Abstract
Purpose
Grounded in social cognitive career theory, this study investigates how employees' perceptions of job security and well-being affected their performance during the COVID-19 pandemic. It also examines the moderating effects of perceived organizational support and psychological capital on well-being and performance.
Design/methodology/approach
Using a two-wave time-lagged design, data were collected from 279 frontline employees in public service organizations in Saudi Arabia.
Findings
The study’s results show that perceived job security significantly affects job performance. Employee well-being significantly and positively influences job performance and partially mediates the relationship between perceived job security and job performance. Additionally, perceived organizational support and psychological capital positively moderated the relationship between employee well-being and job performance during the pandemic.
Practical implications
This study suggests that policymakers and practitioners need to prioritize addressing the job security concerns and well-being of frontline employees during a pandemic to enhance employee performance.
Originality/value
Our findings present significant implications for policymakers in the context of job security and performance within public organizations in emerging countries.
研究目的
本研究以社會認知生涯理論為基礎,去探討在2019冠狀病毒病疫情期間,僱員對職業保障和福祉的看法如何影響他們的工作績效; 研究亦擬探討感知組織支持和心理資本對福祉和工作績效所起的調節作用。
研究方法
研究人員使用雙波時間遞延設計收集數據; 數據取自於沙特阿拉伯的公共服務組織內工作的279名一線員工。
研究結果
研究結果顯示,僱員的感知職業保障會對他們的工作績效有顯著的影響; 另外,僱員的福祉會正面和顯著地影響他們的工作績效; 而且,僱員的福祉也會局部地調節感知職業保障與工作績效之間的關係。再者,研究人員發現,在大流行期間,感知組織支持和心理資本正面調節了僱員福祉與工作績效之間的關係。
研究的啟示
研究結果建議政策制定者和從業人員必須於大流行肆虐期間,優先處理有關職業保障的關注和一線員工的福祉,俾能提升僱員的工作績效。
研究的原創性
本研究的結果為政策制定者提供了重要的啟示,幫助他們於公共組織的環境內,能更有效地處理關於職業保障和僱員工作績效的事宜。
Keywords
Citation
Al Nahyan, M.T., Al Ahbabi, J.M., Alabdulrahman, M.A., Alhosani, I., Jabeen, F. and Farouk, S. (2024), "Employee job security and job performance: the mediating role of well-being and the moderating role of perceived organizational support and psychological capital", European Journal of Management and Business Economics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJMBE-01-2023-0011
Publisher
:Emerald Publishing Limited
Copyright © 2024, Moza Tahnoon Al Nahyan, Jawaher Majdi Al Ahbabi, Mesheal Abdulmohsen Alabdulrahman, Ibrahim Alhosani, Fauzia Jabeen and Sherine Farouk
License
Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The COVID-19 pandemic has triggered a high degree of uncertainty into the business landscape (Charoensukmongkol and Pandey, 2023). Tuzovic and Kabadayi (2021) suggest that the service sector has experienced significant disruption, with companies struggling to maintain their viability. While many individuals lost their jobs, those who remained employed had to grapple with the pandemic’s psychological impacts while working remotely. The pandemic has severely disrupted service delivery channels, causing many companies to struggle to maintain service continuity (Suthatorn and Charoensukmongkol, 2023). This high level of business environment volatility during the pandemic provides a backdrop for studying employee behavior and its outcomes in service organizations.
Job (in)security presents an invisible problem for organizations and is a distraction that impacts individual performance (Jones et al., 1998). Witte’s seminal study (1999) validated that job (in)security is a catalyst for diminishing employee well-being. While the benefits of various employee-related practices were explored in many contexts before the pandemic (Haque, 2023), the pandemic’s impacts have posed challenges for all organizations (Charoensukmongkol and Pandey, 2023). The pandemic has significantly altered employee behavior and disrupted work outcomes (Mehmood et al., 2023) and hence, the perceived job security of public sector employees in an emerging Arab country, where the pandemic has altered employment dynamics and made them as vulnerable to job loss as their private sector counterparts, warrants further examination (Mehmood et al., 2022).
It is crucial to determine whether the quality of organizational support is adequate to alleviate employees' job security concerns (AlHashmi et al., 2019). Another factor that could impact employees during the pandemic is the management of work within the organization (Puyod and Charoensukmongkol, 2021). Essentially, perceived organizational support (POS) and psychological capital (PsyCap) lay the groundwork for adding resources that enhance the workforce’s collective mindset and encourage excellence in their respective workplaces (Froman, 2010). Understanding employees' challenges regarding their POS and PsyCap is critical to improving their overall well-being during and after the COVID-19 pandemic. Moreover, social distancing measures hinder resource and emotional support that rely on the organization’s culture (Jabeen and Isakovic, 2018), playing a significant role in fostering positive psychology, which is vital for optimal employee productivity.
Furthermore, employee reactions can vary based on organizational and external environmental factors (Morgeson et al., 2015). Rapid changes in job categories (Kramer and Kramer, 2020) and employees' acceptance of remote work (Akgunduz et al., 2018) can lead to job insecurity (Blustein et al., 2020). In a recent study, Aguiar-Quintana et al. (2021) highlighted that the employees' job security did not affect their performance. However, some studies (Staufenbiel and König, 2010) reported a positive relationship between job insecurity and job performance and related it to the suppressor effect on employees' performance. According to them, employees with high job insecurity will be motivated to work harder to prove their worth to the organization. However, the impact of these reactions on performance is not adequately documented in the context of developing countries during the COVID-19 pandemic. Hence, such varying findings advocate the existence of some mediating and moderating variables that could alleviate or even converse the influence of job (in)security on job performance.
Social cognitive career theory (SCCT) provides a framework for predicting changes in human and cognitive behaviors (Duffy et al., 2014). Human behaviors are shaped by the interplay of internal feelings, emotions, and surroundings (Chang and Edwards, 2015). SCCT helps construct a research framework that articulates the relationship between work activities, environmental factors, and psychological needs (Jemini-Gashi et al., 2021). Therefore, this study, grounded in SCCT, aims to explore the influence of employees' perceptions of job security and well-being on their performance during the COVID-19 pandemic. Consequently, this study seeks to answer the following research questions:
How does job security and employee well-being affect job performance during social distancing protocols?
How does the mediating effect of employee well-being affect perceived job security and job performance?
How does POS moderate the role of employee well-being and job performance such that the relationship strengthens as POS increases?
How does PsyCap moderate the role of employee well-being and job performance such that the relationship strengthens as PsyCap increases?
This study offers three significant contributions to the existing literature. First, we base our research model on the theoretical foundations of the SCCT framework, a novel approach in COVID-19 research. A comprehensive understanding of SCCT factors can significantly improve employee performance, even after a pandemic. Second, we examine the moderating role of PsyCap and POS, demonstrating how to enhance employee job performance. Lastly, our study concentrates on frontline employees in public organizations in Saudi Arabia, a context that has previously received insufficient academic attention. The study findings shall provide insights to the service organizations to establish a work environment to improve their employee’s well-being and performance.
2. Literature review and hypotheses development
2.1 Employee’s well-being versus job performance
Employee well-being pertains to their optimal mental and physical health, which is influenced by organizational dynamics and occasionally extends to factors beyond the workplace. The enhancement of employee well-being is a primary task for many global employers and leaders, because it directly impacts individual and organizational performance (Johari et al., 2019). Usman (2017) further emphasizes that an employee’s mental state is a critical determinant of job performance. Given the pandemic’s impact on well-being, researchers have focused on exploring the organizational factors that affect employees' work and psychological outcomes (Puyod and Charoensukmongkol, 2021). The psychological well-being of employees encompasses various organizational factors such as innovation, creativity, and engagement in organizational roles and job-related activities, all of which promote improved individual performance.
