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The Effect of Work Engagement and Perceived Organizational Support on Turnover Intention among Nurses: A Meta-Analysis Based on the Price–Mueller Model

Abstract

Aim:

To elaborate on the relationship between work engagement, perceived organizational support, and the turnover intention of nurses by analysing some potential moderators.

Background:

Nurses’ turnover intention is negatively impacted by their level of work engagement and perceptions of organizational support. However, it is challenging to reach a consistent conclusion.

Methods:

Data were acquired from six electronic databases. Each study was evaluated using the quality assessment tool for cross-sectional studies of the Agency for Healthcare Research and Quality (AHRQ). STATA 15.0 was used to analyse the data, and a random effects model was used. The groups that included two or more studies were added to the moderator analysis.

Results:

A total of 40 study articles involving 23,451 participants were included. The turnover intention of nurses was inversely associated with work engagement (coefficient: −0.42) and perceived organizational support (coefficient: −0.32). A substantial moderating role was played by cultural background, economic status, working years, and investigation time (P < 0.05).

Conclusion:

Work engagement and organizational support significantly reduced turnover intention among nurses. Considering the acute shortage of nurses worldwide, nurses with lower wages, fewer working years, and lower levels of work engagement should be given more attention and support from their organizations. Implications for Nursing Management. The meta-analysis suggested that managers should give their employees a more organizational support and promote their work engagement to motivate nurses’ retention intention and maintain a stable workforce with little employee turnover.

Authors: Li-Li Zhu ⚬, Hui-Juan Wang ⚬, Ya-Fei Xu ⚬, Shu-Ting Ma ⚬, Yan-Yan Luo ⚬

Affiliations: School of Nursing, Xinxiang Medical University, Xinxiang, China; School of Nursing, Xinxiang University, Xinxiang, China

License: Copyright © 2023 Li-Li Zhu et al. CC BY 4.0 This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article links: DOI: 10.1155/2023/3356620  |  PubMed: 40225681  |  PMC: PMC11918525

Relevance: Relevant: mentioned in keywords or abstract

Full text: PDF (329 KB)

1. Introduction

In 2021, the International Council of Nurses (ICN) proposed a policy brief indicating the need for 10.6 million additional nurses by 2030. However, the COVID-19 pandemic exacerbated the global nursing shortage and increased nurses’ professional challenges, such as occupational risk of infection, stress, and a severe workload [ref. 1]. Nurses are experiencing increasing burnout, depression, and dissatisfaction with their profession. Subsequently, work engagement and quality of care declined, increasing the risk of medical errors and lower patient satisfaction. Researchers have even discovered an increase in the number of nurses leaving the profession. The Swedish Nurses Association announced that 7% of the nursing workforce (5,700 nurses) considered resigning due to the increased pressure and workloads brought on by the pandemic [ref. 2]. In Egypt, a study revealed that over 95% of nurses intended to leave their present jobs in a COVID-19 triage hospital [ref. 3]. Perceived organizational support (POS) is defined as employees’ perception of the extent to which their organizations value their contributions and care about their well-being [ref. 4]. Increasing POS might help promote nurses’ work engagement and reduce their burnout and turnover intention.

In analysing turnover factors, the Price–Mueller model is commonly used [ref. 5]. It contains four major domains: environmental, individual, structural, and intervening (Figure 1). Environmental variables represent constraints on intent to stay resulting from social conditions external to an organization. The individual variables mainly refer to training, job engagement, and emotions, with job engagement being the model’s essential variable [ref. 6]. Structural variables include equity, job pressure, awards, and promotion opportunities. Award and promotion opportunities can directly predict turnover intention [ref. 7] and indirectly alter turnover intention through job satisfaction. The intervening factors mainly include job satisfaction, organizational commitment, job search behaviour, and turnover intention, which directly influence turnover behaviour. Among them, job satisfaction and organizational commitment are the essential influencing variables of turnover, and economists have noted job search behaviour.

PMC11918525 – fig1
Figure 1: Price–Mueller model.

Accordingly, our research is based on the Price–Mueller model, which primarily explores the impact of nurses’ work engagement and perceived organizational support on turnover intentions from a positive psychological perspective.

