Emotional Labour During the COVID-19 Pandemic—Current Inquiry and Suggested Future Research Directions

Abstract

The objective of this article is to examine the impact of macro-extreme emotional experience (MEEE) and the new societal norms during the COVID-19 pandemic on health and well-being and their situational consequences on emotional labour of frontline employees. The vast literature on emotional labour in the past has focused on several situational cues, and individual and organizational factors as antecedents. We did a systematic review of available literature on emotional labour, literature on sentiment analysis and emotional experience during the pandemic and analysed COVID-19 related blogs using Natural Language Processing (NLP) in RStudio. At the same time, we attempted to look at the possible intervention of individual factors of MEEEs and social aspects of the new societal norms as antecedents on emotion regulation process and its outcome and propose a conceptual framework for future research on emotional labour under the ‘new normal’. It was concluded that perceived risk, fear and anxiety are extreme emotions that individuals are experiencing during the pandemic.

Keywords

COVID-19 emotional labour employee and organizational well-being extreme emotional experience new normal sentiment analysis

Introduction

Hochschild (1983, p. 7) introduced the concept of ‘emotional labour’ (EL) in her book, The Managed Heart. She coined the term and defined emotional labour as ‘the management of feeling to create a publicly observable facial and bodily display’. In other words, emotional labour means the regulation and expression of desirable emotions exhibited in organizations while interacting with clients to meet or fulfil the display rules set by the organization (Ashforth & Humphrey, 1993; Grandey, 2000; Hochschild, 1983). In a statement to the press on 26 March 2020, Dr. Hans Henri P. Kluge, the WHO Regional Director for Europe said, ‘It is natural for each of us to feel stress, anxiety, fear and loneliness during COVID-19. During the time, the pandemic has had an effect on our mental health and psychological well-being’ (WHO, n.d.). The COVID-19 pandemic has created a significant impact on the physical and mental well-being of human beings. The ‘normal’ has undergone change and the ‘new normal’ is going to define new social and organizational norms. The extreme or intense emotional experience felt during the pandemic and the new societal norms of maintaining physical distance and wearing masks may add to the complexities of emotional labour. Hence, the emotional labour during ‘normal’ times is not going to the same under the ‘new normal’. It is essential to understand how the ‘new normal’ is going to impact the health and well-being of frontline employees. Aligning future research with a focus on the impact of the extreme emotional experiences and the ‘new normal’ societal norms on the emotional labour process and their outcomes on the health and well-being of employees can provide new insight and valuable information. The topic of the present article will provide a theoretical and conceptual framework for future research by throwing light on the impact of the pandemic on health and well-being through the process of emotional labour.

Traditionally, ‘service with a smile’ defines the organizational display rules in the emotional labour process (Hochschild, 1983). However, aligning new social norms such as ‘wearing masks’ and ‘maintaining physical distance’ with organizational norms may define new display rules in the emotional labour process. Over four decades, researchers have studied emotional labour as one of the critical aspects of the performance of customer-facing service employees and the performance of the organization. The emotional stress associated with work can have serious consequences on the well-being of an individual, leading to psychological and other health issues (Chen et al., 2019; Lee & Chelladurai, 2018). While an individual’s emotions are based on several antecedents such as situational incidences, social and organizational factors, the circumstances or possible outcomes from customer interactions that may have potential risks associated with an individual’s health and well-being could impinge on one’s motivation to work. For example, a care-giving professional such as a nurse who is engaged in treating a patient is expected to express emotions of sympathy, empathy, care and love.

The nurse’s internal feelings and expression of desirable external emotions might depend on several situational cues, individual and organizational factors. However, if a patient is found to have affected with COVID-19, the nurse’s evaluation of the risk of treating such a patient would be associated with anxiety and fear and could affect her emotional state (Lovrić et al., 2020; Man et al., 2020; Miotto et al., 2020). The emotional labour to meet the socio-organizational display rules may have severe consequences on the nurse’s physical and mental health and well-being. The situation would not be different for employees engaged in social control jobs. Similarly, other frontline employees such as police personnel are equally exposed to high-risk environments. While frontline professional employees may engage with difficult situations or circumstances that are riskier, a pandemic such as COVID-19 poses a different risk perception as the risk is not only with one getting infected with the virus but also with the risk of being a carrier and spreading the disease. Along with the perceived risk, the associated fear and anxiety may also affect the emotional labour process and its outcome. While most of the past empirical studies focused on the positive affect, a few such as Gabriel et al. (2015) studied dispositional positive and negative affectivity on emotional labour strategies (ELS). It is a possibility that ‘extreme emotional experiences’ during a pandemic situation may have a long-lasting impact on an individual’s emotional health and could affect the emotional labour process and its outcome.

People across the world experienced intense emotions during the COVID-19 pandemic. During pandemics such as H1N1, Ebola and COVID-19, an individual’s risk perception (de Zwart et al., 2009; Dryhurst et al., 2020; Ibuka et al., 2010; Leppin & Aro, 2009; Poletti et al., 2011; Smith, 2006), anxiety (Jungmann & Witthöft, 2020; Maaravi & Heller, 2020), fear (Asmundson & Taylor, 2020; Lee, 2020; Lee et al., 2020) and the new societal norms will have a combined effect on the emotional well-being of the employee, thereby affecting their physical and mental health (Restubog et al., 2020; Wang et al., 2020). Managing one’s emotions through effective emotion regulation can help bring about positive psychological outcomes and socio-psychological benefits and may provide improved long-term health and well-being of the employees. Hence it will be worthwhile to explore emotional labour in the backdrop of the extreme emotional experiences endured during the pandemic. As Restubog et al. (2020) put it:

Do employees experience different types of emotions in response to this pandemic? …study more closely and scientifically how working individuals can respond to a pandemic of this magnitude from an emotion regulation perspective. (Restubog et al., 2020, p. 4)

We reviewed the literature on emotional labour, its antecedents and consequences during normal times and the extreme emotional experiences endured during the COVID-19 pandemic as factors that could be antecedents. We then integrated the extreme emotional experiences into the emotional labour process. We have examined each theoretical framework that researchers have used to logically explain the research design. They have argued that the Conservation of Resource (COR) Theory, the Affective Events Theory (AET) and the Job Demand Resource (JD-R) Theory could best fit the research domain under the current pandemic situation. This article, therefore, attempts to integrate the extreme emotional experiences felt (Taylor, 2019) during a pandemic with the pool of situational, individual and socio-organizational factors (Grandey, 2000, 2003) affecting emotional labour and its outcomes. While there is extant literature available on emotional labour per se, literature pertaining specifically to the pandemic is scant and underdeveloped. We, therefore, (1) reviewed existing literature on emotional labour, (2) reviewed the literature on sentiment analysis during the COVID-19 pandemic, (3) analysed COVID-19 related blog posts using Natural Language Processing (NLP) in RStudio, and (4) reviewed the literature on extreme emotional experiences endured during the COVID-19 pandemic and the new normal societal norms that can be an antecedent in the emotional labour process.

