Sage Journals: Discover world-class research

Abstract

Although traveler risk perception is a widely used term in the hospitality and tourism literature, the pandemic related risk perception of tourist destination lacks a clear conceptualization and measurement. This study develops and validates a six item scale to measure Covid-19 risk perception of travel destinations. The scale is developed using multiple data collection methods including interviews, focus group, and survey questionnaires. The exploratory stage involved in-depth interviews and the subsequent quantitative stage gathered data through a survey questionnaire. The scale offers a framework for future empirical research on Covid-19 risk perception that will shape tourism decisions in years to come. Implications for hospitality industry and limitations are also discussed.

Keywords

COVID-19 tourist risk perception travel destination scale development

Introduction

Since Covid-19 surfaced, the travelers around the world fear catching the disease since traditionally destinations with risky hygiene and health conditions are avoided so that they might not get ill or infected (Liu & Pennington-Gray, 2015). Since risk perception affect traveling decisions (Fuchs & Richel, 2006) which includes physical risk, psychological risk, functional risk, and political risk (Lepp & Gibson, 2003; Reichel et al., 2007; Reisinger & Mavondo, 2005; Sönmez & Graefe, 1998b). While traveling a risk free travel is every tourist’s need and it affects the tourism demand of travel destinations, different studies have focused on various types of perceived risks associated with traveling which includes terrorism risk (Fuchs, 2013; Rittichainuwat & Chakraborty, 2009) life threatening hazards (Sarman et al., 2016) health risk (Chien et al., 2017) country risk (Shemma, 2014).

Since tourists perceive the tourist sites as risky in terms of active spread of Covid-19 (Ahmed & Memish, 2020), however the extant tourism and hospitality literature fails to answer two important questions which have become the focal apprehensions of the tourists during Covid-19. Firstly, we don’t have the answers to important research questions like how the Covid-19 risk perception of travel destination is affecting tourism decision. Secondly, we don’t have a robust instrument to measure the perceived covid-19 pandemic risk associated with that destination to travel. These questions are pressing as future of hospitality and tourism and hospitality industry in coming years is coupled with tourist assessment of travel destination’s perceived Covid-19 risk.

Our study thus contributes in two important ways in tourism and hospitality literature. First, we will attempt to theoretically explain how the perception of pandemic risk can affect tourism decisions. Secondly, we will develop a robust scale to measure Covid-19 pandemic risk perception which will help future studies to correctly quantify the risk perception and to see how it will affect tourism and hospitality related decisions in years to come.

Literature Review

Covid-19 Pandemic’s Effect on Hospitality and Tourism

The Covid-19 is one of the major pandemics world has ever seen (Ying et al., 2020) and it has hit every sector but the hospitality sector is among the hardest hit. The travel restrictions and social distancing policies have had a serious effect on the industry (Millán-Oñate et al., 2020). The World Tourism Organization UNWTO (2020) reports that either all the hotel chains are closed or 6 out of 10 rooms in hotels are empty. Moreover, tourists are apprehensive to travel due to the perceived risk that lurks in their minds. Thus, developing negative thoughts and fears about a travel destination (Reichel et al., 2007).

In past, outbreak of diseases badly affected tourism industry in various parts of the world which includes Ebola in Africa (Mizrachi & Fuchs, 2016), Swine Flu in UK (Page et al., 2012), and SARS in Taiwan (Mao et al., 2010). Studies suggest that tourist fear of becoming ill due to pandemic should be given attention in literature (Bagnoli et al., 2007).

Even though, researchers around the world have been interested in the influences of natural disasters on tourism, little research has occurred into exploring how much impact they have at the destination level (Becken & Hughey, 2013). Considering the fact that most of the risk perception emerges as a psychological reaction to the prevailing conditions at the destination (Chien et al., 2017), it is imperative for the traveling companies to alleviate the worry associated with the traveling. Risk that is linked with traveling actually is the product of uncertainty and the consequences (Conchar et al., 2004). Moreover, the level of risk perceived by the travelers is different for each individual and is according to one’s interest and concern toward the destination (Cheron & Ritchie, 1982). The theory of perceived risk provides possible explanation how and why consumers move from the desired state to the action state (Tian-Que, 2012) and can be applied on travel-related risks (Fuchs & Reichel, 2006; Wildavsky & Dake, 1990). Earlier studies provide evidence of pandemics affecting tourism in different parts of the world like foot and mouth disease (Blake et al., 2003), SARS (Yang & Chen, 2009), and Swine flu (Page et al., 2012). The Covid-19 risk emerged after a pandemic spread from Wuhan—China (Bogoch et al., 2020) resulting in a worldwide lockdown since human interaction became hazardous and, in a Covid-19 infected world, people were obligated to act upon social distancing. Bearing in mind the ability of this virus, to infect through human interaction either by physical contact or inhalation in the same atmosphere, has taken down industries across the globe, triggering the multinational businesses to make tough choices with scant data availability (Ayittey et al., 2020).

