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Article

Exploring the Associations between Social Support, Perceived Uncertainty, Job Stress, and Emotional Exhaustion during the COVID-19 Crisis

by
Aslı Ersoy
1,*,
Zahid Mahmood
2,
Shahbaz Sharif
3,
Nazlı Ersoy
4 and
Rüya Ehtiyar
5
1
Department of Tourism Management, Alanya HEP University, Alanya 07400, Turkey
2
Department of Management, College of Business Administration, King Saud University, Riyadh 11451, Saudi Arabia
3
School of Business and Management Sciences, Minhaj University Lahore, Lahore 54000, Pakistan
4
Department of Business, Kilis 7 Aralık University, Kilis 79000, Turkey
5
Department of Tourism Management, Akdeniz University, Antalya 07600, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2150; https://doi.org/10.3390/su15032150
Submission received: 1 December 2022 / Revised: 11 January 2023 / Accepted: 16 January 2023 / Published: 23 January 2023
(This article belongs to the Section Tourism, Culture, and Heritage)
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Versions Notes

Abstract

:
Building on the social support theory and the job demands-resources (JD-R) model, the current research explores the role of coworker task support on the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees affected by the COVID-19 crisis. Moreover, this research investigates the moderating impact of supervisor support and family support on the relationship between perceived uncertainty and emotional exhaustion. The data were collected from 353 hospitality employees currently working in the hospitality industry in Pakistan. Partial least squares structural equation modeling (PLS-SEM) was employed using SmartPLS 3.3.3 software to examine the proposed hypotheses and to analyze the research model. The results point out that coworker task support has no significant relationship with emotional exhaustion. Furthermore, perceived uncertainty and job stress fully mediated the association between coworker task support and emotional exhaustion. Additionally, supervisor support and family support significantly moderated the association between perceived uncertainty and emotional exhaustion. This research contributes to the literature by expanding our knowledge of the role of social support in alleviating the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees during the COVID-19 crisis. The theoretical and practical implications of the study are further discussed.

1. Introduction

COVID-19 has pushed entire industries worldwide, including the tourism and hotel industry, into an unprecedented crisis [1]. The tourism and hotel industry, despite being one of the largest and fastest-growing industries, is highly sensitive to health risks [2]. Many hotel businesses went bankrupt due to the COVID-19 crisis, and numerous hotel reservations were canceled due to fears of being infected with the coronavirus and travel restrictions [3]. Moreover, employee layoffs and leave during the pandemic and the uncertainty caused by this crisis increase the sense of job insecurity of tourism frontline employees, causing them anxiety and fear [4]. Job insecurity is a major source of job-related stress [5]. Past studies have shown that job insecurity has negative effects on employees’ psychological health, job performance [6,7], and absenteeism [5], leading to increased emotional exhaustion [6,8].
Furthermore, hospitality employees ineluctably experience emotional exhaustion while doing their jobs [9] because the hospitality industry is known for long working hours, low wages, and a high level of social intensity with customers and team members [10]. Despite these challenges, frontline employees play a significant role as they represent hospitality and interact directly with customers [11]. When all these factors are considered, hotel organizations should take care to protect the safety and health of their employees in serious crises such as COVID-19 [12]. Previous researchers have shown that social support helps reduce the emotional exhaustion of hospitality employees [13,14]. In this sense, it is possible to say that social support is a significant psychological resource for employees [15].
However, to our knowledge, there is no research into the role that coworker task support plays in alleviating the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees during the COVID-19 crisis. This research seeks to fill this gap. This study aims to extend the hospitality literature in various ways. First, the attempt of this research responds to the call that the effects of coworker task support in lowering individuals’ perceived uncertainty and emotional exhaustion during the COVID-19 crisis need to be studied in diverse sectors and on a larger sample [16]. Second, this research also enhances the knowledge regarding the role of supervisor support and family support in mitigating the effect of perceived uncertainty on emotional exhaustion during the pandemic. Third, this research intends to provide clues for managers to alleviate the psychological impacts of the COVID-19 crisis on employees.
This research set out to explore the roles of coworker task support in lessening the perceived uncertainty and job stress of hospitality employees during the pandemic and its effect on their emotional exhaustion. The theoretical background for the current research comes from (1) social support theory [17] and (2) the job demands-resources (JD-R) model [18] to explain the roles of coworker task support in reducing the perceived uncertainty and job stress of hospitality employees and its successive impact on their emotional exhaustion. Based on the JD-R model, job resources (e.g., family support and supervisor support) can buffer the impact of job demands (e.g., perceived uncertainty) on emotional exhaustion. Hence, the second purpose of the study is to explore the moderating effect of supervisor support and family support on the association between perceived uncertainty and emotional exhaustion.

