1. Introduction
Following the worldwide spread of COVID-19 in January 2020, many countries have been battling for survival [1,2,3]. Fortunately, with the effective promotion of vaccines, the world now seems to be gradually moving in a safer direction [4,5,6]. At this time, many countries have announced that the pandemic will coexist with humankind for a long time and have shifted their energy to economic recovery. Frontline medical staff is responsible for nucleic acid testing and they deserve attention. Nevertheless, in China, the concept of “coexistence” refers to eliminating the threat posed by the pandemic to each patient instead of the safety provided by group immunization [7]. This means that as long as a pandemic occurs in one place, the area will be quarantined and all members in the closed area will be PCR-tested.
As a special service group, healthcare personnel not only have the main responsibility of saving people from imminent death and healing the wounded but also must have the psychological strength for the high pressure of providing healthcare during the pandemic [5,8,9]. The public is no stranger to the high workload of people who work in the medical industry. In addition to unexpected events, the frequently tense doctor–patient relationships in the daily work of medical staff [10] and the noncooperation of tested patients at the nucleic acid testing site during the pandemic have become a source of the exhaustion for this group [9,11]. According to the description of such occupations in the previous literature, high work load, high health risk, high pressure, low income, poor welfare, and difficultly in reflecting personnel value have become recognized terms associated with medical personnel [12,13]. Various psychological problems and work-related adverse consequences are also often mentioned, such as job burnout, decreased job satisfaction, and high turnover intention [14,15]. Among them, turnover intention is considered to be sensitive to many external factors, and it can more effectively reflect the real level of organizational management than the actual turnover behavior [16]. The 2020 Hays Asia Salary Guide published a survey from five Asian countries/regions (China, HK, Malaysia, Singapore, Japan), indicating that 36% of respondents were actively looking for a new job [17]. In addition, China’s medical and health development report released in 2020 shows that the turnover rate of medical staff fluctuated between 10.2% and 11.2% throughout the year, whereas the turnover intention was as high as 56.94% [18].
Given the research on turnover intention, Zakariya et al. [19] believed that continuous improvement of work engagement is the best way to reduce turnover intention, emotional exhaustion, stress, and burnout. The definition of work engagement refers to a positive state related to work, which contrasts with the concept of burnout [20,21]. As the main form of burnout, disengagement is the best explanation for the loss of motivation, helplessness, separation, and hesitation that employees are experiencing. Thus, having a high number of disengaged employees increases the risk of voluntary turnover. Demerouti et al. [22] considered that providing job resources would “instigate a motivational process leading to job-related items, work engagement, and organizational commitment”. Additionally, the job demands–resources (JD-R) theory explains engagement as a motivation process that connects job resources and work outcome. Previous studies mainly focused on the relationship between job resources as a whole and employees’ work-related behavior or work attitude [23], which is not applicable to all situations and is not targeted. Until the outbreak of COVID-19, the fear of the virus and its consequential recessionary effect on the Chinese economy led the provision of job resources to tilt toward the demands of job security and personal healthcare [24]. As the existing vaccines provide security, people’s fear of the pandemic is gradually decreasing. However, the mental exhaustion and emotional loss caused by overwork may be important obstacles for frontline healthcare staff. Some scholars have analyzed the relationship of the major associated factors with work engagement and turnover intention, but no existing studies were specific to front-line medical staff in the post epidemic situation.
Given that previous studies have mentioned the need for identifying job resources to meet employees’ emotional and psychological needs [25,26], as a kind of social support, perceived organizational support (POS) can be obtained from the organization to help nervous and work-conscious people [24,27,28]. The purpose of this study is to shed new light on another psychological resource by adopting the widely recognized and well-established job demands–resources (JD-R) theory. Employee care (EC) is regarded an informal job resource, where managers or superiors provide friendly care, and offer empathic listening [29,30]. Ma et al. and Yang et al. [31,32] verified that when medical staff receive a professional identity and empathetic understanding from their superiors and society, it helps them improve their engagement and enthusiasm. This study clarifies the job resources from the organizational level and the manager level in the context of the current post-pandemic period, and it tests whether they are related to the working state that plagues frontline medical staff. Increased employee care may affect the psychological state of management employees, such that the scope of work resources is no longer limited to the level of transformational leadership or management strategies.
Furthermore, this study examines the mediated moderation age on the relationship between work engagement and turnover intention. Existing studies focused on intergenerational differences rather than on behavioral trends with age [33,34]. It is understood that among the registered medical personnel, 39.4% are over 35 years old and have more than 10 years of work experience, of which 56.94% are willing to leave [18]. Therefore, this article studied perceived organizational support (POS), work engagement (WE), turnover intention (TI), and whether age moderates the relationship work engagement and turnover intention in such groups with a unique working nature, which warrants careful examination. This study can help to better understand the factors related to the emotional health and work results of front-line health care employees in the post—COVID-19 pandemic stage. The data obtained can provide insight and a theoretical basis for the management of stakeholders in potential crises in the future.
2. Literature Review
2.1. Perceived Organizational Support (POS)
The use of POS within organizations is the assessment of employees’ work contributions and psychological well-being after receiving aid from the organization to meet their social–emotional needs [35,36,37,38,39]. To and Huang [40] emphasized that POS could relieve employees’ stress and encourage efficient work. Chen and Eyoun [24] found that POS could play a positive role in reducing employees’ emotional exhaustion and providing guidance for autonomy. Jin and Tang [41] considered whether employees could engage in work more effectively depending on the perception of organizational support obtained from individuals’ strong short-term resilience. Similarly, Hobfoll [26] believed that organizational support is a kind of job resource, which helps to cultivate employees’ sense of self-efficacy, intrinsic motivation, and positive emotion. This is because employees gain positive self-esteem, recognition, sense of belonging, and respect through organizational support [24,42]. The psychological comforting effect of organizational support on employees has been recognized by more scholars since the outbreak of COVID-19. According to Khattak et al. [43], the panic caused by COVID-19 affects employees’ mental health and organizational support has a negative relationship with job insecurity [44]. In addition, the transformational leadership of the supervisor of the work unit during the pandemic can improve the work–life balance of employees, which is the basis for obtaining employees’ work performance [45].
2.2. Employee Care (EC)
The use of EC is an informal way for managers or superiors to listen to employees’ concerns and provide care. EC involves valuing work, improving employees’ wellbeing and assisting their career development [46]. According to Paterson et al. [47], a caring climate is also necessary for supervisors. Managers must eliminate the negative impact of workplace conflicts on their employees. An orderly workplace environment has been shown to be a solid foundation for employee participation [28]. Yang et al. [48] emphasized that a supervisor’s support, such as task guidance, training opportunities, and deviation feedback, can contribute to increasing their employees’ commitment, work engagement, and target performance.
Considering the current psychological needs of frontline healthcare staff [24,31,32], this study chose perceived organizational support and employee care as potential variables of job resources. Previous studies on relevant issues did not focus on the knowledge of the target interviewees under the current situation of the pandemic. Work engagement depends on the organization’s facilitation of workplace mindfulness, conflict management climate, and organizational justice [28]. In addition, Chen et al. and Asghar et al. [24,49] observed that to satisfy the needs of service employees, job resources should also acknowledge the importance of multitasking. Furthermore, improving work attachment and leadership’s commitment to safety could stimulate work engagement when employees return to work after the pandemic becomes stable [5].
