Work Engagement and the Validity of Job Demands–Resources Model Among Nurses in Japan: A Literature Review

Abstract

Background:

Work engagement (WE) is defined as a positive, fulfilling, and work-related state of mind. Enhancing WE leads to positive outcomes in both individuals and organizations. Although cultural factors may influence the antecedents and outcomes of WE, no reviews summarized these factors among nurses, mainly in Asia. This review aimed to identify the antecedents and outcomes of WE among nurses in Japan. This may offer novel insights into the influencing factors on WE.

Methods:

A literature search was conducted using six data sources: PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), PsycINFO, Business Source Premier, Ichushi-Web, and CiNii. A total of 38 studies met the inclusion criteria. Studies that included registered nurses and/or midwives at hospitals or facilities were included in this review. The antecedents and outcomes were classified into four themes based on the job demands–resources model (JD-R model).

Findings:

Many variables in three components of the JD-R model (i.e., job resources, personal resources, and favorable outcomes) exhibited positive relationships with WE as theorized. Many other variables in job demands demonstrated negative relationships with WE as hypothesized. Many factors that were identified in this review were consistent with the JD-R model. The model in turn was found to be applicable among nurses. The antecedents and outcomes in this review were similar to those in previous studies in Western countries, while this review also revealed teamwork nursing as a possible antecedent of WE, which has not previously been reported.

Conclusion/Application to Practice:

Improving job resources (e.g., teamwork nursing) and personal resources may effectively enhance WE among Japanese nurses.

Keywords

antecedents and outcomes Japan job demands–resources model (JD-R model)nurses work engagement

Background

Traditionally, occupational mental health has focused on mental health problems such as burnout and occupational stress. However, in the recent past, studies in this domain have also been conducted within the framework of positive psychology and they have focused on human strengths and optimal functioning (Seligman & Csikszentmihalyi, 2000). One newly proposed concept related to occupational mental health within the framework of positive psychology is work engagement, which is defined as a positive, fulfilling, and work-related state of mind that is characterized by (a) vigor, (b) dedication, and (c) absorption (Schaufeli et al., 2002). Vigor is characterized by high levels of energy, mental resilience, a willingness to invest effort in work, and persistence despite difficulties. Dedication refers to a sense of significance, enthusiasm, inspiration, pride, and challenge. Absorption refers to complete concentration and happy engrossment in work (Schaufeli & Bakker, 2004).

Engaged employees have high levels of energy and are enthusiastically involved in their work (Bakker et al., 2008). Past studies have found that highly engaged employees demonstrate greater organizational commitment (Orgambídez & Almeida, 2020), exhibit more positive behaviors (Wang et al., 2019), experience lower levels of physical and psychological distress (Kunie et al., 2017; Oshio et al., 2018), and report weaker intentions to leave (Rafiq et al., 2019). Past studies have also found that antecedents such as rewards (Keyko et al., 2016; Mukaihata et al., 2019), job control (Keyko et al., 2016; Oshio et al., 2018), social support (Cao & Chen, 2019; García-Sierra et al., 2016), and self-efficacy (Bakker & van Wingerden, 2021) predict greater work engagement.

The job demands–resources model (JD-R; Hakanen & Roodt, 2010; Keyko et al., 2016) has routinely been adopted as a theoretical framework to delineate the antecedents and outcomes of work engagement. It includes a dual process through which job demands initiate a health-impairment process, which leads to negative health-related outcomes. In contrast, job and personal resources initiate a motivational process through which positive performance-related outcomes can be achieved. Burnout mediates the relationship between job demands and negative health-related outcomes in the health-impairment process, whereas work engagement mediates the relationship between resources and positive outcomes in the motivational process (Bakker & Demerouti, 2008).

When compared with workers with other occupational backgrounds, such as engineers and office workers, nurses have been found to be more likely to experience burnout because of an excessive workload, high quality-of-care requirements, and psychological stressors, such as emotional exhaustion, depersonalization, and reduced personal accomplishment (Keyko et al., 2016; López-López et al., 2019). Studies have reported that burnout leads to physical and mental health problems (López-López et al., 2019), absenteeism, and turnover intentions (Kim et al., 2019) among nurses. Over time, burnout may adversely affect the quality of care and patient satisfaction, which in turn can worsen patient health outcomes (Boamah et al., 2017). Past studies conducted in Western countries (García-Sierra et al., 2016; Keyko et al., 2016) have found that nurses’ work engagement plays an important role in promoting positive individual and organizational outcomes. Therefore, enhancing work engagement may effectively prevent burnout.

Over the past decade, several literature reviews have identified the antecedents and outcomes of work engagement among nurses, mainly in Western countries. They include job and personal resources, social support, leadership, and self-efficacy (García-Sierra et al., 2016; Keyko et al., 2016). Past studies have also found that employee work engagement is significantly lower in Japan than in Western and other Asian countries (Shimazu et al., 2010). Cultural factors may influence the antecedents and outcomes of work engagement. However, no published reviews have focused on work engagement among nurses in Eastern countries.

Japan has the largest number of hospital beds in the world (Organisation for Economic Co-operation and Development, 2020) and the nurse to patient ratio is low. Because of greater quantitative workload, nurses in Japan have more overtime hours for involuntary reasons than their counterparts in the United States (Bacon & Stewart, 2017; Kikuchi et al., 2014; Watanabe & Yamauchi, 2018). In addition, greater medical demands within the social context of rapid aging in the Japanese population (i.e., the crisis of increasing older adult population in 2025; Han, 2012) has also led to a nursing shortage. To address these issues, researchers have been paying greater attention to work engagement among Japanese nurses. Identifying the antecedents and outcomes of work engagement among Japanese nurses may offer novel insights into the factors that improve work engagement. The purpose of this literature review was to identify the antecedents and outcomes of work engagement among nurses in Japan within the framework of the JD-R model.