The Covid-19 pandemic brought aviation and hospitality industry at a standstill by a massive demand halt (Stephany et al., 2020) since prominent tourist destinations like Rome, Paris, Madrid, Venice, Germany, Saudi Arabia, Dubai, Malaysia, Thailand, Singapore, Vietnam, and Korea were abandoned (Ahmed & Memish, 2020; Fernandes, 2020; Karim et al., 2020; Mao et al., 2020). The mobility across borders increases the risk of interacting with the disease more and more and creates a perception that hinders the travelers’ ability to choose a certain destination (Smith, 2006).

Existing Scales on Tourist Risk Perceptions

Risk perception is “subjective assessment of the probability of a specified type of accident happening and how concerned we are with the consequences” (Rundmo & Moen, 2006) and it involves valuation of a negative outcome. An overview of risk perception scales shows various dimensions but is inadequate for evaluation of tourist risk perception during COVID-19. The pioneering work of Roehl and Fesenmaier (1992), identified three major dimensions of perceived risk tourist risk i.e. physical-equipment risk, vacation risk, and destination risk. Later Tsaur et al. (1997) restricted the perceived risk into two dimensions namely physical risk including injury and sickness, and equipment risk including malfunctioning equipment and inadequate telecom services. Another study by Boshoff (2002), evaluates the influence of three independent variables on potential service buyers ‘risk perceptions. Floyd and Pennington-Gray (2004) has profiled the types of tourists according to their risk perception. Continuing on the research of aftershock following September 11, 2001, Floyd et al. (2004) published another research on the effect of perceived risk on travel intentions in view of the dimensions of income, travel experience, social risk. Cui et al., (2016), have precisely categorized tourist risk perception into three dimensions which are subjective feelings, objective evaluation as well as tourist cognition.

Apart from above mentioned studies various other research papers have been published on the major dimensions of risk perception which include; financial risk and is concerned with the undervalued and unworthy service consumption (Boksberger et al., 2007) psychological risk meaning indulgence in the buying of some tourism products will result in a loss of self-image (Boshoff, 2002; Liu & Gao, 2008) social risk, in which, the tourists develop a sense of social insecurity due to factors such as political unrest, terrorist activities, crime, etc. performance risk is when the tourists feel that the service quality available at tourist destinations may not meet the expected benchmarks of service quality and Health risk, that is, the tourists’ sense of a particular risk as a result of body injury due to any ailment, accidental mishap or security issue during the trip (Li et al., 2020; Liu & Gao, 2008; Sönmez & Graefe, 1998a).

Need to Develop Covid-19 Risk Perception Scale

Various risk perception scales; terrorism (Rittichainuwat & Chakraborty, 2009), natural disasters (Park & Reisinger, 2010), health risk perception (Jonas et al., 2011), Ebola free destinations (Baker et al., 1992; Mizrachi & Fuchs, 2016; Sridhar et al., 2016), mobile device use (González-Reverté et al., 2018), service buyer risk perception (Boshoff, 2002), have been introduced for research purposes through decades of work after thorough consideration pertaining to many aspects that trigger perceived risk. This has also shed light on the relationship between risk and tourists’ involvement with the decision of visiting a certain place. Relationship between risk perception and risk handling is more marked after the level of risk exceeds the individual’s acceptable level of risk (Dowling & Staelin, 1994). It is evident that over a period of three decades, researchers have attempted to develop tourist risk perception scales keeping in view all the important aspects of touring and traveling. With the exploration of new places and outbreak of diseases and also natural disasters there is need for re-evaluation of already developed risk perception scales.

In a study cautioned that there will be pandemic out breaks in future, hence more systematic research should be conducted on its deadly consequences. Some studies (e.g., see Liu & