study is that
reducing job demands is the key to manage people
under WFH environment. Although adequate job
demands have a positive effect on work engagement,
exceeded job demands strengthen exhaustion.
Therefore, managers need to pay more attention tojob quantity and quality as well as the level of
exhaustion, particularly at the period of pandemic.
HR staffs needs to develop people management skills
in order to take care of all employees working in
office and working remotely.
Furthermore, fulfilling job resources is an
important factor for performing jobs properly in
WFH environment. Remote working is an
irreversible trend of work styles since COVID-19.
Providing autonomy and delegation is an ideal
direction for maintaining individual performance inremote work settings. Teamwork and social support
from coworkers are the sources of assistance forremote workers. Supervisory coaching helps
employees
achievesuccessful
performance
independently. All those practices function as
resources that individual workers can make use of ineither working in office or at home.

8. CONCLUSION
This study focused attention on the mechanism
how to increase work engagement based on the JD-R
model by comparing WFH and traditional work
settings. In conclusion, this study made an important
contribution to the theory of job demand andresources under the influence of pandemic.

7. LIMITATIONS AND FUTURE
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