Methods

A comprehensive literature review was conducted using six databases. PubMed, CINAHL, PsycINFO, and Business Source Premier were searched for English articles, and Ichushi-Web and CiNii Articles were searched for Japanese articles. To search PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), PsycINFO, and Business Source Premier, we used the following search terms and method: “engagement or engagements(title)” AND “nurs*(all fields)” AND “Japan*(all fields).” To search Ichushi-Web and CiNii Articles, we used search terms that are the Japanese equivalent of “work engagement” or “engagement” or “engagements” and “nurs*.” Synonyms were identified using Medical Subject Headings (MeSH) terms. In addition, we conducted an additional literature search, using Google Scholar with the same terms. No time restrictions were specified when the databases were searched, and all papers published up to April 5, 2020 were included.

The inclusion criteria were as follows: (a) studies that quantitatively examined the association between work engagement and its antecedents and outcomes among nurses and/or midwives in hospitals, facilities, or home-visiting nursing stations in Japan; (b) studies published in academic journals; and (c) studies published in English or Japanese. We did not restrict the search based on the countries of the authors if they had quantitatively examined the antecedents and outcomes of work engagement among Japanese nurses. The exclusion criteria were as follows: (a) proceedings or brief reports that did not provide details about the research methods used, (b) studies in which more than half of the participating professionals were not nurses, (c) studies that compared the work engagement scores of nurses with those with other occupations, and (d) studies that examined the antecedents and outcomes of work engagement among only public health nurses. The fourth exclusion criterion was used because there are differences in work characteristics and environments, including working styles, between nurses at hospitals/facilities and public health nurses.

The first screening was conducted based on the titles and abstracts of all the retrieved articles. During this phase, articles that did not meet the inclusion criteria were excluded. The remaining articles were subjected to a second screening, which was conducted based on the contents of the full-text articles. Ambiguities were discussed between the authors until consensus was reached.

We appraised the included studies based on the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies (Moola et al., 2020). One of the eight checklist items that was not used in this study pertains to the objective and standard criteria for the measurement of patient conditions, which is not relevant. The remaining seven items were used to evaluate the methodological aspects of each study, namely, whether (a) the criteria for inclusion in the sample were clearly defined, (b) the study subjects and the setting were described in detail, (c) the exposure was measured in a valid and reliable way, (d) confounding factors were identified, (e) the strategies to deal with confounding factors were stated, (f) the outcomes were measured in a valid and reliable way, and (g) the appropriate statistical analysis was used. Regarding (h), we took into account whether the purpose of the analyses and/or the definitions of the explanatory and outcome variables, as well as how to analyze data, were described. Ambiguities in the ratings were discussed between the authors and consensus was reached. Quality appraisal was not used to make decisions about inclusion; hence, none of the studies were excluded based on quality appraisal. The questions were answered with choices of “Yes,” “No,” “Unclear,” or “Not applicable.” We calculated percentages of “Yes” responses by each study, and the quality of each study was interpreted as percentages as follows: high = 80% to 100%, fair = 50% to 79%, and low = <50% (Lam et al., 2019; Poudel et al., 2018).

We extracted the following information from the selected studies: study characteristics (first author, year of publication, study design, measurement of work engagement, and the theoretical model used in the study, including the JD-R model), number of participants, participant characteristics (age, gender, and settings), variables examined in relation to work engagement, and the main findings (variables that were significantly related to work engagement).

Framework for Data Classification and Synthesis

The antecedents and outcomes of work engagement that were identified in the reviewed studies could be situated within the JD-R model (Bakker & Demerouti, 2008; Goering et al., 2017). Therefore, they were classified into four themes in accordance with the JD-R model. The four themes included three antecedent-related themes, including job demands, job resources, and personal resources, and one outcome-related theme (i.e., outcomes of work engagement), such as lower psychological distress (Kunie et al., 2017) and care quality (Keyko et al., 2016). Next, job demands were classified into (a) challenging demands, and (b) hindrance demands based on the methodologies used in past studies (Goering et al., 2017; Lepine et al., 2005; Mauno et al., 2007). In addition, in accordance with the definition of job and personal resources and the classification used in past studies (García-Sierra et al., 2016; Hakanen & Roodt, 2010; Hobfoll et al., 2003; Kanemune & Takahashi, 2019; Keyko et al., 2016; Schaufeli & Bakker, 2004), job resources were subdivided into (a) organizational, (b) interpersonal relationship, and (c) task factors. Similarly, personal resources were subdivided into professional, psychological, family, and personal behavioral factors. All the variables analyzed in each study were qualitatively sorted based on the aforementioned themes. In addition, we synthesized the antecedents and outcomes of work engagement, which met all the following criteria: the antecedents and outcomes of work engagement, which (a) were examined in the whole sample in each study; (b) were examined in two or more studies, that is, the factors that were examined in only one study were excluded; and (c) exhibited higher than 70% of consistency of positive or negative associations between work engagement and the antecedents and outcomes. For example, support from supervisors, as one of job resource factors, was examined for its association with work engagement in six studies (Asahina et al., 2019; Mukaihata et al., 2019; Naruse et al., 2016, 2013; Ogiso & Itoh, 2019; Sato & Miki, 2014). All of the six articles examined the association in the total sample in each study. Support from supervisors was significantly and positively related to work engagement in all six studies. In this case, the consistency of positive association was 100%. Thus, support from supervisors met the criteria to be synthesized as the antecedents or outcomes of work engagement.

Findings

Search results

The electronic database search yielded 228 articles (Figure 1). After removal of duplicates, 164 articles were screened based on titles and abstracts, following the inclusion and exclusion criteria. This process yielded 41 articles, which were subjected to full-text review. Of these 41 full-text articles, 38 met the eligibility criteria and were included in this review.

Figure 1.

Flow diagram illustrating the literature search and selection process.

Quality appraisal ratings for each study are summarized in Table 1. Fifteen of the 38 studies had “high,” 16 had “fair,” and seven had “low” methodological quality. The quality appraisal process revealed that approximately 40% of the studies met most of the quality criteria. Overall, the included articles appropriately described the participants and study settings, the exposure and outcomes variables were measured in a valid and reliable manner, and appropriate statistical analyses were used. Fifteen out of 38 studies defined confounding factors, whereas 23 did not.

Table 1.

Summary of the Quality Appraisal for the Included Studies Using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies

Author (year)	1. Were the criteria for inclusion in the sample clearly defined?	2. Were the study subjects and the setting described in detail?	3. Was the exposure measured in a valid and reliable way?	4. Were confounding factors identified?	5. Were strategies to deal with confounding factors stated?	6. Were the outcomes measured in a valid and reliable way?	7. Was appropriate statistical analysis used?	Appraisal(high/fair/low) [Score %]
Matsumoto et al (2010)	Yes	Yes	No	Yes	Yes	Yes	Yes	High [85.7%]
Kawauchi and Ohashi (2011)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Kubota et al (2011)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Fukuoka et al (2013)	Yes	No	Yes	No	No	Yes	Yes	Fair [57.1%]
Naruse et al. (2013)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Fukuoka et al. (2014)	Yes	No	Yes	No	No	Yes	No	Low [42.9%]
Sakai et al. (2014)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Sasaki et al. (2014)	Yes	Yes	No	No	No	Yes	Yes	Fair [57.1%]
Sato and Miki (2014)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Kagata et al. (2015)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Sasaki et al. (2015)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Inaba and Hioki (2016)	Yes	Yes	Yes	No	No	Yes	No	Fair [71.4%]
Ishizuka and Miki (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Itabashi et al. (2016)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Nakamura and Yoshioka (2016)	Yes	Yes	No	No	No	Yes	Yes	Fair [57.1%]
Naruse et al. (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Inaba and Hioki (2017)	Yes	Yes	No	No	No	Yes	Yes	Fair [57.1%]
Inoue (2017)	Yes	Yes	No	No	No	Yes	Yes	Fair [57.1%]
Kaneko and Konagaya (2017)	Yes	No	Yes	No	No	Yes	Yes	Fair [57.1%]
Kunie et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Mineta (2017)	Yes	No	Yes	No	No	Yes	No	Low [42.9%]
Suto and Ishii (2017)	Yes	Yes	Yes	No	No	Yes	No	Fair [57.1%]
Toyama and Mauno (2017)	Yes	No	Yes	Yes	Yes	Yes	Yes	High [85.7%]
Inaba (2018)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Ito et al. (2018)	Yes	Yes	No	No	No	Yes	No	Low [42.9%]
Matsuo (2018)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Saito et al. (2018)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Watanabe and Yamauchi (2018)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Asahina et al. (2019)	Yes	Yes	No	No	No	Yes	No	Low [42.9%]
Inaba and Mirbod (2019)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Kanemune and Takahashi (2019)	Yes	Yes	Yes	No	No	Yes	Yes	Fair [71.4%]
Morishima et al. (2019)	Yes	Yes	No	No	No	Yes	No	Low [42.9%]
Mukaihata et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Ogiso and Itoh, (2019)	Yes	Yes	No	No	No	Yes	No	Low [42.9%]
Okada et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Shingu and Ambo (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Watanabe and Yamauchi (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High [100%]
Inoue et al. (2020)	Yes	Yes	No	No	No	Yes	Yes	Fair [57.1%]

Note. The questions were answered with choices of “Yes,” “No,” “Unclear,” or “Not applicable.” The overall scores for each article were calculated as percentages of “Yes” and the quality of each study was rated as high (80%–100%), fair (50%–79%), or low (< 50%; Lam et al., 2019; Poudel et al., 2018).

A summary of the articles included in this review is presented in Table 2. The 38 articles were published between 2010 and 2020. Among them, most articles (68.4%) were published after 2015 (data not shown). Thirteen articles were published in English and 25 were published in Japanese.

Table 2.

Characteristics of the Articles Included in This Review and the Main Variables Significantly Related to Work Engagement