2.3. Relationship among Perceived Organizational Support, Employee Care, and Work Engagement
Job demands–resources (JD-R) theory emphasizes the social exchange among organizations, managers, and employees [50,51], indicating it as a management process to alleviate or balance employees’ adverse reactions to work, so as to stimulate their job-related learning and behavior [20,22]. Rhoades and Eisenberger [27] explained the reciprocity effect according to the social exchange theory and they believe that perceived supportive work resources would affect employees’ emotional attitude toward the organization. Anasori et al. [52] believe that when employees perceive ineffective or suspicious management practices, their negative and helpless emotions will be reflected in their work attitudes, which will ultimately affect the potential performance of the organization. Previous studies have affirmed the positive significance of relevant resources from the perspective of employees, such as support from superiors and colleagues, reward satisfaction [53], organizational justice [54], and professional identity [55]. On the basis of an analysis of the current demands of frontline medical personnel, this study focuses on expanding psychological comfort to cope with the work pressure of medical personnel. Based on the existing empirical evidence, we assume that the perception of job resources (i.e., POS and EC) could enhance work engagement by proposing the following hypotheses:
Perceived organizational support is positively related to work engagement.
Employee care positively is positively related to work engagement.
2.4. Relationship between Work Engagement and Turnover Intention
Turnover intention is a predictor of voluntary turnover [55]. Compared with actual turnover, turnover intention is easier to express [1,56]. The negative emotion of turnover intention is mainly accompanied by a psychological transmission process under the influence of professional environment and organizational conditions [8]. Employees who stay in positions without actual turnover intention could display a form of withdrawal from participating at work, such as “aloofness, absence, delay, poor performance and even looking for a new job” [39]. Increased turnover intention has been shown to cause employee inefficiency and increase potential personal pressure, which in turn could increase the recruitment costs and decrease productivity in the organization unless the organization takes preventive measures [40,57].
Work engagement has been found to be a psychological response to meet job demands, which significantly reduces turnover intention [58]. Work engagement is considered as an energy or identification related to employees’ wellbeing [59]. De Simone et al. and Wang et al. [8,40] pointed out that by ensuring job satisfaction, organizations encourage their employees’ job engagement, and, thus, reduce their turnover intention. Wan et al. and Shahpouri et al. [54,59] provided empirical evidence sharing that work engagement is closely related to reducing turnover intention. Kim et al. [60] reiterated that dedicated employees perform better, preventing negative performance due to turnover intention. Wang et al. and Wan et al. [55,59] found that work engagement was inversely correlated with turnover intention. Therefore, the following hypothesis is posited:
Work engagement is negatively related to turnover intention.
2.5. The Mediating Effect of Work Engagement
In the literature, work engagement was found to be directly related to different forms of work performance and withdrawal behaviors, such as absenteeism and turnover intention, which would be observed more intuitively [61]. Especially following the outbreak of COVID-19, the uncertainty of employee engagement was found to be related to the possible loss of their work and questioning their own financial capability in the face of uncertainty. According to the job demands–resources theory, employees will suffer from emotional exhaustion, burnout, and slackness when they sense a threat of resource reduction and insufficient resources. Therefore, the literature in the outbreak stage mostly establishes the path of using work resources to improve work input and finally achieve positive work results. Therefore, most of the literature at the outbreak stage of the pandemic established ways to improve work engagement and achieve positive work results using job security [31,62], professional identity [40], and leadership safety commitment [5]. Similarly, earlier studies also used work engagement as an intermediary variable. Kim et al. and Shahpouri et al. [54,60] observed that the mediating effect of work engagement on the relationship between job resources and turnover intention was statistically significant. Job resources based on the consideration of job characteristics lead to specific incentive potential, which is conducive to obtaining employees’ work engagement, which can be extended to alleviate employees’ turnover intention [59]. Therefore, job resources focusing on reducing occupational stress while improving distributive justice [54,62] and occupational sustainability [63] are frequently mentioned. The psychological path to support medical staff’s work engagement should be through evidence-based measures, supportive organizational policies, and providing a safe and reliable working environment [64,65,66]. Nevertheless, social support in the post pandemic period is suggested to focus on reducing psychological load [31]. The literature still lacks more specific research studies on the current situation. Hence, the current study redefines POS and EC as indicators to meet the psychological needs of frontline healthcare personnel through the following hypotheses:
Work engagement mediates the effects of POS on turnover intention.
