# Exploring the consequences of COVID-19 on tourist behaviors: perceived travel risk, animosity and intentions to travel

Villy Abraham (Department of Technological Marketing, Sapir Academic College, Hof Ashkelon, Israel)
Kerstin Bremser (Faculty of Business and Law, Pforzheim University of Applied Sciences, Pforzheim, Germany)
Mercedes Carreno (Department of Humanities, Rey Juan Carlos University, Madrid, Spain)
Lynda Crowley-Cyr (Department of Marketing and Communications, School of Law and Justice, University of Southern Queensland, Toowoomba, Australia)
Maria Moreno (Department of Marketing and Communications, Rey Juan Carlos University, Madrid, Spain)

ISSN: 1660-5373

Article publication date: 18 December 2020

Issue publication date: 27 July 2021

6218

## Abstract

### Purpose

This paper aims to report on the findings emerging from an international study focused on the COVID-19 pandemic impact on travel attitudes and behavioral intentions .

### Design/methodology/approach

An online survey created with SurveyMonkey was distributed to a sample of 216 international travelers who were at least 18 years of age.

### Findings

The findings suggest that attribution theory (locus of control) may account for international travel. Individuals attributing the spread of COVID-19 to their own countries (internal locus of control) are more likely to travel abroad. Statistically significant differences are observed between various generational cohorts concerning perceived travel risk, domestic and international travel.

### Originality/value

The impact of a health crisis on domestic and international travels conceptualized in a single model is absent from the literature. The authors propose a model to account for the influence of pandemics on tourists’ attitudes and intentions to travel and whether attribution of blame influences travel destination choices (domestic or international).

### Propósito

En el presente trabajo se muestran los resultados de un estudio internacional centrado en el impacto de la pandemia de COVID 19 sobre las actitudes e intenciones de viaje.

### Diseño/Metodología/Enfoque

Se diseñó una encuesta online mediante la aplicación SurveyMonkey que fue distribuida a una muestra de 216 viajeros internacionales mayores de 18 años.

Los resultados sugieren que la teoría de la atribución (locus de control) puede ser aplicada para explicar los viajes internacionales. Las personas que atribuyen la propagación de COVID 19 as sus propios países (locus de control interno) tienen más probabilidades de viajar al extranjero. Se observan diferencias estadísticamente significativas entre las distintas cohortes generacionales en relación con la percepción de riesgo en los viajes, tanto nacionales como internacionales.

La conceptualización en un único modelo del impacto de una crisis sanitaria sobre los viajes tanto internacionales como nacionales está ausente de la literatura. Se propone un modelo que pretende explicar la influencia de las pandemias en las actitudes e intenciones de los turistas para viajar y si la atribución de culpas influye en la elección de destino, ya sea nacional o internacional.

## Citation

Abraham, V., Bremser, K., Carreno, M., Crowley-Cyr, L. and Moreno, M. (2021), "Exploring the consequences of COVID-19 on tourist behaviors: perceived travel risk, animosity and intentions to travel", Tourism Review, Vol. 76 No. 4, pp. 701-717. https://doi.org/10.1108/TR-07-2020-0344

## Publisher

:

Emerald Publishing Limited

## Introduction

The novel “COVID-19” coronavirus disease was first detected in Wuhan, China, in December 2019 (Zhu et al., 2019). Three months later, the World Health Organization declared a global pandemic. Containment measures to stop the spread of the virus, including lockdowns and border closures in most of the countries, brought tourism to a halt. In May 2020, the United Nations World Tourism Organization noted that the COVID-19 pandemic had caused a 22% fall in global international tourism in Q1 of 2020, with a potential annual decline by 60%–80%, leading to an estimated loss of US$80bn (UNWTO, 2020a, 2020b, 2020c). This is not the first disease outbreak to lead to tourism uncertainty, travel interruptions and have economic impact; some on a global scale (Law, 2006). The severe acute respiratory syndrome (SARS) epidemic and later the H1N1 pandemic, for example, led to a considerable decrease in international arrivals with an estimated financial loss of US$88bn (UNWTO, 2020a, 2020b, 2020c). The impact of the SARS outbreak in 2003 on the economies of China, Hong Kong, Singapore and Vietnam is estimated at \$20bn in lost GDP (Wilder-Smith, 2006). With a decline in around 70% of tourist arrivals across Asia, the outbreak was a major setback to the region’s travel and tourism economy. The industry’s growth rate decreased from 5% to 2.9% (Hong, 2009). In time, the world stopped the spread of the virus and eradicated the disease (Wilder-Smith and Freedman, 2020).

The COVID-19 pandemic is a major (if not the greatest) adverse health event of the 21st century. Early predictions of finding a vaccine or effective treatments within 6–12 months, allowing travel and tourism to resume to pre-crisis levels, was over-optimistic. As Gössling et al. (2020 p.2) note, “within the space of months, the framing of the global tourism system moved from overtourism[…]to non-tourism”.

Despite continuous, timely research on the social impacts of COVID-19 on tourism and hospitality (Deloitte.com, 2020; Melly and Hanrahan, 2020; Wen et al., 2020) and future travel (Hotle et al., 2020; Gössling et al., 2020), there is a lack of theory-based research (Jackson, 2019) to explain the effect of pandemics on the attitude and behavior of tourists. This study provides a new conceptual model underpinned by the attribution theory framework to provide new knowledge of tourists’ attitudes and travel intentions.

The attribution theory is mainly used to explore whether a service failure is caused by the service provider or the customer (Choi and Cai, 2017). A service provider can be an airline, hotel or a destination. A service failure can stem from either the process of service provision or its outcome (Huang et al., 2020). Recent findings on public perceptions of government culpability for the spread of COVID-19 support the use of attribution theory as a theoretical framework when studying people’s attitudes and travel intentions to particular holiday destinations. A case in point is China. A recent survey of the British public suggests that 64% of respondents appear to blame the Chinese Government (i.e. the Ministry of Tourism) for not taking sufficient measures to contain the spread of the virus (Karyotis, 2020a, 2020b). Tourists who believe a foreign country could have done more to control a disease’s spread may be less inclined to visit that country in the future. Similarly, domestic travel could decrease where populations believe that their governments could have done more. Based on the attribution theory, it is plausible that travelers could avoid China as a holiday destination if they perceive an outcome-based service failure (i.e. lack of biosecurity risk mitigation strategies and public health measures to keep tourists safe).