Author (year)Study design, measurement for work engagementTheoretical model	Subjects (number)M age ± SD Female (%)Settings	Variables examined for their relationship with work engagement	Main statistical analysis	Main findings (variables significantly and positively/negatively related to work engagement)
	Subjects (number)M age ± SD Female (%)Settings		Main statistical analysis	Job demands[subtypes]	Job resources[subtypes]	Personal resources[subtypes]	Outcomes	Others
Matsumoto et al. (2010) Cross-sectional^b	RN (N = 828) 33.7 ± 9.0, 100% one hospital	Demographics in nurses	Multiple linear regression analysis, ANCOVA					Age (+), marriage (+)Working pattern (+)
Kawauchi and Ohashi (2011) Cross-sectional^a	RN and MW(N = 114)32.1 ± 7.6,not statedsix hospitals	Job satisfaction and turnover intention	Unpaired t test, Pearson’s product–moment correlation coefficient			[P2]No turnover intention (+), job satisfaction (+)
Kubota et al. (2011) Cross-sectional^b	RN (N = 447)31.0 ± 7.87, 95.7%three hospitals	Sleep quality and work performance	Structural equation modeling				Sleep quality (+), work performance (+)
Fukuoka et al. (2013) Cross-sectional^b (b)	RN (N = 249)36.4 ± 11.3, 100%one hospital	Job stress and turnover intention	χ² test, ANOVA	High strain [no details] (−)		[P2]No turnover intention (+)
Naruse et al. (2013) Cross-sectional^b (a)	RN (N = 175)43.4 ± 7.2, 100%28 HVNS	Job-related variables	Multiple linear regression analysis		[JR2]Support from supervisors (+)[JR3]Job control (+)	[P3]Work–family positive relationship (+)		Higher job position (+)
Fukuoka et al. (2014) Cross-sectional^b	RN (N = 249)36.4 ± 11.3, 100%one hospital	Turnover intention and sleep quality	χ² test, correlation analysis, ANOVA			[P2]No turnover intention (+)	Sleep quality (+)
Sakai et al. (2014) Cross-sectional^b	RN (N = 184)44.0 ± 7.9, 99.5%62 HVNS	Attitudes toward caring for dying patients and their families	Multiple linear regression analysis				Attitudes toward caring (+)
Sasaki et al. (2014) Cross-sectional^b	RN managers(N = 749)49.2 ± 7.8, 94.9%749 HVNS	The personal characteristics and organizational factors	t test, one-way ANOVA		[JR1]Business condition (+)[JR3]Meaning of work (+), Self-enlightenment (+)			Year of managing experiences (+)
Sato and Miki (2014) Cross-sectional^b	RN (except managers; N = 1,194)Not stated,90.7%four hospitals	Stress-coping profile and social support	Hierarchical multiple regression analysis	[JD2]Role ambiguity (−)Role conflict (−)	[JR2]Support from supervisors (+)	[P1]Active coping (+)Changes in viewpoints (+)
Kagata et al. (2015) Cross-sectional^b	RN (except managers; N = 306)Not stated,100%one hospital	Each element of emotional labor and stress response	Pearson’s product–moment correlation coefficient, multiple linear regression analysis			[P1]Exploring and understanding (+)Suppressed expression (−)	Physical and mental stresses (−)
Sasaki et al. (2015) Cross-sectional^b (c)	RN managers(N = 652)48.9 ± 7.7, 95.9%652 HVNS	Job retention and structural empowerment	Structural equation modeling		[JR1]Structural empowerment (+)		Intention to keep working (+)
Inaba and Hioki (2016) Cross-sectional^b	RN (N = 191)34.5 ± 9.0, 100%one hospital	Sleep quality and burnout	Single correlation analysis, partial correlation analysis				Sleep quality (+)Burnout (−)
Ishizuka and Miki (2016) Cross-sectional^a	RN (N = 1,014)32.5 ± 9.0, 93.7%three hospitals	Personal resources	Multiple linear regression analysis			[P2]Optimism (+)
Itabashi et al. (2016) Cross-sectional^b	RN (N = 324)LPN (N = 4)43.4 ± 10.7, 60.9%two psychiatric hospitals	Emotional involvement with patients	Spearman rank correlation coefficient			[P1]Exploring and understanding (+), Deep adjustment (+), Suppressed expression (−) Polite tone and behavior (+)
Nakamura and Yoshioka (2016) Cross-sectional^b	RN and MW(N = 433)33.1 ± 9.3, 94.9%one hospital	Resources factors and demographic variables	Unpaired t test, ANOVA		[JR2]Work atmosphere (+)Social support (+)Presence of lively nurses (+)	[P2]Intention to keep working (+)No turnover intention (+)[P3]Having children (+)		Age (+)Higher job position (+)
Naruse et al. (2016) Cross-sectional^b (a)	RN (N = 93)43.3 ± 8.0, 97.8%15 HVNS	Relational coordination and human resources	Pearson’s product–moment correlation coefficient, multiple linear regression analysis		[JR2]Support from supervisors/coworkers (+), Human resources (+)
Inaba and Hioki (2017) Cross-sectional^b	RN (N = 188)34.5 ± 9.0, 100%one hospital	Frequency of laughing	multiple logistic regression analysis				Frequency of mirthful laughs (+)
Inoue (2017) Cross-sectional^b	RN (except Managers; N = 29),Not stated,Not Stated,one hospital	The level of fatigue and sleep difficulty	t test, ANOVA			[P2]The level of fatigue (−), Sleep difficulty (−)
Kaneko and Konagaya (2017) Cross-sectional^b	RN (N = 102)LPN (N = 81)Not stated,90.7%long-term care facilities	Job resources factors, view of health and job satisfaction	Structural equation modeling		[JR3]Meaning of work (+)	[P2]View of health (+)	Job satisfaction (+)
Kunie et al. (2017) Cross-sectional^b (d)	RN (N = 789)31.4 ± 8.51, 96.2%three hospitals	Manager’s communication behavior, job demands, and job resources	Hierarchical multiple regression analysis	Job demands [no details] (−)	[JR1]Head nurse’s communication behavior (+)[JR2]Support from coworkers (+)[JR3]Job control (+)		Psychological distress (−)	Year of nursing experience(+)
Mineta (2017) Cross-sectional^b	RN (N = 246)34.8 ± 8.3, 94.7%hospitals	Approval, self-determination, and individual factors	Multiple linear regression analysis		[JR2]Approval factors (+)[JR3]Self-determination factors (+)	[P2]Optimism (+)
Suto and Ishii (2017) Cross-sectional^a	RN (N = 334)Not stated,95.2%123 hospitals	Self-efficacy	Multiple linear regression analysis			[P2]Self-efficacy (+)
Toyama and Mauno (2017) Cross-sectional^a (a)	RN (N = 489)39.5 ± 11.0, 82.0%special nursing home	Social support, emotional intelligence, and creativity	Multilevel moderation analysis		[JR2]Social support (+)		Creativity (+) Emotional intelligence (+)
Inaba (2018) Cross-sectional^b	RN (N = 318)34.5 ± 9.0, 100%one hospital	Subjective well-being	Multiple logistic regression analysis			[P2]Subjective well-being (+)
Ito et al. (2018) Cross-sectional^b	RN (N = 1275)33.5 ± 9.3, 91.5%13 hospitals	Job resources and personal resources	Pearson’s product–moment correlation coefficient, multiple linear regression analysis		[JR2]Teamwork nursing (+), Leadership for support nurses (+),Collaboration with other professions (+)[JR3]Self-enlightenment (+)Job control (+)	[P1]Active coping (+)[P2]Self-efficacy (+)[P3]Private supports (+), Having children (+)[P4]Health promoting behavior (+)
Matsuo (2018) Cross-sectional^b (a)	RN (N = 150)/PHN (N = 123)Not stated,91.8%one hospital/one prefecturalgovernment	Learning goal orientation, reflection, and job crafting	Multilevel moderation analysis		[JR3]Learning goal orientation (+) Reflection (+)	[P1]Job crafting (+)
Saito et al. (2018) Cross-sectional^b (a)	RN (N = 2,162)LPN (N = 1,088)44.2 ± 11.0, 94.2%257 long-term care hospitals	Workplace environment factors and nurses’ work values	Multiple linear regression analysis	[JD2]Skill-underutilization (−)	[JR1]Adequate education system (+)[JR2]Workplace atmosphere (+), Collaboration with other professions (+)[JR3]Intrinsic work values (+)
Watanabe and Yamauchi (2018) Cross-sectional^b (a)	RN (N = 1,075)31.3 ± 8.4, 93.6%four hospitals	The factors of overtime work	Multilevel structural equation modeling	[JD1]Overtime work due to conformity/workload/money and fame (−),Overtime work due to self-development/fun of work (+)
Asahina et al. (2019) Cross-sectional^b	RN (N = 173)LPN (N = 107)43.2 ± 11.2, 73.9%four psychiatrichospitals	Job resources and personal resources	Mann–Whitney U test, Kruskal–Wallis test		[JR1]Environment accepting change (+)[JR2]Presence of model staffs (+), Support from supervisor (+)[JR3]Job control (+)	[P1]Exploring and understanding (+), Attention to expressive feelings (+), Polite tone and behavior (+), Deep adjustment (+)
Inaba and Mirbod (2019) Cross-sectional^b	RN (N = 324)35.7 ± 11.3, 100%one hospital	Job crafting	Pearson’s product–moment correlation coefficient, Kruskal–Wallis test			[P1]Job crafting (+)
Kanemune and Takahashi (2019) Cross-sectional^c (a)	RN (exceptmanagers)(N = 589)48.7 ± 5.2, 100%87 hospitals	Menopausal symptoms and health promotion behavior	Pearson’s product–moment correlation coefficient, multiple regression analysis, covariance structure analysis			[P2]Self-efficacy (+)[P4]Health promotion behavior (+)	Menopausal symptoms (−)
Morishima et al. (2019) Cross-sectional^b	RN (except managers; N = 460)37.0,89.8%,seven hospitals	Attitudes toward work	Mann–Whitney U test, Kruskal–Wallis test		[JR2]Rewards (+)[JR3]Career direction (+)	[P2]Intention to keep working (+), Job satisfaction (+)
Mukaihata et al. (2019) Cross-sectional^b (a)	RN (N = 356)LPN (N = 69)42.1 ± 11.4, 66.8%nine psychiatrichospitals	Patient’s unpleasant attitude, social support, and emotional intelligence	Hierarchical multiple regression analysis, Pearson’s product–moment correlation coefficient	[JD2]Patient’s unpleasant attitudes (−)	[JR2]Reward (+),Support fromsupervisors/coworkers (+),Collaboration withother professions(+)[JR3]Job control (+)	[P1]EmotionalIntelligence(+)
Ogiso and Itoh (2019) Cross-sectional^b	RN (N = 570)42.0 ± 10.0, 94.0%137 community-based care hospitals	Workplace environment factors	Spearman rank correlation coefficient, multiple linear regression analysis		[JR1]Adequate education systems (+)[JR2]Teamwork nursing (+), Support from supervisor (+) Collaboration with other profession (+), Exchange of ideas among occupations (+)	[P3]Private support (+)
Okada et al. (2019) Cross-sectional^b (a)	RN (N = 612)34.0,100%three hospitals	Multiple roles in family life, job demands, and personalresources	Hierarchical multiple regression analysis	[JD1]Qualitative and quantitative workload (−)		[P1]Changes inviewpoints (+)Active coping (+)Avoidance andsuppression (−)[P3]Work–family positive relationship (+)		Year of nursing experience(+)Multiple roles in family life (+)
Shingu and Ambo (2019) Cross-sectional^b	RN (N = 508)42.0 ± 11.2, 90.7%12 hospitals	Structural empowerment and affective commitment	Spearman rank correlation coefficient, structural equation modeling		[JR1]Structural empowerment (+)		Affective commitment (+)
Watanabe and Yamauchi (2019) Cross-sectional^b	RN (N = 1,075)31.1, 93.6%four hospitals	The reasons of overtime work	ANCOVA	[JD2]Overtime work by intrinsicmotivation (+)Overtime work by involuntary/conformity/workload (−)
Inoue et al. (2020) Cross-sectional^b	RN (N = 546)Not stated,100%one hospital	Job demands, coping skill, and recovery activity	Multiple linear regression analysis	[JD1]Quantitative workload (−)[JD2]Role conflict (−)		[P1]Active coping (+)		Recovery activities (talking with others/using SNS or email during break time) (+)

Note. Types of the conceptual models used by the articles reviewed in this study: (a) job demands–resource model, (b) job demands–control model, (c) work empowerment theory, and (d) motivating language theory. Job demands include two subtypes of job demands: Challenging demands [JD1] and Hindrance demands [JD2]. Job resources include three subtypes of job resources: Organizational factors [JR1], Interpersonal relationship factors [JR2], and Task factors [JR3]. Personal resources include four subtypes of personal resources: Professional factors [P1], Psychological factors [P2], Family factors [P3], and Personal behavioral factors [P4]. Others signifies the variables (i.e., demographics and recovery experiences–related variables) that could not be applicable to the job demands–resources model. (+) means significant and positive association with work engagement, (−) means significant and negative association with work engagement. RN = registered nurse; ANCOVA = analysis of covariance; MW = midwives; ANOVA = analysis of variance; HVNS = home visiting nursing station; JR = job demands–resources model; PHN = public health nurse; LPN = licensed practical nurse; SNS = simple notification service.

The Japanese version of Utrecht Work Engagement Scale (17 items). ^bThe Japanese short version of the Utrecht Work Engagement Scale (nine items). ^cThe Japanese ultra-short version of the Utrecht Work Engagement Scale (three items).

The mean age of the study participants ranged from 31.0 (Kubota et al., 2011) to 49.2 years (Sasaki et al., 2014). Among 38 studies, 31 studies were conducted in hospitals, five studies in home-visiting nursing stations, one study in long-term care facilities, and one study in nursing homes, respectively. A majority of the participants were women, except in studies conducted among nurses in psychiatric hospitals. Six studies (Ishizuka & Miki, 2016; Ito et al., 2018; Saito et al., 2018; Sato & Miki, 2014; Watanabe & Yamauchi, 2018, 2019) included at least 1,000 participants.

All the studies examined the association between work engagement and related variables using a cross-sectional design. Of the 38 studies, 18 used (hierarchical) multiple linear regression analysis, six used structural equation modeling, and two used multilevel moderation analysis. In contrast, 12 studies used correlation analysis or analysis of variance/analysis of covariance/Kruskal–Wallis test. Twelve of the 38 articles specified the theoretical models that were adopted. Of these, nine studies used the JD-R model. The short form of the Japanese version of the Utrecht Work Engagement Scale (Shimazu et al., 2008) was used to assess work engagement in 33 of the 38 studies.

Antecedents of work engagement

A total of 65 variables were identified as antecedents of work engagement, which exhibited significant and positive/negative associations with work engagement. Each variable was classified into job resources, personal resources, and job demands.

With regard to job resources, each variable was subsumed under the following three factors: (a) organizational factors, (b) interpersonal relationship factors, and (c) task factors. Five variables were classified into organizational factors. Among them, structural empowerment (Sasaki et al., 2015; Shingu & Ambo, 2019) and adequate educational systems (Ogiso & Itoh, 2019; Saito et al., 2018) were consistently and positively associated with work engagement. Twelve variables were classified into interpersonal relationship factors. Among them, some factors, such as teamwork nursing (Ito et al., 2018; Ogiso & Itoh, 2019), support from supervisors (Asahina et al., 2019; Mukaihata et al., 2019; Naruse et al., 2016, 2013; Ogiso & Itoh, 2019; Sato & Miki, 2014), work atmosphere (Nakamura & Yoshioka, 2016; Saito et al., 2018), and social supports (Nakamura & Yoshioka, 2016; Toyama & Mauno, 2017) were consistently and positively related to work engagement. Among eight variables classified into task factors, meaning of work (Kaneko & Konagaya, 2017; Sasaki et al., 2014), job control (Asahina et al., 2019; Ito et al., 2018; Kunie et al., 2017; Mukaihata et al., 2019; Naruse et al., 2013), and self-enlightenment (Ito et al., 2018; Sasaki et al., 2014) exhibited consistent and positive relationships with work engagement.