Work engagement mediates the effects of EC on turnover intention.
2.6. The Moderating Role of Age
Socioemotional selectivity theory (SST) is a lifespan theory of motivation which states that with the narrowing of the lifetime range of adults, their goals become guided by current interests [67]. Carstensen and Pasupathi [68] noted that young people are more in pursuit of personal development and progress. Torsello [34] found that Millennial employees put forward more requirements for personal career growth strategies set by the organization. They pursue skill acquisition, prefer a participatory management style, avoid hierarchy, emphasize benevolence, trust, personal respect, and seek an appropriate work–life balance. Charles et al. [69] revealed that young, middle-aged, and older groups will show different tendencies when faced with positive or negative images, i.e., older adults tend to have meaningful and positive entertainment, whereas younger adults prefer experiencing negative emotions, relieving their boredom, or seeking entertainment. Similarly, Wilson et al. [70] found that young people have greater psychological panic when facing the risk of contracting COVID-19, whereas older people show a better psychological state. According to the analysis of resource conservation theory, Charoensukmongkol and Puyod [45] believe that as the physical strength of the population starts to decline due to age, they choose to optimize other relatively abundant resources to obtain calm and rationality, but young people are more prone to emotional fatigue under work pressure. Cavanagh et al. [71] observed that age-related motivation and goal differences might be reflected in their work. Kooij et al. [72] confirmed the focus of human resources staff focuses on age differentiation as the driving force to improve the abilities of people at different ages. Through the theory of career stage development, Spurk et al. [73] affirmed that age factors may be the essence of changes in individuals’ cognition of career development, goal selection, and career success. In addition, Tordera et al. [74] emphasized that human resource practices and age are the antecedents for improving employees’ welfare and performance. The current registered medical employee account for a relatively low proportion, with only 47.2% of the registered staff having more than 10 years of experience [18]. It is worth paying attention to whether the current situation of high turnover rate and turnover intention is disturbed by the age characteristics of the on-the-job staff. Among these factors, age can play a regulatory role [51].
De Vos et al. [75] once proposed that SST theory is helpful to understand the change in individual career adaptation, so as to understand the sustainability of career. However, most studies focused on cohort theory, which is the group classification theory used to distinguish the inherent characteristics of Generation X and Generation Y in work [33,76,77]. In Section 2.3, we reviewed the theories and evidence supporting the prediction of the relationship of work engagement with employees’ turnover intention. However, this predictive power may change as employees age (Figure 1). Therefore, this study addresses this systematically through the following hypothesis:
Age strengthens the relationship between work engagement (WE) and Turnover Intention (TI) such that the TI of employees of a lower age is higher than the TI of employees of an older age.
3. Methodology
3.1. Research Model
This study emphasized that job resources (perceived organizational support and engagement care) are the antecedence of work engagement and turnover intention of front-line healthcare staff. In this study, we used a cross-sectional study design. Because the data can be collected within a time frame, the type of research design is more convenient and cost-effective for researchers. This design is feasible, beneficial, and supportive when assumptions are based on mature and tested theories. The research model is evaluated using the using partial least square (PLS) path modeling tool, which is a technique with structural equation based on variance [78,79]. Version 3.3 of SmartPLS will show a more comprehensive and intuitive data analysis for the test. In summary, the deductive method is used in this study, followed by the quantitative technology of data collection.
3.2. Sample and Data Collection
After sorting out the pandemic situation in China from January to March 2022, samples from the pandemic-prone cities Zhengzhou, Suzhou, and Nanjing were selected for determination. The data was collected from three main types of epidemic prevention units (including public hospitals, specialized hospitals, and community health service stations) and sent through email and professional questionnaire program links via the human resource system of each work unit. Overall, a total of 533 medical workers from 11 units participated in the questionnaire survey in three different types of medical places. A total of 448 valid questionnaires were submitted, of which 33.6% were from public hospitals, 28.8% were from infectious disease hospitals, and 37.6% were from community health service stations. The respondents from the three surveyed places (Zhengzhou, Suzhou and Nanjing) were 176, 121, and 151 respectively. Characteristics of respondents are shown in Table 1.