2. Conceptual Relationships and Hypothesis Development

Turnover intention is a psychological and behavioural tendency that occurs when employees plan to leave their current organization or occupation. It is a crucial factor for predicting turnover behaviour directly [ref. 8].

2.1. Correlation between Turnover Intention and Work Engagement

Work engagement (WE) is a positive, fulfilling, work-related mental state with vigour, dedication, and absorption features [ref. 9]. Individuals who are deeply engaged in their work frequently have a strong sense of hope, meaning, pride, competence, progress, and a positive psychological experience. This higher level of job satisfaction, fulfilment, and recognition are necessary for employees, such as nurses, to remain in an organization. However, the relationship between work engagement and turnover intentions varies. An Australian study found a somewhat negative association between nurses’ willingness to leave their jobs and their level of work engagement [ref. 10]. An Italian analysis revealed a moderately negative relationship between nurses’ work engagement and turnover intention [ref. 11].

2.2. Correlation between Perceived Organizational Support and Turnover Intention

Perceived organizational support includes two types of rewards. Intrinsic rewards are found within the job role, whereas extrinsic rewards include pay, benefits, and advancement opportunities [ref. 12]. Research has shown that perceived organizational support positively affects nurses’ job satisfaction, increases nurses’ emotional attachment to the organization, and improves organizational commitment [ref. 13, ref. 14]. It also fosters a sense of responsibility to contribute to the achievement of organizational advantages and goals, which lowers the likelihood of leaving. Research on organizational support and turnover intention has gradually increased in recent years. However, the effects of perceived organizational support on nurses’ turnover intention were inconsistent. For example, one study found strong negative relationships between perceived organizational support and nurse turnover intention in China [ref. 15]. Perceived organizational support had a weak negative relation to hospital nurses’ turnover intention in Egypt and Italy [ref. 16, ref. 17]. In the UK, there was a moderately unfavourable association between nurses’ intention to leave and the support they felt from the company [ref. 18].

2.3. Hypothesis Development and Research Questions

These abovementioned studies confirmed that perceived organizational support and work engagement were positive indicators of turnover intention. However, the strength of the relationship between these studies was inconsistent. The meta-analysis model was used to synthesize the effects of work engagement/perceived organizational support on nurse turnover intention.

  • Question 1: What is the degree to which the intention to leave nursing is correlated with nurses’ work engagement?
  • Question 2: How strong is the link between nurses’ perceived organizational support and their turnover intention?
  • Question 3: Is there a relationship between turnover intention and work engagement and perceived organizational support, moderated by backgrounds, organizational characteristics, measurement instruments, and individual differences?

3. Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines [ref. 19].

3.1. Literature Search

Relevant articles were systematically searched in scientific databases (PubMed, Embase, the Cochrane Library, Web of Science, Medline, and Scopus). The core search consisted of Medical Subject Headings (MeSH), Embase subject headings, and keywords. We used the subject headings and keywords “nurse” and “perceived organizational support” and “turnover intention;” “nurse” and “work engagement” and “turnover intention” to search the literature. A supporting information file presents detailed search strategies (see Supplementary Information S1). In addition, references to related articles were manually searched. The retrieval period was from their inception to July 5, 2022.

3.2. Inclusion and Exclusion Criteria

All eligible studies met the following criteria: (1) focused on nursing staff, (2) contained Pearson correlation coefficients for the relationship between work engagement and turnover intention or perceived organizational support and turnover intention, and (3) were articles published in English.

The following studies were excluded: (1) reviews, conference abstracts, incomplete data, and studies without full text, (2) studies repeatedly published literature, and (3) low-quality literature.

3.3. Study Selection

All search results were imported into EndNote X9 software. First, duplicates were eliminated according to titles and abstracts. Second, two researchers independently screened the full texts of the papers according to the inclusion and exclusion criteria.

3.4. Quality Assessment

To evaluate the quality of the included studies, two researchers independently used the quality assessment tool for cross-sectional studies recommended by the Agency for Healthcare Research and Quality (AHRQ) [ref. 20], a questionnaire containing 11 items. If an item was answered “YES,” it scored “1;” if the answer was “NO” or “UNCLEAR,” it scored “0”. Literature quality was evaluated as follows: high quality (8–11), medium quality (4–7), and low quality (0–3). Articles with quality scores below 4 were deleted.