With regard to personal resources, each variable was subsumed under the following four factors: (a) professional factors, (b) psychological factors, (c) family factors, and (d) personal behavioral factors. Ten variables were classified into professional factors. Among them, the positive strategies toward stress and emotions, that is, active coping (Inoue et al., 2020; Ito et al., 2018; Okada et al., 2019; Sato & Miki, 2014), changes in viewpoints (Okada et al., 2019; Sato & Miki, 2014), job crafting (Inaba & Mirbod, 2019; Matsuo, 2018), and emotional intelligence (Mukaihata et al., 2019) were positively related to work engagement. The negative strategies, such as suppressed expressions (Itabashi et al., 2016; Kagata et al., 2015) and avoidance and suppression (Okada et al., 2019) were negatively associated with work engagement. Regarding psychological factors, nine factors were identified. Among them, optimism (Ishizuka & Miki, 2016; Mineta, 2017), self-efficacy (Ito et al., 2018; Kanemune & Takahashi, 2019; Suto & Ishii, 2017), no turnover intention (Fukuoka et al., 2013, 2014; Kawauchi & Ohashi, 2011; Nakamura & Yoshioka, 2016), intention to keep working (Morishima et al., 2019; Nakamura & Yoshioka, 2016), and job satisfaction (Kawauchi & Ohashi, 2011; Morishima et al., 2019) showed consistent and positive association with work engagement. As family factors, having children (Ito et al., 2018; Nakamura & Yoshioka, 2016), private support (Ito et al., 2018; Ogiso & Itoh, 2019) and work–family positive relationships (Naruse et al., 2013; Okada et al., 2019) were identified. All three variables were positively associated with work engagement. One factor (i.e., health promoting behaviors) was classified into personal behavioral factor. Two studies reported that nurses with many health promoting behaviors had higher work engagement (Ito et al., 2018; Kanemune & Takahashi, 2019).

With regard to job demands, a total of 17 variables were identified, which related to work engagement. Among them, quantitative workload was consistently and negatively related to work engagement (Inoue et al., 2020; Okada et al., 2019). Variables subsumed under hindrance demands (e.g., role ambiguity, Sato & Miki, 2014, and unpleasant patient attitudes, Mukaihata et al., 2019) were negatively related to work engagement, while each variable was examined in only one study. One study conducted by Watanabe and Yamauchi (2018) reported that overtime work due to workload was negatively associated with work engagement, while overtime work when work is fun was positively related to work engagement.

In addition to the factors included in the JD-R model, demographic characteristics were also examined in relation to work engagement in many studies. Among them, a higher job position showed a consistent and positive relationship with work engagement (Nakamura & Yoshioka, 2016; Naruse et al., 2013). Recovery activities (i.e., talking with others and using SNS/email during a break time) were positively related to work engagement (Inoue et al., 2020), which factor could not be applicable to four themes in the JD-R model.

Outcomes of work engagement

A total of 12 variables were identified as outcomes of work engagement. High work engagement exhibited significant associations with each favorable outcome (Figure 2). Among them, sleep quality was positively related to work engagement consistently in all three studies (Fukuoka et al., 2014; Inaba & Hioki 2016; Kubota et al., 2011). The other 11 variables, such as attitudes toward caring (Sakai et al., 2014) and emotional intelligence (Toyama & Mauno, 2017), were examined in only one study, respectively.

Figure 2.

The antecedents and outcomes of work engagement among nurses in Japan within the framework of the JD-R model.

Discussion

In this literature review, we identified the antecedents and outcomes of work engagement among nurses in Japan. These factors were classified into four themes using the JD-R model. Variables subsumed under the following three factors included in the JD-R model consistently showed positive relationships with work engagement: (a) job resources (e.g., nursing teamwork, the meaning of work, job control, and support from supervisors), (b) personal resources (e.g., job crafting, changes in viewpoints, intentions to keep working, job satisfaction, self-efficacy, and optimism), and (c) outcomes (i.e., sleep quality). In contrast, only one variable related to job demands (i.e., quantitative workload) was consistently and negatively related to work engagement. In addition to the factors included in the JD-R model, a higher job position exhibited a positive relationship with work engagement.

The findings of this review are largely consistent with the antecedents and outcomes of work engagement that have been identified in past studies conducted among nurses in Western countries. For example, the meaning of work and job crafting consistently showed a positive relationship with work engagement. This finding concurs with the results of past studies conducted in Western countries (Bakker, 2017; Deetz et al., 2020). Several past interventional studies have focused on job crafting to enhance employee work engagement (Frederick & VanderWeele, 2020; Sakuraya et al., 2016). Future studies should aim to conduct such interventional studies among nurses.

This review revealed that there is a significant positive relationship between nursing teamwork and work engagement among Japanese nurses. Studies conducted in Western countries have yielded inconsistent results. (Keyko et al., 2016; Montgomery et al., 2015). Factors related to work engagement vary across countries because of cultural influences (Mukae, 2019). Therefore, the positive relationship observed in this study may be attributable to Japanese cultural values, which emphasize group harmony and self-definition through relationships with others (Komiya et al., 2016). Thus, promoting nursing teamwork as a job resource may effectively improve work engagement, at least among Japanese nurses.

Many variables, including emotional intelligence, recovery experiences (i.e., talking with others and/or using SNS or email during break time), and outcomes of work engagement (e.g., attitudes toward caring), were examined in only one or two of the studies included in this review. This indicates that sufficient evidence has not yet been accumulated. For example, emotional intelligence is defined as “the ability to monitor one’s own and others’ feelings and emotions, to discriminate among them, and to use this information to guide one’s thinking and actions” (Salovey & Mayer, 1990, p.189). The findings of past studies conducted among nurses in China and Spain suggest that enhancing emotional intelligence can improve work engagement (Pérez-Fuentes et al., 2018; Zhu et al., 2015). Accordingly, intervention programs that enhance emotional intelligence have been developed (Sharif et al., 2013). Recovery experiences restore the psychosocial resources drained by stressful experiences to baseline levels (Kubota et al., 2014). However, this variable is not included in the conventional JD-R model. Such factors play an important role in maintaining and improving work engagement. Therefore, future studies should examine the relationship between these variables and work engagement among nurses.