Kundi et al. (2020) underscore the importance of promoting psychological well-being, while García-Cabrera et al. (2018) suggest that a healthy organization stems from the enhancement of employees' mental well-being, leading to improved job performance. This context clarifies the necessity for employees to maintain robust mental and physical health to positively impact both individual and organizational performance. Workplace culture, encompassing various facets of employer-employee interactions, can foster greater job satisfaction (Jabeen et al., 2018). When employees are satisfied with their specific job duties, it facilitates higher individual job performance, thereby boosting overall organizational performance. However, the social distancing measures imposed due to the COVID-19 pandemic have created conditions that challenge employees' ability to sustain a psychological state conducive to fulfilling their organizational roles effectively (Kundi et al., 2020). In essence, the shift to remote work, necessitated by pandemic protocols, has disrupted employees' engagement with workplace culture, a critical factor in their psychological and physical well-being. Therefore, we propose the following:
Employee well-being positively affects job performance during social distancing protocols.
2.2 Perceived job security and job performance
An organization can cultivate a workplace culture that promotes intrinsic motivation among employees, which is rooted in employee-employer relationships and interactions. Gerhart and Fang (2015) argue that this intrinsic motivation significantly impacts employee performance through various micro-factors. An individual’s intrinsic perspectives, emotions, and environment play a role in shaping their behavior (Chang and Edwards, 2015). SCCT has become a crucial framework for illustrating the interplay between cognitive, environmental, and behavioral factors (Liguori et al., 2020). This theory helps structure an investigation into the significance of environmental influences, psychological factors, and work activities. These elements encompass individual differences in self-perception and personality, variations in temperament, decision-making processes, workplace interaction, and consultative skills.
Ganta (2014) argued that workplace motivation significantly shapes employees' sense of job fulfillment. This suggests that during remote work, the absence of the workplace culture fostered by the broader organizational culture could potentially impact employees' perceived job security, thereby inducing stress (Gerhart and Fang, 2015; Jabeen et al., 2020). Employees under stress often demonstrate reduced performance, leading to diminished organizational output. Moreover, fears about job security typically arise when employees are required to alter their work schedules, resulting in decreased performance. This is because employees may view themselves as dispensable, given the organization’s apparent capacity to operate effectively with a smaller workforce.
Darvishmotevali and Ali (2020) argue that job security boosts employee well-being, which in turn influences long-term performance. Cheng and Chan (2008) found that job security positively impacts employees' mental and physical health. These conditions affect job security, which subsequently influences overall well-being and performance. Lowe (2020) contends that the foundation of healthy organizations is employee well-being. Prior research supports the importance of examining employee well-being to understand the relationships between various HR dimensions and employee outcomes (Khoreva and Wechtler, 2018). Additionally, Darvishmotevali and Ali (2020) demonstrated that a sense of job security correlates with higher scores on several work-related well-being indicators, such as elevated levels of emotional engagement. This suggests that an increase in perceived job security can enhance individual resources. Therefore, our study aims to clarify the relationship between job security and job performance by improving employee well-being. Consequently, we propose the following hypotheses:
Perceived job security positively affects employee job performance during social distancing protocols.
There is a direct effect of perceived job security on job performance.
There is an indirect effect of perceived job security on job performance through the mediating role of employee well-being.
2.3 Moderating role of POS on employee well-being and job performance
Eisenberger et al. (1986, p. 501) define POS as “employees” beliefs concerning the extent to which the organization values their contributions and cares about their well-being.” Organizations provide crucial training that enables employees to handle job-related stress and adopt new skills and strategies to address organizational challenges (Ariza-Montes et al., 2018). This acquisition of effective work skills simplifies tasks, leading to enhanced job satisfaction. Cheng and Yi (2018) demonstrate how attributes such as hope and resilience affect an employee’s well-being, thereby influencing their job performance. Prior studies (Charoensukmongkol and Suthatorn, 2022; Shen et al., 2018) contend that certain human resource actions, including organizational leadership and culture, forecast positive relationships. Social distancing disrupts specific human resource management practices, resulting in reduced employee motivation, a situation that often leads to decreased job performance (Khoreva and Wechtler, 2018). Previous research (Guang and Charoensukmongkol, 2022) confirmed that POS is a crucial practice that can strengthen the effects of workplace factors on work outcomes and employee well-being.
Human practices significantly influence the role of POS in employee well-being and job performance. Haddon (2018) discusses the correlation between employee productivity and human resource practices, particularly in relation to employee well-being and performance. Kosinski et al. (2015) reveals that human resource initiatives, such as individual or group motivation, can boost job performance, which in turn leads to improved organizational productivity. Therefore, POS can be a vital tool in managing stress and fostering a positive outlook on life, which promotes a mindset conducive to achieving goals (Roemer and Harris, 2018). While POS traits may be inherent in an employee’s character, organizations and human resource managers should proactively invest time and resources in prioritizing employee well-being (Naseer et al., 2018). Therefore, we propose the following hypothesis:
POS positively moderates the relationship between employee well-being and job performance, such that the relationship strengthens as POS increases.
2.4 The moderating role of PsyCap on employee well-being and job performance
PsyCap is defined as an individual’s positive psychological state of development, encompassing elements such as self-efficacy, hope, optimism, and resilience (Luthans et al., 2015). Each of these characteristics positively influences overall health and work-related outcomes (Alarcon et al., 2013). Luthans et al. (2015) further characterize PsyCap as a state that demonstrates a readiness for change. Self-efficacy pertains to an individual’s confidence in their ability to successfully perform organizational roles. People with high self-efficacy are typically determined to reach their goals and often strive to achieve organizational objectives as if they were their own (Kotzé, 2018). Optimism, on the other hand, is the trait of expecting positive outcomes from one’s efforts (Carver et al., 2010). Optimistic individuals usually maintain a positive attitude, which helps them remain steadfast when addressing crises and seeking positive change in personal and organizational challenges (Krok, 2015). Lastly, resilience, a common trait among highly adaptable individuals, is crucial when encountering challenges or adversities (Friedman et al., 2017). It enables employees to bounce back from difficult situations while simultaneously learning from the experience (Luthans and Youssef-Morgan, 2017).
Regarding employees' PsyCap, a positive correlation can be established between their mental state and job performance. Thus, PsyCap can serve as a dependable resource that enables workers to manage stressful situations (Fu and Charoensukmongkol, 2022). Moreover, while PsyCap can be inherent in an employee, organizations and human resource managers need to invest more time and resources in prioritizing employee well-being. The social distancing measures implemented during the current pandemic have created a challenging environment for meeting employees' developmental needs. Consequently, employee well-being has become a priority during this pandemic. Therefore, we propose the following hypothesis:
PsyCap moderates the relationship between employee well-being and job performance, such that the relationship strengthens as PysCap increases.
Figure 1 presents the conceptual model of this study.
3. Methodology
3.1 Sample and data collection
The data for this study were gathered from frontline employees in public service organizations across five municipal bureaus in Riyadh, Saudi Arabia, from September to December 2021. These bureaus, which include municipal public offices and transport, civil affairs, land and housing, and healthcare, provide essential public services. After securing approval from the respective organizations' HR departments, a list of employees with at least two years of experience was compiled. The survey was then distributed to a random sample of frontline employees within each public organization.
In this study, we used a time-lagged design to mitigate the risk of common method bias (CMB) (Podsakoff et al., 2012). Data were gathered in two waves, six weeks apart. In the first wave (T1), we collected data on perceived job security, employee well-being, and demographic information. With the help of the HR department, we distributed the survey to 500 employees and received 350 valid responses, a response rate of 70%. In the second wave (T2), six weeks later, we collected data on POS, PsyCap, and job performance from the initial 350 respondents. We received 279 valid responses, a response rate of 79.71%. The final sample consisted of these 279 employees, 45.9% of whom were male and 54.1% female. Additionally, 69.1% held a bachelor’s degree, and 30.9% had a master’s degree.