3.5. Data Extraction and Statistical Methods

The following data were collected for each study: name of the first author, region (country), publication year, hospital type, number of participants, age, sex, education level, working years, evaluation tools, and the correlation coefficient between variables, as shown in Table 1. If there was missing or uncertain information, we tried to contact the original author to obtain it.

Table 1: Characteristics of studies and participants.

Study Country Culture Economy Type of facility Sample size Average age Bachelor or above Working years Measure tool Variable
1 Abou Hashish 2015 Egypt Islamic Developing country Hospital 500 — 35.00 POS POS
2 Adriaenssens 2017 Belgium Western Developed country Hospital 318 45.70 97.50 19.30 UWES WE
3 Armstrong-Stassen 1998 Canada, Jordan — Developed country Medical institution 826 33.58 7.68 POS POS
4 Austin 2020 Canada Western Developed country Medical institution 635 29.20 — 2.22 UWES WE
5 Bobbio 2015 Italy Western Developed country Public hospital 711 42.06 — 12.99 POS POS
6 Brunetto 2016 Australian Western Developed country Public hospital 242 38.20 59.60 POS POS
7 Brunetto 2013 Australian, Western Developed country Private hospital 1228 — — — POS, Non-UWES POS, WE
USA
8 Cao 2020 China East Asian Developing country Public hospital 569 23.91 31.46 2.19 UWES WE
9 Cao 2021 China East Asian Developing country Public hospital 528 29.10 49.20 — UWES WE
10 Cao 2021 China East Asian Developing country Public hospital 496 29.26 50.20 — UWES WE
11 Chen 2022 China East Asian Developing country Public hospital 278 — — — UWES WE
12 Cohen 2005 Israeli Western Developed country Public hospital 233 33.17 — 11.77 Non-POS POS
13 Dåderman 2016 Poland Western Developed country Hospital 188 41.00 — 19.50 UWES WE
14 De Simone 2018 Italy Western Developed country Public hospital 194 43.50 31.90 16.50 UWES WE
15 El Akremi 2014 Italy Western C Developed country Hospital 945 37.10 — 14.00 POS POS
16 Filipova 2011 USA Western Developed country Nursing home 656 — — — POS POS
17 Forbes 2014 UK Western Developed country Medical institution 231 25.00 — 1.50 UWES WE
18 Galletta 2011 Italy Western Developed country Public hospital 1240 — 37.13 14.16 POS POS
19 Galletta 2016 Italy Western Developed country Hospital 304 35.60 12.40 POS POS
20 Ghazawy 2019 Egypt Islamic Developing country Public hospital 535 31.60 27.60 — UWES WE
21 Li 2021 Taiwan, China East Asian Developing country Public hospital 512 — — — Non-POS POS
22 Karatepe 2017 Cyprus Western Developed country Public hospital 212 — 69.80 — UWES WE
23 Li 2019 China East Asian Developing country Community hospital 410 33.92 45.61 — UWES WE
24 Liu 2018 China East Asian Developing country Public hospital 1716 — 59.40 — POS-SVS POS
25 Ma 2022 China East Asian Developing country Public hospital 522 — POS POS
26 Marques-Pinto2018 Portugal Western Developed country Hospital 1927 34.50 98.00 11.30 UWES WE
27 McKenna 2020 Ireland Western Developed country Hospital 89 — — — UWES WE
28 Sharif 2021 Iran Islamic Developing country Public hospital 341 — — — Non-POS POS
29 Peng 014 Taiwan, China East Asian Developing country Public hospital 349 31.80 36.10 7.90 UWES WE
30 Pennbrant 2021 Sweden Western Developed country Medical institution 807 42.00 — 13.80 UWES WE
31 Robson 2016 UK Western Developed country Medical institution 433 43.00 — — POS POS
32 Rodwell 2016 Australian Western Developed country Medical institution 459 — — — Non-UWES WE
33 Sabokro 2013 Iran Islamic Developing country Hospital 494 — — — Non-POS POS
34 Shacklock 2013 Australian Western Developed country Community hospital 510 46.50 — Non-UWES, WE, POS
POS
35 Shahpouri 2015 Iran East Asian Developing country Private hospital 208 — — UWES WE
36 Sheehan 2019 Australian Western Developed country Medical institution 1039 48.67 22.47 UWES WE
37 Takase 2014 Japan East Asian Developed country Hospital 766 32.20 34.65 9.62 Non-POS POS
38 Wan 2018 China East Asian Developing country Public hospital 778 36.35 57.60 — UWES WE
39 Yang 2015 China East Asian Developing country Public hospital 526 47.00 — Non-POS POS
40 Zeytinoglu 2011 Canada Western Developed country Hospital 1396 42.00 18.00 Non-POS POS