3.3. Measurement
The measures used in this study were withdrawn from the existing literature. The survey was initially drafted in English, then translated into Chinese using the translation and back translation procedure to the target Chinese employee [80]. The seven-item scale adopted from Chen et al. and Kotze et al. [24,30] measures perceived organizational support and employee care. Sample items such as “My supervisor considers my goals and values” and “At a difficult time, my boss is willing to lend an ear” were rated on a five-point Likert-type scale from “strongly disagreement” to “strongly agreement” adopted from Jung et al. [1] to measure work engagement. Participants were asked to report the frequency of symptoms (e.g., “I find the work that I do full of meaning and purpose”). Turnover intention was assessed using four items from Romeo et al. [81]. A sample item was “I sometimes feel compelled to quit my job in my current workplace”. Detailed questionnaire contents are shown in Table 2.
3.4. Data Analysis
Data analysis consisted of several steps. Firstly, the core of measurement model analysis was to determine the relationship between structure and project, with a focus on determining the relationship between structures in the model [82]. The outcomes obtained for the model demonstrated adequate convergent validity given their outer loading, composite reliability, and average variance extracted (AVE). Discriminant validity involves determining that all structures in the established model are empirically different from each other using common methods such as the Fornell–Larker criterion and heterotrait–monotrait ratio (HTMT). Secondly, the significance of the hypothesized relationships in the structural model was assessed by coefficient of determination (R2), cross-validated redundancy (Q2), and standardized root-mean-square residual (SRMR) analysis [83,84]. Thirdly, a structural equation model (SEM) was estimated to test the mediation hypotheses and following the recommendations of [85], whereas the maximum-likelihood estimation method was used to conduct 1000 repeated deviation corrections for each path. Lastly, we tested the interaction of X (perceived organizational support), Y (turnover intention), M (work engagement), and W (age) using the Johnson–Neyman technique [86], which is supported by Model 14 in the PROCESS macro for SPSS [85]. This procedure focuses on “solving for the values of M for which the effect of X on Y becomes or ceases to be significant” [86].
4. Results
4.1. Measurment Model
The results show that the outer loads were higher than the recommended threshold of 0.7. The comprehensive reliability ranged from 0.939 to 0.961, and the extracted mean variance ranged from 0.773 to 0.814. Therefore, the measurement model had good reliability for all its contracts. A satisfactory comparison between structures was tested using the Fornell–Larker criterion, which requires that the average variance of the structure to be greater than the square of its maximum correlation with any other structure (Table 3). In addition, the latest coefficient threshold of HTMT proposed by [87] of no more than 0.85 was also included in the discriminate validity (Table 4).
4.2. Structured Model
The analysis revealed that the values of f2 (0.052 for EC→WE, 0.157 for POS→WE, and 0.012 for WE→TI) were above the recommended 0 threshold. The coefficient of determination (R2) of WE was greater than 0.333 and considered moderate (Table 5). The cross-validated redundancy (Q2) was positive and greater than zero (Table 5), indicating that the structural model had predictive relevance and stable model estimates. The SRMR value was 0.048 (Table 5), meeting the rated requirement of being lower than 0.08, indicating good fitting [88].