Few studies focus on the effect of attribution on tourism attitudes and behavioral intentions (Badu-Baiden et al., 2016; Çakar, 2020), and none consider the effect on future visitation intentions. This study extends the attribution theory’s application to include perceptions of responsibility for the spread of COVID-19 by one’s own country and other countries. Studies have examined the relationships between adverse health crises and international travel (Valencia and Crouch, 2008) or domestic travel (Cahyanto et al., 2016). The impact of a health crisis on domestic and international travels conceptualized in a single model is absent from the literature. This study tests a model examining tourists’ attitudes and intentions to travel during a global pandemic and whether attribution of blame influences travel destination choices (domestic or international). Moving forward, this can inform tourism industry practitioners and policymakers how to better respond to tourists’ health concerns in the planning and implementation of their risk mitigation strategies and measures for tourism recovery.

## Theoretical background

### External/internal locus of control and behavioral intentions

Weiner’s attribution theory explains how behavior is affected by common thoughts and influenced by expectations of satisfaction based on experience. The theory accounts for how people draw conclusions on the causes and effects of events (Weiner, 1980). This theory comprises four dimensions: internal vs external locus of attribution and internal vs external locus of control (Weiner, 2018). An underlying assumption is that a comparison between the outcome of an event and people’s expectations forms the basis of their affective responses (Kim et al., 2014).

The attribution theory has been used to demonstrate that consumer reactions to product failure (i.e. gaps between expectations and outcomes) are predictable (Folkes, 1984). In tourism studies, it has been used to explain tourists’ overall satisfaction with their travel experiences (Breitsohl, and Garrod, 2016; Choi and Cai, 2017; Jackson, 2019) and reactions to destination social responsibility (Su et al., 2020).

A key concept associated with our study of attribution theory is locus of control. The concept refers to whether an individual interprets events in their lives as deriving primarily from their own doing or control (internal locus) or as caused by the behavior of another person or external circumstances (external locus) (Rotter, 1966). People’s willingness to accept responsibility for what happens to them depends on their values and personality (Madrigal, 1995). Individuals leaning toward internal locus of control tend to believe that “they can take control of their lives.” In contrast, those leaning toward external locus of control “tend to feel powerless” about events in their lives (Madrigal, 1995, pp.130–131). Locus “influences beliefs about who should solve problems,” based on whoever’s actions create the problem (Folkes, 1987, p. 556).

Locus of control has been used to study future behavioral intentions (Hareli and Hess, 2008), including visitation of an international destination (Hsu and Chen, 2019). If tourists are harassed at a holiday destination, it may deter them from revisiting that location (Badu-Baiden et al., 2016). Perceptions of controllability can generate a range of emotions. Bad experiences mediated by uncontrollable causes may result in empathy or sympathy, whereas controllable causes can result in disgust and emotional reactions such as anger (Weiner, 2000).

When an event is considered preventable by a foreign entity’s actions (i.e. external control) and it fails to act, negative emotion may develop toward that entity. Ang, Jung, Kau, Leong, Pornpitakpan and Tan (2014) found that Malaysians, who thought that the USA could have controlled the development of the Asian economic crisis, were more likely to harbor animosity toward the USA. Karyotis (2020a, 2020b) found that some individuals hold foreign and local governments responsible for the spread of COVID-19. This could influence tourists’ future travel destination choices.

Since there are no previous studies on attribution of responsibility against a government for failing to stop the spread of a virus, this study uses local governments (one’s government) as the internal locus of control and China as the foreign entity (external locus of control). China was chosen because the extent of media reports that suggest it had a role in the spread of COVID-19 outweighs similar reports against other countries. We consider the following:

H1a.

External locus of control will be positively associated with animosity toward China.

H1b.

External locus of control will be negatively associated with travelers’ willingness to visit China.

H2a.

Internal locus of control will be positively associated with travelers’ willingness to visit China.

H2b.

Internal locus of control will be positively associated with travelers’ willingness to travel internationally.

H2c.

Internal locus of control will be negatively associated with travelers’ willingness to travel domestically.

## Animosity

Klein et al. (1998) define animosity as “anger related to previous or ongoing political, military, economic, or diplomatic events” (p. 90). Tourism (Abraham and Poria, 2020; Moufakkir, 2014; Sanchez et al., 2018; Stepchenkova et al., 2017; Farmaki et al., 2019b) and hospitality research (Kim, 2019) suggest that consumer animosity is likely to have long-term effects on travel behavior.

Countries/entities and private individuals inhabiting a target country as a collective can be targets of animosity (Abraham and Poria, 2019; Campo and Alvarez, 2019; Stepchenkova et al., 2017). Media reports on the outbreak of COVID-19 describe concerns about emerging animosity toward people of Asian descent and Chinese nationals (Clarke, 2020). Since the spread of the virus, anti-Asian assaults, harassment and hate crimes have been reported in the USA, Italy, France and other countries. In the UK, there were more than 200 reported offences against Chinese nationals in the first three months of 2020 (Mercer, 2020). In Australia, a survey reported 178 incidents of racism against Asian Australians in a two-week period (Zhou, 2020). By May 8, 2020, the United Nations Secretary General posted on Twitter that “the pandemic continues to unleash a tsunami of hate and xenophobia, scapegoating and scare-mongering” and urged governments to “act now to strengthen the immunity of our societies against the virus of hate” (Gutteres, 2020). Animosity toward Chinese nationals is coupled with animosity harbored toward the Chinese Government, mainly over inadequate measures taken to halt the spread of the disease to other parts of the world (Silver et al., 2020).

### Animosity and intentions to visit China

Past research points to a relationship between animosity and intentions or willingness to visit a holiday destination (Sanchez et al., 2018). A study by Stepchenkova et al. (2017) found that ongoing political discord between the USA and Russia resulted in Russians harboring animosity toward the USA. Consequently, the number of Russian tourists willing to visit the USA decreased considerably (Statistica, 2020). The USA imposed “designated persons sanctions” against Russia for invading and occupying Ukraine’s Crimea region and parts of eastern Ukraine. In 2016, Russian tourists traveling to the USA decreased by 26% (Stepchenkova et al., 2017). We consider the following:

H3.

Animosity toward Chinese nationals and the Chinese Government will be negatively associated with willingness to visit China.

### Risk perception and willingness to travel

This study considers the relationship between risk perception, willingness to travel domestically or internationally for business or leisure and infectious disease. Domestic travel covers travel within a country of residence and international travel covers all other travel. Risk perception can influence tourists’ destination choices, with most tending to choose low-risk destinations for holidays (Sönmez and Graefe, 1998) or the perceived safety of domestic travel (Dolnicar, 2005). More venturous types of travelers can be more inclined to travel abroad for holidays, even if the destination is affected by a crisis (Hajibaba et al., 2015; Sönmez and Graefe, 1998). Much appears to depend on individual characteristics, the types of activities to be undertaken, perceived benefits of the risk-handling activity and ability to absorb monetary losses (Dowling and Staelin, 1994) or other demographic characteristics (Roehl and Fesenmaier, 1992).