The analysis was conducted with STATA 15.0. For the correlation coefficient r: (1) the rs values in some studies were converted into r values; (2) the Fisher’s Z values were converted by using the formula [ref. 21]; (3) the Fisher’s Z values and the standard error SE were input into the STATA software, and the Summary Fisher’s Z values were derived by using the inverse variance method, and finally, converted to Summary r values. We identified heterogeneity with the Q test and assessed it by I2 and P values. The I2 statistic represents the percentage of total variability between studies caused by heterogeneity. I2 values of 75%, 50%, and 25% correspond to high, medium, and low levels of heterogeneity, respectively. When the heterogeneity was greater than 50%, the random effects model was used. Publication bias was assessed by observing the symmetry of the funnel plot and Egger’s test. P < 0.05 was considered to be statistically significant.

All authors worked together to discuss and analyse information from the included literature to identify the moderator. Individual differences, organizational characteristics, backgrounds, and measurement instruments are some possible moderators for the surveyed relationships. Finally, we combined the previous studies and the Price–Mueller model to summarize eight variables: culture, economy, survey time, hospital type, years of work, age, education level, and measurement instrument.

4. Results

4.1. Search Result

The PRISMA flow chart (Figure 2) describes the literature search and study selection process. A total of 2644 articles were retrieved from six databases and other sources. A total of 1628 remained after removing duplicates, and 175 full text articles were reviewed. Finally, 40 relevant articles were identified in the meta-analysis.

PMC11918525 – fig2
Figure 2: PRISMA flow diagram of the study selection process.

4.2. Characteristics of Studies and Participants

These studies were conducted in 18 different countries across 5 continents, with a sample of 24,351 nurses working in hospitals, medical institutions or nursing homes. In 31 studies, the female sex was significantly predominant (59.1%∼100%), while it was not mentioned in 9 studies. The average age and the average working years were the detailed features of each study, as shown in Table 1.

4.3. Data and Publication Bias

4.3.1. Correlations between Work Engagement and Turnover Intention

Of the 22 studies included in the review, 7 studies found a weakly correlated relationship between work engagement and turnover intention [ref. 11, ref. 22–ref. 27]; 14 studies found a moderate correlation [ref. 15, ref. 28–ref. 41]; and one study found a strong correlation [ref. 15]. We analysed the correlations between work engagement and turnover intention for 11,988 nurses. The I2 was 94.3%, indicating significant heterogeneity. Therefore, we used the random-effects model, as shown by the pooled Fisher’s value of 0.45 (95% CI (−0.53, −0.37), P < 0.001) and the transformed r value of −0.42, 95% CI (−0.49, −0.35). The meta-analysis revealed that nurses’ work engagement was moderately negatively correlated with their turnover intention (Figure 3).

PMC11918525 – fig3
Figure 3: Forest plot of work engagement and turnover intention.

In addition, no publication bias was detected by Egger’s test (t = 0.54, P = 0.595), with the funnel plot being substantially symmetrical in Figure 4(a).

PMC11918525 – fig4
Figure 4: (a) Funnel plot of the work engagement and turnover intention. (b) Funnel plot of the perceived organizational support and turnover intention.

4.3.2. Correlations between Perceived Organizational Support and Turnover Intention

Of the 20 studies included in the review, 5 studies found the relationship between perceived organizational support and turnover intention to be very weakly correlated [ref. 14, ref. 16, ref. 17, ref. 42, ref. 43], 9 studies found a weak correlation [ref. 13, ref. 41, ref. 44–ref. 50], and 6 studies found a moderate correlation [ref. 18, ref. 41, ref. 51–ref. 54]. We analysed correlations between perceived organizational support and turnover intention for 14,101 nurses. The I2 was 93.4%, indicating significant heterogeneity. Therefore, we used the random effects model, as shown by the pooled effect size Fisher’s value of 0.34 (95% CI (−0.40, −0.27), P < 0.001) and the transformed r value of −0.32, 95% CI (−0.38, −0.26). The meta-analysis found that nurses’ perceived organizational support was weakly negatively correlated with their turnover intention (Figure 5).