We, finally, attempted to draw the linkage by integrating the extreme emotional experiences and the new societal norms as antecedents to the emotional labour process to propose a framework for future research. While emotional labour research framework is complex in nature, we believe that pandemic related factors which were not taken into consideration earlier will add value to the research. This article will thus add to the existing body of literature on emotional labour through its conceptual and proposed theoretical research framework for emotion regulation under the extreme conditions of a pandemic for future researchers to explore and study.

Literature Review

Emotional Labour and Emotion Regulation Strategy

Individuals follow the feeling rules to manage the expression of their emotions as per social norms or organizational display rules. The ‘new normal’ as a result of the pandemic may add the new societal norms to the organizationally acceptable display rules. Hochschild (1983) argued that individuals performing emotional labour could adopt ELS of Surface Acting (SA) in which individuals regulate and modify their expressive behaviour to express their emotions in accordance with the requirements or rules or Deep Acting (DA) in which individuals regulate their internal thoughts and feelings through cognitive reappraisal and feel the emotions before they are expressed. ‘Reappraisal’ (Gross, 1998a) is a form of emotion regulation accomplished by modifying one’s perception of a situation either through attention deployment or through cognitive changes (Grandey, 2000), and hence it is deep acting (Hochschild, 1983). On the other hand, ‘suppression’ (Gross, 1998a) is inhibiting emotion-expressive behaviour after the emotion has been aroused; therefore, it is response-focused (Gross, 1998a) and is surface acting (Hochschild, 1983). We argue that frontline service providers under intense macro-extreme conditions such as the COVID-19 pandemic, may have to control and manage their emotion regulation process to mitigate the adverse effects and consequences of the extreme emotional experience (Restubog et al., 2020). The question is whether one should manage the emotions by attending to the feelings or ignoring them (Gross, 1998b). The type of emotion and the time to express those emotions are decided and controlled by individuals using a variety of emotional strategies (Gross, 1998a, 1998b).

Even with standard operating procedures to deal with COVID-19 patients and the infected community in place, the emotional trauma experienced by nurses, doctors, paramedics, police and social service professionals is a matter of concern, discussion and media focus. Researchers, in the initial stages, focused mostly on the outcome of emotional labour. As theorists explored the concept beyond the initial developmental work, they proposed several antecedents to emotional labour. They argued that an individual’s adoption or selection of a particular emotion regulation strategy is influenced by several situational cues as well as individual and organizational factors (Grandey, 2000). Whether it is the management of feelings and expressions (Hochschild, 1983) or observable behaviour (Ashforth & Humphrey, 1993) for impression management or a conscious effort put in by the employees to express their emotions in a social environment (Morris & Feldman, 1996), emotional labour is subjected to and influenced by several individual, organizational, situational and societal factors (Grandey, 2000). However, heightened stress, anxiety, fear and exhaustion during the pandemic can lead to a substantial divergence between the emotions felt and those required; a state of high emotional dissonance can also sometimes result in intentional or unintentional emotional deviance (Gabriel et al., 2015; Grandey & Gabriel, 2015; Rafaeli & Sutton, 1989). Suppression of emotions or surface acting can lead to emotional dissonance which affects individual and organizational well-being, mental and physical health, job performance, turnover intention and quality of work–life and can result in burnout, emotional exhaustion and job dissatisfaction (e.g., Alsakarneh et al., 2019; Becker et al., 2018; Bhave & Glomb, 2016; Brotheridge & Grandey, 2002; Côté & Morgan, 2002; Diefendorff et al., 2011; Gabriel et al., 2015; Grandey, 2000, 2003; Grandey et al., 2019; Kim et al., 2019; Lee & Madera, 2019).

To summarize, researchers and theorists believe that emotional labour and regulation strategies can take any form of SA (Grandey, 2000; Hochschild, 1983), DA (Grandey, 2000; Hochschild, 1983), spontaneous expression of genuine emotion (Diefendorff et al., 2005), non-expression of emotion—which is emotional deviance (Gabriel et al., 2015; Grandey & Gabriel, 2015; Rafaeli & Sutton, 1989), or a combination of both surface and deep acting as per the latent profile of individuals (Gabriel et al., 2015). As the consequences of emotional labour can affect the well-being of individuals and the organization, so also can situational cues such as organizational expectations on customer interactions, emotional events (positive or negative), individual factors (e.g., gender, emotional intelligence, emotional expressivity, positive and negative affects) and organizational factors (e.g., perceived organizational support (POS), job characteristics and emotional demands) (Grandey, 2000), influence ELS (Grandey, 2000, 2003; Hochschild, 1983) and their outcomes as antecedents, mediators/moderators. The conceptual and process models of emotional labour with its antecedents and consequences developed by Grandey (2000), Gross (1998a) and Lee and Madera (2019) formed the basis for the research framework. Also, the new societal and organizational norms on extreme emotional experiences such as risk perception, fear and anxiety (Taylor, 2019) under macro-extreme situations such as the COVID-19 pandemic can add to the pool of antecedent variables of emotional labour.

Macro-Extreme Emotional Experience Under the New Normal

Extremes are often identified in the context of risk, disruption and emergencies arising out of significant events such as pandemics, dangerous weather, acts of terrorism, political upheaval, war, and so on. The macro or intense extremes appear major even to external observers as individuals often go through traumatic experiences (Wankhade et al., 2020). We consider the COVID-19 pandemic as a macro-extreme or intense-extreme condition that can lead to traumatic emotional experiences. Emotions under macro-extreme conditions are influenced not only by one’s evaluation of the situation but also by other factors such as interaction and communication with friends, relatives and media and governmental announcements. These emotions find the easiest way of expression through social media which plays a significant role in quick communication, expression of one’s opinion and views, and expression of one’s sentiments (Lwin et al., 2020; Pellert et al., 2020; Zhang et al., 2020). Platforms of micro-blogging such as Twitter, Facebook, Instagram and YouTube become sources for easy and fast communication and expression of one’s views, opinions and sentiments (e.g., Das & Dutta, 2020; de las Heras-Pedrosa et al., 2020; Lwin et al., 2020; Pellert et al., 2020; Zhang et al., 2020). At the same time, more detailed views, logical expressions, opinions and suggestions are expressed in individual and organizational blogposts. Also, academicians, scholars and researchers further contribute to the huge body of knowledge through their qualitative, quantitative and empirical studies and findings (Asmundson & Taylor, 2020; Chao & Wang, 2020; Choi et al., 2020; Dryhurst et al., 2020; Lee, 2020; Lee et al., 2020; Lovrić et al., 2020; Maaravi & Heller, 2020; Ripp et al., 2020).