2. Literature Review: Theoretical Framework and Hypothesis

2.1. Emotional Exhaustion among Hospitality Employees during the COVID-19 Crisis

A crisis is “a sudden and unexpected event that threatens to disrupt an organization’s operations and poses both a financial and a reputational threat” [19] (p. 163). The COVID-19 crisis, besides leading to financial instability worldwide, has also caused some psychological problems such as stress, anxiety, and uncertainty [1]. In this sense, emotional exhaustion is a common psychological problem experienced by employees when faced with uncertainties during a crisis [20]. Emotional exhaustion is defined as “a state of depletion in which one is overextended to the point of having nothing left to give” [21] (p. 275). Employees’ emotions are a significant determinant of attitude towards the organization. Emotionally exhausted employees’ participation in organizational activities and their desire to stay in the organization decreases and they have a sense of helplessness [22].
In service industries—particularly the hospitality and tourism industry—front-line workers are considered the connecting point between the customers and the organization, therefore, it is crucial for them to manage their feelings and interact with customers [23]. However, hospitality employees are extremely worried and feel stressed about working during the COVID-19 crisis [2]. Since COVID-19 is an extremely infectious disease, hospitality employees working in a high-contact setting face high risk [11]. Considering that those working on the front lines of the tourism industry have longer working hours and more frequent customer interactions than other industries [24], it is possible to say that COVID-19 may further increase the stress and emotional exhaustion of those employees.

2.2. Coworker Task Support and Emotional Exhaustion of Hospitality Employees

Social support indicates the support and care that people perceive they receive from others [25]. Social support acts as a buffer by reducing the mental and psychological damage caused by stressful events [26]. On the other hand, coworkers are confidants, they help lighten the workload, make challenging work environments enjoyable, and, finally, act as a reference of support for people [27]. Coworker support is stated as “the extent to which one’s coworkers are helpful, can be relied upon in times of need, and are receptive to work-related problems” [28] (p. 1360). Research has usually classified coworker support as emotional and instrumental [29,30]. Emotional support can take the form of sympathy, comfort, caring, and encouragement [31], while instrumental support includes assistance in the process of performing work-related tasks by colleagues [32]. This research revolves around instrumental support.
The lack of social support, especially with the effect of social distancing and quarantining applied at the beginning of COVID-19, is cited as a major stress factor that can harm the well-being of hospitality and tourism employees [33]. Because coworker support is an important social resource in itself, it can reduce feelings of emotional exhaustion and thus promote strong mental health [34]. Similarly, research has shown that coworker support buffers employees from burnout [35,36].
The relation between coworker task support and emotional exhaustion can also be explicated by social support theory [17]. The theory proposes that social support improves both physical and psychological well-being and enables individuals to deal better with stressful situations [37]. Recent research also indicates that social support helps reduce employees’ emotional exhaustion [16,38,39]. Overall, from the theoretical point of view of social support theory, it can be suggested that instrumental support may reduce hospitality employees’ emotional exhaustion during the COVID-19 crisis and help alleviate its negative consequences. Depending on the social support theory and the above results, the following hypothesis is offered:
Hypothesis 1 (H1). 
Coworker task support is negatively associated with hospitality employees’ emotional exhaustion.

2.3. The Mediating Role of Perceived Uncertainties

The psychological impact of the crisis on individuals is usually manifested in the form of perceived uncertainty [20]. In a similar vein, the rising deaths and infection, combined with the lack of treatment, have caused individuals to feel fear and uncertainty about COVID-19 [3]. Therefore, COVID-19 has caused fear, worry, and anxiety in individuals around the world. In terms of the hospitality and tourism industry, the pandemic and the resulting global recession have created extraordinary uncertainty and risk. Uncertainty is defined as “a potential deficiency in any phase or activity of the process, which can be characterized as not definite, not known or not reliable” [40] (p. 683). Individuals who do not tolerate uncertainty experience it as threatening and unacceptable. As a result, they have “a tendency to react negatively on an emotional, cognitive and behavioral level to uncertain situations” [41]. In this context, the uncertainties caused by the COVID-19 crisis have caused panic, anxiety, and frustration among tourism employees [42].
In the service industry, coworkers can be a source of both stress and support [43]. If coworkers are rude to each other, this will cause the employee to leave the job and reduce performance [44]. In addition, considering the diversity of the workforce in the hospitality industry, communication difficulties are considered a factor that hinders support among employees and has a negative impact on organizational outcomes [45]. On the other hand, employees’ positive relationships with their coworkers and supervisors can have a strong effect on job burnout [39]. Considering that social support reduces uncertainty [46], we assume that coworker support, which is one of the types of social support, may reduce the uncertainty and the emotional exhaustion of employees.
The role of coworker task support on the perceived uncertainty and emotional exhaustion of hospitality employees during the pandemic can also be explicated by the job demands-resources (JD-R) model [47]. Accordingly, working conditions are divided into two, as job demands and job resources. Job demands are “physical, social, or organizational aspects of the job that require sustained physical or mental effort and are therefore associated with certain physiological and psychological costs” [47] (p. 501). Examples of job demands are unsafe workloads, working conditions, and work pressure. Moreover, job resources are “physical, psychological, (i.e., cognitive and emotional), and social, or organizational aspects of the job that may do any of the following: (1) be functional in achieving work goals, (2) reduce job demands at the associated physiological and psychological costs, and (3) stimulate personal growth and development” [47] (p. 501). Examples of job resources are coworker support, supervisor support, feedback, and autonomy. The JD-R model assumes that burnout emerges when employees have limited resources to cope with high job demands [18,47]. The recent research literature has highlighted that employees’ perceived uncertainty during COVID-19 can be considered a kind of job demand that impacts their emotional exhaustion [16,20]. On the other hand, coworker task support can be considered a job resource as it is social support that can reduce employees’ anxiety about the uncertainties arising from the pandemic. For example, this role of coworker task support is argued in the research of Usman et al. [16]. They discovered that coworker task support plays a significant role in enabling employees to cope effectively with perceived uncertainty and emotional exhaustion. Given the relationship between coworker task support and emotional exhaustion, it can be expected that coworker task support has an impact on hospitality employees’ emotional exhaustion via perceived uncertainty during COVID-19. Thus, this research suggests the following:
Hypothesis 2 (H2). 
Perceived uncertainty mediates the effect of coworker task support on hospitality employees’ emotional exhaustion.

2.4. The Mediating Role of Job Stress

The COVID-19 crisis has had a huge effect on the hospitality industry worldwide, with millions of employees losing their jobs [48]. Hospitality employees are very worried about the financial losses caused by unpaid leave and job insecurity during the epidemic [49]. As a result, hospitality employees experienced high levels of anxiety and job stress during the COVID-19 crisis [2,50]. Moreover, given the fact that the hospitality industry is largely based on human contact, hospitality employees may feel psychological burdens and excessive stress during the pandemic period due to the contact that occurs during the service process to customers [49]. Thus, hospitality employees’ infection risk and job insecurity are the two main stress factors faced during the pandemic [51].
Job stress is defined as “the change in one’s physical or mental state in response to the workplace that pose a clear challenge or threat to that employee’s wellbeing” [52] (p. 90). Job stress has negative impacts on both employees and the organization. Some studies have indicated that job stress is linked with employee job dissatisfaction [2,53], low job performance [54], and high turnover intention [10,55]. Moreover, some research in the hospitality and tourism industry has explored the association between COVID-19 and job stress. Aguiar-Quintana et al. [56] examined how job insecurity creates anxiety and depression among hospitality employees during the COVID-19 crisis. Chen and Eyoun [4] exposed that COVID-19-based job insecurity can lead to emotional exhaustion among restaurant front-line employees. Wong et al. [2] found that hotel occupational stressors that emerged after COVID-19 reduced organizational commitment and job satisfaction and created a state of extreme anxiety. Based on all these results, it can be said that the more employees worry about the pandemic, the more they will perceive job-related risks and the more emotionally exhausted and stressed they will feel [11].
According to the JD-R model, job demands “evoke a health impairment process that exhausts employees’ mental and physical resources and therefore leads to burnout” [57] (p. 74). The JD-R model also refers to how job resources (e.g., coworker task support) can mitigate the negative impact of job demands (e.g., job stress) on emotional exhaustion [58]. Recent research has indicated that job stress, which is connected with risk and job insecurity during the COVID-19 crisis, causes emotional exhaustion in employees [4,11]. As working in the hospitality industry has become more difficult during the pandemic, employees expect more specific and targeted support during the pandemic [48]. In this context, instrumental support provided by coworkers can reduce job stress by reducing their workload [59]. Consistent with the JD-R model, social support supplied by coworkers can assist in avoiding the negative impacts of job stress [43]. Thus, we expect that coworker task support has an effect on the emotional exhaustion of hospitality employees via job stress during the pandemic. Hence, we offer the following hypothesis:
Hypothesis 3 (H3). 
Job stress mediates the impact of coworker task support on hospitality employees’ emotional exhaustion.

2.5. The Moderating Role of Social Support

Supervisor support is defined as “the degree to which supervisors value employees’ contributions and care about their well-being” [60] (p. 700). Supporting employees to help achieve organizational goals is a basic aspect of supervisory labor [61]. Employees supported by their supervisors can cope with the maladaptive demands of the workplace, and thus experience less emotional exhaustion [62]. Bakker and Schaufeli [63] assert that social support from the supervisor buffers the impact of job demands on burnout levels through instrumental assistance and emotional support. In a similar vein, previous research has indicated that supervisor support can reduce the emotional exhaustion of hotel frontline employees [14,62]. Therefore, supervisor support plays a vital role in employees’ well-being during a crisis [64].
The JD-R model can also demonstrate the buffering impact of supervisor support in moderating the relationship between perceived uncertainty and emotional exhaustion. According to the JD-R model, job resources can buffer the association between job demands and emotional exhaustion [65]. Employees’ perceived uncertainty during the COVID-19 crisis can be considered as a job demand affecting their well-being [20]. On the other hand, supervisor support is seen as a job resource that prevents disengagement and burnout [47] and reduces employees’ perceived uncertainty [66]. Hence, in line with the JD-R model, we posit that supervisor support will moderate the link between perceived uncertainty and employees’ emotional exhaustion. Therefore, we predict the following:
Hypothesis 4 (H4). 
Supervisor support positively moderates the association between perceived uncertainty and the emotional exhaustion of hospitality employees.
Family support is defined as the individual’s perception of being valued, cared for, and esteemed by his family, which increases his personal functionality and helps him cope with stressors [67]. Family is one of the crucial social support factors that individuals have [68]. The support of family members is valuable for those working at risk of COVID-19 because their understanding and support are helpful in such situations [48]. In addition, strong ties developed with family members often serve as sources of help in uncertain situations [69].
Moreover, family support produces a positive psychological state, which can reduce the psychological requirements of the employee’s work [70]. It is stated that when family relationships are adequate and supportive, they act as a buffer against stress, and when they are perceived as deficient and inadequate, they may be a risk factor for depression [71]. A study managed by Lee et al. [72] found that family support reduces the emotional exhaustion of employees and makes them less stressed. Usman et al. [16] expressed that family support has a moderating impact on the association between individuals’ perceived uncertainty and emotional exhaustion during the pandemic. Hence, we hypothesize that family support has a moderating impact on the association between perceived uncertainty and emotional exhaustion. Therefore, the following hypothesis is developed:
Hypothesis 5 (H5). 
Family support positively moderates the association between perceived uncertainty and the emotional exhaustion of hospitality employees.
Figure 1 shows the proposed research model.

3. Methodology

3.1. Data Collection Procedure

This study, using a convenience sampling technique, collected data from two- to five-star hotels in Pakistan between 10 June and 20 July 2022. Convenience sampling is the most commonly used technique in quantitative research [73]. Participants were required to answer the structured questions on the prepared online questionnaire survey, which was emailed to respondents and also sent through WhatsApp. In the end, the researchers used a personal phone call to complete the process of keeping the data private and secure. The researcher was able to obtain email addresses and other contact information from the hotel websites. In total, 650 questionnaires were distributed and 395 of them were returned (response rate of 60.77%). After a thorough review of the returned questionnaires, 353 were found fit for data analysis.

3.2. Measurements

The survey comprises two sections. The first section collects hospitality employees’ demographic information such as age, gender, educational level, and job tenure. Section two consists of questions related to coworker task support, perceived uncertainty, job stress, emotional exhaustion, supervisor support, and family support.
Coworker task support. This was measured using six items developed by Eisenberger et al. [74] and later adapted by Settoon and Mossholder [75]. A sample item included: “My coworkers go out of their way to help me with work-related problems.” “Items were rated on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree”. The Cronbach’s α of this scale was 0.93.
Uncertainty about COVID-19. This was measured using 10 items developed by Wu et al. [76] with reference to the Mishel Uncertainty in Illness Scale (1981). The scale consisted of 5 items on lack of information and clarity (α = 0.75) and 5 items on unpredictability (α = 0.85). A sample item is: “I am unsure when the COVID-19 outbreak will end.” “Each item was rated on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree”.
Job stress. This was measured using five items developed by Crank et al. [77] and later adapted by Lambert et al. [78]. One sample item is: “A lot of time my job makes me very frustrated or angry.” “Items were assessed on a 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree”. The Cronbach’s α of this scale was 0.82.
Emotional exhaustion. A 9-item scale from Maslach and Jackson [79] was used to measure hospitality employees’ emotional exhaustion. A sample item is: “I feel fatigued when I get up in the morning and have to face another day on the job.” “The items were rated on 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree”. The Cronbach’s α of this scale was 0.92.
Supervisor support. Six items from Anderson et al. [80] were utilized to measure supervisor support. An example item included: “My supervisor is understanding when I talk about personal or family issues that affect my work.” “Each of the supervisor support used a five-point scale ranging from 1 = strongly disagree to 5 = strongly agree”. The Cronbach’s α of this scale was 0.89.
Family support. This was measured with the 8-item Family Support Inventory for Workers [81]. The original scale consisted of 44 items on instrumental (α = 0.85) and emotional support (α = 0.80). In this study, eight items with the highest item-total correlations associated with instrumental and emotional support were selected. One sample item is: “When I’m having a difficult week at my job, my family members try to do more of the work around the house.” “Respondents reported on a five-point scale, ranging from 1 = strongly disagree to 5 = strongly agree”.

3.3. Demographic Information

The study found that 59.49% (210) of respondents were men and 40.51% (143) were women. Moreover, 26.91% (95) of respondents were 18–25 years old, 51.84% (183) of respondents were 25–35 years old, 20.40% (72) of respondents were 35–50 years old, and only 0.85% (3) of respondents were above 50 years of age. In terms of education, 34.84% of employees (123) had completed an intermediate-level education, 51% of employees (180) had completed a college-level education, and 14.16% of employees (50) had completed a university-level education. Finally, 27.20% (96) of employees had experience of less than 1 year working in the hospitality industry, 54.11% (191) of them had 1–5 years of working experience, 11.90% (42) of them had 6–10 years working experience, and 6.79% (24) of them had more than 10 years working experience.

3.4. Data Analysis

There are two ways to implement the strategy: the first is the covariance-based SEM (CB-SEM), which uses programs such as AMOS and LISREL, and the second is PLS-SEM, also known as PLS path modeling, which makes use of programs such as Smart PLS. While the basic goal of both approaches is the same—to study the links between variables—they diverge statistically when verifying the measurement scale [82]. The PLS-SEM method explains the variance of unobserved predictor variables, while the CB-SEM method shows the variance–covariance matrices [83,84]. The advantages of PLS-SEM outweigh the disadvantages of CB-SEM and vice-versa. As a result, researchers should view the two methodologies as complementing rather than competitors [83]. Therefore, the study used SMARTPLS 3.3.3 to test the proposed research hypotheses. SMART PLS is a multivariate technique that can handle a complex model in structural equation modeling [85]. The study will use co-variance-based SEM to ensure the validity and reliability of the measurement constructs [86]. First, the study applied the algorithm technique with 5000 sub-samples to ensure the validity and reliability of the measurement constructs. Second, the study applied a bootstrapping technique with 5000 sub-samples in Smart PLS [87,88]. The study uses a 5% significance level with a 95% confidence interval with 5000 bootstrapping sub-samples. Third, the study used the blindfolding technique to test the models’ adequacy and fitness.

4. Findings of the Study

4.1. Assessment of Measurement Model

The judgment of whether or not a scale assesses the idea of what it is supposed to measure is exactly what is meant by the term “validity.” Again for the evaluation of the data of measurement items in Smart PLS, the convergent validity evaluation is a prerequisite, including factor loadings (value > 0.60) and average variance extracted (>0.50) [89]. The degree to which one measure relates to other measurements of the same event is what we mean when talking about its convergent validity [90]. In these situations, researchers have the option of condensing multiple measurements into one construct instead of carrying out a measurement analysis [91]. The concept of convergent validity describes the degree to which a new scale relates to other factors and other measurements of the same construct [92]. The study applied a series of algorithm techniques and found that two items of family support (FS1, FS2) and four items of perceived uncertainty (UC1, UC2, UC3, UC4) had lower factor loadings, so these items have been deleted from the model. Finally, Table 1 shows the good factor loadings of the constructs.
AVE is a measure of the quantity amount of variability recorded by a construct concerning the percentage amount of variation due to estimation errors. In other words, AVE compares the amount of variance taken by a construct to the amount of variance attributable to estimation errors.
The extracted amount of variance must be greater than 0.5, which is the minimal requirement [90]. According to Memon et al. [92], if the AVE is lower than 0.5 but the reliability value is greater than 0.6, the convergent validity of the factor is still sufficient as long as the reliability coefficient is higher [91]. The AVE is determined by the mean squared residuals linked with each indication part of a construct (Table 1). Finally, the study proved the convergent validity.
On the other hand, when doing research, scores of composite reliability or Cronbach alpha that fall between 0.60 and 0.70 are considered acceptable; however, when conducting research at a more advanced level, the value should be greater than 0.70. The study used the advanced threshold value for Cronbach alpha and composite reliability, which should be higher than 0.70 [88,92]. Table 1 shows that the values for each construct are higher than 0.70 for Cronbach alpha and composite reliability, so there was also good reliability.
The discriminant validity of the model was assessed by the Heterotrait–Monotrait (HTMT) ratio. The researchers argued that the HTMT value should be less than 0.90, so Table 2 showed that the value for each construct was lower than 0.90. Finally, the study also proved discriminant validity.

4.2. Multi-Collinearity Statistics (VIF)

In applied multiple regression, tolerance is utilized to determine the extent of multi-collinearity. A model’s tolerance provides insight into the degree to which the beta coefficients are influenced by adding additional predictor variables [90]. Less generous values of tolerance are indicative of greater degrees of multi-collinearity. The term “multi-collinearity” refers to the presence of more than one predictor variable in the model. In most cases, we have been told that a VIF of roughly 10 does not pose an issue; nonetheless, this does not pose a problem for the estimating procedure. In general, if the value of the variable of interest (VIF) is greater than 4 or the tolerance is less than 0.25, multi-collinearity may exist, and further research is necessary. When the value of the variable of interest (VIF) is greater than 10 or the tolerance is less than 0.1, severe multi-collinearity must be adjusted [91,92]. In PLS-SEM, the Variance Inflation Factor (VIF) is examined to determine the degree of collinearity in the data. Two rules of thumb are generally recognized: if the VIF is 5 or greater, it suggests a potential concern with the collinearity [83]. The study used the threshold value of 5 and reported that the values of both the outer model and inner model were lower than 5. So, there was no problematic concern with collinearity (see Table 3).

4.3. Assessment of Path Model

The study tested the path coefficients using structural equation modeling (SEM) to assess the proposed research hypotheses. A path coefficient represents the direct influence that one variable, presumed to be a cause, has on another variable, which is presumed to be an effect. The standardization of path coefficients is necessary because they are derived from relationships (i.e., path regression coefficient). When writing out path coefficients, two subscripts are required. It is possible for path coefficients, which are similar to ordinary regression coefficients, to be greater than 1. In contrast to a correlation coefficient, these are not limited to the range of −1 to 1 [83,90]. Path analysis also allows us to determine which model best fits the data. The degree of each independent variable’s effect on its own is compared using a standardized beta coefficient. The dependent variable is the focus of this comparison. The magnitude of the effect increases proportionally with the exact amount of the beta coefficient. For instance, a beta value of −9 has a more significant impact than a beta value of +.

4.4. Direct Effects

The study found the direct effects of structural equation modeling (SEM) to accept or reject the research hypotheses using beta values, t-values, and p-value. Table 4 reports that coworker task support did not significantly affect emotional exhaustion (beta = −0.082, t-value = 1.188, p-value = 0.235), and hypothesis H1 was rejected and not supported. The structural model for this research is shown in Figure 2.

4.5. Mediation Effects

The study also elaborated on the mediating roles of perceived uncertainty and job stress between coworker task support and emotional exhaustion. Table 5 shows that perceived uncertainty significantly and negatively mediated the relationship between coworker task support and emotional exhaustion (beta = −0.242, t-value = 5.681, p-value = 0.000), and hypothesis H2 was accepted. The mediating relationship was full mediation because the direct effect was not significant. On the other hand, job stress also significantly and negatively mediated the relationship between coworker task support and emotional exhaustion (beta = −0.096, t-value = 3.360, p-value = 0.001), and hypothesis H3 was also accepted based on the full mediation testing.

4.6. Moderation Effects

The study also tested the moderation of supervisor support and family support. Table 6 shows that supervisor support with perceived uncertainty significantly and negatively affected emotional exhaustion (beta = −0.142, t-value = 2.192, p-value = 0.029), and hypothesis H4 was accepted. On the other hand, family support with perceived uncertainty significantly and negatively affected emotional exhaustion (beta = −0.185, t-value = 2.871, p-value = 0.004), and hypothesis H5 was accepted. Finally, both supervisor support and family support moderated the relationships.

4.7. Model Adequacy and Fitness

To check the model adequacy and fit indices, this study assesses R2 and f2. R2 values range from 0.25 to 0.50 to 0.75, indicating a weak, moderate, or strong effect, respectively [87,88]. The study reported that coworker task support, perceived uncertainty, and job stress explained 57.9% of the total variance in emotional exhaustion, which is a moderate effect. Additionally, coworker task support explained 37.3% of the total variance in perceived uncertainty while it explained 30.3% of the total variance in job stress. Both effects were weak (Table 7). Additionally, f2 values range within ≥0.02, ≥ 0.15, and ≥ 0.35 indicating a weak, medium, or high impact [93]. Table 7 presents that coworker task support had a large effect (>0.35) on both perceived uncertainty and job stress. Perceived uncertainty had a medium effect (>0.15) on emotional exhaustion. At last, job stress had a small effect (>0.02) on emotional exhaustion. As a result, the study supports that the model had good fit indices and relevance.

5. Discussion and Conclusions

On the basis of social support theory and the job demands-resources (JD-R) model, this research explored the impact of coworker task support on the perceived uncertainty, job stress, and emotional exhaustion of hospitality employees. This study also investigated whether the impact of perceived uncertainty on employees’ emotional exhaustion could be moderated by supervisor support and family support. Unpredictably, the results showed that coworker task support had no significant impact on emotional exhaustion and failed to directly impact emotional exhaustion (beta = −0.082, p = 0.235), which contradicts the current findings of Charoensukmongkol et al. [39] and Usman et al. [16]. This can be explicated by the JD-R model because supervisor support is more powerful than coworker task support in providing the resources less-educated employees need [94]. Moreover, this result can also be explained by the following reasons. First, hospitality businesses have taken various measures to protect themselves from the negative impacts of the pandemic, such as taking employees off unpaid leave, dispersing employees to various departments, and terminating their jobs [95,96]. In addition, many hospitality employees worked remotely during the COVID-19 crisis [97]. This may have caused coworker task support to be less effective on the hospitality employees’ emotional exhaustion compared to other types of support during COVID-19.
This research explored the mediating role of perceived uncertainty and job stress on the link between coworker task support and emotional exhaustion. As predicted, perceived uncertainty was found to have a fully mediated association between coworker task support and emotional exhaustion (beta = −0.242, p = 0.000). The findings are consistent with Usman et al. [16] by showing that employees who receive task support from coworkers tend to show less perceived uncertainty and subsequently experience low emotional exhaustion. Especially in the context of a crisis, positive relations and mutual support among employees in the hospitality industry, where effective teamwork is crucial, can provide positive outcomes. Therefore, this finding also confirms the view that coworker support is linked with psychological well-being [98]. Second, the research confirmed the importance of job stress for assessing the mediating relationship between coworker task support and emotional exhaustion (beta = −0.096, p = 0.001). This result proposes that employees who have coworker support will be less stressed while performing their jobs, which will reduce their emotional exhaustion. The JD-R model [58] allows us to explain this finding. Coworker support, which is stated as a job resource, reduces the negative effect of job stress on emotional exhaustion. In addition, this result confirms Poor et al.’s [99] assertion that job stress occurs when employees feel that they do not receive enough support from managers, officials, and coworkers.
This study also found that supervisor support (beta = −0.142, p = 0.029) and family support (beta = −0.185, p = 0.004) significantly moderated the nexus between perceived uncertainty and emotional exhaustion negatively. In this vein, employees who have supervisor and family support are more likely to mitigate the potential negative effects of perceived uncertainty on emotional exhaustion than employees who do not have these types of support. Our findings are in harmony with the assumptions of the buffering effect of the JD-R model [65], which indicates that social support buffers the negative impact of perceived uncertainty on emotional exhaustion. This result also echoes the argument from Karatepe and Kilic [14] and Karatepe [62] that social support such as supervisor support can lessen hospitality frontline employees’ emotional exhaustion. This is because, in the context of the hospitality industry, supervisors play a critical role in shaping subordinates’ perceptions of the workplace [100]. On the other hand, due to the intense working hours of the hospitality industry, employees cannot spare enough time for their family and friends [101]. Despite this, this study proved the moderating effect of family support. This finding is also in line with other research that discovered the moderating impact of family support on the negative impacts of perceived uncertainty during the pandemic [16]. This can also be explicated by reason of the fact that family has become a crucial source of social support for employees during COVID-19, with the effect of employees spending more time at home during the pandemic [102].

5.1. Theoretical Implications

The present study contributes to the hospitality literature in many ways. Firstly, this study extends the hospitality literature on the role of coworker task support in the context of the pandemic, as well as being the first to investigate the impact of coworker task support on emotional exhaustion in the hospitality context. Second, previous research examined the effect of coworker support on job stress [103] and, in turn, on perceived uncertainty [16]. However, the mediating impact of perceived uncertainty and job stress between coworker task support and emotional exhaustion in the context of the hospitality industry has always been disregarded, and this research supports the fully mediating impact of perceived uncertainty and job stress. Third, the current research also contributes to the body of hospitality literature by examining how the role of social support from supervisors and family helps employees in coping with the negative impacts of perceived uncertainty. Hence, high social support from supervisors and family in the hospitality industry is significantly linked with a lower risk of emotional exhaustion. This is also important in terms of the Pakistani context. In a collectivist culture, the role of social support becomes even more apparent when employees with emotional exhaustion need the support of their supervisors or their families.

5.2. Practical Implications

This empirical research also has some practical implications for the hospitality industry. This research provides important clues for the sustainability of the hospitality industry. In the hospitality industry, which is known for its labor-intensive feature, the psychological state of the employees is the determinant of service quality, customer satisfaction, and the success of the business in general. Therefore, the main key to the sustainability of the hospitality industry is to improve the psychological state of employees and ensure their well-being, especially in the face of crises such as COVID-19. Positive coworker relationships can help employees’ well-being by reducing their job stress and perceived uncertainty. Hence, hospitality managers should provide an environment that permits employees to develop interpersonal relationships with their colleagues and develop policies in this direction.
On the other hand, the results of the study revealed that job stress and perceived uncertainty are important indicators of the possibility of employees experiencing emotional exhaustion. Therefore, hospitality organizations should train their employees to adopt coping techniques to enable them to cope with the negative consequences of job stress and perceived uncertainty. In addition, establishing psychological counseling units in hospitality organizations may help employees find solutions to job-related stress and problems.
The moderating role of supervisor support and family support revealed in the study is also important for the sustainability of the hospitality industry. This study revealed that managers should be aware of their role in lessening employees’ perceived uncertainty and thus reducing their emotional exhaustion. Given the importance of supervisors in reducing employee uncertainty during the COVID-19 crisis, hotel management should give them greater authority and autonomy to take necessary action during this process. It may be beneficial for supervisors to be responsive to the needs of employees and to approach them with empathy. Thus, supervisors should establish a supportive work environment and communicate more frequently with employees to help them cope with the problems they face and improve their well-being. Moreover, hospitality organizations should organize awareness training and develop strategies in order to train their supervisors to provide more support to their employees. Second, employees need to be aware that the family is an important source of social support because when family relationships are adequate and supportive, they act as a buffer against the negative impacts of the COVID-19 crisis on employees. Employees should take care of their families, communicate with them regularly, share their difficulties with them, and seek their support [70]. Organizations should also devise policies that allow hospitality employees to avoid excessive workloads and overtime and take vacations so that they can spend quality time with their family members [104].

5.3. Limitations and Future Directions

This research contains several limitations. First, the use of the convenience sampling method in the study limits the generalizability of the findings to hospitality employees in Pakistan. Another limitation of the study is the use of the uncertainty about COVID-19 scale, developed by Wu et al. [76], without adapting it to the context of Pakistan. In this case, it is likely that there are some cultural differences in the meanings attributed to some concepts. Future studies may use this scale by adapting it to the Pakistani context. Third, our research emphasized the role of supervisor support and family support in mitigating the impact of perceived uncertainty on emotional exhaustion in the hospitality industry. Future research may further explore the effect of social support in the hospitality industry and relate it to other variables, such as job engagement and organizational commitment.

Author Contributions

Conceptualization, A.E., Z.M. and S.S.; Methodology, A.E., Z.M., S.S. and N.E.; Software, A.E., Z.M. and S.S.; Validation, A.E., Z.M., S.S. and N.E.; Formal analysis, A.E., S.S. and N.E.; Investigation, A.E., Z.M., S.S., N.E. and R.E.; Resources, N.E. and R.E.; Data curation, A.E. and S.S.; Writing—original draft, A.E.; Writing—review & editing, Z.M., N.E. and R.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study because the survey questionnaire used in the study was sent anonymously to the participants, did not contain any personal sensitive data, and the information collected did not identify specific individuals.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 02150 g001 550
Figure 1. Proposed research model.
Figure 1. Proposed research model.
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Sustainability 15 02150 g002 550
Figure 2. Structural model.
Figure 2. Structural model.
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Table
Table 1. The results of the measurement model.
Table 1. The results of the measurement model.
ConstructsItems CodeFactor LoadingAVECronbach Alpha (α)Composite Reliability
Coworker Task SupportCTS10.7240.5720.8500.854
CTS20.725
CTS30.778
CTS40.804
CTS50.755
CTS60.749
Emotional ExhaustionEE10.7400.5920.8840.888
EE20.745
EE30.780
EE40.680
EE50.785
EE60.829
EE70.818
Family SupportFS30.7090.6290.8820.894
FS4NS *
FS5NS *
FS60.861
FS70.787
FS80.819
JS10.748
Job StressJS20.8900.7190.9010.904
JS30.871
JS40.874
JS50.848
SS10.689
SS20.800
Supervisor SupportSS30.7660.6050.8690.887
SS40.830
SS50.833
SS60.736
UC50.647
UC60.766
Perceived UncertaintyUC70.7390.5080.8380.845
UC80.667
UC90.722
UC100.698
UC110.741
* NS = non-significant.
Table
Table 2. The results of discriminant validity analysis.
Table 2. The results of discriminant validity analysis.
Constructs12345678
Coworker task support
Emotional exhaustion0.652
Family support0.7530.664
Job stress0.6260.6530.660
Perceived uncertainty0.7130.7890.6970.630
Supervisor support0.7030.6270.7610.6310.710
Supervisor support x perceived uncertainty0.1230.2750.1950.2440.3670.208
Family support x perceived uncertainty0.2070.3810.3000.3170.3800.1850.840
Table
Table 3. Multi-collinearity (VIF).
Table 3. Multi-collinearity (VIF).
Constructs12345678
Coworker task support 2.200 1.0001.000
Emotional exhaustion
Family support 2.562
Job stress 1.830
Perceived uncertainty 2.197
Supervisor support 2.352
Supervisor support × perceived uncertainty 3.716
Family support × perceived uncertainty 3.824
Table
Table 4. Direct effects.
Table 4. Direct effects.
HypothesesRelationshipBT Statistics (|O/STDEV|)p-ValuesDecision
H1Coworker task support → emotional exhaustion−0.0821.1880.235Rejected
Table
Table 5. Mediation analysis.
Table 5. Mediation analysis.
HypothesesRelationshipBT Statistics (|O/STDEV|)p-ValuesDecision
H2Coworker task support → perceived uncertainty → emotional exhaustion−0.2425.6810.000Accepted
H3Coworker task support → job stress → emotional exhaustion−0.0963.3600.001Accepted
Table
Table 6. Moderation analysis.
Table 6. Moderation analysis.
HypothesesRelationshipBT Statistics (|O/STDEV|)p-ValuesDecision
H4Supervisor support x perceived uncertainty → emotional exhaustion−0.1422.1920.029Accepted
H5Family support x perceived uncertainty → emotional exhaustion−0.1852.8710.004Accepted
Table
Table 7. R2 and f2 (model adequacy and fitness).
Table 7. R2 and f2 (model adequacy and fitness).
Constructs12345R-SquareR-Square Adjusted
Coworker task support 0.007 0.4340.594
Emotional exhaustion 0.5790.570
Family support 0.007
Job stress 0.039 0.3030.301
Perceived uncertainty 0.170 0.3730.371
Supervisor support 0.011
Supervisor support × perceived uncertainty 0.021
Family support × perceived uncertainty 0.040
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MDPI and ACS Style

Ersoy, A.; Mahmood, Z.; Sharif, S.; Ersoy, N.; Ehtiyar, R. Exploring the Associations between Social Support, Perceived Uncertainty, Job Stress, and Emotional Exhaustion during the COVID-19 Crisis. Sustainability 2023, 15, 2150. https://doi.org/10.3390/su15032150

AMA Style

Ersoy A, Mahmood Z, Sharif S, Ersoy N, Ehtiyar R. Exploring the Associations between Social Support, Perceived Uncertainty, Job Stress, and Emotional Exhaustion during the COVID-19 Crisis. Sustainability. 2023; 15(3):2150. https://doi.org/10.3390/su15032150

Chicago/Turabian Style

Ersoy, Aslı, Zahid Mahmood, Shahbaz Sharif, Nazlı Ersoy, and Rüya Ehtiyar. 2023. "Exploring the Associations between Social Support, Perceived Uncertainty, Job Stress, and Emotional Exhaustion during the COVID-19 Crisis" Sustainability 15, no. 3: 2150. https://doi.org/10.3390/su15032150

APA Style

Ersoy, A., Mahmood, Z., Sharif, S., Ersoy, N., & Ehtiyar, R. (2023). Exploring the Associations between Social Support, Perceived Uncertainty, Job Stress, and Emotional Exhaustion during the COVID-19 Crisis. Sustainability, 15(3), 2150. https://doi.org/10.3390/su15032150

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