PMC11918525 – fig5
Figure 5: Forest plot of perceived organizational support and turnover intention.

Using Egger’s test, no publication bias was found (t = 0.48, P=0.638), and the funnel plot was symmetrical (Figure 4(b)).

4.4. Sensitivity Analysis

We performed sensitivity analysis by removing each study sequentially. These results indicated that in the overall meta-analysis, no single study significantly changed the pooled correlation coefficient (Figures 6(a) and 6(b)). The outcomes of the meta-analysis are statistically stable and reliable.

PMC11918525 – fig6
Figure 6: (a) Plot of sensitivity analysis of work engagement and turnover intention. (b) Plot of sensitivity analysis of perceived organizational support and turnover intention.

4.5. Moderator Analysis

The results of the moderator analysis are provided in Table 2. They revealed that regional culture, economic level, years of work, and survey time were significant moderators for work engagement and turnover intention (P < 0.05).

Table 2: Moderator analysis results/work engagement-turnover intention.

Concepts K N Zscore Upper CI Lower CI Q P Egger’s
Moderator (culture) 22 11988 −0.45∗∗ −0.53 −0.37 7.37 0.025∗ 0.595
Islamic culture 1 −0.58 −0.66 −0.49
Western culture 13 −0.39 −0.50 −0.27
East Asian culture 8 −0.53 −0.62 −0.44
Moderator (economy) 22 11988 −0.45∗∗ −0.53 −0.37 4.82 0.028∗ 0.595
Developed country 13 −0.39 −0.50 −0.27
Developing country 9 −0.54 −0.61 −0.46
Moderator (hospital type) 22 11988 −0.45∗∗ −0.53 −0.37 2.98 0.084 0.595
Public hospital 9 −0.52 −0.61 −0.44
Another type 13 −0.40 −0.51 −0.29
Moderator (average age) 16 9514 −0.45∗∗ −0.54 −0.35 3.64 0.162 0.660
21∼30 5 −0.56 −0.69 −0.44
31∼40 6 −0.51 −0.59 −0.43
41∼50 5 −0.25 −0.55 0.06
Moderator (bachelor’s degree) 11 6316 −0.41∗∗ −0.55 −0.27 2.61 0.106 0.656
Below 50% 6 −0.52 −0.65 −0.39
Above 50% 5 −0.29 −0.54 −0.04
Moderator (years of work) 9 5908 −0.40∗∗ −0.56 −0.23 7.81 0.005∗ 0.648
Less than 10 years 3 −0.64 −0.80 −0.48
10+ years 6 −0.27 −0.47 −0.07
Moderator (measurement instrument) 21 11776 −0.45∗∗ −0.53 −0.37 0.03 0.855 0.674
UWES 18 −0.46 −0.55 −0.36
Non-UWES 3 −0.44 −0.62 −0.25
Moderator (survey time) 22 11988 −0.45∗∗ −0.53 −0.37 5.56 0.018∗ 0.595
2010∼2019 15 −0.40 −0.50 −0.30
After 2020 7 −0.56 −0.65 −0.47

∗∗P < 0.001; ∗P < 0.05.

Table 3 shows the moderator analysis results of perceived organizational support and turnover intention. We found that regional culture and years of work were significant moderators for this relationship (P < 0.05).

Table 3: Moderator analysis results/perceived organizational support-turnover intention.