3.2 Measures
We used measurement items from previous studies for our survey. The survey was translated into Arabic, and then back-translated into English by a certified local translation service. Perceived job security was measured using a four-item scale adapted from Filimonau et al. (2020), with a sample item being, “When the Covid-19 crisis is over, my job will be secure” (α = 0.896). Employee well-being was measured using an 11-item scale adapted from Tennant et al. (2007), with a sample item being, “I’ve been feeling good about myself” (α = 0.937). Job performance was measured using an 18-item scale from Ramos-Villagrasa et al. (2019), with a sample item being, “I managed to plan my work so that I finished it on time” (α = 0.884). POS was measured using an eight-item scale adapted from Rhoades and Eisenberger (2002), with a sample item being, “My organization strongly considers my goals and values” (α = 0.720). PsyCap was measured using a 24-item scale adapted from Luthans (2007), with a sample item being, “I feel confident analyzing a long-term problem to find a solution” (α = 0.914).
3.3 Data analysis
Initially, we examined the characteristics of the data and the significance of the individual variables using descriptive statistics and a one-sample t-test, respectively. Following these analyses, we conducted a correlation analysis of the variables using Pearson’s correlation. We also employed a hierarchical least squares regression to test the models outlined in the previous section. These analyses included tests for autocorrelation, multicollinearity, and the normality of residuals. All tests were performed using IBM® SPSS® Statistics software.
4. Results
4.1 Reliability, validity, and CMB
As shown in Table 1, we used a confirmatory factor analysis (CFA) to assess reliability, convergent validity, and discriminant validity. The reliability of the constructs was confirmed by Cronbach’s alpha values exceeding the 0.70 threshold (Nunnally, 1970; Elkhwesky et al., 2023a). Convergent validity (Table 1) was then evaluated using factor loadings and average variance extracted (AVE) measures. All items had significant factor loading (p < 0.001), and the AVE for perceived job security and employee well-being exceeded 0.50, establishing convergent validity (Table 1; Fornell and Larcker, 1981). Although the AVE for job performance, POS, and PsyCap was less than 0.5, the composite reliability exceeded 0.6, making the construct’s convergent validity acceptable. Furthermore, the AVE was greater than the shared variance, that is, the square of the intercorrelations among constructs, indicating the constructs' discriminant validity (Hair et al., 2019). We used Harman’s single-factor test to examine the measurement context effect, which revealed multiple distinct factors. The first factor accounted for 31.676% of the variance, suggesting that CMB was unlikely to be a concern in our survey.
4.2 Descriptive statistics and one-sample t-test
Following the verification of response reliability, we integrated descriptive statistics and one-sample t-test results. The descriptive statistics are presented in Table 2.
The results in Table 2 reveal that perceived job security has a mean score of 3.9229 and a standard deviation of 0.91494, indicating minimal volatility and slight variation in responses. Employee well-being has a mean score of 3.46 and a standard deviation of 0.82337, also suggesting low volatility in responses. Similar low volatility is observed in the statistics for job performance, POS, and PsyCap. Furthermore, the skewness coefficient for all variables is close to zero, implying a normal distribution of the data.
Table 3 displays the results of the one-sample t-test, which was used to determine whether the data variation was significant. The perceived job security variable has a t-statistic of 71.618, indicating its statistical significance at a 0.01 level of significance. The t-statistics for employee well-being and job performance are 70.345 and 103.827, respectively, both of which are statistically significant at a 0.01 level of significance. The variables POS and PsyCap are also significant at the 0.01 level, because their two-tailed significant statistics are close to zero. Their respective t-statistics, 98.745 and 110.420, further confirm their statistical significance.
4.3 Correlation analysis
Table 4 displays the results of Pearson’s correlation with significance. There is a significant positive correlation between perceived job security and employee well-being, although the correlation is moderate, at 39.2%. Job performance also shows a statistically significant positive correlation with perceived job security, with a correlation of 38.3%. Both variables are directly proportional to job security during the COVID-19 pandemic. Furthermore, both POS and PsyCap show a strong correlation with employee well-being and job performance.
4.4 Impact of perceived job security on employee well-being
We use a hierarchical least square (HLS) regression to examine the effect of perceived job security during the COVID-19 pandemic, considering the moderating variables of POS and PsyCap. The results of the HLS regression are displayed in Tables 5 and 6.
The SPSS software performed the HLS regression on Model I and Model II concurrently. According to the HLS analysis results in Table 5, Model I accounted for 15.3% of the population, while Model II represented 40.9% of the population. Furthermore, our models did not display any autocorrelation.
Table 6 provides the coefficient statistics. In Model I, there is a positive correlation between perceived job security and employee well-being, with a t-statistic of 7.085, indicating a highly significant probability at a 0.01 level of significance. Model II further illustrates the positive effect of perceived job security on employee well-being, even when considering the presence of POS and PsyCap. The t-statistic for perceived job security in this model is 4.214, and with a significance level of 0.01, this variable, along with POS and PsyCap, are statistically significant, explaining their impact on employee well-being. Based on these statistics, Hypothesis 2 (H2) remains valid, owing to the significant positive influence of perceived job security on employees' job performance. Additionally, both moderators significantly and positively affected employee well-being during the pandemic, so we cannot reject Hypotheses 3 (H3) or 4 (H4).
4.5 Impact of perceived job security on job performance
According to the results presented in Table 7, Model III accounted for 14.7% of the population results, whereas Model IV accounted for 61.4%. Furthermore, the regression showed no signs of autocorrelation.
According to Table 8, there is a positive correlation between perceived job security and job performance. This correlation is statistically significant, as evidenced by a t-statistic of 6.89 and a near-zero probability value. Model IV further illustrates the positive correlation between perceived job security and job performance, moderated by POS and PsyCap. The coefficient of perceived job security in this model has a t-statistic of 3.359 and a significance probability of 0.001, indicating a significant positive correlation at the 0.01 level of significance. Additionally, no multicollinearity issues were found among the independent variables.
4.6 Mediation analysis
This study applied the method proposed by Baron and Kenny (1986) to assess the mediating role of employee well-being in the relationship between perceived job security and job performance. Simple and multiple regression analyses were used to evaluate the four critical conditions suggested by Baron and Kenny (1986) for confirming the mediating relationship.
Table 9 presents the results of the simple regression analysis, demonstrating a positive correlation between perceived job security and employee well-being (β = 0.33, t = 6.81, p < 0.00), thereby satisfying the first condition of Baron and Kenny’s (1986) mediation methodology. The second condition is also met, because perceived job security correlates significantly with job performance (β = 0.22, t = 6.50, p < 0.00) (H2a). The third condition is fulfilled because employee well-being is positively and directly linked to job performance (β = 0.38, t = 11.31, p < 0.00). For the fourth condition, Table 10 reveals that employee well-being partially mediates the relationship. Specifically, Table 10 shows that the correlation between perceived job security and job performance remains significant, but is reduced (β = 0.10, t = 3.33) when employee well-being is introduced as a mediating variable. Thus, employee well-being partially mediates the relationship between perceived job security and job performance (H2b). Figure 2 presents the structural model.
4.7 Moderation analysis
We conducted a moderation analysis using the PROCESS macros in SPSS software, as introduced by Hayes (2018). This tool employs a conditional process analysis to investigate the relationship between two variables, moderated by a third variable. It determines the existence of a moderating effect by incorporating a linear interaction term into a multiple regression model. This analysis necessitates the consideration of both the lower limit confidence interval (LLCI) and the upper limit confidence interval (ULCI). Given that the LLCI value was 0.3859 and the ULCI was 0.4367, both of which are not equal to zero, the output is based on the p-value (p < 0.05), indicating a significant effect, as outlined in the hypothesis.