Law (2006) distinguishes types of risk as infectious diseases, natural disasters and terrorism. Typically, there is more readily available information on domestic than international destinations influencing risk perceptions. Media reports of an outbreak of the Ebola virus disease (EVD) in Africa, for instance, deterred tourists from visiting Gambia, which was EVD free (Novelli et al., 2018).

Similarly, studies of risk perception and willingness to travel during the outbreaks of SARS in Asian countries demonstrate that tourists avoided these destinations, regardless of actual infection rates (Cooper, 2005; Wilder-Smith, 2006). Rittichainuwat and Chakraborty (2009) observed that first-time travelers perceived health risks from SARS and H1N1 bird flu outbreaks more severely than frequent travelers, and may refrain from future travel. Studies also suggest that major disease outbreaks are associated with greater perceived risk concerning international travel (Cahyanto et al., 2016). This can also be observed with COVID-19-related government recommendations. For example, Germany’s government recommended domestic instead of international travel for holidays in the fall and winter of 2020 (Sönnichsen, 2020). We consider the following:

H4a.

Perceived travel risk (PTR) will be negatively associated with willingness to travel to China.

H4b.

PTR will be negatively associated with willingness to travel internationally.

H4c.

PTR will be positively associated with willingness to travel domestically.

### Past and future travel relationships

Past research suggests that past travel experience predicts future travel intentions (Lam and Hsu, 2006). Tourists who have visited a destination in the past are more likely to perceive it as safe (Sönmez and Graefe, 1998) and are likely to be less fearful to revisit destinations (Floyd et al., 2004). According to the past research, tourist demographics (Sönmez and Tasci, 2019), coupled with several destination-inherent factors (facilities, quality of services, promotional activities, cost of living, cost of transportation, package price), determine revisit intentions, even in times of political instability (Seddighi and Theocharous, 2002). Hence, it may be argued that tourists who have visited an international destination pre-COVID-19 are likely to revisit it once travel restrictions are relaxed. We consider the following:

H5a.

Past international travel will be positively associated with willingness to travel internationally once travel restrictions are lifted.

H5b.

Past travel to China will be associated with a willingness to travel to China once travel restrictions are lifted.

## Methodology

The target population for this study was travelers. An online survey questionnaire created with SurveyMonkey was distributed to a sample of individuals of at least 18 years of age. The conceptual model tested in the present study comprises four latent variables (i.e. external control, internal control, animosity and perceived risk) and 15 indicators. The proportion of indictors (r) to latent variables is 3.75:1. The modest sample size is consistent with Boomsma’s (1982a, 1982b) minimum sample size recommendation (n = 100) for a proportion of 4:1 (r = 4).

The questionnaire comprised two major parts. In the first part, seven items were employed to measure PTR with a five-point Likert scale (1 – strongly disagree; 5 – strongly disagree) adapted from Cahyanto et al. (2016). Intentions to travel domestically and internationally for business or leisure in the next 12 months were measured with items adapted from Floyd et al. (2004). Animosity toward China was measured on a scale adapted from Klein et al. (1998). Locus of control (internal and external) was measured with items adapted from Ang et al. (2004). The second part of the questionnaire included sociodemographic characteristics such as gender, generational cohort and education. Data was collected over the month of April 2020 during lockdowns and border closures in most of the countries. In line with previous research (Chin et al., 2018), we conducted a preliminary analysis using SPSS (version 25) before conducting measurement and structural analyses. A total of 256 questionnaires were distributed. Of that, 40 questionnaires were omitted owing to incomplete data. The final data set comprises a total of 216 usable questionnaires. SmartPLS (version 3.3.2) was used to assess the proposed research model.

## Findings

### Sample profiling

The sample (n = 216) comprises 36.6% males and 60.7% females. The rest (2.6%) selected “prefer not to say” as their answer (Table 1). Generation-Y (born between 1980 and 2000) comprises the majority of the sample (66.8%), followed by baby-boomers (born between 1946 and 1965) who comprise 13.7% of the sample and generation-X (born between the mid-1960s and late 1970s), which accounts for 16.8% of the sample. The overwhelming majority of respondents are highly educated (87.4% hold a university degree).

### Validation of the conceptual model using confirmatory factor analysis

Before analysis, all relevant items were reversed-scored. We used SmartPLS to test for internal consistency, fit of the proposed model (Figure 1) and path analysis. Omitted from further analysis were items with loadings below the recommended 0.5 cutoff in the structured model (Filieri et al., 2015). Four items were dropped from the PTR construct (PTR7-10) and one item from internal control (IC3) owing to falling below the 0.5 threshold. As shown in Table 2, all items loaded significantly on their respective constructs (p < 0.001). Factor loadings were all above 0.7 and significant (p < 0.01), suggesting an acceptable level of internal validity (Cheng et al., 2006). Furthermore, loadings were within the acceptable 0.6–0.9 range, thus indicating unidimensionality. Convergent validity was assessed by estimating composite reliability and average variance extracted (henceforth referred to as AVE). Internal consistency was examined by assessing Cronbach’s α and construct reliability values. As can be seen from Table 2, Cronbach’s α values were at or above the 0.7 cutoff recommended by Fornell and Larcker (1981). The composite reliability values of all latent variables were at or above the recommended threshold of 0.6 (Fornell, 1992). In line with Fornell and Larcker (1981) recommendations, discriminant validity was estimated by assessing AVE (Table 3).

### Assessing structural model

The results of the hypothesized relationships are shown in Table 5. The proposed model accounts for 40% of the variance in animosity, 19% in willingness to travel to China, 18.5% in the intentions to travel internationally and 3.7% in travel intentions domestically in the coming 12 months. Bootstrapping using a sample of 5,000 was performed to calculate the t-statistic and strength of the relationships between the endogenous and exogenous constructs in the proposed model (Hair et al., 2017). According to H1a, external locus of control will be positively associated with animosity toward China. This was supported by the data (β = 0.63, t  = 16.976, р < 0.001). According to H1b, external locus of control will be negatively associated with willingness to travel to China. This was confirmed (β = −0.021, t = 2.877, р < 0.05). A negative association was hypothesized between internal locus of control and the willingness to travel to China. The path is negative but insignificant. Hence, H2a is not supported (β = −0.07, t  = 1.102, р > 0.05). H2b posits a positive relationship between internal locus of control and the willingness to travel internationally. This was corroborated (β = 0.15, t  = 2.182, р < 0.05). According to H2c, internal locus of control will be negatively associated with the willingness to travel domestically. This was not confirmed by the data (β = −0.03, t = 0.382, р > 0.05). H3 posits that animosity will be negatively associated with willingness to travel to China, and this was supported by the data (β = −0.20, t  = 3.022, р < 0.05). According to H4a, PTR will be negatively associated with the willingness to travel to China. However, this was not corroborated by the data (β = 0.07, t = 1.774, р > 0.05). H4b posits that PTR will be negatively associated with the willingness to travel internationally. This was supported by the data (β = −0.21, t = 3.028, р < 0.05). In contrast to the expectations of H4c, PTR was negatively associated with the willingness to travel domestically (β = −0.18, t = 2.493, р < 0.05). H4c is not supported by the data. Finally, according to H5a, past international travel will be positively associated with willingness to travel internationally in the next 12 months. This was not confirmed (β = −0.35, t = 5.600, р < 0.001). H5b posits past travel to China will be positively associated with a willingness to travel to China in the future. This is not corroborated by the data (β = 0.10, t = 1.751, р > 0.05).