All path analysis results are shown in Figure 2 and Table 6. Figure 2 shows the direction and size of the direct relationship through the positive and negative symbols of the path coefficient and the absolute value of the standardization coefficient, indicating that all direct suspension paths were statistically significant [89]. More specifically, the results from Table 6 show that there was a strong positive correlation between perceived organizational support and employee engagement (β = 0.42), which was higher than that between employee care and employee engagement (β = 0.273). The p-value and p-value also have reference significance, whereby the t-value must be more than 1.96, whereas the p-value must be less than 0.05 [87]. Through the comparison, we could draw some conclusions. Perceived organizational support and employee care were significantly associated with employee engagement (POS: β = 0.472, t = 5.330, p < 0.001; EC: β = 0.281, t = 2.727, p < 0.01), which in turn were significantly associated with turnover intention (β = −0.238, t = 3.000, p < 0.01), as depicted in Figure 1. Thus, hypotheses H1, H2, and H3 were supported. Work engagement did not appear to mediate the relationship between employee care and turnover intention because the indirect effect failed to reach significance (β = −0067, t = 1.905, p = 0.057). Hypothesis H5 was not supported. On the contrary, the indirect effect of perceived organizational support on turnover intention via work engagement was significant (β = −0.112, t = 2.519, p = 0.012), providing support for hypothesis H4.
There was a significant difference in the scores of work engagement and turnover intention depending on the frontline healthcare personnel’s demographics and work-related characteristics, including their gender, professional title, education background, and unit attribute. The staff of community health service stations had a higher turnover intention. In contrast, respondents from public hospitals were more engaged in work. In addition, a higher position level was correlated with a lower turnover intention (Table 6).
The direct effect of X on Y showed an upper confidence interval of −0.5849 and lower confidence interval of −0.1477, indicating a partial mediating effect in the model. As depicted in Figure 3, there was a significant effect of work engagement on turnover intention at the value of age ≥3.0731 (β = −0.274, t = −1.971, p = 0.05, 95% CI = −0.549, 0.000). Precisely, work engagement was associated with low turnover intention at a relatively higher level of age (i.e., age ≥3.0731). However, no significant effect was identified for age <3.0731. Thus, hypothesis H6 was supported.
5. Discussion
The purpose of this study was to examine the impact of perceived organizational support and employee care on turnover intention and to test the mediating effect of work engagement on frontline medical staff. In addition, since most former employees or those who have the intention to leave are young employees, it was worth monitoring whether age would affect employees’ work attitude. In summary, the essential role of the JD-R model was confirmed as almost all hypotheses were supported, except hypothesis H5 that suggested the mediating role of work engagement in the relationship between employee care and turnover intention of frontline healthcare staff. These findings are in line with Wan et al. and Shahpouri et al. [39,59] who concluded that medical employees have substantial work resources. The psychological construction and emotional adjustment of medical staff under heavy pressure with no fixed rest day in their working environment will affect employees’ engagement, leading to a negative working state. These results are in line with those of previous studies on the impact of job resources on work engagement and job results [1,47,55] and of work engagement on turnover intention [90]. If frontline medical employees receive organizational support to adjust their work intensity, mediate work conflicts, and improve the working environment, they can experience a high level of physical, psychological, social, and organizational working conditions. In turn, these experiences can also lead to a high level of work engagement and work behavior, rather than turnover intention. Lastly, these results are consistent with the trend of career adaptability mentioned in the social emotion selectivity theory found in a previous study by De Vos et al. [75].
6. Theoretical Implications
This study contributes to the academic literature in several directions. First, this study focused on a major research gap in which previous studies have adopted a scattered approach in terms of work engagement and turnover intention of frontline healthcare staff. In addition, since most former employees or those who have the intention to leave are young employees, it was worth monitoring whether age would affect employees’ work attitude. Specifically, the aspects of job resources were addressed in a partial and disconnected manner. Previous studies have described the turnover intention of medical staff in a specific working environment to a certain extent (usually through interviews with objects in specific areas or surveys of the job demands and work status of medical staff under nonemergency conditions). Second, this study makes up for the research gap of the strong psychological needs of frontline medical staff due to continuous high-pressure frontline work in the post-pandemic era. It expands the definition of job demand in the JD-R theory, from generally recognized career development, salary, training demand, life–work balance, and flexible work arrangement [12,13] to resource construction on the premise of reducing psychological load and improving work identity. Furthermore, it is the first study to investigate employee care and expand the scope of social support. Through the correlation analysis of formal social support, perceived organizational support, and informal social support in employee care with employee engagement, it was verified that employee care, as an informal and leader-led channel of psychological construction, echoes the responsibility of leaders to create a positive and dynamic working atmosphere. Moreover, a critical significance of this study is its clarification of problems that have not been addressed in existing research through a variable “age” on a new theoretical basis (SST), rather than a “generation”, which commonly appeared in previous studies, while emphasizing the need to expand the theories of other studies which focused on simple trend analysis.
7. Practical Implications
This study provides some practical enlightenment to remind hospital executives that their frontline healthcare employees need psychological support and a comfortable working environment, not just material assistance and personal career promotion. Particularly for Chinese hospital stakeholders, domestic medical staff need more care and support under the unique background of a post-pandemic clearance policy. The results show that formal support from the organization and management can improve the fulfillment of frontline healthcare staff, which can provide a reference for hospital managers in the face of possible emergency situations in the future. In addition, the mediating variable of age realization explained that the proportion of registered medical personnel over the age of 35 was small, and the turnover rate and turnover intention of younger employees were high, which can provide a new management entry point for the future management of hospital executives.
8. Limitations
This study had several noteworthy limitations that could provide avenues for future research. First of all, this study used a cross-sectional method to collect data. The cross-sectional method has been criticized by the academic community for its lack of consideration of the impact of changes over time on the attitude of the interviewed group. However, this shortcoming can be improved by establishing covariates to reduce the deviation caused by the complexity of time flow [91]. Moreover, this study did not classify and describe the nature of the hospital to which the medical staff belonged. Chinese hospitals are divided into public hospitals and private hospitals. At present, public hospitals have carried out institutional reform in accordance with national policies, i.e., the “iron rice bowl” with occupational security has become the contract management system of enterprises. Whether profitable hospitals can increase the professional pressure of medical staff will be the research topic of the team at a future stage.
9. Conclusions
Using the JD-R theory, this paper reported the relationship among POS, EC, and TI through work engagement. This paper put forward important opinions on the inhibitors of medical staffs’ TI in the post-pandemic situation, which was not previously studied. With respect to employee care, on the one hand, this study confirmed that the informal care of managers as a job resource can meet the psychological needs of frontline medical staff in the current environment. On the other hand, it updated the scope of work resources. In addition, the application of socioemotional selectivity theory verified the mediating moderator of age and confirmed previous speculation in the literature with respect to the restraining effect of age on employee behavior.
Conceptualization, Q.L., R.M., A.M. and H.K.; Methodology, Q.L. and R.M.; Data collection, Q.L.; Data calculation, Q.L.; Writing—original draft, Q.L.; Writing—review and editing, Q.L. and R.M. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of UNIVERSITI PUTRA MALAYSIA (JKEUPM-2022-269, 16 June 2022).
Informed consent was obtained from all subjects involved in the study.
Data Available Statement: Not applicable.
The author declare that they have no known competing interests or personal relationship that could have appeared to influence that work reported in this paper.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 2. PLS—SEM (Inner Model: Path Coefficient and p-value; Outer Model: T-value).
Enlarge this image.Figure 3. The effect of workplace Work Engagement on Turnover Intention is significant at relative high level of age.