To understand the macro-extreme emotional experience (MEEE) during the COVID-19 pandemic, we reviewed, analysed and consolidated three different kinds of literature as described below:

Review of literature on sentiment analysis of microblog posts such as Twitter pertaining to the COVID-19 pandemic.

Analysis of 245 blogs (retrieved on 5 September 2020 from Google using the Google Search API and the BeautifulSoup Library in Python 3.x) through Natural Language Processing (NLP) using RStudio (Google, 2020).

Review of literature on emotional experience during the COVID-19 pandemic.

The literature on sentiment analysis and scholarly articles on COVID-19 emotional experience indicated that perceived risk, anxiety and fear are the emotional experiences and their impacts the individuals experienced during the COVID-19 pandemic. Our analysis of the COVID-19 pandemic related blogs further reiterated these findings as depicted in Figure 1. The review of literature on sentiment analysis, analysis of blogposts and the review of literature on emotional experience during COVID-19 are discussed.

Sentiment Analysis and Analysis of Blogs

Sentiment analysis using NLP and other analytical tools such as Machine Learning (ML) and Artificial Intelligence (AI) to infer public sentiment and the emotional and mental state of individuals from postings on social media and microblogs is a recent trend which has seen considerable growth in the last decade (Keith Norambuena et al., 2019). There are quite a few sentiment analyses carried out recently to study public sentiment during COVID-19, and most of these analyses are done using text mining and Natural Language Processing (NLP) of tweets (de las Heras-Pedrosa et al., 2020; Low et al., 2020; Lwin et al., 2020; Pellert et al., 2020; Zhang et al., 2020). Lwin et al. (2020), while examining public sentiment using the Crystalfeel sentiment analytic algorithm, observed that while fear and anger showed a reducing trend, there was an increasing amount of sadness due to the loss of loved ones. It was also observed that gratitude towards frontline employees also increased. Low et al. (2020) observed heightened anxiety in their NLP of Reddit postings using ML. Zhang et al. (2020) examined the depression trend on Twitter during the COVID-19 pandemic using the Crimson Hexagonplot. They observed a significant increase in depression signals among people as more and more discussions on COVID-19 continued. Pellert et al. (2020), while analysing the emotional experience of Australian people, observed that anxiety, anger and sadness continued to show up as critical emotions. However, the intensity of the changes was seen over time. Das and Dutta (2020) found depression, anxiety and stress to be common psychological responses to the COVID-19 pandemic in their Twitter data analysis of India. In multiple microblog analyses in Spain using IBM Watson Analytics, de las Heras-Pedrosa et al. (2020) found that the pandemic caused additional strain on the emotional well-being of the people of Spain who felt negative emotions such as those of sadness, disgust, anger and fear. To conclude, fear remained one of the most expressed emotions, while anxiety, sadness, anger and disgust continued to show up as other negative emotions. Psychological and mental health issues of depression and stress were observed to be high on the reported analysis from the microblog postings.

Our analysis of blogs also indicates similar results and reiterated the findings from other sentiment analysis literatures. We used NLP to carry out the sentiment analysis of the COVID-19 blogs. NLP involves four basic steps—data mining, data preparation, data exploration and sentiment analysis.

Data Mining: Using Google Search API and the BeautifulSoup library in Python 3.x, we mined 245 blog posts from Google. The blog links were retrieved from Google on the 5 September 2020 using the search query, ‘emotional well-being during COVID blogs’ and the content of the blogs was extracted using BeautifulSoup.

Data Preparation: The corpus was cleaned using data processing techniques such as stop word removal which finds and removes words like articles and single character words that are irrelevant to the study. These stop words were then used to expand the pre-defined stop word list in the Natural Language Tool Kit (NLTK) library and were used to clean up the corpus entirely. To remove noise and reduce space, the ‘re’ library was used to remove punctuation and URLs from the data. HTML tags from the blog posts were removed as well. To normalize the dataset to avoid duplication and to be able to accurately analyse key terms, the text was transformed into the lower case using the String function in Python and each word in the corpus was reduced to its stem form for better feature extraction. For example, ‘worry’ and ‘worrisome’ were transformed into ‘worri’.

Data Exploration and Sentiment Analysis: The cleaned corpus was tokenized which means it essentially breaks down the text corpus into individual words. A frequency chart was plotted to analyse the distribution of the unigram word count of the processed corpus in RStudio. Through this frequency chart, a word cloud was formed to better visualize the data points. The word cloud plot of the text-mined data using NLP Library in RStudio is shown in Figure 1. Word clouds are visual representations of words that give greater prominence to words that appear more frequently. It helps identify the most common and frequently used words in the blog posts, thus identifying the main sentiment expressed in blogs.

Figure 1.

Word Cloud of the Analysis of Blogs Posted During COVID-19.

As can be seen in the word cloud, words such as risk, anxiety, fear, stress and depression are the most prominent emotions expressed in the blogs, and these extreme emotional experiences can be seen affecting public health not only physically but also mentally and psychologically. From the literature on sentiment analysis and our own analysis of the blogs, we conclude that the perceived risk associated with the virus leads to the fear of getting infected or the fear of transmission of the virus and these fears create anxiety, stress and depression in individuals. At the same time, other negative emotions such as sadness, anger and disgust are also felt during this time due to loss of lives, social restrictions, inadequate health care facilities, and so on. However, people also expressed hope and positivity regarding the future that things would change for the better. They expressed joy and gratitude for the services being provided by the frontline health workers. Similar emotional experiences are also reported in scholarly articles.

Extreme Emotional Experiences

Extant literature related to the COVID-19 pandemic elaborates on the extreme emotions experienced during this time because of the perceived risk associated with the virus (Dryhurst et al., 2020; Harper et al., 2020; Khosravi et al., 2020; Miotto et al., 2020; Ripp et al., 2020), the fear of getting infected with the virus or being a carrier for transmission (Asmundson & Taylor, 2020; Chao & Wang, 2020; Lee, 2020; Lee et al., 2020; Ornell et al., 2020; Ripp et al., 2020), and the accompanying anxiety (Choi et al., 2020; Jungmann & Witthöft, 2020; Lee, 2020; Maaravi & Heller, 2020; Moghanibashi-Mansourieh, 2020; Nikčević & Spada, 2020). Previous studies during the earlier pandemic of H1N1 have also indicated similar emotional experiences. In their theoretical and conceptual article on pandemic influenza risk perception, Leppin and Aro (2009) suggested that the pandemic influenza was high on the dreaded risk factor. The dreaded risk perception creates a ‘climate of fear’ in individuals and in society (Smith, 2006). Further, uncertainty about the future, social isolation, physical distancing, worrying about one’s health and that of near and dear. The fear of loss of loved ones can induce stress and anxiety. Hence, during extraordinary events like the COVID-19 pandemic, feeling fearful and stressed out is common (Ripp et al., 2020).

From the analysis of the literature on sentiment analysis, literature on extreme emotional experiences, and the analysis of blogposts during the pandemic, we concluded that extreme emotional experience is a combination of perceived risk, fear and anxiety. These combined emotional experiences during a pandemic can affect individual well-being which includes physical and mental health, job satisfaction, organizational well-being such as job performance, turnover intention, quality of work–life and customer outcomes such as customer satisfaction. Most of the studies during the pandemic have focused on the physical and mental well-being of the public and frontline health workers. The frontline service providers not only go through extreme emotional experiences due to the pandemic but they also face the brunt of delivering emotional labour that is socially and organizationally acceptable. The long-term effect of these extreme emotional experiences on individual, organizational and customer outcomes cannot be ignored. However, control and management of emotions through proper emotion regulation strategies can act as a coping strategy to mitigate the negative consequences of these extreme emotional experiences (Restubog et al., 2020) and help in maintaining the good health and well-being of employees. An exploration of MEEEs as an antecedent of the emotion regulation process remains a research gap which needs to be explored.

Factors Affecting Emotional Labour Under the New Normal

While current research focused on the effect of the COVID-19 pandemic on individual and organizational well-being, economic, social and customer outcomes, there is a lack of research on ‘factors of vulnerability’ (e.g., anxiety) and ‘coping’ (e.g., emotion regulation strategies) focusing on the pandemic (Asmundson & Taylor, 2020; Jungmann & Witthöft, 2020; Restubog et al., 2020). In a study of anxiety among the German population during the COVID-19 pandemic, Jungmann and Witthöft (2020) observed that ‘dysfunctional emotion regulation has a positive relationship and adaptive emotion regulation has a negative association with the current virus anxiety’. Emotional labour under the ‘new normal’ will be more complicated due to factors of extreme emotional experience during the pandemic and new societal norms of physical distancing, wearing of masks and repetitive hand hygiene. Thus, we argue that MEEE will add to the pool of individual factors and new social norms will add to the existing collection of social and organizational norms and rules. At the micro-level, individual factors such as the individual’s emotional intelligence (i.e., ability to perceive, understand and properly manage one’s own emotions and that of others), self-efficacy (i.e., one’s belief in his ability to engage in risky tasks) (de Zwart et al., 2009), values and beliefs (i.e., one’s values based on the social culture—e.g., caring, loving, respect for elders and honesty), extreme emotional experience (perceived risk, fear and anxiety), gender, and so on.; at the meso level, social factors such as social distancing, wearing of masks and culture (individualistic and collectivistic); and at the macro level, organizational factors such as specific measures, policies and display rules may affect emotional labour and its individual, organizational and customer outcomes. In the proposed emotional labour framework, we consider MEEE as an individual antecedent and social distancing, wearing of masks and cultural norms as social antecedents of the emotion regulation process.

Individual Factors

Macro-Extreme Emotional Experiences (Perceived Risk, Fear and Anxiety)

The extreme emotional experience of frontline service providers caused by their perceived risk, fear and anxiety regarding the COVID-19 pandemic (e.g., Asmundson & Taylor, 2020; Chao & Wang, 2020; Choi et al., 2020; Dryhurst et al., 2020; Fernandez et al., 2020; Harper et al., 2020; Jungmann & Witthöft, 2020; Khosravi et al., 2020; Lee, 2020; Lee et al., 2020; Maaravi & Heller, 2020; Moghanibashi-Mansourieh, 2020; Nikčević & Spada, 2020; Ornell et al., 2020; Ripp et al., 2020) can create emotional dissonance in their emotion regulation process while interacting with customers. These may result in negative psychological well-being in the form of emotional exhaustion, stress, self-alienation, de-personalization and low job satisfaction. Looking at emotion from a dynamic emotional perspective, emotions due to perceived risk may start even before situational events happen at the workplace. Hence, we argue that macro-extreme conditions can influence emotions even before the commencement of the emotional labour process. Thus, in the emotional labour process, the feelings arising out of macro-extreme conditions may precede situational cues.

Situational emotional events arising from customer events (customer interactional expectations) and co-worker events (workplace positive and negative events) can induce emotions such as those of happiness/unhappiness, enthusiasm/boredom, and so on. These build up emotion regulation motives which subsequently lead to emotion regulation. In a similar vein, Mann (2005) outlined the Emotional Labour Model for healthcare nurses based on emotional labour-inducing events, subsequent emotional conflict, emotional labour performance and finally, the emotional labour outcome. While Mann (2005) focused on emotion labour-inducing events such as uncertain outcomes from treatments, dealing with patients’ emotions, pain and expectations, we argue that macro-extreme conditions such as the COVID-19 pandemic can induce severe negative emotions due to the higher dreaded risk, fear and anxiety associated with it, all of which can lead to emotional conflict and subsequent emotional dissonance. Hence MEEE may be an antecedent upon the emotion regulation process during the ‘new normal’ as an individual factor.

Social Factors

Social Distancing

Social distancing (physical distance) has become a new social norm in the ‘new normal’ under the current COVID-19 pandemic. While in a collectivistic society, physical distancing may be seen as distancing from a personal relationship and personal care, the same may not be the case in an individualistic society. Hence, we argue that social distancing may affect customer perception regarding the quality of service being provided. Maintaining physical distance from each other is an essential health measure to control the spread of the virus. The same can also lead to grief (Wallace et al., 2020) and pose a mental health risk due to our embedded social culture. However, it may also be seen as a pro-social behaviour of empathy (Pfattheicher et al., 2020) and a predictor of positive behavioural changes among people (Harper et al., 2020). Social distancing can be a double-edged sword. While, on one side, individuals are expected to maintain social distance in their social interactions, on the other side, healthcare professionals such as nurses, doctors, paramedics and social control professionals such as the police are expected to interact more closely with their clients, patients, or controlling public in social gatherings. Hence, these professionals may find it difficult to maintain social distancing while on duty and this may affect their emotional labour.

Social distancing is also viewed differently by people from individualistic and collectivistic societies due to differing social cultures and beliefs. Thus, setting ‘social distancing’ as a social norm could affect the cultural opinion of people and hence affect their psychological (Wallace et al., 2020) and cognitive thought process that may affect their emotional labour process. From our review of emotional labour literature in the context of the current pandemic situation, we opine that, in addition to individual and organizational factors, social factors are essential. The ‘new normal’ has set ‘social distancing’ as a social norm that can affect emotional labour.

Wearing of the Mask

Wearing masks in the wake of the COVID-19 pandemic brought challenges to both individuals, the one who is expected to express the desired emotions and the receiver who is expected to perceive the expressed emotions. Facial expression is one of the most essential and effective ways of non-verbal communication. This would be hampered when part of the face is covered with a mask, throwing new challenges to this non-verbal form of communication as against full facial expression (Nestor et al., 2020). Also, there is an uneasiness felt while wearing a mask. Also, individuals may find it strange to wear a mask in public as it alters their appearance. However, the mask is seen as protection from getting infected and prevention of the spread of the virus. In a recent study, Pfattheicher et al. (2020) observed that both social distancing and wearing of face masks are seen as behaviour related to empathy—a genuine pro-social emotion. From an emotional labour point of view, expressing desired emotions by the employees in customer interactions may be difficult and challenging when wearing a mask. As Nestor et al. (2020) put it:

By cutting the visual surface area of our faces in half, masks make it incredibly challenging to display and perceive each other’s facial expressions… If this is the case, COVID-19 brings with it not only a pandemic of global health but a pandemic of emotional communication as well. (Nestor et al., 2020, p. 2158)

However, the only thing that is not masked is the inner feeling controlled by the mind in that masked interactive process. Hence, we believe that the ‘wearing of the mask’ as a new social norm will only add to the complexities of the emotional labour process.

Cultural Norms

Cultural norms of collectivistic and individualistic societies differ in many ways (Cheung et al., 2011). People in collectivistic societies such as those in Asian countries, will try to control or hide their negative emotions when encountered with provocative negative emotional situations. In contrast, those in individualistic societies such as those in western countries, may tend to express negative emotions under similar circumstances. In the collectivistic league, group harmony and group relationship play a significant role in social interactions (Cheung et al., 2011) and societal expectations are high for serving the family, group or community. In collectivistic societies, where societal norms are built on emotional attachments and interpersonal relationships, the ‘new normal’ may add further complexities to the already complex emotional labour model.

The Proposed Model to Examine Emotional Labour During the Pandemic

Frontline service professionals involved in caring, social control and customer service professions could be under constant fear, anxiety and risk but are required to perform emotional labour (EL) by controlling and regulating their emotions to express socially or organizationally desired emotions in customer interactions (Grandey, 2000, 2003; Hochschild, 1983). Researchers in the past have looked at various factors affecting emotional labour and its outcomes. However, to the best of our knowledge, little or no research has been done on emotional labour under macro-extreme conditions. Hence, we attempted to bring together the factors that may have a significant effect on the emotion regulation process under macro-extreme conditions, especially in pandemic situations such as the COVID-19. We reviewed the conceptual model of Grandey (2000), the process model of Gross (1998a) and the model developed by Lee and Madera (2019) and built our conceptual model of emotional labour in the backdrop of the pandemic. In our conceptual research model of emotional labour, we proposed to include the MEEEs of perceived-risk, fear and anxiety and the ‘new normal’ socio-organizational norms of social distancing and wearing of marks as antecedents of emotion regulation strategies (refer Figure 2).

Figure 2.

Notes: 1. This model is built on the conceptual and process model of Grandey (2000), Gross (1998a), and Lee and Madera (2019).

Based on the theoretical framework of the COR theory (Hobfoll, 1989) and the JD-R theory (Demerouti et al., 2001), we argue that pandemics or similar situations which create an intense or extreme condition can cause resource drain-outs even before the commencement of an individual’s emotional labour process or emotion regulation. This is because of the psychological impact of fear and anxiety associated with the perceived risk. Thus, organizational and social support can play an important role in the conservation of resources. The examples we cite here are the highly demanding jobs of professionals such as nurses, doctors and the paramedics, social control professionals such as the police and other public servants engaged in the control of the spread of the virus. Whereas perceived risk, fear and anxiety can severely impact the psychological resource, the perceived organizational support (POS) in dealing with virus-infected patients, the infrastructure support, the support from other team members and supervisors can help in replenishing the loss of psychological resource. Hence POS can moderate the relationship between extreme emotional experiences and emotion regulation.

On the basis of our conceptual framework, we have suggested a research model in Figure 2 and a research framework in Table 1 with causative and intervening models that can be explored by future researchers to study emotional labour under macro-extreme conditions. However, the research framework in Table 1 is only suggestive and not exhaustive. Future researchers can explore other causative and intervening models as well.

Table 1.

Suggested Research Frameworks.

Possible Frameworks	Consequences	Justifications
MEEE, EI, SE, SI, POS, SD, WM, CN >> ELS MEEE, EI, SE, SI, POS, SD, WM, CN >> PS, EE, JS, JP, QWL, CS ELS >> PS, EE, JS, JP, QWL, CS	• PS • EE • JS • JP • QWL • CS	Causative model—direct and independent
MEEE >> ELS MEEE X POS >>ELS POS >>ELS MEEE >> ELS MEEE X SD >>ELS SD >> ELS MEEE>>ELS MEEE X WM >> ELS WM >> ELS		Intervening model—intervening components either as moderators or mediators
MEEE>> EE, PS, JS, JP, QWL, CS MEEE >>ELS>> EE, PS, JS, JP, QWL, CS ELS >> EE, PS, JS, JP, QWL, CS

Note: MEEE: macro-extreme emotional experience; EI: emotional intelligence; SE, self-efficacy; SI, social identity; POS, perceived organizational support; SD, social distancing; WM, wearing mask; CN, cultural norms; ELS, emotional labour strategies; PS, psychological stress; EE, emotional exhaustion; JS, job satisfaction; JP, job performance; QWL, quality of work-life; CS, customer satisfaction; ?, ??, and ???, unknown.

Implications and Limitations

This study contributes to the body of literature on emotional labour by focusing on emotional labour under macro-extreme conditions such as the COVID-19 pandemic. First, this study provided a conceptual model and framework for future researchers to explore emotional labour under pandemic situations. Thus, future researchers using the proposed research framework may provide new findings to use the appropriate ELS to counter the negative impact of extreme emotional experiences and new social norms for better health and well-being. Second, by focusing on the pandemic situation as a high-risk situation, this study included ‘perceived risk’, ‘fear’ and ‘anxiety’ as a critical individual factors in the emotional labour process. Third, past empirical studies mostly focused on individual and organizational factors affecting emotion regulation and its outcomes. This study included ‘societal factors’ of ‘social distancing’, ‘wearing of masks’ and ‘cultural norms’ as factors influencing emotional labour which future empirical studies can explore further. Fourth, though this study builds the logic based on frontline service providers, the conceptual model and framework can be extended to other service organizations as well as specific professional groups for empirical studies as the effect of the pandemic can be felt across society and organizations. Fifth, employees within an organization also perform emotional labour within and across departments, functions and hierarchies. Working conditions, rules and processes are changing under the ‘new normal’. Hence, the proposed framework can be further extended to emotional labour within departments, functions, hierarchies and leadership positions within the same organization.

Our study has some practical implications as well. Findings from the research based on the proposed conceptual framework can provide ways to reduce the adverse outcomes of emotional labour under macro-extreme conditions. It may throw new light on the selection, recruitment, training, development and support of personnel which may help select ELS that would reduce subsequent adverse outcomes to ensure ‘healthy lives and promote well-being’. We believe that this study is an attempt to provide a basic framework to study emotional labour under macro-extreme conditions.

One of the limitations of the proposed framework is that it does not consider other possible factors that may also play a significant role in the emotional labour process under a pandemic situation. Second, the study has relied on the literature available on the emotional experience during the pandemic, on sentiment analysis and on the analysis of blog posts. The views and opinions of experts might add further to the proposed conceptual framework. Lastly, the proposed conceptual research model adds to the complexities of the previously suggested research models on emotional labour. This limitation is partly addressed in our proposed research framework (see Table 1). However, we believe that this study will open new opportunities to explore beyond what we have concluded, and future researchers will be able to identify other factors and refine the model to address specific research objectives and questions.

Conclusion and Future Scope

Scholars, theorists and researchers in the past four decades have immensely contributed to the literature on emotional labour from concept development and augmentation to consolidation. Researchers have expanded the conceptual framework based on various theories that integrate the emotional labour process from its antecedents to its consequences. However, the prevailing intense macro-extreme conditions such as the COVID-19 pandemic may have a severe effect on individuals, society and organizations that may add complexities to the emotional labour process. The MEEE of heightened risk perception, fear and anxiety and the ‘new normal’ social norms of social distancing and wearing of masks may add to the complexities of emotional labour and the emotion regulation process. Hence, our proposed conceptual framework with these factors would augment the literature on emotional labour.

We believe that our proposed conceptual framework can be extended for future empirical studies on emotional labour among professionals who take care of others, social control professionals and service professionals. The current situation of the COVID-19 pandemic has affected every individual, society, organization and frontline service provider. Hence, our conceptual construct of emotional labour research will be able to fill the void in the ‘new normal’ created due to the COVID-19 pandemic. Thus, this article will add to the existing literature on emotional labour and will also contribute to the practical application among different professions to identify the factors affecting emotional labour and plan to mitigate the negative effects through proper socio-organizational support, training and recognition of better health and well-being of employees.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Alsakarneh

A. A. A.

, Hong

S. C.

, Eneizan

B. M.

, & Al-Kharabsheh

K. A.

(2019). Exploring the relationship between the emotional labor and performance in the Jordanian insurance industry. Current Psychology, 38(5), 1140–1151. 10.1007/s12144-018-9935-2

Ashforth

B. E.

, & Humphrey

R. H.

(1993). Emotional labor in service roles: The influence of identity. Academy of Management Review, 18(1), 88–115. 10.5465/amr.1993.3997508

Asmundson

G. J. G.

, & Taylor

(2020). Coronaphobia: Fear and the 2019-nCoV outbreak. Journal of Anxiety Disorders, 70, 102196. 10.1016/j.janxdis.2020.102196

Becker

W. J.

, Cropanzano

, Van Wagoner

, & Keplinger

(2018). Emotional labor within teams: Outcomes of individual and peer emotional labor on perceived team support, extra-role behaviors, and turnover intentions. Group & Organization Management, 43(1), 38–71. 10.1177/1059601117707608

Bhave

D. P.

, & Glomb

T. M.

(2016). The role of occupational emotional labor requirements on the surface acting–job satisfaction relationship. Journal of Management, 42(3), 722–741. 10.1177/0149206313498900

Brotheridge

C. M.

, & Grandey

A. A.

(2002). Emotional labor and burnout: Comparing two perspectives of ‘people work’. Journal of Vocational Behavior, 60(1), 17–39. 10.1006/jvbe.2001.1815

Chao

, & Wang

(2020). The panic emotions and coping strategies of in the background of COVID-19. Journal of Psychiatry and Behavioral Sciences, 3(1), 1040.

Chen

K.-Y.

, Chang

C.-W.

, & Wang

C.-H.

(2019). Frontline employees’ passion and emotional exhaustion: The mediating role of emotional labor strategies. International Journal of Hospitality Management, 76, 163–172. 10.1016/j.ijhm.2018.05.006

Cheung

, Tang

C. S.

, & Tang

(2011). Psychological capital as a moderator between emotional labor, burnout, and job satisfaction among school teachers in China. International Journal of Stress Management, 18(4), 348–371. 10.1037/a0025787

10.

Choi

E. P. H.

, Hui

B. P. H.

, & Wan

E. Y. F.

(2020). Depression and anxiety in Hong Kong during COVID-19. International Journal of Environmental Research and Public Health, 17(10), 3740. 10.3390/ijerph17103740

11.

Côté

, & Morgan

L. M.

(2002). A longitudinal analysis of the association between emotion regulation, job satisfaction, and intentions to quit. Journal of Organizational Behavior, 23(8), 947–962. 10.1002/job.174

12.

Das

, & Dutta

(2020). Characterizing public emotions and sentiments in COVID-19 environment: A case study of India. Journal of Human Behavior in the Social Environment. 10.1080/10911359.2020.1781015

13.

de las Heras-Pedrosa

, Sánchez-Núñez

, & Peláez

J. I.

(2020). Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems. International Journal of Environmental Research and Public Health, 17(15), 5542. 10.3390/ijerph17155542

14.

de Zwart

, Veldhuijzen

I. K.

, Elam

, Aro

A. R.

, Abraham

, Bishop

G. D.

, Voeten

H. A. C. M.

, Richardus

J. H.

, & Brug

(2009). Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: Results of an international survey. International Journal of Behavioral Medicine, 16(1), 30–40. 10.1007/s12529-008-9008-2

15.

Demerouti

, Bakker

A. B.

, Nachreiner

, & Schaufeli

W. B.

(2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512.

16.

Diefendorff

J. M.

, Croyle

M. H.

, & Gosserand

R. H.

(2005). The dimensionality and antecedents of emotional labor strategies. Journal of Vocational Behavior, 66(2), 339–357. 10.1016/j.jvb.2004.02.001

17.

Diefendorff

J. M.

, Erickson

R. J.

, Grandey

A. A.

, & Dahling

J. J.

(2011). Emotional display rules as work unit norms: A multilevel analysis of emotional labor among nurses. Journal of Occupational Health Psychology, 16(2), 170–186. 10.1037/a0021725

18.

Dryhurst

, Schneider

C. R.

, Kerr

, Freeman

A. L. J.

, Recchia

, van der Bles

A. M.

, Spiegelhalter

, & van der Linden

(2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 1–13. 10.1080/13669877.2020.1758193

19.

Fernandez

P. R.

, Lord

, Halcomb

P. E.

, Moxham

P. L.

, Middleton

, Alananzeh

, & Ellwood

(2020). Implications for COVID-19: A systematic review of nurses’ experiences of working in acute care hospital settings during a respiratory pandemic. International Journal of Nursing Studies. 10.1016/j.ijnurstu.2020.103637

20.

Gabriel

A. S.

, Daniels

M. A.

, Diefendorff

J. M.

, & Greguras

G. J.

(2015). Emotional labor actors: A latent profile analysis of emotional labor strategies. Journal of Applied Psychology, 100(3), 863–879. 10.1037/a0037408

21.

Grandey

A. A.

(2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. Journal of Occupational Health Psychology, 5(1), 95–110. 10.1037/1076-8998.5.1.95

22.

Grandey

A. A.

(2003). When ‘the show must go on’—Surface acting and deep acting as determinants of emotional exhaustion and peer-rated service delivery. The Academy of Management Journal, 46(1), 86–96.

23.

Grandey

A. A.

, & Gabriel

A. S.

(2015). Emotional labor at a crossroads: Where do we go from here? Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 323–349. 10.1146/annurev-orgpsych-032414-111400

24.

Grandey

A. A.

, Houston

, & Avery

D. R.

(2019). Fake it to make it? Emotional labor reduces the racial disparity in service performance judgments. Journal of Management, 45(5), 2163–2192. 10.1177/0149206318757019

25.

Gross

J. J.

(1998a). Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. American Psychological Association, 74(1), 224–237.

26.

Gross

J. J.

(1998b). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2(3), 271–299.

27.

Harper

C. A.

, Satchell

L. P.

, Fido

, & Latzman

R. D.

(2020). Functional fear predicts public health compliance in the COVID-19 pandemic. International Journal of Mental Health and Addiction, 19(5), 1875–1888. 10.1007/s11469-020-00281-5

28.

Hobfoll

S. E.

(1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. 10.1037/0003-066X.44.3.513

29.

Hochschild

A. R.

(1983). The Managed Heart: Commercialization of human feeling. University of California Press.

30.

Ibuka

, Chapman

G. B.

, Meyers

L. A.

, Li

, & Galvani

A. P.

(2010). The dynamics of risk perceptions and precautionary behavior in response to 2009 (H₁N₁) pandemic influenza. BMC Infectious Diseases, 10(1), 296. 10.1186/1471-2334-10-296

31.

Jungmann

S. M.

, & Witthöft

(2020). Health anxiety, cyberchondria, and coping in the current COVID-19 pandemic: Which factors are related to coronavirus anxiety? Journal of Anxiety Disorders, 73, 102239. 10.1016/j.janxdis.2020.102239

32.

Keith Norambuena

, Lettura

E. F.

, & Villegas

C. M.

(2019). Sentiment analysis and opinion mining applied to scientific paper reviews. Intelligent Data Analysis, 23(1), 191–214. 10.3233/IDA-173807

33.

Khosravi

(2020). Perceived risk of COVID-19 pandemic: The role of public worry and trust. Electronic Journal of General Medicine, 17(4), em203. 10.29333/ejgm/7856

34.

Kim

T.-Y.

, Gilbreath

, David

E. M.

, & Kim

S.-P.

(2019). Self-verification striving and employee outcomes: The mediating effects of emotional labor of South Korean employees. International Journal of Contemporary Hospitality Management, 31(7), 2845–2861. 10.1108/IJCHM-08-2018-0620

35.

Lee

, & Madera

J. M.

(2019). A systematic literature review of emotional labor research from the hospitality and tourism literature. International Journal of Contemporary Hospitality Management, 31(7), 2808–2826. 10.1108/IJCHM-05-2018-0395

36.

Lee

S. A.

(2020). Coronavirus Anxiety Scale: A brief mental health screener for COVID-19 related anxiety. Death Studies, 44(7), 393–401. 10.1080/07481187.2020.1748481

37.

Lee

S. A.

, Jobe

M. C.

, Mathis

A. A.

, & Gibbons

J. A.

(2020). Incremental validity of coronaphobia: Coronavirus anxiety explains depression, generalized anxiety, and death anxiety. Journal of Anxiety Disorders, 74, 102268. 10.1016/j.janxdis.2020.102268

38.

Lee

Y. H.

, & Chelladurai

(2018). Emotional intelligence, emotional labor, coach burnout, job satisfaction, and turnover intention in sport leadership. European Sport Management Quarterly, 18(4), 393–412. 10.1080/16184742.2017.1406971

39.

Lee

Y. H.

, Lee

S. H. B.

, & Chung

J. Y.

(2019). Research on how emotional expressions of emotional labor workers and perception of customer feedbacks affect turnover intentions: Emphasis on moderating effects of emotional intelligence. Frontiers in Psychology, 9, 2526. 10.3389/fpsyg.2018.02526

40.

Leppin

, & Aro

A. R.

(2009). Risk perceptions related to SARS and avian influenza: Theoretical foundations of current empirical research. International Journal of Behavioral Medicine, 16(1), 7–29. 10.1007/s12529-008-9002-8

41.

Lovrić

, Farčić

, Mikšić

Š.

, & Včev

(2020). Studying during the COVID-19 pandemic: A qualitative inductive content analysis of nursing students’ perceptions and experiences. Education Sciences, 10(7), 188. 10.3390/educsci10070188

42.

Low

D. M.

, Rumker

, Talkar

, Torous

, Cecchi

, & Ghosh

S. S.

(2020). Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19: An observational study [Preprint]. PsyArXiv. 10.31234/osf.io/xvwcy

43.

Lwin

M. O.

, Lu

, Sheldenkar

, Schulz

P. J.

, Shin

, Gupta

, & Yang

(2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447. 10.2196/19447

44.

Maaravi

, & Heller

(2020). Not all worries were created equal: The case of COVID-19 anxiety. Public Health, 185, 243–245. 10.1016/j.puhe.2020.06.032

45.

Man

M. A.

, Toma

, Motoc

N. S.

, Necrelescu

O. L.

, Bondor

C. I.

, Chis

A. F.

, Lesan

, Pop

C. M.

, Todea

D. A.

, Dantes

, Puiu

, & Rajnoveanu

R.-M.

(2020). Disease perception and coping with emotional distress during COVID-19 pandemic: A survey among medical staff. International Journal of Environmental Research and Public Health, 17(13), 4899. 10.3390/ijerph17134899

46.

Mann

(2005). A health-care model of emotional labor: An evaluation of the literature and development of a model. Journal of Health Organization and Management, 19(4/5), 304–317. 10.1108/14777260510615369

47.

Miotto

, Sanford

, Brymer

M. J.

, Bursch

, & Pynoos

R. S.

(2020). Implementing an emotional support and mental health response plan for healthcare workers during the COVID-19 pandemic. Psychological Trauma: Theory, Research, Practice, and Policy, 12(S1), S165–S167. 10.1037/tra0000918

48.

Moghanibashi-Mansourieh

(2020). Assessing the anxiety level of Iranian general population during COVID-19 outbreak. Asian Journal of Psychiatry, 51, 102076. 10.1016/j.ajp.2020.102076

49.

Morris

J. A.

, & Feldman

D. C.

(1996). The dimensions, antecedents, and consequences of emotional labor. The Academy of Management Review, 21(4), 986. 10.2307/259161

50.

Nestor

M. S.

, Fischer

, & Arnold

(2020). ‘Masking’ our emotions: Botulinum toxin, facial expression, and well-being in the age of COVID-19. Journal of Cosmetic Dermatology, 19(9), 2154–2160. 10.1111/jocd.13569

51.

Nikčević

A. V.

, & Spada

M. M.

(2020). The COVID-19 anxiety syndrome scale: Development and psychometric properties. Psychiatry Research, 292, 113322. 10.1016/j.psychres.2020.113322

52.

Ornell

, Schuch

J. B.

, Sordi

A. O.

, & Kessler

F. H. P.

(2020). ‘Pandemic fear’ and COVID-19: Mental health burden and strategies. Brazilian Journal of Psychiatry, 42(3), 232–235. 10.1590/1516-4446-2020-0008

53.

Pellert

, Lasser

, Metzler

, & Garcia

(2020). Dashboard of sentiment in Austrian social media during COVID-19. ArXiv:2006.11158 [Cs]. http://arxiv.org/abs/2006.11158

54.

Pfattheicher

, Nockur

, Böhm

, Sassenrath

, & Petersen

M. B.

(2020). The emotional path to action: Empathy promotes physical distancing and wearing face masks during the COVID-19 pandemic [Preprint]. PsyArXiv. 10.31234/osf.io/y2cg5

55.

Poletti

, Ajelli

, & Merler

(2011). The effect of risk perception on the 2009 H1N1 pandemic influenza dynamics. PLoS ONE, 6(2), e16460. 10.1371/journal.pone.0016460

56.

Rafaeli

, & Sutton

R. I.

(1989). The expression of emotion in organisational life (Vol. 11, pp. 1)–42). Research in Organisation Behavior, JAI Press Inc.

57.

Restubog

S. L. D.

, Ocampo

A. C. G.

, & Wang

(2020). Taking control amidst the chaos: Emotion regulation during the COVID-19 pandemic. Journal of Vocational Behaviour, 119, 103440. 10.1016/j.jvb.2020.103440

58.

Ripp

, Peccoralo

, & Charney

(2020). Attending to the emotional well-being of the health care workforce in a New York City health system during the COVID-19 pandemic. Academic Medicine, 95(8), 1136–1139. 10.1097/ACM.0000000000003414

59.

Smith

R. D.

(2006). Responding to global infectious disease outbreaks: Lessons from SARS on the role of risk perception, communication and management. Social Science & Medicine, 63(12), 3113–3123. 10.1016/j.socscimed.2006.08.004

60.

Taylor

(2019). The psychology of pandemics: Preparing for the next global outbreak of infectious disease (2019th ed.). Cambridge Scholars Publishing.

61.

Wallace

C. L.

, Wladkowski

S. P.

, Gibson

, & White

(2020). Grief during the COVID-19 pandemic: Considerations for palliative care providers. Journal of Pain and Symptom Management, 60(1), e70–e76. 10.1016/j.jpainsymman.2020.04.012

62.

Wang

, Pan

, Wan

, Tan

, Xu

, McIntyre

R. S.

, Choo

F. N.

, Tran

, Ho

, Sharma

V. K.

, & Ho

(2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, Behaviour, and Immunity, 87, 40. 10.1016/j.bbi.2020.04.028

63.

Wankhade

, Stokes

, Tarba

, & Rodgers

(2020). Work intensification and ambidexterity – The notions of extreme and ‘everyday’ experiences in emergency contexts: Surfacing dynamics in the ambulance service. Public Management Review, 22(1), 48–74. 10.1080/14719037.2019.1642377

64.

World Health Organization (WHO). (n.d.). Statement – Physical and mental health key to resilience during COVID-19 pandemic. https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/

65.

Zhang

, Lyu

, Liu

, Zhang

, Wang

, & Luo

(2020). Monitoring depression trend on Twitter during the COVID-19 pandemic. ArXiv:2007.00228 [Cs]. http://arxiv.org/abs/2007.00228