To assess the PLS path model’s predictive relevance, Stone Geisser’s Q2 was estimated (Hair et al., 2017). Using the blindfolding technique, estimates were employed to supplant actual data points recursively at an omission distance of 7 (the default omission distance in SmartPLS). The analysis results corroborate the model’s predictive relevance for all the variables in the proposed model (Table 4). Finally, variation inflation factor (VIF) values were examined to test for possible multicollinearity issues, and these values are below the maximum value of 10, as shown in Table 5 [AQ8]. Thus, no multicollinearity exists between the constructs comprising the proposed research model (Bock et al., 2005).

## Discussion

Previous infectious disease outbreaks emerging from Asian countries (such as SARS or H1N1 influenza pandemic) have had damaging impacts on travel and tourism. In this paper, we consider the impact of widespread travel restrictions during the COVID-19 pandemic on travel attitudes and behavioral intentions. This is the first study to also question whether a theoretical relationship between animosity toward a foreign country (China), blamed (attribution) for failing to contain the spread (locus of control) of an infectious disease beyond its borders, is associated with future domestic and international travel intentions.

The results indicate that at the time of the survey, some participants harbored animosity toward China and its people for the new virus and its spread to other countries. This is consistent with similar suggestions in media reports (Karyotis, 2020a, 2020b; Willson, 2020).

Weiner’s attribution theory suggests that tourists are more likely to have positive affective responses (i.e. loyalty to a destination) if they attribute responsibility for a crisis to forces outside a destination’s sphere of control. Conversely, a negative affective response will likely occur if tourists believe the harm was within the destination’s control (Breitsohl and Garrod, 2016; Lee, 2004; Weiner, 1985). Studies suggest that attribution of blame is associated with consumer attitudes and behavior (Folkes, 1987). Jorgensen (1994) demonstrates that a fatal air crash, attributed to the airline rather than a force outside its control, can diminish consumer attitudes toward the company. Our findings suggest travelers holding a country liable for failing to control the spread of a highly contagious disease are likely to harbor animosity (an attitude) and avoid traveling to that country in the future (behavior). These findings support a growing body of research demonstrating the detrimental impact that animosity can have on travel behavioral intentions (Khalilzadeh, 2018; Stepchenkova et al., 2018).

In line with previous research, our findings point to an inverse relationship between PTR and international travel (Park and Reisinger, 2010). This may be owing to a positive relationship between self-efficacy and travel avoidance observed in previous research (Liao et al., 2010). The mean score on the self-efficacy scale used in our study is well above the midpoint (M = 4.13), suggesting that, overall, respondents believe that they are at a lower level of infection. This suggests that self-efficacy is a possible moderator in the relationship between PTR and travel avoidance and that tourism researchers should consider its inclusion in future studies.

In contrast to previous research (Floyd et al., 2004; Sönmez and Graefe, 1998), our study found a negative and statistically significant relationship between past international travel and willingness to travel internationally once travel restrictions are lifted. No significant relationship was observed between previous travel to China and willingness to revisit it in the future. This suggests that if a country is perceived as responsible for the spread of a disease, previous travel to that country may not be a reliable predictor of future travel intentions. This is perhaps distinguishable from the case of less extreme circumstances. The observed relationship between past travel and future intentions might be accounted for by a heightened risk perception associated with current international travel (M = 4.12) and consequential desire to postpone travel, especially among those (75.9%) who had recently traveled abroad (i.e. within the last 12 months).

### Theoretical implications

This presumably represents a frontier study, attempting to empirically explore the relationship between attribution theory and animosity in the context of future travel intentions, domestically and internationally, in the backdrop of a global pandemic. Some contextual factors should be taken into account. Animosity toward China was likely influenced by various factors, including unproven blame claims by other governments and the reiteration of these ideas in news media. This likely contributed to a rise in negative emotions. This disease has led to extremely difficult circumstances, giving rise to economic insecurity and mental well-being issues. The conceptualization of tourism attitudes and behaviors should include these factors. Past experience, while it is a critical variable in predicting future travel, other context-specific variables such as governments’ control and management measures deployed during the pandemic and previous travel timing may be relevant in future predictions.

### Practical implications

In contrast to previous research, our findings suggest that attribution theory (locus of control) can account for both international and domestic travel. Individuals attributing the spread of COVID-19 to their own countries may be less likely to visit local holiday locations. This may stem from a loss of trust by local populations in their governments’ approach to controlling the virus’s spread. Moving forward, in an attempt to restore or improve public confidence in engaging in travel and visiting tourism destinations, tourism practitioners and government authorities should consider adopting and enforcing the hygiene and containment measures recommended by public health officials and the World Health Organization (social distancing, avoiding crowds, hand hygiene).

This study indicates that once travel restrictions are relaxed, some people (e.g. one-third of those surveyed) intend to revisit China within the next 12 months for business purposes. Restoring the confidence of leisure, travelers are likely to be the most critical factor in luring tourists back. Tourism practitioners and policymakers should be mindful of the possibility of animosity against China when planning future strategies for resuming tourism services. Open communication about the pandemic and its implications for tourists will be required to develop targeted public relations (PR) campaigns. This equally applies to those organizations with interest in attracting Chinese tourists to their destinations. It is essential to be cognizant of the potential fear, anxiety and loss of trust that can develop in tourists of countries that have been the target of negative publicity and allegations about a disease outbreak. Recommendations in other studies (Wen et al., 2020) on changes in Chinese travelers’ consumption patterns associated with concerns about the COVID-19 pandemic may help develop possible future responses to these changes to attract tourists.

Experience suggests that countries and businesses should also have crisis-management plans ready to know how to react and which issues to address (Novelli et al., 2018). Measures taken in response to crises often center on “(1) infrastructure and reconstruction, (2) provision of financial assistance and human resources for tourism enterprises, (3) development of communication and marketing campaigns to promote tourism in existing and new markets” (Ritchie and Jiang (2019) p. 9).

## Conclusions, limitations and future research

The COVID-19 disease has taken an unprecedented toll on travel and tourism, lives and livelihoods. This study demonstrates the value of attribution theory in explaining tourists’ future travel intentions and behaviors in the context of widespread disease outbreaks. It joins an emerging body of tourism studies that explain the need for policymakers and tourism and hospitality businesses to be aware and sensitive to the necessity of open communication and collaboration in planning and designing appropriate risk management measures and public relations campaigns to boost confidence in traveling.

Most notably, our study raises awareness that possible animosity against countries or governments (foreign or domestic) considered responsible for disease-related health threats can affect future tourists’ travel intentions and behaviors. Animosity may be a useful factor to consider in future tourism studies associated with disease outbreaks. The intention of tourists to visit a holiday destination in the future may depend on how much they believe the destination acted on behalf of the greater social good (external motivation) in addressing the health, social and economic consequences of the disease and its attempt to contain its spread, locally and globally.

Our study is limited in terms of the sample’s size, and it is predominantly university educated, young and female. Hence, extrapolation to other parts of the population or the broader global tourist population should be treated with care. Future research would benefit from a replication of the study using a larger sample. Since senior travelers tend to have more disposable income, this age group warrants further investigation. Income can be a predictor of travel intention but was not included in our survey instrument. We considered income too challenging to capture in our international study, which would involve comparing different currencies, varying levels of income and standards of living. Income would be a useful control variable in future research.

Owing to the multifaceted nature of the issues, further study needs to be conducted to determine whether the locus of attribution changes when travel restrictions are lifted, people can return to work, treatments and a vaccine become available, and so on. At the time of the survey, little was known about the virus. Borders were closed, and containment measures to stop the disease spreading were in place in most countries. These factors may have contributed to biased responses. Although beyond the scope of our study, future research would benefit from examining the impact of COVID-19 with a focus on the internal vs external locus of attribution dimensions of the attribution theory framework.

However, this may not be the only theoretical framework to explore the impact of COVID-19 on tourism behavior. Other theoretical frameworks may provide a different lens through which tourism behavior can be explored in the context of a health crisis. A case in point is the Theory of Reasoned Action (henceforth referred to as TRA). TRA comprises seven constructs, three of which (perceived risks, attitude and intention to visit) are part of our proposed conceptual model (Fishbein, 1979).

Future research would also benefit exploring the interplay of other demand variables such as length of stay, travel distance and preferred holidays/activities, with animosity in the context of the COVID-19 pandemic. It can also be argued that the mishandling of a health crisis may taint a destination’s image for a considerable time. Exploring the impact of global pandemic on destination image is worthy of further research.

The present study suggests travelers harbor animosity toward China as they attribute its spread to the initial mishandling of the pandemic by the Chinese Government. COVID-19 became politicized leading to political conflicts especially between the USA and China. Past research suggests political conflict leads to the development of negative stereotypes, which may be difficult to change (Farmaki et al., 2019a). Hence, the exploration of the relationship between the politicization of pandemics, stereotyping and tourism behavior is worthy of further investigation.

The findings suggest that past travel experience may not be a reliable predictor of future travel intentions in times of major global pandemics. The growth in use of videoconferencing by business travelers, for instance, may account for a reduction in the necessity of business travel, and may continue to influence future business travel. However, the distinction between business and leisure travel was beyond the scope of the present study. This may be a worthwhile avenue for future research.

Proposed model

## Table 1

Demographic description of data

Variable Feature N (%)
Gender Male 70 36.6
Female 116 60.7
Prefer not to say 5 2.6
Generational cohort Baby-boomers 26 13.7
X-generation 32 16.8
Y-generation 127 66.8
Z-generation 5 2.6
Education Less than high-school education 1 0.5
Primary education 3 1.6
Secondary education 16 8.4
University education 167 87.4

## Table 2

Confirmatory factor analysis

Factor and corresponding item Indicators Factor loading SE t-statistic Cronbach’s α Composite reliability AVE
Perceived travel risk (PTR) 0.898 0.918 0.614
Domestic travel is risky now PTR1 0.778 0.005 9.159
*Domestic travel is safe now PTR2 0.797 0.005 10.057
*I would feel comfortable traveling domestically PTR3 0.824 0.005 17.594
*I would feel comfortable traveling internationally PTR4 0.747 0.003 11.808
International travel is risky now PTR5 0.789 0.004 10.908
*International travel is safe now PTR6 0.762 0.006 7.966
Dangerous to travel internationally right now PTR11 0.785 0.005 9.544
External control (EC) 0.814 0.890 0.729
The Chinese could have prevented my country’s economic crisis from happening EC1 0.874 0.001 35.875
The Chinese Government could have done more to prevent the spread of the virus globally EC2 0.875 0.001 46.708
This is not the first time a global pandemic has emerged in China. The local population should have known to engage in preventive actions faster to avoid spreading the virus EC3 0.811 0.001 23.220
Internal control (IC) 0.701 0.834 0.717
My country’s government could have avoided the current economic problems resulting from the coronavirus IC1 0.913 0.006 9.248
My country’s government could have done more to prevent/slow down the spread of the virus IC2 0.775 0.007 6.831
Animosity (AN) 0.762 0.861 0.673
China’s Government is responsible for the global spread of the virus AN1 0.824 0.001 30.979
Chinese citizens travelling on business have brought the virus into my country AN2 0.815 0.002 21.167
Tourists from China and other countries brought the virus into my country AN3 0.822 0.001 29.400

Notes: AVE, average variance extracted; * reverse scored items

## Table 3

Discriminant validity

Construct AN IC EC PTR
AN 0.820
IC 0.322** 0.846
EC 0.651** 0.314** 0.853
PTR 0.009 −0.022 0.005 0.783
Notes:

AN, animosity; IC, internal control; EC, external control; PTR perceived travel risk. Diagonals represent the square root of the AVE. Other entries represent the correlations.

*p ≤ 0.05

**

p ≤ 0.01 (two-tailed)

## Table 4

Stone Geisser’s Q2 values

Variable SSO SSE Q² = (1 − SSE/SSO)
Animosity 648.000 478.133 0.262
International travel 216.000 179.793 0.168
WTT China 216.000 183.202 0.152
Domestic travel 216.000 214.565 0.007
External control 648.00 648.00
Internal control 432.000 432.000
Past international travel 216.000 216.000
Past travel to China 216.000 216.000
Perceived travel risk 1,512.00 1512.00
Notes:

Q2 values greater than zero point to a model’s predictive relevance for a dependent latent variable; SSE = sum of squares of observation; SSO = the sum squares of prediction error; WTT = willingness to travel

## Table 5

Standardized path estimated and hypotheses testing

Hypothe-sis (H) Paths Path coefficients SD t-Statistic Result VIF
H1a EC → AN 0.638 0.038 16.976** Supported 1.000
H1b EC → WTT China −0.215 0.075 2.877* Supported 1.735
H2a IC → WTT China −0.072 0.073 1.102 Unsupported 1.162
H2b IC → International travel 0.151 0.074 2.182* Supported 1.009
H2c IC → Domestic travel −0.031 0.077 0.382 Unsupported 1.003
H3 AN → WTT China −0.204 0.067 2.945* Supported 1.743
H4a PTR → WTT China 0.077 0.059 1.774 Unsupported 1.009
H4b PTR → Intentions to travel internationally −0.211 0.064 3.028** Supported 1.004
H4c PTR → Intentions to travel domestically −0.187 0.073 2.493* Supported 1.003
H5a Past international travel → international travel −0.358 0.062 5.600** Unsupported 1.008
H5b Past travel to China → intentions to travel to China 0.107 0.062 1.715 Unsupported 1.065
Notes:

SD, = standard deviation; VIF, variation inflation factor; AN, animosity; IC, internal control; PTR perceived travel risk;

*

p  <  0.05;

**

p  <  0.001

## References

Abraham, V. and Poria, Y. (2019), “A research note: exploring socially visible consumption in tourism”, Tourism Management, Vol. 70, pp. 56-58, doi: 10.1016/j.tourman.2018.07.012.

Abraham, V. and Poria, Y. (2020), “Political identification, animosity, and consequences on tourist attitudes and behaviors”, Current Issues in Tourism, Vol. 23 No. 24, pp. 3093-3110, doi: 10.1080/13683500.2019.1679095.

Ang, S.H., Jung, K., Kau, A.K., Leong, S.M., Pornpitakpan, C. and Tan, J.S. (2004), “Animosity towards economic giants: what the little guys think”, The Journal of Consumer Marketing, Vol. 21 No. 3, pp. 190-207, available at: https://doi.org/10.1108/07363760410534740

Badu-Baiden, F., Adu-Boahen, A.E. and Otoo, E.F. (2016), “Tourists' response to harassment: a study of international tourists to Ghana”, Anatolia, Vol. 27 No. 4, pp. 468-479.

Bock, G.W., Zmud, R.W., Kim, Y.G. and Lee, J.N. (2005), “Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate”, MIS Quarterly, Vol. 29 No. 1, pp. 87-111.

Boomsma, A. (1982a), “Robustness of LISREL against small sample sizes in factor analysis models”, in Joreskog, K.G. and Wold, H. (Eds), Systems under Indirect Observations, Causality, Structure, Prediction (Part 1), North-Holland, Amsterdam, pp. 149-173.

Boomsma, A. (1982b), “The robustness of LISREL against small sample sizes in factor analysis models”, in Jצreskog, K.G. and Wold, H. (Eds), Systems under Indirect Observation: Causality, Structure, Prediction, Vol. 149, North-Holland, Amsterdam, pp. 149-173.

Breitsohl, J. and Garrod, B. (2016), “Assessing tourists' cognitive, emotional and behavioural reactions to an unethical destination incident”, Tourism Management, Vol. 54, pp. 209-220.

Cahyanto, I., Wiblishauser, M., Pennington-Gray, L. and Schroeder, A. (2016), “The dynamics of travel avoidance: the case of Ebola in the US”, Tourism Management Perspectives, Vol. 20, pp. 195-203.

Çakar, K. (2020), “Tourophobia: fear of travel resulting from man-made or natural disasters”, Tourism Review, doi: 10.1108/TR-06-2019-0231.

Campo, S. and Alvarez, M.D. (2019), “Animosity towards a country in the context of countries as destinations as tourism products”, Journal of Hospitality & Tourism Research, Vol. 43 No. 7, doi: 10.1177/1096348019840795.

Cheng, T.C.E., Lam, D.Y.C. and Yeung, A.C.L. (2006), “Adoption of internet banking: an empirical study in Hong Kong”, Decision Support Systems, Vol. 42 No. 3, pp. 1558-1572.

Chin, C.-H., Chin, C.L. and Wong, W.P.-M. (2018), “The implementation of green marketing tools in rural tourism: the readiness of tourists?”, Journal of Hospitality Marketing & Management, Vol. 27 No. 3, pp. 261-280, doi: 10.1080/19368623.2017.1359723.

Choi, S.H. and Cai, L.P.A. (2017), “An experiment on the role of tourist attribution: evidence from negative nature-based incidents”, Current Issues in Tourism, Vol. 20 No. 5, pp. 455-458, doi: 10.1080/13683500.2016.1164673.

Clarke, J. (2020), “On friendship with China [online]”, Eureka Street, Vol. 30 No. 8, pp. 11-12.

Cooper, M. (2005), “Japanese tourism and the SARS epidemic of 2003”, Journal of Travel & Tourism Marketing, Vol. 19 Nos 2/3, pp. 117-131, doi: 10.1300/J073v19n02_10.

Deloitte.com (2020), “Deloitte access economics 2020”, (accessed October 9 2020).

Dolnicar, S. (2005), “Understanding barriers to leisure travel: tourist fears as a marketing basis”, Journal of Vacation Marketing, Vol. 11 No. 3, pp. 197-208, doi: 10.1177/1356766705055706.

Dowling, G.R. and Staelin, R. (1994), “A model of perceived risk and intended risk-handling activity”, Journal of Consumer Research, Vol. 21 No. 1, p. 119, doi: 10.1086/209386.

Farmaki, A., Antoniou, K. and Christou, P. (2019a), “Visiting the ‘enemy’: visitation in politically unstable destinations”, Tourism Review, Vol. 74 No. 3, pp. 293-309, doi: 10.1108/TR-11-2018-0159.

Farmaki, A., Khalilzadeh, J. and Altinay, L. (2019b), “Travel motivation and demotivation within politically unstable nations”, Tourism Management Perspectives, Vol. 29, pp. 118-130.

Filieri, R., Alguezaui, S. and Mcleay, F. (2015), “Why do travellers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth”, Tourism Management, Vol. 51, pp. 174-185.

Fishbein, M. (1979), “A theory of reasoned action: some applications and implications”, Nebraska Symposium on Motivation, Vol. 27, pp. 65-116.

Floyd, M.F., Gibson, H., Pennington-Gray, L. and Thapa, B. (2004), “The effect of risk perceptions on intentions to travel in the aftermath of september 11, 2001”, Journal of Travel & Tourism Marketing, Vol. 15 Nos 2/3, pp. 19-38.

Folkes, V.S. (1984), “Consumer reactions to product failure: an attributional approach”, Journal of Consumer Research, Vol. 10 No. 4, pp. 398-409.

Folkes, V.S., Koletsky, S. and Graham, J. (1987), “A field study of causal inferences and consumer reactions: the view from the airport”, Journal of Consumer Research, Vol. 13 No. 4, pp. 534-539.

Fornell, C. (1992), “A national customer satisfaction barometer: the Swedish experience”, Journal of Marketing, Vol. 56 No. 1, pp. 6-21.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

Gössling, S., Scott, D. and Hall, M. (2020), “Pandemics, tourism and global change: a rapid assessment of COVID-19”, Journal of Sustainable Tourism, Vol. 29 No. 1, doi: 10.1080/09669582.2020.1758708.

Gutteres, A. (2020), #COVID19 does not care who we are, where we live, or what we believe. Yet the pandemic continues to unleash a tsunami of hate and xenophobia, scapegoating and scare-mongering. That’s why I’m appealing for an all-out effort to end hate speech globally”, Twitter,

Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2017), A Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM), Sage, Thousand Oaks, CA.

Hajibaba, H., Gretzel, U., Leisch, F. and Dolnicar, S. (2015), “Crisis-resistant tourists”, Annals of Tourism Research, Vol. 53, pp. 46-60, doi: 10.1016/j.annals.2015.04.001.

Hareli, S. and Hess, U. (2008), “The role of causal attribution in hurt feelings and related social emotions elicited in reaction to other’s feedback about failure”, Cognition & Emotion, Vol. 22 No. 5, pp. 862-880, doi: 10.1080/02699930701541641.

Hong, W.C. (2009), “Global competitiveness measurement for the tourism sector”, Current Issues in Tourism, Vol. 12 No. 2, pp. 105-132.

Hotle, S., Murray-Tuite, P. and Singh, K. (2020), “Influenza risk perception and travel-related health protection behavior in the US: insights for the aftermath of the COVID-19 outbreak”, Transportation Research Interdisciplinary Perspectives, Vol. 5, p. 100127, doi: 10.1016/j.trip.2020.100127.

Hsu, C.H.C. and Chen, N. (2019), “Resident attribution and tourist stereotypes”, Journal of Hospitality & Tourism Research, Vol. 43 No. 4, pp. 489-516.

Huang, Y., Zhang, M., Gursoy, D. and Shi, S. (2020), “An examination of interactive effects of employees’ warmth and competence and service failure types on customer’s service recovery cooperation intention”, International Journal of Contemporary Hospitality Management, Vol. 32 No. 7, pp. 2429-2451, doi: 10.1108/IJCHM-01-2020-0028.

Jackson, M. (2019), “Utilizing attribution theory to develop new insights into tourism experiences”, Journal of Hospitality and Tourism Management, Vol. 38, pp. 176-183, doi: 10.1016/j.jhtm.2018.04.007.

Jorgensen, B.K. (1994), “Consumer reaction to company-related disasters: the effect of multiple versus single explanations”, Advances in Consumer Research, Vol. 21, pp. 348-352.

Karyotis, G. (2020a), “British people blame Chinese government more than their own for the spread of coronavirus”, (accessed October 8 2020).

Karyotis, G. (2020b), “The conversation. British people blame Chinese government more than their own for the spread of coronavirus”,

Khalilzadeh, J. (2018), “Demonstration of exponential random graph models in tourism studies: is tourism a means of global peace or the bottom line?”, Annals of Tourism Research, Vol. 69, pp. 31-41.

Kim, J.-H. (2019), “Animosity and switching intention: moderating factors in the decision making of Chinese ethnic diners”, Cornell Hospitality Quarterly, Vol. 60 No. 2, pp. 1-15, doi: 10.1177.

Kim, Y., Chang, Y., Wong, S. and Park, M. (2014), “Customer attribution of service failure and its impact in social commerce environment”, International Journal of Electronic Customer Relationship Management, Vol. 8 Nos 1/2/3, pp. 136-158.

Klein, J.G., Ettenson, R. and Morris, M.D. (1998), “The animosity model of foreign product purchase”, Journal of Marketing, Vol. 62 No. 1, pp. 89-101.

Lam, T. and Hsu, C. (2006), “Predicting behavioral intention of choosing a travel destination”, Tourism Management, Vol. 27 No. 4, pp. 589-599, doi: 10.1016/j.tourman.2005.02.003.

Law, R. (2006), “The perceived impact of risks in travel decisions”, International Journal of Tourism Research, Vol. 8 No. 4, pp. 289-300, doi: 10.1002/jtr.576.

Lee, B.K. (2004), “Audience-oriented approach to crisis communication: a study of Hong Kong consumers' evaluation of an organizational crisis”, Communication Research, Vol. 31 No. 5, pp. 600-618.

Liao, Q., Cowling, B., Lam, W.T., Ng, M.W. and Fielding, R. (2010), “Situational awareness and health protective responses to pandemic influenza A(H1N1) in Hong Kong: a cross sectional study”, PloS One, Vol. 5 No. 10, p. e13350.

Madrigal, R. (1995), “Personal values, traveler personality type and leisure travel style”, Journal of Leisure Research, Vol. 27 No. 2, pp. 125-142.

Melly, D. and Hanrahan, J. (2020), “Tourist biosecurity awareness and risk mitigation for outdoor recreation: management implications for Ireland”, Journal of Outdoor Recreation and Tourism, Vol. 31, doi: 10.1016/j.jort.2020.100313.

Mercer, D. (2020), “Coronavirus: hate crimes against Chinese people soar in UK during COVID-19 crisis. Sky news”,

Moufakkir, O. (2014), “What’s immigration got to do with it? Immigrant animosity and its effects on tourism”, Annals of Tourism Research, Vol. 49, pp. 108-121, doi: 10.1016/j.annals.2014.08.008.

Novelli, M., Burgess, L.G., Jones, A., Ritchie, B.W. (2018), “No Ebola…. still doomed―the Ebola-induced tourism crisis”, Annals of Tourism Research, Vol. 70, pp. 76-87.

Park, K. and Reisinger, Y. (2010), “Differences in the perceived influence of natural disasters and travel risk on international travel”, Tourism Geographies, Vol. 12 No. 1, pp. 1-24, doi: 10.1080/14616680903493621.

Ritchie, B. and Jiang, Y. (2019), “A review of research on tourism risk, crisis and disaster management: launching the annals of tourism research curated collection on tourism risk, crisis and disaster management”, Annals of Tourism Research, Vol. 79 No. Nov, doi: 10.1016/j.annals.2019.102812.

Rittichainuwat, B.N. and Chakraborty, G. (2009), “Perceived travel risks regarding terrorism and disease: the case of Thailand”, Tourism Management, Vol. 30 No. 3, pp. 410-418.

Roehl, W.S. and Fesenmaier, D.R. (1992), “Risk perceptions and pleasure travel: an exploratory analysis”, Journal of Travel Research, Vol. 30 No. 4, pp. 17-26.

Rotter, J.B. (1966), “Generalized expectancies for internal versus external control of reinforcement”, Psychological Monographs: General and Applied, Vol. 80 No. 1, pp. 1 -28.

Sanchez, M., Campo, S. and Alvarez, M.D. (2018), “The effect of animosity on the intention to visit tourist destinations”, Journal of Destination Marketing & Management, Vol. 7, pp. 182-189, doi: 10.1016/j.jdmm.2016.11.003, doi: 10.1177/004728759203000403.

Seddighi, H.R. and Theocharous, A.L. (2002), “A model of tourism destination choice: a theoretical and empirical analysis”, Tourism Management, Vol. 23 No. 5, pp. 475-487, doi: 10.1016/S0261-5177(02)00012.

Silver, L. Devlin, K. and Huang, C. (2020), “Americans fault China for its role in the spread of COVID-19”, (accessed October 7 2020).

Sönmez, S. and Tasci, A.D.A. (2019), “Characteristics and behaviors of anti-gun and pro-gun travelers”, Tourism Review, Vol. 75 No. 2, pp. 347-368, doi: 10.1108/TR-01-2019-0002.

Sönmez, S.F. and Graefe, A.R. (1998), “Influence of terrorism risk on foreign tourism decisions”, Annals of Tourism Research, Vol. 25 No. 1, pp. 112-144, doi: 10.1016/S0160-7383(97)00072-8.

Sönnichsen, B. (2020), “Gesundheitsminister spahn zu herbst- und winter urlaub: ‘schönheit deutschlands genießen’ [television broadcast]”, ARD, (accessed October 9 2020).

Statistica (2020), “Number of visitors to the United States from Russia from 2011 to 2019”, (accessed October 11 2020).

Stepchenkova, S., Shichkova, E., Kim, M. and Rykhtik, M.I. (2017), “Do strained bilateral relations affect tourists’ desire to visit a country that is a target of animosity?”, Journal of Travel & Tourism Marketing, Vol. 35 No. 5, pp. 1-14, doi: 10.1080/10548408.2017.1374907.

Stepchenkova, S., Su, L. and Shichkova, E. (2018), “Marketing to tourists from unfriendly countries: should we even try?”, Journal of Travel Research, Vol. 57, pp. 1-17.

Su, L., Lian, Q. and Huang, Y. (2020), “How do tourists' attribution of destination social responsibility motives impact trust and intention to visit? the moderating role of destination reputation”, Tourism Management, Vol. 77, doi: 10.1016/j.tourman.2019.103970.

UNWTO (2020a), “Impact assessment of the COVID-19 outbreak on international tourism”,

UNWTO (2020b), “International tourist numbers could fall 60-80% in 2020”, )

UNWTO (2020c), “UNWTO world tourism barometer: special focus on the impact of COVID-19”,

Valencia, J. and Crouch, G.I. (2008), “Travel behavior in troubled times: the role of consumer self-confidence”, Journal of Travel & Tourism Marketing, Vol. 25 No. 1, pp. 25-42.

Weiner, B. (1980), “A cognitive (attribution)-emotion-action model of motivated behavior: an analysis of judgments of help-giving”, Journal of Personality and Social Psychology, Vol. 39 No. 2, pp. 186-200, doi: 10.1037/0022-3514.39.2.186.

Weiner, B. (1985), “An attitudinal theory of achievement motivation and emotion”, Psychological Review, Vol. 92 No. 4, pp. 548-573.

Weiner, B. (2000), “Attributional thoughts about consumer behavior”, Journal of Consumer Research, Vol. 27 No. 3, pp. 382-387.

Weiner, B. (2018), “The legacy of an attribution approach to motivation and emotion: a no-crisis zone”, Motivation Science, Vol. 4 No. 1, pp. 4-14.

Wen, J., Kozak, M., Yang, S. and Liu, F. (2020), “COVID-19: potential effects on Chinese citizens’ lifestyle and travel”, Tourism Review, doi: 10.1108/TR-03-2020-0110.

Wilder-Smith, A. (2006), “The severe acute respiratory syndrome: impact on travel and tourism”, Travel Medicine and Infectious Disease, Vol. 4 No. 2, pp. 53-60, doi: 10.1016/j.tmaid.2005.04.004.

Wilder-Smith, A. and Freedman, D.O. (2020), “Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak”, Journal of Travel Medicine, Vol. 27 No. 2, doi: 10.1093/jtm/taaa020.

Willson, R. (2020), “The fragile world of Donald J. Trump”, (accessed October 4 2020).

Zhou, N. (2020), “Survey of Covid-19 racism against Asian Australians records 178 incidents in two weeks”, The Guardian,

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G.F. and Tan, W. (2019), “China novel coronavirus investigating and research team”, New England Journal of Medicine, Vol. 382 No. 8, pp. 727-733, doi: 10.1056/NEJMoa2001017.

Abraham, V. and Reitman, A. (2018), “Conspicuous consumption in the context of consumer animosity”, International Marketing Review, Vol. 35 No. 3, pp. 412-428.

Kim, H., Yilmaz, S. and Choe, Y. (2019), “Traveling to your match? Assessing the predictive potential of Plog’s travel personality in destination marketing”, Journal of Travel & Tourism Marketing, Vol. 36 No. 9, pp. 1025-1036, doi: 10.1080/10548408.2019.1683485.

Levantis, T. and Gani, A. (2000), “Tourism demand and the nuisance of crime”, International Journal of Social Economics, Vol. 27 Nos 7/8/9/10, pp. 959-967.

Shoham, A., Davidow, M., Klein, J. and Ruvio, A. (2006), “Animosity on the home front: the intifada in Israel and its impact on consumer behavior”, Journal of International Marketing, Vol. 14 No. 3, pp. 92-114, doi: 10.1509/jimk.14.3.92.

Weiner, B. (1984), “Principles for a theory of student motivation and their application with an attributional framework”, Research on Motivation in Education: Student Motivation, Vol. 6, pp. 15-38.

World Health Organization (2020), “WHO coronavirus disease (COVID-19) dashboard”, available at: https://covid19.who.int/

Zeng, B., Carter, R.W. and De Lacy, T. (2005), “Short-term perturbations and tourism effects: the case of SARS in China”, Current Issues in Tourism, Vol. 8 No. 4, pp. 306-322, doi: 10.1080/13683500508668220.

## Corresponding author

Villy Abraham is the corresponding author and can be contacted at: abraham.villy@gmail.com