Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Dec 12;54:119–127. doi: 10.1016/j.jhtm.2022.12.010

Losing talent due to COVID-19: The roles of anger and fear on industry turnover intentions

Iuliana Popa a, Lindsey Lee b, Heyao Yu c, Juan M Madera a,∗
PMCID: PMC9742209

Abstract

Early in the COVID-19 pandemic, the US hospitality industry workforce experienced significant job loss via furloughs and job eliminations. Over a year later, the American hospitality industry is now facing a labor shortage. However, there is a dearth of literature explaining why the hospitality industry's response due to a mega-event, like the pandemic, can motivate employees to leave the hospitality industry. Instead, theory and research have primarily focused on organizations as the focal point for understanding turnover, while neglecting the industry. Using the affect theory of social exchange, this paper examined how anger and fear related to job status changes (i.e., being furloughed or laid-off) due to the pandemic, influence intentions to leave the industry. Study 1 used a survey of management-level employees, whereas Study 2 used an experiment to test the proposed model. Both studies showed that employees who lost their job due to the pandemic felt more anger and fear than those still employed. However, mediation analyses revealed anger, but not fear, as the primary driver of industry turnover intentions. These results highlight a potentially problematic trend. Should skilled hospitality workers switch industries due to job loss amidst an industry-wide negative event, it may become difficult for hospitality businesses to find qualified employees once the industry recovers and rehiring begins.

Keywords: COVID-19, Industry turnover intensions, Negative emotions, Job loss, Turnover

1. Introduction

Since March of 2020, the US hospitality industry workforce, particularly in the lodging and food and beverage sectors, has experienced significant furloughs and job loss due to the COVID-19 pandemic (Gursoy & Chi, 2020; U.S. Bureau of Labor Statistics, 2020). Travel and dining out declined greatly, placing a financial strain on the industry and threatening the job security of millions (Nicola et al., 2020). While many companies from other industries have adapted their operating style to accommodate social distancing measures, hospitality companies face greater challenges in adapting to remote work and service delivery methods (Baum et al., 2020). In the lodging sector, approximately 1.6 million workers have lost their jobs, with an average vacancy of eight out of ten hotel rooms during 2020 (American Hotel & Lodging Association, 2020). The food and beverage sector was especially hard hit early on, with two-thirds of employees losing their jobs during the first two months of the pandemic, amounting to over 8 million jobs lost (National Restaurant Association, 2020).

With the rollout of vaccinations, the hospitality industry is slowly regaining thousands of jobs; but the industry is now facing a labor shortage (Picchi, 2021). Employers are struggling to attract talent—both who were furloughed or laid-off due to the pandemic—back to the industry despite offering signing bonuses and competitive benefits. For example, Taco Bell is offering paid family leave to company store managers and Jimmy Johns and other fast-food restaurants are offering $250 signing bonuses (Haddon, 2021). However, many hospitality industry employees have left for jobs in other industries, particularly ones that were less affected by the pandemic and were able to adjust to social distancing and remote work (Wiener-Bronner, 2021; Yu et al., 2021). Although our current study focuses on the US workforce, these trends have also been found in Australia (Powell, 2022), Asia (TTG Asia, 2021), and Europe (Diazgranados, 2021).

Despite these realities, there is a dearth of literature surrounding the mechanisms that explain why the industry's response to a mega-event, such as the COVID-19 pandemic, can motivate employees to leave the hospitality industry. Event-system theory (Morgeson et al., 2015) states that mega-events are unexpected, external disasters that require organizational actions. Accordingly, mega-events can have a negative impact on employees' emotions, attitudes, and behaviors via organizations' actions, such as layoffs and furloughs. While research shows that employment decisions (e.g., layoffs and furloughs) do indeed elicit negative emotions (e.g., Grandey et al., 2021; Huffman et al., 2022; Shepherd & Williams, 2018), a question remains of whether these emotions trigger negative attitudes toward not only the organization that made them unemployed, but to the industry they work in. In other words, although it is expected that employees will experience negative emotions when they are made unemployed, research has primarily focused on how employees experience negative emotions within the context of the specific organization that made them unemployed. However, it is not clear whether these emotions will also be felt targeting the industry they work in. The hospitality industry's response to the COVID-19 pandemic provides a unique context to examine this research gap.

First, the hospitality industry was hit the hardest by the pandemic. Approximately two-thirds of jobs lost in the US were from the hospitality industry (U.S. Bureau of Labor Statistics, 2020), thereby placing a negative spotlight on the hospitality industry as an employer. Second, the hospitality industry relies on a pipeline of talent with industry-specific education, competencies, and skills that are transferable across organizations within the industry, thereby requiring talent to remain within the industry (King et al., 2021). Third, talent in the industry is ingrained in the industry. Specifically, the hospitality industry commonly requires a diversity of frontline, operational experience, across organizations within the industry for supervisory and management positions (Suh et al., 2012). This necessitates that talent stays within the industry and the industry needs to retain talent with the relevant skills and experiences to maintain a pipeline for management positions. Thus, hospitality organizations have invested time and resources on training management-level employees, who likewise, have invested their time working in the industry. It is therefore important to understand why hospitality employees, particularly those in management roles, leave the industry during the COVID-19 pandemic.

To address this gap in the literature, our study is guided by the affect theory of social exchange (Lawler, 2018), which provides insight on why hospitality employees might leave the industry when they experience furloughs or job loss during the pandemic. This theory contends that exchanges at work almost invariably produce positive or negative emotions. In the context of this study, employees who were furloughed or laid-off will most likely feel stronger negative emotions than employees who were able to work during the pandemic. Although the affect theory of social exchange (Lawler, 2018) limits its scope within organizations, the current research expands on this theory by examining how in exchange for being laid-off or furloughed, and subsequently feeling negative emotions to this action, employees will be motivated to leave the industry. We test our hypotheses by leveraging a combination of methods and samples. Study 1 uses a survey with management-level employees, whereas Study 2 uses an experiment to further test our proposed model using a sample of aspiring entrants into hospitality management (i.e., management in training sample of hospitality management students).

The current study makes several contributions to the literature on understanding why hospitality employees leave the industry. First, event-system theory (Morgeson et al., 2015) limits its focus on organization-focused attitudes and behaviors, despite the fact that industries can face mega-events that negatively impact them more than other industries, such as the COVID-19 pandemic having the greatest impact on the hospitality industry in regard to furloughs and job loss (U.S. Bureau of Labor Statistics, 2020). We expand on this theory by showing that the industry can also be the target of negative emotions related to a mega-event. Likewise, we expand on the theory of social exchange (Lawler, 2018) by showing that negative emotions felt by decisions made by organizations can also motivate employees to make career changes, such as leaving the industry they work in.

Second, and related to the first point, is that much of the literature on why employees, including managers, leave the hospitality industry has focused on personal attributes, job attributes, and organizational-specific attitudes, such as job satisfaction, internal motivation, perceived organizational support, and psychological contracts (e.g., Ann & Blum, 2020; Blomme et al., 2010; Brown et al., 2015; Guchait et al., 2015). In other words, the literature has primarily focused on the organization as the foci for understanding turnover, while neglecting the industry. For example, in their critique of the literature, King et al. (2021) argued that the difficulties the hospitality industry faces in attracting and retaining talent, particularly during the pandemic, “also seem to reside at the industry level, and not solely at the organizational level where most research is focused” (p. 252). Therefore, the current paper focuses on industry turnover.

2. Theoretical background and hypotheses

2.1. Job status and feeling anger and fear

The current paper uses the affect theory of social exchange (Lawler, 2001) as the guiding framework. This theory argues that emotions are produced when two or more entities exchange valued outcomes (e.g., payment, rewards, or goods for work). Entities can include people and/or social units, such as organizations they work for. Accordingly, after exchanges, employees process information and interpret intentions from their organization, and emotionally respond to an exchange. The emotional reactions can involve positive or negative emotions, depending on the exchange (e.g., positive emotions like pride after a promotion or negative emotions like fear after a furlough). Per this theory, these emotional reactions also lead to attributions, in which emotions are linked to people or social unites. For example, employees who feel negative emotions, like fear after a furlough, will then attribute these emotions to their organizations. Thus, this theory provides an important lens through which to understand how the hospitality industry response to the COVID-19 pandemic can drive industry turnover.

One of the broadest effects of the COVID-19 pandemic on employees’ jobs is their job status. In the early months of the pandemic, many jobs were changed to a remote format, hours were reduced, job tasks were modified, or they were eliminated temporarily through furloughs or indefinitely through layoffs (Brynjolfsson et al., 2020). Current literature further indicates that COVID-19 has created an environment where hospitality employees who experienced job insecurity had lower work motivation (Bajrami et al., 2021) and those who are high in career adaptability are particularly prone to developing high industry turnover intentions in situations of low supervisor support (Lee et al., 2021), indicating a high-stakes situation for the hospitality industry in terms of talent loss.

In addition, as reported by Wong et al. (2021, p. 102798), employee “job satisfaction, organizational commitment, job performance, subjective well-being, and prosocial behavior had each significantly decreased after the pandemic took hold, whereas turnover intention was significantly higher after COVID-19 had become quite prevalent,” demonstrating a widespread negative impact of COVID-19 on employee well-being, with negative implications for the hospitality industry in turn. Further contributing to these indications of COVID-19 as a deeply negative, impactful crisis for the hospitality industry, a study by Yan et al. (2021) has found that lower job satisfaction strengthens the relationship between employee perceptions of COVID-19 risk perception and experiencing depressive symptoms.

Thus, given the unprecedented hospitality industry job loss and rapid change in operations due to the pandemic (US Bureau of Labor Statistics, 2020), employees are prone to experiencing significant levels of negative emotions (Mimoun et al., 2020). Although there are a number of negative emotions that employees can feel, Lebel's (2017) model of negative emotion regulation points to fear and anger as particularly relevant for the current study. Whether laid off, furloughed, or still employed, hospitality employees are facing substantial uncertainty, which can result in negative emotions such as fear and anger. For example, managers still employed might feel fear from the uncertainty of working during a pandemic, or anger with the direction or lack of guidance provided by their organization (Chen & Eyoun, 2021; Guzzo et al., 2021).

Anger is defined as feeling indignation with desires to redress a perceived wrongdoing or violation of a perceived contract (Greenbaum et al., 2020); whereas fear is defined as feeling uncertainty and a lack of control or efficacy (Osborne et al., 2012). Anger and fear are both negatively valanced, high-arousal, discrete emotions and have been found to have strong counterproductive effects. For instance, aggression and other deviant behavior are common responses to anger (Fox & Spector, 1999), whereas silence on important matters is often a result of fear (Kish-Gephart et al., 2009).

Furthermore, anger and fear are often felt when losing important resources, such as one's job, whether it is through layoffs or furloughs (Pugh et al., 2003; Osborne et al., 2012). As suggested by affect theory of social exchange (Lawler, 2018), discrete emotions, such as anger and fear, are felt as a result of the decisions organizations make that have implications to one's well-being. For example, one's employment is an exchange for the time and effort one gives to their organization. When an organization makes an employee unemployed, whether from a layoff or furlough, the employee might see this as a violation of the time and effort they gave their organization.

In addition, losing one's job is a form of relative deprivation and can lead to viewing one's financial circumstances as undeserved and worse in comparison to coworkers who remained employed, which often results in feeling negative emotions such as anger and fear (Smith & Pettigrew, 2014). One's job is not only related to financial resources, but also to one's identity, job satisfaction, sense of accomplishment, and affiliation (Miscenko & Day, 2016). Therefore, losing one's job not only deprives oneself of financial resources, but also of resources that one gains through work. Such deprivation can lead to feelings of anger, due to a perceived wrongdoing or violation of trust on behalf of their organization, and fear, due to the uncertainty for one's financial future. Thus, managers who lost their jobs due to the pandemic are more likely to feel anger and fear relative to managers still employed in the hospitality industry.

H1a

Unemployed and furloughed managers will feel greater anger than managers working during the pandemic.

H1b

Unemployed and furloughed managers will feel greater fear than managers working during the pandemic.

2.2. Hospitality industry turnover: the mediating effects of anger and fear

The affect theory of social exchange (Lawler, 2018) suggests that negative emotions felt due to an exchange—being unemployed by one's organization—can decrease future exchanges or terminate a relationship with an organization. The current paper advances this theory by suggesting that the employees who feel negative emotions, like anger or fear, might attribute these emotions to working in an industry that was vulnerable to the pandemic. In other words, equally important is that people can look beyond one's organization as the cause of the negative emotions. For example, managers who were laid-off or furloughed by their hospitality organizations are not only likely to feel anger and fear, but also to attribute these emotions to working in the hospitality industry. In response to losing one's job, managers might terminate their relationship with working in the hospitality industry due to the anger and fear elicited by the unemployment status. Thus, the current study examined anger and fear as mediators of the relationship between job status (i.e., employed or unemployed) and hospitality industry turnover intentions.

It is important to note here that while anger and fear may sometimes be perceived as passing emotions, they are emotional experiences that can influence our perceptions and behaviors in the long-term. Specifically, research shows that discrete emotions, such as anger and fear, influence how people appraise situations, which then influences how people judge and plan for future events (Han et al., 2007; Lerner & Tiedens, 2006). For instance, discrete emotions have been linked to helping behavior, sabotage, pro-environmental behavior, organizational commitment, and absenteeism, suggesting that discrete emotions influence long-term behaviors and decisions at work (Conroy et al., 2017; Rubino et al., 2013).

Therefore, considering the important effects anger and fear have on future behavior, it is important to examine how unemployment related to the COVID-19 pandemic is affecting hospitality employees’ industry career change intentions. Industry turnover refers to a change in an occupation that is not part of a normal career evolution (McGinley et al., 2014). For instance, a job change from hotel front desk agent to front desk supervisor is a natural career progression, whereas a change from hotel front desk agent to real estate agent is considered industry turnover. Although industry turnover intentions and organizational turnover in the hospitality industry have been the focus of previous research (Brown et al., 2015), a gap exists. Past research largely focused on the factors that contribute to hospitality industry turnover during normal times and has identified causes such as working environment and career progression (Haldorai et al., 2019). Considering the global reach and magnitude of the COVID-19 pandemic, this presents a unique chance to gain insight into the effects of such a crisis on hospitality industry employee turnover intentions.

Anger and fear, are negative emotions, frequently associated with counterproductive results and often act as motivators that lead individuals to taking proactive measures to remedy these feelings (Lebel, 2017). For the purposes of this study, proactive behavior can be defined as an action that is geared towards a future goal (Parker et al., 2010), which can include becoming disengaged at work or looking for alternative employment opportunities (Osborne et al., 2012). Both anger and fear have been associated with fight and flight responses, motivating individuals to be proactive in their approach (Lebel, 2017). Specifically, anger is associated with high certainty situations and motivates the one experiencing it to take corrective action, or a “fight” response (Crisp et al., 2007). Meanwhile, fear arises from a sense of uncertainty surrounding a negative event, which in turn leads to a “flight” response – a proactive effort to cut off the source of fear (Dasborough et al., 2020). In both cases, these two negative emotions incite proactive behavior, since they motivate the individual experiencing them to assess their situation and address it (Lazarus & Folkman, 1984).

In the current context—that is, the pandemic's negative effect on the hospitality industry—the anger and fear managers feel can be directed towards the industry. In other words, because the hospitality industry suffered the most furloughs and job losses during the pandemic, industry turnover intention can serve as a ‘fight and flight’ response triggered by anger and fear, respectively. Industry turnover intention can be a corrective action triggered by employees' anger because they believe working in the hospitality industry is at fault. In addition, industry turnover can be a flight response triggered by fear because employees' perceive the unstable nature of the hospitality industry as a source of their fear. Therefore, as suggested by the affect theory of social exchange (Lawler, 2018), these negative emotions might drive talent to eliminate future exchanges with the source of the negative emotions. Thus, managers who were laid-off or furloughed by their hospitality organization might attribute their anger and fear to working in the hospitality industry and therefore are motivated to leave the industry. The conceptual model is illustrated in Fig. 1 .

H2a

Anger will mediate the relationship between employee status (still employed or furloughed/unemployed) and career change intentions.

H2b

Fear will mediate the relationship between employee status (still employed or furloughed/unemployed) and career change intentions.

Fig. 1.

Fig. 1

Open in a new tab

Conceptual model.

Note. *Employment status = still employed or furloughed/unemployed during the pandemic.

3. Methodology: study 1

3.1. Data collection

Data was collected using an online survey distributed through the Amazon Mechanical Turk (or MTurk) platform. MTurk is a website that helps match workers on the platform to tasks requiring human intelligence, such as research surveys. Human intelligence task (HIT) requesters can set demographic parameters that individuals must meet in order to complete the requested HIT, such as age, HIT-performance history (past task approval rate), and location data. The data was collected in June and July of 2020, after the first wave of shutdowns.

Eligibility criteria for participants in this study included employment in the hospitality industry in management-level positions who (1) have worked in the industry for at least one year and (2) are still employed in the industry or have been laid off or furloughed due to COVID-19 within the three months of the quarantine mandates (e.g., workplace restriction orders) executed in March 2020. In addition, participants were required to be US residents and be at least 18 years or older. Those who met the criteria were asked to complete the survey. To further ensure quality responses, the survey included various “attention-check” questions throughout, such as, “Select ‘Strongly disagree’.” These questions served to drop inattentive respondents from the survey before completion. In addition, only participants with HIT approval rates above 98% and over 5000 submitted HITs were qualified. All data collection for this study was remote, and respondents were paid $1.50 for successfully completing the survey.

3.2. Participants

A total of 350 participants were surveyed, but 24 respondents were not used due to incomplete or inaccurate responses, resulting in 326 participants. In regard to employment status, 53% were currently working, and 38% were furloughed, and 9% were laid off as a result of COVID-19 workplace restriction orders; 44% were from hotel and lodging and 56% were from the food and beverage operations. The majority were White (57%), male (76%), paid a salary (89%), and had a 4-year college degree (64%) or professional degree (25%). The majority were between 25 and 34 (58%) or 35 to 44 (22%) years old. Lastly, the participants reported an average of 7.03 (SD = 6.30) years working in the hospitality industry.

3.3. Measures

Employment status. Respondents were asked to specify the effects that the pandemic had on their job, such as whether they were laid off, furloughed, or were still employed. Participants who were laid off or furloughed were coded as “unemployed” (47%) and all others were coded as still “employed” (53%). A planned contrast showed no significant differences in fear [t(323) = 0.68, p = 0.49], anger [t(323) = −0.39, p = 0.69], and industry turnover intentions [t(323) = −1.54, p = 0.12] between the ‘laid off’ and ‘furloughed’ participants, thereby providing evidence for combining both groups as “unemployed.”

Anger and fear. Fear and anger were measured using the anger and fear subscale of Izard's (1991) Differential Emotion Scale III (DES III). This was a 5-point Likert scale with answer choices ranging from “very much” to “not at all.” Participants were asked to select responses on this scale in reference to their experiences of fear, anger, and related emotions, upon learning about the effects of COVID-19 on their job. The items for fear were “scared,” “fearful,” and “afraid,” (Cronbach's alpha = 0.77) and the items for anger were “enraged,” “angry,” and “mad” (Cronbach's alpha = 0.74).

Industry turnover intention. Items by McGinley and Mattila (2020) were used to measure hospitality industry turnover intentions. Some example items include: “You think a lot about leaving the industry,” and “You are actively searching for an alternative to this industry.” Participants were asked to respond to these questions using a 5-point Likert scale, with answer choices that ranged from “strongly disagree” to “strongly agree” (Cronbach's alpha = 0.83).

Control variables. The control variables used were age and type of operation (hotel/lodging or restaurant). We controlled for age because age is negatively related to career change intentions (e.g., Carless & Arnup, 2011). In addition, we also controlled for whether the participants worked in food and beverage or hotel and lodging to control for any potential idiosyncratic differences in turnover between these two types of operations.

4. Results

4.1. Psychometric analyses

A confirmatory factor analysis (CFA) and the heterotrait-monotrait ratio (HTMT) of the correlations were used to examine the psychometric properties of the measures. A CFA of a three-factor model with fear, anger, and industry turnover intentions demonstrated adequate fit: χ2 = 39.86, df = 24, NFI = 0.97, IFI = 0.98, CFI = 0.98; RMSEA = 0.045. All loadings were statistically significant and were larger than 0.50 (they varied from 0.66 to 0.84), and the composite reliabilities were greater than the 0.70 threshold, indicating convergent validity (Hair et al., 2010). This model was compared to a one-factor-model, which demonstrated poor fit: χ2 = 398.86, df = 27, NFI = 0.62, IFI = 0.63, CFI = 0.63; RMSEA = 0.21.

As shown in Table 1 , the average variance extracted (AVE) for each measure was greater than the 0.50 cutoff (Bagozzi & Yi, 1988). In addition, the maximum shared variance (MSV) for each construct was less than the AVEs and square root of the AVEs for each measure were greater than the correlations among the measures, thereby demonstrating discriminant validity (Fornell & Larcker, 1981). Lastly, the HTMT of the correlations was used to further assess discriminant validity (Henseler et al., 2015). The results of the HTMT showed that the values ranged from 0.24 to 0.69, which were less the 0.85 threshold (Kline, 2011).

Table 1.

Validity results for the measures for Study 1.

Means (SD) CR AVE MSV Fear Anger Turnover Intentions
Fear 3.53 (0.91) 0.75 0.51 0.48 0.71
Anger 3.34 (1.01) 0.75 0.51 0.48 0.69* 0.71
Turnover intentions 3.49 (1.05) 0.83 0.62 0.26 0.23* 0.51* 0.79
Open in a new tab

Note. CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance. The square root of the AVEs are in bold.

*p < 0.01.

4.2. Test of hypotheses

The hypotheses were tested using PROCESS Model 4 with two parallel mediators and a bootstrap function extracting 5,000 samples for the analysis (95% confidence interval [CI]) was used to test the conceptual model (Hayes, 2017). The results showed that unemployed participants felt more anger than employed participants (β = 0.27, p = 0.02; CI = [0.05, 0.49]). Similarly, unemployed participants felt more fear than employed participants (β = 0.10, p = 0.03; CI = [0.02, 0.42]), thereby supporting H1a and H1b.

The results for the indirect effect showed that the relationship between employment status (unemployed vs. employed) and industry turnover intentions was mediated by anger (effect = 0.11; CI = [0.02, 0.21]), supporting H2a. However, fear did not mediate the relationship between employment status (unemployed vs. employed) and industry turnover intentions (effect = −0.01; CI = [-0.05, 0.03]), which did not support H2b. Table 2 shows the results of the main and indirect effects.

Table 2.

Direct and indirect effect for Study 1.

Effect SE LLCI ULCI
H1a Employment status → anger 0.27 0.11 0.05 0.49
H1b
 Employment status → fear
 0.10
 0.10
 0.02
 0.42
Indirect effects
BootSE
 LLCI
 ULCI
H2a Employment status → anger → industry turnover intentions 0.11 0.05 0.02 0.21
H2b Employment status → fear → industry turnover intentions −0.01 0.02 −0.05 0.03
Open in a new tab

Note. SE = standard error; LLCI = lower limit confidence interval; ULCI = upper limit confidence interval.

4.3. Discussion: study 1

Study 1 examined management-level employees’ emotional reactions to how the pandemic has affected their job status, such as being laid-off or furloughed, and influences their intentions to leave the industry. The results showed that unemployed participants felt more anger and fear than employed participants. The results for the indirect effect showed that the relationship between employment status (unemployed vs. employed) and industry turnover intentions was mediated by anger but not fear.

Despite using a sample of managers with industry experience, which is a strength, Study 1 is a cross-sectional survey. Therefore, an experiment was used in Study 2 to not only address the limitations of Study 1 but also examine the causal effect of why job loss due to mega-events, such as the COVID-19 pandemic, can motivate employees to leave the hospitality industry. Specifically, in Study 2 we used an experiment to further test our proposed model with a sample of hospitality management students with hospitality work experience. Using hospitality management students with work experience in the industry allows us to further test the model with a sample who are currently investing in a hospitality degree and working in the industry, with the goal of attaining management-level positions in the hospitality industry.

5. Methodology: study 2

5.1. Sample

The target sample for Study 2 was aspiring entrants into hospitality management, therefore, we targeted hospitality management students with work experience in the industry. Hospitality management students with work experience in the industry are an appropriate sample considering that the current study is focusing on industry turnover intentions and hospitality management students are important stakeholders of the industry because they are part of a significant pipeline of hospitality management talent (King et al., 2021). This sample additionally represents stakeholders who are currently investing in a hospitality degree and working in the industry, with the goal of attaining management-level positions in the hospitality industry. The data was collected in the fall of 2021, September and October, after the first wave of available vaccinations.

A total of 150 senior-level students currently majoring in the hospitality industry with at least one year of work experience were contacted via email to participate in a study about their career interest. Of these, 104 (39% men, 61% women) completed the study. Of those currently working (81%), the majority, 69%, had an hourly, non-supervisor job and 31% had a supervisor/management level job. They had an average age of 24.35 (SD = 7.7) and an average of 4.92 (SD = 5.39) years of work experience in the industry. Most identified as Caucasian (43.4%), Asian (24.2%), Latinx (20.2%), African-American/Black (6.1%), and multiracial (6.1%).

5.2. Design and procedure

A 2-group (employee status: still employed or job eliminated) between-subjects experimental design was used. Participants read a scenario in which their job was either eliminated or not, adapted from the scenario used by Guzzo et al. (2021). They were instructed to imagine that they are attending a shift meeting with their manager at a company similar to their current or past job. For the eliminated job condition (n = 48), the participants read “You are informed that the company will be eliminating job positions due to the COVID-19 pandemic. You are told that your job at this company has been affected. Although some jobs were saved, your job has been eliminated due to the pandemic.” For the not eliminated job condition (n = 52), the participants read “You are informed that the company will be eliminating job positions due to the COVID-19 pandemic. You are told that your job at this company has not been affected. Although some jobs were eliminated due to the pandemic, you will continue to be employed despite the pandemic.” After reading the manipulated statements, the participants completed the measures.

5.3. Measures

Anger and fear. The same measures for anger (Cronbach's alpha = 0.96) and fear (Cronbach's alpha = 0.93) from Study 1 were used.

Industry turnover intention. The same measure from Study 1 was used (Cronbach's alpha = 0.86).

Control variables. We controlled for current hours working and whether respondents were furloughed or lost their job due to COVID-19 in the past. We controlled for these in case currently either working in the industry during the pandemic and/or losing a job due to the pandemic had an effect on their reactions to the manipulated scenarios.

Manipulation check. The participants were asked to rate the extent to which their job was affected by the pandemic described in the scenario using a 5-point Likert-type scale from “not at all” to “very much.” As expected, the participants’ rating was higher in the condition in which their job was eliminated (M = 4.14, SD = 1.39) than when it was not eliminated (M = 2.62, SD = 1.14), F(1, 98) = 21.61, p = 0.001.

6. Results

6.1. Psychometric analyses

A CFA and the HTMT ratio of the correlations were used to examine the psychometric properties of the measures. A CFA of a three-factor model with fear, anger, and industry turnover intentions demonstrated adequate fit: χ2 = 42.32, df = 24, NFI = 0.96, IFI = 0.98, CFI = 0.98; RMSEA = 0.09. All loadings were statistically significant and were larger than 0.50 (they varied from 0.65 to 0.96), and the composite reliabilities were greater than the 0.70 threshold, indicating convergent validity (Hair et al., 2010). This model was compared to a one-factor-model, which demonstrated poor fit: χ2 = 352.92, df = 27, NFI = 0.63, IFI = 0.65, CFI = 0.65; RMSEA = 0.34.

As shown in Table 3 , the average variance extracted (AVE) for each measure was greater than the 0.50 cutoff (Bagozzi & Yi, 1988). In addition, the maximum shared variance (MSV) for each construct was less than the AVEs and square root of the AVEs for each measure were greater than the correlations among the measures, thereby demonstrating discriminant validity (Fornell & Larcker, 1981). Lastly, the HTMT of the correlations was used to further assess discriminant validity (Henseler et al., 2015). The results of the HTMT showed that the values ranged from 0.50 to 0.69, which were less the 0.85 threshold (Kline, 2011).

Table 3.

Validity results for the measures for Study 1.

Means (SD) CR AVE MSV Fear Anger Turnover Intentions
Fear 2.85 (1.10) 0.93 0.83 0.35 0.91
Anger 2.15 (1.28) 0.96 0.90 0.47 0.59* 0.95
Turnover intentions 2.82 (1.17) 0.86 0.69 0.47 0.50* 0.69* 0.83
Open in a new tab

Note. CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance. The square root of the AVEs are in bold.

*p < 0.01.

6.2. Test of hypotheses

The hypotheses were tested using PROCESS Model 4 with two parallel mediators, the control variables, and a bootstrap function extracting 5,000 samples for the analysis (95% confidence interval [CI]) (Hayes, 2017). The results showed that the participants who read that their job was eliminated due to the pandemic felt more anger than the participants who read that their job was not eliminated (β = 1.51, p = 0.001; CI = [1.10, 1.93]). Similarly, the participants who read that their job was eliminated due to the pandemic felt more anger than the participants who read that their job was not eliminated (β = 0.56, p = 0.01; CI = [0.13, 1.01]), thereby supporting H1a and H1b.

The results for the indirect effect showed that the relationship between employment status (job eliminated vs. employed) and industry turnover intentions was mediated by anger (effect = 0.65; CI = [0.30, 1.04]), supporting H2a. However, fear did not mediate the relationship between employment status (job eliminated vs. employed) and industry turnover intentions (effect = 0.11; CI = [-0.01, 0.26]), which did not support H2b. Table 4 shows the results of the main and indirect effects.

Table 4.

Direct and indirect effect for Study 2.

Effect SE LLCI ULCI
H1a Employment status → anger 0.27 0.11 0.05 0.49
H1b
 Employment status → fear
 0.10
 0.10
 0.02
 0.42
Indirect effects
BootSE
 LLCI
 ULCI
H2a Employment status → anger → industry turnover intentions 0.11 0.05 0.02 0.21
H2b Employment status → fear → industry turnover intentions −0.01 0.02 −0.05 0.03
Open in a new tab

Note. SE = standard error; LLCI = lower limit confidence interval; ULCI = upper limit confidence interval.

7. Discussion

Theory and research have primarily focused on organizations as the target for understanding turnover, while neglecting the industry. To address this limitation in the literature, the current paper used the hospitality industry as the foci for understanding turnover. Using two different samples (management-level employees in Study 1 and hospitality management students with hospitality work experience in Study 2) and methodology (survey for Study 1 and experiment for Study 2), the results showed being made unemployed due to the pandemic resulted in more anger and fear than being employed. Both studies, however, converged to show that the relationship between employment status (unemployed vs. employed) and industry turnover intentions was mediated by anger but not fear. These findings provide implications to explain why the industry's response to a mega-event, such as the COVID-19 pandemic, can motivate employees to leave the hospitality industry.

7.1. Theoretical implications

The current paper offers several theoretical implications. First, it advances the event-system theory (Morgeson et al., 2015) by showing how an industry's response to a mega-event, such as the COVID-19 pandemic, has a direct influence on employees' attitudes toward the industry. Specifically, event-system theory (Morgeson et al., 2015) focuses on how major events can affect the organization and how the actions taken by the organization can affect employee attitudes toward their organization. In addition, much of the literature on understanding why employees, including managers, leave the hospitality industry has focused on personal attributes, job attributes, and organizational-specific attributes (e.g., Ann & Blum, 2020; Blomme et al., 2010; Brown et al., 2015; Guchait et al., 2015). By using the affect theory of social exchange (Lawler, 2018) as a framework, however, the current paper showed how emotional reactions related to job status (i.e., being laid-off or furloughed) due to the pandemic influences intentions to leave the industry. Thus, an industry's response to a mega-event, such as the pandemic, can elicit negative emotions, such as anger and fear, which then motivate employees to leave the industry.

Second, the current study also has important implications for Lebel's (2017) model of fear and anger regulation. Lebel contends that fear and anger can act as motivators to lead individuals to take proactive measures to remedy these feelings, such as intentions to leave an industry. However, across both studies, anger, but not fear, was the primary driver of industry turnover intentions. One possible explanation is that anger is associated with high certainty situations and motivates a “fight” response (Novaco, 2016), whereas fear is associated with the “flight” response (Ohman & Wiens, 2003). While both emotions motivate a proactive effort to cut off the source of fear (Lebel, 2017), anger might motivate action to leave, whereas fear might motivate emotional withdrawal (Kish-Gephart et al., 2009). For example, people who feel higher intensity of anger than fear are more confident in their actions and more likely to take risks than people who feel higher intensity of fear than anger (Lerner & Keltner, 2001). These opposing views in risk appraisal from feeling anger or fear might explain why feeling anger was a stronger motivator of intentions to leave the industry. Because Study 1 had a sample of managers and Study 2 had a sample of hospitality management students with work experience, both samples had employees who have invested resources in working in the industry. Therefore, leaving the industry is a risk, which research has shown to be motivated by feelings of anger rather than feelings of fear.

Third, the current paper also builds on the hospitality turnover literature by demonstrating why job loss due to mega-events, such as the COVID-19 pandemic, can motivate employees to leave the hospitality industry (e.g., Akkermans et al., 2020; Bufquin et al., 2021; Chen & Chen, 2021; Yu et al., 2021). For example, a recent study showed how stress related to the COVID-19 pandemic can subsequently generate negative employee attitudes towards the hospitality industry (e.g., Yu et al., 2021). Similarly, Chen and Chen (2021) found that the stress related to job loss due to the pandemic was also related to impaired well-being and higher intentions to leave the hospitality industry. Unlike Yu et al. (2021) and Chen and Chen (2021), the current studies compared those who were made unemployed and those who remained employed during the pandemic in order to understand how discrete emotional reactions—anger and fear—affect their intentions to leave the industry. Specifically, it was the employees who lost their job due to the pandemic who felt more anger and fear than those still employed, and these emotions were related to industry turnover intentions.

Bufquin et al. (2021) also compared those who were made unemployed or remained employed during the pandemic but found that employees still working during the pandemic experienced higher levels of psychological distress, drug, and alcohol use than furloughed employees, all of which were related to industry turnover intentions. One reason for these contrasting findings is that sample from Bufquin et al. (2021) used non-management, hourly restaurant employees, a sample that research has shown to have high tendencies of substance abuse (Hight & Park, 2018; Kitterlin et al., 2015). The current research used management level employees (Study 1) and employed hospitality management students with goals of attaining management level careers (Study 2) —two samples that have invested resources in hospitality industry careers. Thus, remaining employed is consistent with their career aspirations, whereas those who lost their jobs were faced with a situation that was detrimental to their career aspirations.

Given that the hospitality industry was one of the industries most affected by COVID-19, negative attitudes among employees could mean a significant loss of talent as workers and job seekers consider switching industries for greater stability. Those who were furloughed or laid off due to COVID-19 felt greater levels of subjective anger than their counterparts who were still employed during the pandemic. As normal as these reactions to job and income loss may appear, the industry-wide circumstances that led to such widespread furloughs and lay-offs could result in subsequent actions taken by former employees to avoid a reoccurrence of such events; in this case, potentially leaving the hospitality industry altogether. Therefore, hospitality industry employees who lost their jobs due to COVID-19 may not return to work in the industry at all, even as conditions improve.

7.2. Practical implications

The current study additionally provides important practical implications. The results revealed that intentions to leave the industry were related to the anger felt from being unemployed from the hospitality industry. This is a cause for concern, because the hospitality industry relies on a pipeline of professionals who have skills and knowledge that are industry-specific and transferable across organizations within an industry (Baum, 2019). In addition, the hospitality industry relies on a pipeline of talent from which management positions are often filled by employees who have frontline work experience in the industry. Therefore, the hospitality industry may suffer from a loss of talent if industry-related negative work events, such as the pandemic, negatively influence employee attitudes and behaviors toward the industry. As such, these results point to the importance for hospitality organizations to develop strategies that can attenuate employees’ feelings of anger related to being unemployed. For example, hospitality organizations currently recruiting talent might use messages that build trust and/or address how they will avoid layoffs and furloughs in the future (Guzzo et al., 2021).

In addition, the results suggest that the pandemic is a problem for the industry as a whole, rather than a problem for individual organizations within the industry. This suggests that the hospitality industry must rebuild trust among its talent by communicating what was learned for addressing future events and how it plans to recover. This requires trade associations, such as the National Restaurant Association and the American Hotel &Lodging Association, and industry partners, such as university programs, to focus on recovery efforts (King et al., 2021). For example, industry trade associations and major chain corporations could partner to communicate plans for recovering lost jobs and plans to address future pandemics and other negative work events that can affect the industry.

It is also important for hospitality organizations to consider how they may prevent or at least reduce anger-driven industry turnover intentions to begin with, should the industry be faced with similar future crises. Contingency plans such as offering employees continuing benefits, alternative work arrangements, and/or training programs, may go a long way towards attenuating negative emotions towards the organization and hospitality industry. In addition, it is critical that hospitality organizations maintain clear and consistent communication with their employees regarding the situation that they are facing in times of uncertainty, as well as how they plan to address these challenges. These factors, if combined with an emphasis on integrity and employee welfare, could demonstrate to hospitality employees that despite the difficulties faced by their industry, the organizations they work for value them, and are prepared to protect employee interests in the face of hardships (Guzzo et al., 2021).

7.3. Limitations and future research

The main limitation of this study is the fact that the data was collected using samples from one country, the United States. Although similar labor shortages in the hospitality industry have been found on other parts of the world, like Australia (Powell, 2022), Asia (TTG Asia, 2021), and Europe (Diazgranados, 2021), future research can examine these relationships in other cultures, wherein different emotions might predict different outcomes (e.g., Luo et al., 2019). Another limitation is that in both current studies, intentions to leave the industry were used. Future studies should be conducted to establish whether the negative emotions that respondents report do, in fact, result in real industry turnover among those individuals. It is possible, for instance, that individuals who were laid off or furloughed only temporarily experienced greater negative feelings towards the hospitality industry, and their desire to leave the industry altogether decreased over time. An additional limitation is the fact that the data for this study only examines the attitudes of hospitality industry employees. Comparing this with the attitudes of current and laid off or furloughed employees from other industries affected by COVID-19 could provide important context for hospitality industry leaders as well as future researchers.

Despite these limitations, across two studies, we found that hospitality talent who experienced a change in job status due to the COVID-19 pandemic, either in the form of lay-offs or furloughs, consequently felt higher levels of subjective anger and fear than their counterparts who were still employed. In turn, higher levels of anger, but not fear, were related to higher industry turnover intentions. These results highlight a potentially problematic trend. Should skilled hospitality workers switch industries due to job loss amidst an industry-wide negative event, it may become difficult for hospitality businesses to find qualified employees once the industry recovers and rehiring begins. For this reason, it is important that companies within the hospitality industry carefully consider how to approach staffing cuts during difficult times, as talent that is let go may not return.

References

  1. Akkermans J., Richardson J., Kraimer M.L. The Covid-19 crisis as a career shock: Implications for careers and vocational behavior. Journal of Vocational Behavior. 2020;119:1–5. doi: 10.1016/j.jvb.2020.103434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Hotel. Lodging Association COVID-19's impact on the hotel industry. 2020. https://www.ahla.com/covid-19s-impact-hotel-industry Retrieved from.
  3. Ann S., Blum S.C. Motivating senior employees in the hospitality industry. International Journal of Contemporary Hospitality Management. 2020;32(1):324–346. [Google Scholar]
  4. Asia T.T.G. IHG embraces flexible working to tackle hospitality labour shortage. TTG Asia. 2021. https://www.ttgasia.com/2021/09/23/ihg-embraces-flexible-working-to-tackle-hospitality-labour-shortage/
  5. Bagozzi R.P., Yi Y. On the evaluation of structural equation models. Journal of the Academy of Marketing Science. 1988;16(1):74–94. [Google Scholar]
  6. Bajrami D.D., Terzić A., Petrović M.D., Radovanović M., Tretiakova T.N., Hadoud A. Will we have the same employees in hospitality after all? The impact of COVID-19 on employees' work attitudes and turnover intentions. International Journal of Hospitality Management. 2021;94 doi: 10.1016/j.ijhm.2020.102754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Baum T. Does the hospitality industry need or deserve talent? International Journal of Contemporary Hospitality Management. 2019;31(10):3823–3837. [Google Scholar]
  8. Baum T., Mooney S.K., Robinson R.N., Solnet D. COVID-19's impact on the hospitality workforce – new crisis or amplification of the norm? International Journal of Contemporary Hospitality Management. 2020;32(9):2813–2829. [Google Scholar]
  9. Blomme R.J., Van Rheede A., Tromp D.M. The use of the psychological contract to explain turnover intentions in the hospitality industry: A research study on the impact of gender on the turnover intentions of highly educated employees. The International Journal of Human Resource Management. 2010;21(1):144–162. [Google Scholar]
  10. Brown E.A., Thomas N.J., Bosselman R.H. Are they leaving or staying: A qualitative analysis of turnover issues for generation Y hospitality employees with a hospitality education. International Journal of Hospitality Management. 2015;46:130–137. [Google Scholar]
  11. Brynjolfsson E., Horton J.J., Ozimek A., Rock D., Sharma G., TuYe H.Y. National Bureau of Economic Research; 2020. COVID-19 and remote work: An early look at US data (No. w27344) [Google Scholar]
  12. Bufquin D., Park J.Y., Back R.M., de Souza Meira J.V., Hight S.K. Employee work status, mental health, substance use, and career turnover intentions: An examination of restaurant employees during COVID-19. International Journal of Hospitality Management. 2021;93 doi: 10.1016/j.ijhm.2020.102764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carless S.A., Arnup J.L. A longitudinal study of the determinants and outcomes of career change. Journal of Vocational Behavior. 2011;78(1):80–91. [Google Scholar]
  14. Chen C.C., Chen M.H. Well-being and career change intention: COVID-19's impact on unemployed and furloughed hospitality workers. International Journal of Contemporary Hospitality Management. 2021;33(8):2500–2520. [Google Scholar]
  15. Chen H., Eyoun K. Do mindfulness and perceived organizational support work? Fear of COVID-19 on restaurant frontline employees' job insecurity and emotional exhaustion. International Journal of Hospitality Management. 2021;94 doi: 10.1016/j.ijhm.2020.102850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Conroy S.A., Becker W.J., Menges J.I. The meaning of my feelings depends on who I am: Work-related identifications shape emotion effects in organizations. Academy of Management Journal. 2017;60(3):1071–1093. [Google Scholar]
  17. Crisp R.J., Heuston S., Farr M.J., Turner R.N. Seeing red or feeling blue: Differentiated intergroup emotions and ingroup identification in soccer fans. Group Processes & Intergroup Relations. 2007;10:9–26. [Google Scholar]
  18. Dasborough M.T., Hannah S.T., Zhu W. The generation and function of moral emotions in teams: An integrative review. Journal of Applied Psychology. 2020;105(5):433. doi: 10.1037/apl0000443. [DOI] [PubMed] [Google Scholar]
  19. Diazgranados D. Labor shortages in European hospitality industry threaten hopes of tourism boost. Medium. 2021. https://danilodiazgranados.medium.com/labor-shortages-in-european-hospitality-industry-threaten-hopes-of-tourism-boost-7211f1369b7d
  20. Fornell C., Larcker D.F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 1981;18(1):39–50. [Google Scholar]
  21. Fox S., Spector P.E. A model of work frustration–aggression. Journal of Organizational Behavior. 1999;20(6):915–931. [Google Scholar]
  22. Grandey A.A., Sayre G.M., French K.A. A blessing and a curse”: Work loss during coronavirus lockdown on short-term health changes via threat and recovery. Journal of Occupational Health Psychology. 2021;26(4):261–275. doi: 10.1037/ocp0000283. [DOI] [PubMed] [Google Scholar]
  23. Greenbaum R., Bonner J., Gray T., Mawritz M. Moral emotions: A review and research agenda for management scholarship. Journal of Organizational Behavior. 2020;41(2):95–114. [Google Scholar]
  24. Guchait P., Cho S., Meurs J.A. Psychological contracts, perceived organizational and supervisor support: Investigating the impact on intent to leave among hospitality employees in India. Journal of Human Resources in Hospitality & Tourism. 2015;14(3):290–315. [Google Scholar]
  25. Gursoy D., Chi C.G. Effects of COVID-19 pandemic on hospitality industry: Review of the current situations and a research agenda. Journal of Hospitality Marketing & Management. 2020;29(5):527–529. [Google Scholar]
  26. Guzzo R.F., Wang X., Madera J.M., Abbott J. Organizational trust in times of COVID-19: Hospitality employees' affective responses to managers' communication. International Journal of Hospitality Management. 2021;93 doi: 10.1016/j.ijhm.2020.102778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Haddon H. Restaurants serve up signing bonuses, higher pay to win back workers. 2021. https://www.wsj.com/articles/restaurants-serve-up-signing-bonuses-higher-pay-to-win-back-workers-11619359201 Retrieved from.
  28. Hair J.F., Black W.C., Babin B.J., Anderson R.E. Pearson; New York, NY: 2010. Multivariate data analysis: A global perspective. [Google Scholar]
  29. Haldorai K., Kim W.G., Pillai S.G., Park T.E., Balasubramanian K. Factors affecting hotel employees' attrition and turnover: Application of pull-push-mooring framework. International Journal of Hospitality Management. 2019;83:46–55. [Google Scholar]
  30. Han S., Lerner J.S., Keltner D. Feelings and consumer decision making: The appraisal-tendency framework. Journal of Consumer Psychology. 2007;17(3):158–168. [Google Scholar]
  31. Hayes A.F. Guilford publications; 2017. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. [Google Scholar]
  32. Henseler J., Ringle C.M., Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 2015;43(1):115–135. [Google Scholar]
  33. Hight S.K., Park J.Y. Substance use for restaurant servers: Causes and effects. International Journal of Hospitality Management. 2018;68:68–79. [Google Scholar]
  34. Huffman A.H., Albritton M.D., Matthews R.A., Muse L.A., Howes S.S. Managing furloughs: How furlough policy and perceptions of fairness impact turnover intentions over time. International Journal of Human Resource Management. 2022;33(14):2801–2828. [Google Scholar]
  35. Izard C.E. The psychology of emotions. Plenum Press; New York: 1991. [Google Scholar]
  36. King C., Madera J.M., Lee L., Murillo E., Baum T., Solnet D. Reimagining attraction and retention of hospitality management talent–A multilevel identity perspective. Journal of Business Research. 2021;136:251–262. [Google Scholar]
  37. Kish-Gephart J.J., Detert J.R., Treviño L.K., Edmondson A.C. Silenced by fear: The nature, sources, and consequences of fear at work. Research in Organizational Behavior. 2009;29:163–193. [Google Scholar]
  38. Kitterlin M., Moll L., Moreno G. Foodservice employee substance abuse: Is anyone getting the message? International Journal of Contemporary Hospitality Management. 2015;27(5):810–826. [Google Scholar]
  39. Kline R.B. Guilford Press; New York: 2011. Principles and practice of structural equation modeling. [Google Scholar]
  40. Lawler E.J. An affect theory of social exchange. American Journal of Sociology. 2001;107(2):321–352. [Google Scholar]
  41. Lawler E.J. In: Contemporary social psychological theories. Burke P.J., editor. Stanford University Press; 2018. The affect theory of social exchange; pp. 224–248. [Google Scholar]
  42. Lazarus R.S., Folkman S. Springer; New York: 1984. Stress appraisal and coping. [Google Scholar]
  43. Lebel R.D. Moving beyond fight and flight: A contingent model of how the emotional regulation of anger and fear sparks proactivity. Academy of Management Review. 2017;42(2):190–206. [Google Scholar]
  44. Lee P.C., Xu S.T., Yang W. Is career adaptability a double-edged sword? The impact of work social support and career adaptability on turnover intentions during the COVID-19 pandemic. International Journal of Hospitality Management. 2021;94 doi: 10.1016/j.ijhm.2021.102875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lerner J.S., Keltner D. Fear, anger, and risk. Journal of Personality and Social Psychology. 2001;81(1):146–159. doi: 10.1037//0022-3514.81.1.146. [DOI] [PubMed] [Google Scholar]
  46. Lerner J.S., Tiedens L.Z. Portrait of the angry decision maker: How appraisal tendencies shape anger's influence on cognition. Journal of Behavioral Decision Making. 2006;19(2):115–137. [Google Scholar]
  47. Luo A., Guchait P., Lee L., Madera J.M. Transformational leadership and service recovery performance: The mediating effect of emotional labor and the influence of culture. International Journal of Hospitality Management. 2019;77:31–39. [Google Scholar]
  48. McGinley S., Mattila A.S. Overcoming job insecurity: Examining grit as a predictor. Cornell Hospitality Quarterly. 2020;61(2):199–212. [Google Scholar]
  49. McGinley S., O'Neill J., Damaske S., Mattila A.S. A grounded theory approach to developing a career change model in hospitality. International Journal of Hospitality Management. 2014;38:89–98. [Google Scholar]
  50. Mimoun E., Ben Ari A., Margalit D. Psychological aspects of employment instability during the COVID-19 pandemic. Psychological Trauma: Theory, Research, Practice, and Policy. 2020;12(S1):S183. doi: 10.1037/tra0000769. [DOI] [PubMed] [Google Scholar]
  51. Miscenko D., Day D.V. Identity and identification at work. Organizational Psychology Review. 2016;6(3):215–247. [Google Scholar]
  52. Morgeson F.P., Mitchell T.R., Liu D. Event system theory: An event-oriented approach to the organizational sciences. Academy of Management Review. 2015;40(4):515–537. [Google Scholar]
  53. National Restaurant Association Coronavirus information and resources. 2020. https://restaurant.org/Covid19 Retrieved from.
  54. Nicola M., Alsafi Z., Sohrabi C., Kerwan A., Al-Jabir A., Iosifidis C.…Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery. 2020;78:185–193. doi: 10.1016/j.ijsu.2020.04.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Novaco R.W. Stress: Concepts, cognition, emotion, and behavior. Academic Press; 2016. Anger; pp. 285–292. [Google Scholar]
  56. Ohman A., Wiens S. In: Handbook of affective sciences. Davidson R.J., Scherer K.R., Goldsmith H.H., editors. Oxford University Press; New York: 2003. On the automaticity of autonomic responses in emotion: An evolutionary perspective; pp. 256–275. Organizational Psychology and Organizational Behavior, 7, 451-477. [Google Scholar]
  57. Osborne D., Smith H.J., Huo Y.J. More than a feeling: Discrete emotions mediate the relationship between relative deprivation and reactions to workplace furloughs. Personality and Social Psychology Bulletin. 2012;38(5):628–641. doi: 10.1177/0146167211432766. [DOI] [PubMed] [Google Scholar]
  58. Parker S.K., Bindl U.K., Strauss K. Making things happen: A model of proactive motivation. Journal of Management. 2010;36(4):827–856. [Google Scholar]
  59. Picchi A. Restaurants face new COVID-19 challenge: Finding enough workers. 2021. https://www.cbsnews.com/news/restaurants-covid-19-worker-shortage/ Retrieved from.
  60. Powell D. Demand for hospitality staff hits two-year high as union warns on exploitation. Sydney Morning Herald. 2022 https://www.smh.com.au/business/companies/demand-for-hospitality-staff-hits-two-year-high-as-union-warns-on-exploitation-20220317-p5a5ja.html#:~:text=Applicants%20for%20hospitality%20jobs%20are,lowest%20level%20since%20mid%2D2008 [Google Scholar]
  61. Pugh S.D., Skarlicki D.P., Passell B.S. After the fall: Layoff victims' trust and cynicism in re‐employment. Journal of Occupational and Organizational Psychology. 2003;76(2):201–212. [Google Scholar]
  62. Rubino C., Wilkin C.L., Malka A. The role of emotion and emotion regulation in job stress and well being. Emerald Group Publishing Limited; 2013. Under pressure: Examining the mediating role of discrete emotions between job conditions and well-being. [Google Scholar]
  63. Shepherd D.A., Williams T.A. Hitting rock bottom after job loss: Bouncing back to create a new positive work identity. Academy of Management Review. 2018;43(1):28–49. [Google Scholar]
  64. Smith H.J., Pettigrew T.F. The subjective interpretation of inequality: A model of the relative deprivation experience. Social and Personality Psychology Compass. 2014;8(12):755–765. [Google Scholar]
  65. Suh E., West J.J., Shin J. Important competency requirements for managers in the hospitality industry. Journal of Hospitality, Leisure, Sports and Tourism Education. 2012;11(2):101–112. [Google Scholar]
  66. U.S. Bureau of Labor Statistics Impact of the coronavirus (COVID-19) pandemic on the employment situation for august 2020. U.S. Bureau of Labor Statistics, U.S. Bureau of Labor Statistics. 2020 www.bls.gov/covid19/employment-situation-covid19-faq-may-2020.htm 2020. [Google Scholar]
  67. Wiener-Bronner D. A perfect storm': These restaurants survived the pandemic. Now they can't find workers. 2021. https://www.cnn.com/2021/04/21/business/restaurant-labor-shortage/index.html Retrieved from.
  68. Wong A.K.F., Kim S.S., Kim J., Han H. How the COVID-19 pandemic affected hotel Employee stress: Employee perceptions of occupational stressors and their consequences. International Journal of Hospitality Management. 2021;93 doi: 10.1016/j.ijhm.2020.102798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Yan J., Kim S., Zhang S.X., Foo M.D., Alvarez-Risco A., Del-Aguila-Arcentales S., Yáñez J.A. Hospitality workers' COVID-19 risk perception and depression: A contingent model based on transactional theory of stress model. International Journal of Hospitality Management. 2021;95 doi: 10.1016/j.ijhm.2021.102935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Yu H., Lee L., Popa I., Madera J.M. Should I leave this industry? The role of stress and negative emotions in response to an industry negative work event. International Journal of Hospitality Management. 2021;94 [Google Scholar]

Articles from Journal of Hospitality and Tourism Management are provided here courtesy of Elsevier

RESOURCES