Concepts K N Zscore Upper CI Lower CI Q P Egger’s
Moderator (culture) 19 13275 −0.33∗∗ −0.40 −0.26 8.20 0.017∗ 0.662
Islamic culture 3 −0.18 −0.32 −0.04
Western culture 11 −0.32 −0.42 −0.23
East Asian culture 5 −0.41 −0.49 −0.33
Moderator (economy) 20 14101 −0.34∗∗ −0.40 −0.27 0.4 0.527 0.638
Developed country 13 −0.35 −0.44 −0.27
Developing country 7 −0.31 −0.42 −0.19
Moderator (hospital type) 20 14101 −0.34∗∗ −0.40 −0.27 0.33 0.565 0.638
Public hospital 9 −0.31 −0.40 −0.23
Another type 11 −0.35 −0.45 −0.25
Moderator (average age) 10 5856 −0.33∗∗ −0.42 −0.24 0.43 0.511 0.714
31∼40 6 −0.30 −0.44 −0.17
41∼50 4 −0.37 −0.50 −0.23
Moderator (bachelor’s degree) 6 4406 −0.33∗∗ −0.46 −0.20 0.03 0.872 0.438
Below 50% 4 −0.33 −0.53 −0.13
Above 50% 2 −0.35 −0.51 −0.19
Moderator (years of work) 8 6421 −0.29∗∗ −0.38 −0.19 45.07 <0.001∗∗ 0.742
Less than 10 years 2 −0.47 −0.52 −0.42
10+ years 6 −0.22 −0.27 −0.17
Moderator (measurement instrument) 20 14101 −0.34∗∗ −0.40 −0.27 0.53 0.467 0.638
POS 15 −0.35 −0.43 −0.27
Non-POS 5 −0.30 −0.41 −0.18
Moderator (survey time) 20 14101 −0.34∗∗ −0.40 −0.27 2.45 0.294 0.638
Before 2010 2 −0.41 −0.56 −0.26
2010∼2019 15 −0.34 −0.42 −0.26
After 2020 3 −0.25 −0.39 −0.12

∗∗P < 0.001; ∗P < 0.05.

5. Discussion

Over the last two decades, research concerning work engagement and perceived organizational support among nurses has increased. Work engagement and perceived organizational support have become crucial positive indicators of turnover intention. However, there is no meta-analysis based on theoretical models that research work engagement, organizational support, and contextual factors impacting turnover intention. This study aims to fill this gap. At the same time, this study verifies the Price–Muller model in the field of nursing.

Our meta-analysis indicates that the effect size of nurses’ work engagement and turnover intention is moderate. Individual variables have a predictive effect on the turnover intention of nurses. Work engagement has a more significant impact on turnover intention among nurses than employees in other occupations. Zhang [ref. 55] found that the effect size of work engagement on turnover intention is weak among rural doctors in China. Work engagement motivates individuals to continue working. High work engagement implies a high level of energy and resilience [ref. 56]. Nurses work in a high-paced and demanding environment, and they must provide more technical and time-sensitive care to sicker patients. With the increasing demand for high-quality healthcare services, hospitals have emphasized the provision of healthcare that centres on patients’ needs. Nurses must control their emotional expressions to match patients’ experiences. Thus, nurses utilize behaviour and emotional labour to meet organizational goals through daily interactions with patients. Nurses need to show empathetic, sensitive, friendly, and caring emotional behaviour when interacting with patients and their families [ref. 57]. Nurses with low work engagement may perform less emotional labour, thereby consuming additional personal resources with negative effects, such as job burnout, emotional disorders, and exhaustion. In contrast, research shows that individuals with high work engagement actively change themselves to meet job demands [ref. 58]. Therefore, nurses with high work engagement do not need much cognitive processing or self-regulation. They can naturally express emotions to meet the goals of the organization, which reduces personal resource consumption and turnover intention.

Social exchange theory proposes that employees will exhibit beneficial behaviour towards the organization and reduce turnover when they receive affirmation and support from their organization [ref. 14]. According to the results of this study, the effect size of the relationship between perceived organizational support and turnover intention among nurses is negative. Structural variables have a predictive effect on the turnover intention of nurses. However, this relationship has a minor impact on other medical professions [ref. 59]. Nurses are primarily female. Due to the influence of traditional culture, women are the majority of family caregivers globally [ref. 60]. It is not easy to maintain work-family balance while working nursing shifts, doing housework, and performing childcare duties. Nurses need more support to promote the intention to maintain their jobs. Therefore, a high level of organizational support helps nurses reduce physical and mental stress, increase job satisfaction, and decrease turnover intention.

Our study shows that cultural background significantly moderates nurses’ work engagement/organizational support and turnover intention. This difference may derive from the fact that employees define themselves and understand the rules in different cultures. For instance, in the East Asian cultural context, organizational support not only meets the need for employees to be valued and respected but also reinforces a sense of self-identity. Therefore, nurses’ perceived organizational support is more sensitive to turnover intention.