Table 11 demonstrates that POS significantly moderates employee well-being, as evidenced by a t-statistic value of 4.38 and a p-value of 0.00, which is below the 0.05 threshold. This signifies the importance of POS at the 95% confidence level. Similarly, Table 12 reveals that PsyCap significantly moderates employee well-being, with a t-statistic value of 6.70 and a p-value of less than 0.05, indicating its significance at the 95% confidence level. Table 13 presents the model summary.
Figures 3 and 4 show visual representations of the conditional effects of the primary predictors.
Hypothesis 1 (H1) is accepted, indicating a positive correlation between perceived job security and job performance. Similarly, there is a statistically significant positive correlation between employee well-being and job performance, validating Hypothesis 2 (H2). Additionally, perceived job security is significantly related to job performance, supporting Hypothesis H2a. Employee well-being partially mediates the relationship between perceived job security and job performance, confirming Hypothesis H2b. Both moderators significantly enhance the relationship between employee well-being and job performance during the pandemic. Consequently, we can confidently conclude that our analyses substantiate all four hypotheses, demonstrating that the relationships between the variables under consideration are statistically significant.
5. Discussion
This study explored the impact of perceived job security on employee well-being and job performance amid the social distancing measures enforced during the COVID-19 pandemic. While prior research has examined the effects of such measures on employee well-being (Tuzovic and Kabadayi, 2021) and the relationship between job security, subjective well-being, and job performance (Darvishmotevali and Ali, 2020), the influence of perceived job security and employee well-being on job performance has not been investigated. The findings of this study not only confirm the existence of such an effect, but also identified POS and PsyCap as moderating factors. The research showed that perceived job security positively influenced both job performance and employee well-being. Furthermore, this relationship was found to be stronger when moderated by POS and PsyCap.
Our findings align with those of previous research. For instance, Umrani et al. (2019) discovered that job security and organizational support positively influenced employees' job performance. They proposed that ensuring job security and providing organizational support could enhance employees' job performance, a conclusion that resonates with our findings. Usman (2017) also found that improved psychological well-being positively affected job performance among corporate employees. Earlier studies have explored the impact of employee well-being on job performance from various angles. In their research on the relationship between employee well-being and performance, Edgar et al. (2017) found that different aspects of well-being contributed differently to employees' task and contextual performance, with happiness and trust showing a positive correlation with both types of performance. Johari et al. (2019) demonstrated that positive feedback positively affected employee well-being, serving as a significant mediator in the relationship between feedback and job performance.
This study’s findings revealed a significant impact of perceived job security on employees' job performance. Darvishmotevali and Ali (2020) discovered a positive correlation between job security and employees' job performance, primarily through an increase in subjective well-being. The study also tested a second hypothesis (H2), suggesting a positive effect of employee well-being on job performance, which yielded significant positive results. Given its direct influence on both individual and organizational performance, enhancing employee well-being remains a top priority for many companies and executives. Therefore, this study’s findings confirm a positive correlation between both employee well-being and job security with employee job performance.
Moreover, our findings indicate the critical role of employee well-being in the relationship between perceived job security and job performance. The data suggest that employee well-being contributes to a positive reciprocal response, which is manifested in their job performance. Our results confirm that when organizations invest in creating a secure job environment, and this is positively perceived by employees, it leads to an enhancement in job performance (H2a and H2b).
Our findings indicate that POS has a moderating effect on employee well-being and job performance, with the relationship strengthening as POS increases. Cheng and Yi’s (2018) study emphasized how POS elements such as hope and resilience affect employee well-being and performance. They argued that POS plays a crucial moderating role in assessing employee well-being and job performance, because it gauges the degree to which employees believe their employer values their contributions. It also illustrates the company’s concern for its employees' well-being and its potential to meet their socio-emotional needs. This study’s results reveal that these socio-emotional needs influence job performance through the effect of POS on employee well-being, which subsequently has a positive effect on job performance.
This study also used PsyCap as a moderator, which demonstrated a significant and positive impact on employee well-being and job performance. The assumption was that this relationship would strengthen as PsyCap increased. Fida et al. (2015) found that each PsyCap attribute positively influenced overall health and work-related outcomes. PsyCap, encompassing traits such as self-efficacy, hope, optimism, and adaptability, is a set of resources that enhance work performance and success. Our findings indicate a significant positive moderating effect of PsyCap attributes on employee well-being and job performance. This affirms that PsyCap could be one of the essential tools that Folkman and Lazarus (1984) identified as necessary for employees to manage stressful workplace situations.
5.1 Theoretical implications
This study presents three significant contributions to academic knowledge in this field of research. First, it expands upon the evolving body of work on the application of SCCT in the context of job security and performance, offering insights into previously unexplored implications. This is the first empirical study to apply the SCCT model in the setting of the COVID-19 pandemic within an emerging Arab country. Second, the study addresses the lack of prior literature on the effect of job security and employee well-being on work performance. We believe our findings will assist practitioners in uncovering new insights into employee work performance. Researchers will gain a deeper understanding of the contrasting outcomes, because the results support both hypotheses H1 and H2. Furthermore, the application of both POS and PsyCap to assess their effect on job performance yielded a positive effect. Third, this research could offer a fresh perspective on additional ways to amplify the positive effect of employee well-being on job performance. Lastly, this study enriches the existing literature in the context of emerging Arab countries by introducing a new boundary condition of employee well-being, considering PsyCap and POS. In this study, PsyCap and POS were found to bolster the relationship between employee well-being and job performance.
5.2 Practical implications
The study’s findings provide crucial recommendations for practitioners and policymakers. First, managers might observe an uptick in employee job performance as a result of simultaneous enhancements in employees' well-being and perceived job security. Therefore, managers should place a high priority on employees' psychological, social, and health well-being (Elkhwesky et al., 2023b). Our findings suggest that increasing employee well-being should be a primary focus for organizations, given its direct effect on employee job performance (Johari et al., 2019). Practitioners can boost their employees' well-being by providing mentorship, encouragement, assistance when needed, and offering team-building opportunities to make employees feel valued.
Second, this research is significant because it offers organizations a deeper understanding of the effect of well-being on work performance. It forms the foundation for a variety of organizational outcomes, such as innovation, creativity, and employee engagement in organizational tasks. Thus, managers could potentially value employees' psychological well-being to ensure improved job performance. They should develop policies and practices to enhance their employees' PsyCap through training and soft skills programs, thereby improving their mental health and job performance. We also discovered that if a company fails to implement measures for maintaining job security, employees' negative attitudes and behaviors related to work become apparent. Consequently, managers should consider ways to enhance employees' perceived job security factors to ensure optimal professional performance. Organizational strategies aimed at improving employees' job performance may not be effective if the employees lack self-confidence and experience stress. Interventions such as employee participation in planning, information sharing, and execution can reduce job insecurity and improve desired outcomes. By developing training programs and providing opportunities for employees to reflect on their work and learn new knowledge, practitioners can mitigate the effect of work stressors, thereby boosting their confidence and performance at work.
Lastly, it could be advantageous for managers to differentiate between employees with high and low levels of POS. It is crucial for businesses to ensure employees understand their value. By implementing effective performance management systems, managers can make employees feel appreciated and reassure them of their significant contributions to the workplace. Additionally, endorsing employees through a reward system can assist organizations in maintaining high levels of job security and productivity.
6. Conclusion
This study aimed to assess the impact of perceived job security and employee well-being on job performance during the implementation of social distancing protocols. Additionally, it examined the enhanced effect of PsyCap and POS on employee well-being during this period. Our findings emphasized the positive influence of POS and PsyCap on employee well-being and their combined direct effect on job performance. We anticipate that our findings will aid managers in formulating policies that enhance employee well-being and bolster perceived job security. Furthermore, the insights gained from this study could enable managers to better understand their employees' performance and devise effective strategies for improvement.