Sample profile.
| Variables | Items | Sample Size | Frequency (%) |
|---|---|---|---|
| Gender | Men | 182 | 40.63% |
| Women | 216 | 59.37% | |
| Professional title | Primary title | 292 | 65.18% |
| Intermediate title | 138 | 30.80% | |
| Senior title | 18 | 4.02% | |
| Education background | Less than Bachelor’s degree | 22 | 4.91% |
| Bachelor’s degree | 292 | 65.18% | |
| Master’s degree or above | 134 | 29.91% | |
| Unit attribute | Public hospital | 168 | 37.5% |
| Specialized infectious disease hospital | 156 | 34.82% | |
| Community health station | 124 | 27.68% |
Assessment of the measurement model (construct reliability and validly).
| Construct/Variables | Loads | CR | AVE |
|---|---|---|---|
| Employee Care | 0.960 | 0.773 | |
| At a difficult time, would your supervisor be willing to lend an ear? | 0.879 | ||
| Is your supervisor caring? | 0.913 | ||
| Do you feel that your supervisor is empathic and understanding about your work concerns? | 0.890 | ||
| Does your supervisor treat you as you would like to be treated? | 0.888 | ||
| Does your supervisor shoulder some of your worries about work? | 0.864 | ||
| Do you feel your transactions with your supervisor are, in general, positive? | 0.876 | ||
| Do you believe that your employer cares about their staff ’s wellbeing? | 0.842 | ||
| Perceived Organizational Support | 0.961 | 0.778 | |
| My organization considers my goals and values. | 0.880 | ||
| My organization really cares about my wellbeing. | 0.894 | ||
| My organization shows a lot of concern for me. | 0.836 | ||
| My organization would forgive an honest mistake on my part. | 0.900 | ||
| My organization cares about my opinion. | 0.892 | ||
| My organization would never take advantage of me. | 0.895 | ||
| Help is available from my organization when I have a problem. | 0.876 | ||
| Turnover Intention | 0.939 | 0.794 | |
| I sometimes feel compelled to quit my job in my current workplace. | 0.873 | ||
| I will quit my job at my current organization in one year or less. | 0.876 | ||
| I am currently seriously considering leaving my current job to work at another company. | 0.922 | ||
| I will quit this company if the given condition gets even a little worse than now. | 0.893 | ||
| Work Engagement | 0.956 | 0.814 | |
| I find the work that I do full of meaning and purpose. | 0.881 | ||
| I am enthusiastic about my job. | 0.915 | ||
| My job inspires me. | 0.928 | ||
| At my work, I feel bursting with energy. | 0.915 | ||
| I get carried away when I am working. | 0.873 |
Note: CR = composite reliability, AVE = average variance extracted.
Fornell—Larker Criterion.
| EC | POS | TI | WE | |
|---|---|---|---|---|
| EC | 0.874 | |||
| POS | 0.805 | 0.881 | ||
| TI | −0.246 | −0.318 | 0.893 | |
| WE | 0.661 | 0.698 | −0.238 | 0.899 |
Heterotrait–monotrait Ratio (HTMT).
| EC | POS | TI | WE | |
|---|---|---|---|---|
| EC | ||||
| POS | 0.847 | |||
| TI | 0.265 | 0.340 | ||
| WE | 0.698 | 0.734 | 0.250 |
Fitness of the research model.
| R2 | Q2 (=1 − SSE/SSO) | SMRM | |
|---|---|---|---|
| TI | 0.057 | 0.034 | 0.048 |
| WE | 0.515 | 0.407 |
Bootstrapping results.
| β | t | p Values | Conclusion | |
|---|---|---|---|---|
| Research hypothesis testing | ||||
| Employee Care -> Work Engagement | 0.281 | 2.727 | 0.007 | Support |
| POS -> Work Engagement | 0.472 | 5.330 | 0.000 | Support |
| Work Engagement -> Turnover Intention | −0.238 | 3.000 | 0.003 | Support |
| EC -> WE -> TI | −0.067 | 1.871 | 0.062 | Not support |
| POS -> WE-> TI | −0.112 | 2.565 | 0.010 | Support |
| Control variables effect | ||||
| Professional title -> Turnover Intention | 0.466 | 9.613 | 0.000 | Support |
| Professional title -> Work Engagement | −0.085 | 2.072 | 0.039 | Support |
| Unit attribute -> Turnover Intention | −0.220 | 3.586 | 0.000 | Support |
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; Mahomed, Anuar; Khan, Hanna