Currently, global economic development is unbalanced. In developing countries, nurses’ pay packages and working environments may not meet expectations [ref. 61]. Work engagement plays a vital role in the career planning of nurses, which is a motivation to continue working. Our study found that developing countries had a more significant effect on nurses’ work engagement and turnover intention than developed countries. Therefore, the turnover intention of nurses was reduced. We should pay more attention to nurses’ work engagement in developing countries. In addition, nurses who had worked for more than ten years had a lower sensitivity of work engagement/organizational support and turnover intention. It is possible that employees working for a long time had a strong emotional connection to the organization and a high level of organizational identification and loyalty. They, therefore, have a lower turnover intention.

It is worth noting that since 2020, nurses’ work engagement has been more sensitive to turnover intention, probably due to the pandemic. Clinical nurses face negative stress reactions such as fear, anxiety, and burnout during long-term work. They need more motivating and positive emotions to continue working [ref. 62]. In addition, according to the moderator analysis results, survey time did not moderate the relationship between perceived organizational support and turnover intention. However, research shows that perceived organizational support can help reduce nurses’ burnout during the COVID-19 pandemic [ref. 63] while promoting nurses’ retention intention. Accordingly, during the COVID-19 pandemic, we should particularly focus on nurses’ job engagement and perceived organizational support. Work together at individual and organizational levels to improve nurses’ job satisfaction and reduce turnover intention.

5.1. Limitations and Future Research

Despite its comprehensive nature, this study also had limitations. First, the majority of the study samples generally consisted of female nurses; further research should involve more male nurses to enhance the universality of the results. It is worth noting that female nurses’ marriage status was associated with turnover, but the information provided in this review is not suitable for moderating analysis. Second, we only included major databases in the literature search, and database search bias may exist. Finally, most of our studies were cross-sectional, and a few were longitudinal. Future researchers can summarize meaningful information from qualitative research.

6. Conclusion

This study revealed that work engagement and organizational support significantly reduced turnover intentions. This study allowed middle and senior nursing managers to gain more insight into the role of nurses’ work engagement and organizational support. For example, nursing managers have always focused on patient safety issues worldwide, such as patient falls or medication errors. Adverse events not only cause physical and psychological damage to patients but also impose a great psychological burden on clinical nurses. At the same time, adverse events frequently cause turnover intention. Work engagement and perceived organizational support can reduce the rate of adverse events through both personal and organizational aspects and increase the job satisfaction of patients and nurses. High work engagement is closely related to medical quality results through a positive and highly dedicated working state. Organizational support can reduce nursing adverse events by improving the hospital safety management system. Meanwhile, when nurses experience adverse events as secondary victims, effective organizational support can alleviate nurses’ anxiety, sleep disorders, and career burnout. Moreover, nurses’ cultural background, economic level, working years, and investigation time played a significant moderating role in the surveyed relationships.

7. Implications for Nursing Management

Nursing shortages and high turnover rates have been the focus of nursing managers and an obstacle to addressing global public health challenges. In this regard, we have the following suggestions for middle and senior managers. First, hospital managers should change their thinking, attaching importance to the nursing role and enhancing nurses’ positions to reduce the loss of talent. For instance, they can support the growth of nursing disciplines with policies and resources so that nurses can improve their career identification and engagement. Second, nursing managers should enhance humanistic care to improve nurses’ satisfaction and to promote retention. (1) Managers should use inclusive and supportive ways to communicate, which contribute to nurses’ physical and mental health. We suggest that nursing managers use leader gratitude expressions to communicate with nurses. As positive emotional communication, the leader’s gratitude expression helps nurses form a positive evaluation of the organization, increase work satisfaction, and reduce turnover intention. (2) We propose encouraging nurses to have autonomous and elastic working time. For example, managers should upgrade working shifts to increase nurses’ flexibility. Nurses can make appointments according to their needs in online systems to reduce family-work conflict. (3) Managers should identify the nurses’ primary needs and effectively inspire nurses. While maintaining fair pay, managers should broaden the career development path of nurses and support nurses so they may participate in organizational affairs and decision-making that will increase their engagement. Finally, future studies should also focus on path analysis to assess the causal relationship between these variables.

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