Past studies conducted in Western countries have also revealed that greater work engagement can lead to positive individual and organizational outcomes such as improved health, lower turnover rates, and better quality of care (García-Sierra et al., 2016; Keyko et al., 2016). However, only sleep quality was identified as a robust outcome in this review. Past findings suggest that positive employee outcomes nurture the creation of their own resources, which in turn enhance work engagement, thereby creating a positive gain spiral. (Bakker & Demerouti, 2008; Salanova et al., 2010). Therefore, future studies should explore other outcome variables among Japanese nurses. Similarly, few studies conducted among Japanese nurses examined the relationship between nursing-specific variables, such as their passion for nursing, critical reflective practice, and a patient-centered orientation and work engagement (Begat et al., 2004; Keyko et al., 2016). Nursing practice has an impact on the lives of patients and their families, and nurses provide support during patient decision-making. Some nursing-specific variables, such as nursing identity, strength-oriented attitudes, and nursing competency, may enable nurses to derive greater pride from their work and experience greater work engagement.

With regard to the job demands theme, only quantitative workload was a robust antecedent to work engagement. Some variables related to this theme, such as overtime work, exhibited inconsistent relationships. Past studies conducted among employees in Japan have found that there is a nonlinear relationship between the number of hours that one works and job satisfaction (Kuroda & Yamamoto, 2019). In addition, moderate and appropriate job demands, which may enhance work engagement, may differ among workers, including nurses (Goering et al., 2017). Because the optimal level of job demands may be influenced by individual personality traits and perceptions of work (Kuroda & Yamamoto, 2019; Oshio et al., 2018), the inconsistent relationship between job demand–related factors and work engagement is not surprising.

In this review, 12 of the 38 articles specified the theoretical models that were adopted. Some articles in which the adopted theoretical model was not specified did not define work engagement appropriately. These studies may have examined the factors related to work engagement without concrete hypotheses formulated based on a sound model. Many of the antecedents and outcomes of work engagement that were identified in this review were applicable to the JD-R model. Thus, the present conclusions support to be applicable to the JD-R model among nurses in Japan. Instead of interpreting results based on assumptions and preconceived notions, the JD-R model can be used to comprehensively understand work engagement and related factors. (Bakker et al., 2011; Bordage, 2009; Sakai et al., 2012). Therefore, future studies should adopt such models and examine additional variables that are not included in these models. In addition, all the studies included in this review adopted a cross-sectional design. It is said that changes in resources will translate into corresponding changes in work engagement, and steady high job resources will also predict higher work engagement one year or several years later (Adriaenssens et al., 2015; Hakanen et al., 2008). For example, Adriaenssens et al. (2015) found that high job control and social supports predicted higher work engagement 18 months later. However, few studies with longitudinal design on work engagement among nurses have been conducted so far (Sakai et al., 2012). Therefore, future well-designed studies including longitudinal design for one year or several years to examine the causal association between work engagement and related factors are needed.

This review is the first study which identified the antecedents and outcomes of work engagement among nurses in Japan. The findings offer novel insights into the factors that enhance work engagement and the positive effects of work engagement on Japanese nurses.

Since a comprehensive strategy was used to search and retrieve articles, we believe that most eligible articles were included in this review, while some articles may not have been included. The data presented in this review do not seem to appear suitable for rigorous analyses such as meta-analyses due to too high heterogeneity. Instead, a thorough systematic review in the future may reveal the factors that are related to work engagement.

Conclusion

This review identified the antecedents and outcomes of work engagement among nurses in Japan within the framework of the JD-R model. The antecedents and outcomes of work engagement that were identified in this review were largely similar to those identified in past studies conducted among nurses in Western countries. However, in contradistinction to the findings of past studies conducted in Western countries, nursing teamwork emerged as a possible antecedent of work engagement in this review. This positive relationship may be attributable to Japanese cultural values, which emphasize group harmony. All the studies included in this review examined the association between work engagement and related variables using a cross-sectional design. Therefore, future longitudinal studies to examine the causal associations between these variables are needed.

The findings of this review have implications for work engagement among nurses in Japan. Specifically, the findings suggest that improving the work environment (e.g., nursing teamwork, support from supervisors, the meaning of work, job crafting, job control, and self-efficacy) may effectively improve work engagement. The findings also suggest that greater work engagement may have a positive effect on sleep quality among nurses. Thus, improving work engagement may effectively enhance the mental health of nurses.

In Summary

The purpose of this literature review was to identify the antecedents and outcomes of work engagement among nurses in Japan within the JD-R model.

A total of 38 articles were reviewed. The many antecedents and outcomes of work engagement were classified into four themes using the JD-R model. The model in turn was found to be applicable among nurses in Japan.

The antecedents and outcomes of work engagement that were identified in this review are largely similar to those identified in past studies conducted in Western countries. However, in contradistinction to the findings of past studies conducted in Western countries, nursing teamwork was related to work engagement consistently.

This positive relationship may be attributable to Japanese cultural values, which emphasize group harmony. Promoting nursing teamwork as a job resource may effectively improve work engagement among Japanese nurses.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by JSPS KAKENHI under Grant Number 19K11216.

ORCID iD

Yuichi Kato

Author Biographies

Yuichi Kato, BSN, RN, PHN, is a master course student in the Department of Nursing, Graduate School of Health Sciences, at Kobe University. His research interests include work engagement and improvement of mental health among nurses.

Rie Chiba, PhD, RN, PHN, is a professor in the Department of Nursing, Graduate School of Health Sciences, at Kobe University. Her research is primarily focused on the areas of mental health among people with mental illness and their families.

Akihito Shimazu, PhD, CP, CPP, is a professor in the Faculty of Policy Management at Keio University. He is a leading researcher on work engagement. His research focuses on improving the health and productivity of workers and their families.

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