7. Limitations and future directions
The study has several limitations, including the use of self-reported data for analysis, which may introduce social desirability bias and generalization, potentially affecting the validity and credibility of the results. The study focused on the impact of job security and employee well-being on job performance, but other relevant variables could also influence job performance. Future research could incorporate dimensions such as work stress, conscientiousness, adaptability, and integrity. Additionally, the study did not consider the multidimensional structure of well-being, including psychological, social, and health aspects, and their potential impact on employee performance. This leaves unexplored areas for researchers and practitioners to investigate. The data were collected from Saudi Arabia, limiting the generalizability of the results to other national contexts. Future studies should aim to ensure the generalizability of the results across diverse cultures and contexts.
Figures
Figure 1
Conceptual model
Figure 2
Hypothesized structural model
Figure 3
The moderating effect of POS on the association between employee well-being and job performance
Figure. 4
The moderating effect of PsyCap on the association between employee well-being and job performance
Variable reliability
| Variable | Cronbach’s alpha | CR | λ | AVE |
|---|---|---|---|---|
| Perceived job security | 0.896 | 0.927 | 0.872–0.879 | 0.762 |
| Employee well-being | 0.937 | 0.946 | 0.694–0.871 | 0.616 |
| Job performance | 0.884 | 0.894 | 0.645–0.793 | 0.392 |
| POS | 0.720 | 0.824 | 0.684–0.827 | 0.477 |
| PsyCap | 0.914 | 0.922 | 0.549–0.778 | 0.373 |
Note(s): λ, factor loadings
Source(s): Authors
Descriptive statistics
| Mean | Std. Deviation | Skewness | Kurtosis | |||
|---|---|---|---|---|---|---|
| Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| Perceived job security | 3.9229 | 0.91494 | −0.806 | 0.146 | 0.569 | 0.291 |
| Employee well-being | 3.4676 | 0.82337 | −0.462 | 0.146 | 0.195 | 0.291 |
| Job performance | 3.4844 | 0.55955 | −0.012 | 0.146 | 1.407 | 0.291 |
| POS | 3.3759 | 0.57105 | −0.084 | 0.146 | 1.948 | 0.291 |
| PsyCap | 3.5618 | 0.53880 | −0.141 | 0.146 | 2.313 | 0.291 |
Note(s): N = 279
Source(s): Authors
One-sample t-test
| Test value = 0 | ||||||
|---|---|---|---|---|---|---|
| t | df | Sig. (2-tailed) | Mean difference | 95% confidence interval of the difference | ||
| Lower | Upper | |||||
| Perceived job security | 71.618 | 278 | 0.000 | 3.92294 | 3.8151 | 4.0308 |
| Employee well-being | 70.345 | 278 | 0.000 | 3.46758 | 3.3705 | 3.5646 |
| Job performance | 103.827 | 277 | 0.000 | 3.48441 | 3.4183 | 3.5505 |
| POS | 98.745 | 278 | 0.000 | 3.37590 | 3.3086 | 3.4432 |
| PsyCap | 110.420 | 278 | 0.000 | 3.56183 | 3.4983 | 3.6253 |
Source(s): Authors
Pearson’s correlation
| Perceived job security | Employee well-being | Job performance | POS | PsyCap | |
|---|---|---|---|---|---|
| Perceived job security | 1 | ||||
| Employee well-being | 0.392** | 1 | |||
| Job performance | 0.383** | 0.563** | 1 | ||
| POS | 0.293** | 0.526** | 0.668** | 1 | |
| PsyCap | 0.328** | 0.582** | 0.740** | 0.678** | 1 |
Note(s): **Correlation is significant at the 0.01 level (two-tailed)
Source(s): Authors
Hierarchal least squares regression analysis model summary (equations I and II)
| Model | R | R-square | Adjusted R-square | Std. Error of the estimate | Change statistics | Durbin–Watson | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| R-square change | F-change | df1 | df2 | Sig. F change | ||||||
| I | 0.392 | 0.153 | 0.150 | 0.75894 | 0.153 | 50.199 | 1 | 277 | 0.000 | |
| II | 0.640 | 0.409 | 0.403 | 0.63625 | 0.256 | 59.564 | 2 | 275 | 0.000 | 1.998 |
Note(s): Predictors: (constant), perceived job security
Predictors: (constant), perceived job security, POS, PsyCap
Dependent variable: employee well-being
Source(s): Authors
Hierarchical least squares regression analysis coefficient statistics (equations I and II)
| Model | Unstandardized coefficients | Standardized coefficients | t | Sig | Correlations | Collinearity statistics | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| B | Std. Error | Beta | Zero-order | Partial | Part | Tolerance | VIF | ||||
| I | (Constant) | 2.085 | 0.200 | 10.404 | 0.000 | ||||||
| Perceived job security | 0.352 | 0.050 | 0.392 | 7.085 | 0.000 | 0.392 | 0.392 | 0.392 | 1.000 | 1.000 | |
| II | (Constant) | −0.319 | 0.277 | −1.152 | 0.250 | ||||||
| Perceived job security | 0.187 | 0.044 | 0.208 | 4.214 | 0.000 | 0.392 | 0.246 | 0.195 | 0.883 | 1.132 | |
| POS | 0.312 | 0.091 | 0.216 | 3.409 | 0.001 | 0.526 | 0.201 | 0.158 | 0.534 | 1.872 | |
| PsyCap | 0.562 | 0.098 | 0.368 | 5.728 | 0.000 | 0.582 | 0.327 | 0.265 | 0.521 | 1.918 | |
Note(s): Dependent variable: employee well-being
Source(s): Authors
Hierarchical least squares regression analysis model summary (equations III and IV)
| Model | R | R-square | Adjusted R-square | Std. Error of the estimate | Change statistics | Durbin–Watson | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| R-square change | F change | df1 | df2 | Sig. F change | ||||||
| III | 0.383 | 0.147 | 0.144 | 0.51781 | 0.147 | 47.467 | 1 | 276 | 0.000 | |
| IV | 0.784 | 0.614 | 0.610 | 0.34945 | 0.467 | 166.008 | 2 | 274 | 0.000 | 1.906 |
Note(s): Predictors: (constant), perceived job security
Predictors: (constant), perceived job security, POS, PsyCap
dependent variable: job performance
Source(s): Authors
Hierarchical least squares regression analysis coefficient statistics (equations III and IV)
| Model | Unstandardized coefficients | Standardized coefficients | t | Sig | Correlations | Collinearity statistics | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| B | Std. Error | Beta | Zero-order | Partial | Part | Tolerance | VIF | ||||
| III | (Constant) | 2.567 | 0.137 | 18.769 | 0.000 | ||||||
| Perceived job security | 0.234 | 0.034 | 0.383 | 6.890 | 0.000 | 0.383 | 0.383 | 0.383 | 1.000 | 1.000 | |
| IV | (Constant) | 0.363 | 0.152 | 2.383 | 0.018 | ||||||
| Perceived job security | 0.082 | 0.024 | 0.134 | 3.359 | 0.001 | 0.383 | 0.199 | 0.126 | 0.883 | 1.133 | |
| POS | 0.285 | 0.050 | 0.291 | 5.662 | 0.000 | 0.668 | 0.324 | 0.212 | 0.534 | 1.871 | |
| PsyCap | 0.517 | 0.054 | 0.498 | 9.595 | 0.000 | 0.740 | 0.501 | 0.360 | 0.522 | 1.917 | |
Note(s): Dependent variable: job performance
Source(s): Authors
Regression analysis
| IV | DV: employee well-being | DV: job performance | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| β | R2 | Std. Error | t | Sig | β | R2 | Std. Error | t | Sig | |
| Perceived job security | 0.339 | 0.144 | 0.050 | 6.819 | 0.000 | 0.221 | 0.133 | 0.034 | 6.500 | 0.000 |
| Employee well-being | – | – | – | – | – | 0.382 | 0.317 | 0.034 | 11.316 | 0.000 |
Note(s): IV: independent variables, DV: dependent variables
Source(s): Authors
Multiple regression
| Independent variables | Job performance | |||||
|---|---|---|---|---|---|---|
| F | R2 | β | Std. Error | t | Sig | |
| Perceived job security | 42.244 | 0.344 | 0.107 | 0.032 | 3.337 | 0.001 |
| Employee well-being | 0.337 | 0.036 | 9.396 | 0.000 | ||
Source(s): Authors
Results of POS moderation analysis (model I)
| β | se | t | p | LLCI | ULCI | |
|---|---|---|---|---|---|---|
| constant | 0.85 | 0.43 | 1.98 | 0.05 | 0.00 | 1.70 |
| WB | 0.27 | 0.13 | 2.13 | 0.03 | 0.02 | 0.52 |
| POS | 0.58 | 0.13 | 4.38 | 0.00 | 0.32 | 0.84 |
| Int_1 | −0.02 | 0.04 | −0.6 | 0.55 | −0.09 | 0.05 |
Source(s): Authors
Results of PsyCap moderation analysis (model 2)
| Β | se | t | p | LLCI | ULCI | |
|---|---|---|---|---|---|---|
| constant | 0.22 | 0.40 | 0.55 | 0.58 | −0.57 | 1.01 |
| WB | 0.29 | 0.12 | 2.40 | 0.02 | 0.05 | 0.52 |
| PsyCap | 0.79 | 0.12 | 6.70 | 0.00 | 0.56 | 1.02 |
| Int_1 | 0.04 | 0.03 | −1.32 | 0.19 | −0.11 | 0.02 |
Source(s): Authors
Model summary
| R | R2 | MSE | F | DF1 | DF2 | p | |
|---|---|---|---|---|---|---|---|
| Model 1 | 0.71 | 0.51 | 0.16 | 94.61 | 3.00 | 274.00 | 0.00 |
| Model 2 | 0.76 | 0.58 | 0.13 | 124.18 | 3.00 | 274.00 | 0.00 |
Source(s): Authors
Moza Tahnoon Al Nahyan: Writing –review, editing, Writing –original draft, revising the document (Revision 1, Revision 2 and Revision 3), Methodology, Data curation, Conceptualization. Fauzia Jabeen: Writing –original draft, Methodology, Data curation, Conceptualization, Writing –review and editing. 3. Ibrahim Alhosani: Writing –original draft, Methodology, Data curation, Conceptualization. Mesheal Alabdulrahman: Validation, Methodology, Conceptualization. Jawaher Majdi Al Ahbabi: Writing –original draft, Methodology, Data curation, Conceptualization. Sherine Farouk: Methodology, Data curation, Conceptualization.
References
Aguiar-Quintana, T., Nguyen, T.H.H., Araujo-Cabrera, Y. and Sanabria-Díaz, J.M. (2021), “Do job insecurity, anxiety and depression caused by the COVID-19 pandemic influence hotel employees' self-rated task performance? The moderating role of employee resilience”, International Journal of Hospitality Management, Vol. 94, 102868, doi: 10.1016/j.ijhm.2021.102868.
Akgunduz, Y., Alkan, C. and Gök, Ö.A. (2018), “Perceived organizational support, employee creativity and proactive personality: the mediating effect of meaning of work”, Journal of Hospitality and Tourism Management, Vol. 34, pp. 105-114, doi: 10.1016/j.jhtm.2018.01.004.
Alarcon, G.M., Bowling, N.A. and Khazon, S. (2013), “Great expectations: a meta-analytic examination of optimism and hope”, Personality and Individual Differences, Vol. 54 No. 7, pp. 821-827.
AlHashmi, M., Jabeen, F. and Papastathopoulos, A. (2019), “Impact of leader–member exchange and perceived organisational support on turnover intention: the mediating effects of psychological stress”, Policing: An International Journal, Vol. 42 No. 4, pp. 520-536, doi: 10.1108/pijpsm-06-2018-0081.
Ariza-Montes, A., Arjona-Fuentes, J.M., Han, H. and Law, R. (2018), “Work environment and well-being of different occupational groups in hospitality: job demand–control–support model”, International Journal of Hospitality Management, Vol. 73, pp. 1-11, doi: 10.1016/j.ijhm.2018.01.010.
Baron, R.M. and Kenny, D.A. (1986), “The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology, Vol. 51 No. 6, pp. 1173-1182, doi: 10.1037//0022-3514.51.6.1173.
Blustein, D.L., Duffy, R., Ferreira, J.A., Cohen-Scali, V., Cinamon, R.G. and Allan, B.A. (2020), “Unemployment in the time of COVID-19: a research agenda”, Journal of Vocational Behavior, Vol. 119, 103436, doi: 10.1016/j.jvb.2020.103436.
Carver, C.S., Scheier, M.F. and Segerstrom, S.C. (2010), “Optimism”, Clinical Psychology Review, Vol. 30 No. 7, pp. 879-889, doi: 10.1016/j.cpr.2010.01.006.
Chang, Y. and Edwards, J.K. (2015), “Examining the relationships among self-efficacy, coping, and job satisfaction using social career cognitive theory: an SEM analysis”, Journal of Career Assessment, Vol. 23 No. 1, pp. 35-47, doi: 10.1177/1069072714523083.
Charoensukmongkol, P. and Pandey, A. (2023), “The flexibility of salespeople and management teams: how they interact and influence performance during the COVID-19 pandemic”, Asia Pacific Management Review, Vol. 28 No. 2, pp. 99-109, doi: 10.1016/j.apmrv.2022.07.001.
Charoensukmongkol, P. and Suthatorn, P. (2022), “How managerial communication reduces perceived job insecurity of flight attendants during the COVID-19 pandemic”, Corporate Communications: An International Journal, Vol. 27 No. 2, pp. 368-387, doi: 10.1108/ccij-07-2021-0080.
Cheng, G.H.L. and Chan, D.K.S. (2008), “Who suffers more from job insecurity? A meta‐analytic review”, Applied Psychology, Vol. 57 No. 2, pp. 272-303, doi: 10.1111/j.1464-0597.2007.00312.x.
Cheng, J.C. and Yi, O. (2018), “Hotel employee job crafting, burnout, and satisfaction: the moderating role of perceived organizational support”, International Journal of Hospitality Management, Vol. 72, pp. 78-85, doi: 10.1016/j.ijhm.2018.01.005.
Darvishmotevali, M. and Ali, F. (2020), “Job insecurity, subjective well-being and job performance: the moderating role of psychological capital”, International Journal of Hospitality Management, Vol. 87, p. 102462, doi: 10.1016/j.ijhm.2020.102462.
Duffy, R.D., Bott, E.M., Allan, B.A. and Autin, K.L. (2014), “Exploring the role of work volition within social cognitive career theory”, Journal of Career Assessment, Vol. 22 No. 3, pp. 465-478, doi: 10.1177/1069072713498576.
Edgar, F., Geare, A. and Zhang, J.A. (2017), “A comprehensive concomitant analysis of service employees' well-being and performance”, Personnel Review, Vol. 46 No. 8, pp. 1870-1889, doi: 10.1108/pr-05-2016-0108.
Eisenberger, R., Huntington, R., Hutchison, S. and Sowa, D. (1986), “Perceived organizational support”, Journal of Applied Psychology, Vol. 71 No. 3, pp. 500-507, doi: 10.1037//0021-9010.71.3.500.
Elkhwesky, Z., Abuelhassan, A.E., Elkhwesky, E.F.Y. and Khreis, S.H.A. (2023a), “Antecedents and consequences of behavioural intention to use virtual reality in tourism: evidence from Gen-Y and Gen-Z consumers in Egypt”, Tourism and Hospitality Research, p. 14673584231170576, doi: 10.1177/14673584231170576.
Elkhwesky, Z., Derhab, N., Elkhwesky, F.F.Y., Abuelhassan, A.E. and Hassan, H. (2023b), “Hotel employees' knowledge of monkeypox's source, symptoms, transmission, prevention, and treatment in Egypt”, Travel Medicine and Infectious Disease, Vol. 53, 102574, doi: 10.1016/j.tmaid.2023.102574.
Fida, R., Paciello, M., Tramontano, C., Fontaine, R.G., Barbaranelli, C. and Farnese, M.L. (2015), “An integrative approach to understanding counterproductive work behavior: the roles of stressors, negative emotions, and moral disengagement”, Journal of Business Ethics, Vol. 130 No. 1, pp. 131-144, doi: 10.1007/s10551-014-2209-5.
Filimonau, V., Derqui, B. and Matute, J. (2020), “The COVID-19 pandemic and organisational commitment of senior hotel managers”, International Journal of Hospitality Management, Vol. 91, 102659, doi: 10.1016/j.ijhm.2020.102659.
Folkman, S. and Lazarus, R.S. (1984), Stress, Appraisal, and Coping, Springer, New York.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50, doi: 10.2307/3151312.
Friedman, E.M., Ruini, C., Foy, R., Jaros, L., Sampson, H. and Ryff, C.D. (2017), “Lighten UP! A community-based group intervention to promote psychological well-being in older adults”, Aging and Mental Health, Vol. 21 No. 2, pp. 199-205, doi: 10.1080/13607863.2015.1093605.
Froman, L. (2010), “Positive psychology in the workplace”, Journal of Adult Development, Vol. 17 No. 2, pp. 59-69, doi: 10.1007/s10804-009-9080-0.
Fu, L. and Charoensukmongkol, P. (2022), “Benefits of psychological capital on host country nationals' support and burnout of Chinese expatriates in Thailand: the moderating effect of personal characteristics”, Asia-Pacific Journal of Business Administration, Vol. 14 No. 3, pp. 265-284, doi: 10.1108/apjba-06-2020-0181.
Ganta, V.C. (2014), “Motivation in the workplace to improve the employee performance”, International Journal of Engineering Technology, Management and Applied Sciences, Vol. 2 No. 6, pp. 221-230.
García-Cabrera, A.M., Lucia-Casademunt, A.M., Cuéllar-Molina, D. and Padilla-Angulo, L. (2018), “Negative work-family/family-work spillover and well-being across Europe in the hospitality industry: the role of perceived supervisor support”, Tourism Management Perspectives, Vol. 26, pp. 39-48, doi: 10.1016/j.tmp.2018.01.006.
Gerhart, B. and Fang, M. (2015), “Pay, intrinsic motivation, extrinsic motivation, performance, and creativity in the workplace: revisiting long-held beliefs”, Annual Review of Organizational Psychology and Organizational Behavior, Vol. 2 No. 1, pp. 489-521, doi: 10.1146/annurev-orgpsych-032414-111418.
Guang, X. and Charoensukmongkol, P. (2022), “The effects of cultural intelligence on leadership performance among Chinese expatriates working in Thailand”, Asian Business and Management, Vol. 21 No. 1, pp. 106-128, doi: 10.1057/s41291-020-00112-4.
Haddon, J. (2018), “The impact of employees' well-being on performance in the workplace”, Strategic HR Review, Vol. 17 No. 2, pp. 72-75, doi: 10.1108/shr-01-2018-0009.
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2019), Multivariate Data Analysis, 8th ed., Cengage, Boston.
Haque, A. (2023), “The impact of the COVID-19 pandemic on employee motivation and organisational resilience and the role of strategic HRM: renewal of a resource-based conceptual model”, Management Matters, Vol. 20 No. 2, pp. 117-133, doi: 10.1108/manm-02-2022-0022.
Jabeen, F. and Isakovic, A.A. (2018), “Examining the impact of organizational culture on trust and career satisfaction in the UAE public sector: a competing values perspective”, Employee Relations, Vol. 40 No. 6, pp. 1036-1053, doi: 10.1108/er-02-2017-0038.
Jabeen, F., Friesen, H.L. and Ghoudi, K. (2018), “Quality of work life of Emirati women and its influence on job satisfaction and turnover intention: evidence from the UAE”, Journal of Organizational Change Management, Vol. 31 No. 2, pp. 352-370, doi: 10.1108/jocm-01-2017-0016.
Hayes, A.F. (2018), “Partial, conditional, and moderated moderated mediation: quantification, inference, and interpretation”, Communication Monographs, Vol. 85 No. 1, pp. 4-40.
Jabeen, F., Al Hashmi, M. and Mishra, V. (2020), “Should I stay or should I go? The antecedents of turnover intention among police personnel”, Safer Communities, Vol. 19 No. 1, pp. 1-14, doi: 10.1108/sc-05-2019-0013.
Jemini-Gashi, L., Duraku, Z.H. and Kelmendi, K. (2021), “Associations between social support, career self-efficacy, and career indecision among youth”, Current Psychology, Vol. 40 No. 9, pp. 4691-4697, doi: 10.1007/s12144-019-00402-x.
Johari, J., Mohd Shamsudin, F., Fee Yean, T., Yahya, K.K. and Adnan, Z. (2019), “Job characteristics, employee well-being, and job performance of public sector employees in Malaysia”, International Journal of Public Sector Management, Vol. 32 No. 1, pp. 102-119, doi: 10.1108/ijpsm-09-2017-0257.
Jones, G., George, J. and Hill, C. (1998), Contemporary Management, 1st ed., McGraw Hill, New York, NY.
Khoreva, V. and Wechtler, H. (2018), “HR practices and employee performance: the mediating role of well-being”, Employee Relations, Vol. 40 No. 2, pp. 227-243.
Kosinski, M., Matz, S.C., Gosling, S.D., Popov, V. and Stillwell, D. (2015), “Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines”, American Psychologist, Vol. 70 No. 6, p. 543.
Kotzé, M. (2018), “The influence of psychological capital, self-leadership, and mindfulness on work engagement”, South African Journal of Psychology, Vol. 48 No. 2, pp. 279-292, doi: 10.1177/0081246317705812.
Kramer, A. and Kramer, K.Z. (2020), “The potential impact of the COVID-19 pandemic on occupational status, work from home, and occupational mobility”, Journal of Vocational Behavior, Vol. 119, 103442, doi: 10.1016/j.jvb.2020.103442.
Krok, D. (2015), “The mediating role of optimism in the relations between sense of coherence, subjective and psychological well-being among late adolescents”, Personality and Individual Differences, Vol. 85, pp. 134-139, doi: 10.1016/j.paid.2015.05.006.
Kundi, Y.M., Aboramadan, M., Elhamalawi, E.M. and Shahid, S. (2020), “Employee psychological well-being and job performance: exploring mediating and moderating mechanisms”, International Journal of Organizational Analysis, Vol. 29 No. 3, pp. 736-754, doi: 10.1108/ijoa-05-2020-2204.
Liguori, E., Winkler, C., Vanevenhoven, J., Winkel, D. and James, M. (2020), “Entrepreneurship as a career choice: intentions, attitudes, and outcome expectations”, Journal of Small Business and Entrepreneurship, Vol. 32 No. 4, pp. 311-331.
Lowe, G. (2020), Creating Healthy Organizations, University of Toronto Press, Toronto.
Luthans, F., Avolio, B.J., Avey, J.B. and Norman, S.M. (2007), “Psychological capital: measurement and relationship with performance and satisfaction”, Personnel Psychology, Vol. 60 No. 3, pp. 541-572, doi: 10.1111/j.1744-6570.2007.00083.x.
Luthans, F. and Youssef-Morgan, C.M. (2017), “Psychological capital: an evidence-based positive approach”, Annual Review of Organizational Psychology and Organizational Behavior, Vol. 4 No. 1, pp. 339-366, doi: 10.1146/annurev-orgpsych-032516-113324.
Luthans, F., Youssef-Morgan, C.M. and Avolio, B.J. (2015), Psychological Capital and beyond, Oxford University Press, New York.
Mehmood, K., Jabeen, F., Iftikhar, Y., Yan, M., Khan, A.N., AlNahyan, M.T., Alkindi, H.A. and Alhammadi, B.A. (2022), “Elucidating the effects of organisational practices on innovative work behavior in UAE public sector organisations: the mediating role of employees' wellbeing”, Applied Psychology: Health and Well‐Being, Vol. 14 No. 3, pp. 715-733, doi: 10.1111/aphw.12343.
Mehmood, K., Jabeen, F., Al Hammadi, K.I.S., Al Hammadi, A., Iftikhar, Y. and AlNahyan, M.T. (2023), “Disentangling employees' passion and work-related outcomes through the lens of cross-cultural examination: a two-wave empirical study”, International Journal of Manpower, Vol. 44 No. 1, pp. 37-57, doi: 10.1108/ijm-11-2020-0532.
Morgeson, F.P., Mitchell, T.R. and Liu, D. (2015), “Event system theory: an event-oriented approach to the organizational sciences”, Academy of Management Review, Vol. 40 No. 4, pp. 515-537, doi: 10.5465/amr.2012.0099.
Naseer, S., Raja, U., Syed, F. and Bouckenooghe, D. (2018), “Combined effects of workplace bullying and perceived organizational support on employee behaviors: does resource availability help?”, Anxiety, Stress, and Coping, Vol. 31 No. 6, pp. 654-668, doi: 10.1080/10615806.2018.1521516.
Nunnally, J.C.. Jr (1970), “Introduction to psychological measurement”.
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012), “Sources of method bias in social science research and recommendations on how to control it”, Annual Review of Psychology, Vol. 3 No. 1, pp. 539-569, doi: 10.1146/annurev-psych-120710-100452.
Puyod, J.V. and Charoensukmongkol, P. (2021), “Effects of workplace rumors and organizational formalization during the COVID-19 pandemic: a case study of universities in the Philippines”, Corporate Communications: An International Journal, Vol. 26 No. 4, pp. 793-812, doi: 10.1108/ccij-09-2020-0127.
Ramos-Villagrasa, P.J., Barrada, J.R., Fernández-del-Río, E. and Koopmans, L. (2019), “Assessing job performance using brief self-report scales: the case of the individual work performance questionnaire”, Revista de Psicología del Trabajo y de las Organizaciones, Vol. 35 No. 3, pp. 195-205, doi: 10.5093/jwop2019a21.
Rhoades, L. and Eisenberger, R. (2002), “Perceived organizational support: a review of the literature”, Journal of Applied Psychology, Vol. 87 No. 4, pp. 698-714, doi: 10.1037/0021-9010.87.4.698.
Roemer, A. and Harris, C. (2018), “Perceived organizational support and well-being: the role of psychological capital as a mediator”, SA Journal of Industrial Psychology, Vol. 44 No. 1, pp. 1-11, doi: 10.4102/sajip.v44i0.1539.
Shen, J., Dumont, J. and Deng, X. (2018), “Employees' perceptions of green HRM and non-green employee work outcomes: the social identity and stakeholder perspectives”, Group and Organization Management, Vol. 43 No. 4, pp. 594-622, doi: 10.1177/1059601116664610.
Staufenbiel, T. and König, C.J. (2010), “A model for the effects of job insecurity on performance, turnover intention, and absenteeism”, Journal of Occupational and Organizational Psychology, Vol. 83 No. 1, pp. 101-117, doi: 10.1348/096317908x401912.
Suthatorn, P. and Charoensukmongkol, P. (2023), “Effects of trust in organizations and trait mindfulness on optimism and perceived stress of flight attendants during the COVID-19 pandemic”, Personnel Review, Vol. 52 No. 3, pp. 882-899, doi: 10.1108/pr-06-2021-0396.
Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., Parkinson, J., Secker, J. and Stewart-Brown, S. (2007), “The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation”, Health and Quality of Life Outcomes, Vol. 5 No. 1, pp. 1-13, doi: 10.1186/1477-7525-5-63.
Tuzovic, S. and Kabadayi, S. (2021), “The influence of social distancing on employee well-being: a conceptual framework and research agenda”, Journal of Service Management, Vol. 32 No. 2, pp. 145-160, doi: 10.1108/josm-05-2020-0140.
Umrani, W.A., Afsar, B., Khan, M. and Ahmed, U. (2019), “Addressing the issue of job performance among hospital physicians in Pakistan: the role of job security, organizational support, and job satisfaction”, Journal of Applied Biobehavioral Research, Vol. 24 No. 3, e12169, doi: 10.1111/jabr.12169.
Usman, A. (2017), “The effect of psychological wellbeing on employee job performance: comparison between the employees of projectized and non-projectized organizations”, Journal of Entrepreneurship and Organization Management, Vol. 6 No. 01, pp. 8-12, doi: 10.4172/2169-026x.1000206.
Witte, H.D. (1999), “Job insecurity and psychological well-being: review of the literature and exploration of some unresolved issues”, European Journal of Work and Organizational Psychology, Vol. 8 No. 2, pp. 155-177, doi: 10.1080/135943299398302.
Further reading
Charoensukmongkol, P. (2022), “Does entrepreneurs' improvisational behavior improve firm performance in time of crisis?”, Management Research Review, Vol. 45 No. 1, pp. 26-46, doi: 10.1108/mrr-12-2020-0738.
Hayes, A.F. (2017), Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, Guilford, New York.
Acknowledgements
The authors acknowledge financial support from Abu Dhabi University’s Office of Research and Sponsored Programs Grant number: 19300768.
Corresponding author
About the authors
Dr Moza Tahnoon Al Nahyan is a Professor of Management at the College of Business at Abu Dhabi University, where she teaches management principles to business students. She has published her research works in international refereed journals and has received the “best paper award” in international conferences. She is also an entrepreneur and philanthropist and is known for her various charitable activities in the community.
Jawaher Majdi Al Ahbabi is a DBA Candidate at college of Business, Abu Dhabi University (ADU), United Arab Emirates. She holds a Master of Business Administration from ADU. Her research interest is in the areas of Management Sustainability, strategic management, technology. She is currently working in SEHA, since 2013.
Mesheal Abdulmohsen Alabdulrahman is a DBA Candidate at the college of Business, Abu Dhabi University (ADU), United Arab Emirates.
Ibrahim Alhosani: holds a BSc in Telecom Engineering from Khalifa University, UAE, and Master in BA from Zayed University, UAE. He is currently a part time DBA student at Abu Dhabi University, UAE. He spent his professional career working for more than 20 years in Etisalat Group, a telecom operator in the United Arab Emirates, in various departments such as engineering, sales and public relations, and he still works there as VIP’s and Government Relations’ Director.
Fauzia Jabeen (PhD) is a Professor of Management in the College of Business at Abu Dhabi University and a Visiting Professor at Burgundy School of Business, Dijon, France. In addition, she serves as Chapter Advisor for the Beta Gamma Sigma honor society at Abu Dhabi University. She has more than 20 years of experience in teaching, consulting and research in a wide variety of industries including manufacturing, telecom, education, utilities and healthcare. She has published work on behavior, innovation, sustainability, knowledge, and performance in leading journals, including the International Journal of Hospitality Management, Journal of Business Research, Technological Forecasting and Social Change, Journal of Knowledge Management, Business Strategy and the Environment.
Prof. Sherine Farouk has been an academic leader for more than 25 years at various international universities in the UAE, UK, and Egypt. She is the Associate Provost for academic projects and Internationalization, Associate Dean of the College of Business for Enrollment and student success, and Academic Mentor for the Sustainability Society at Abu Dhabi University.
Prof. Sherine is currently working on various academic projects at Abu Dhabi University including global engagement, international accreditations, study abroad programs, and institutional academic ranking and reputation. She also leads projects on retention, enrollment management, and student success. She is also involved in numerous initiatives related to industry engagement, including MOUs, professional development programs, and others.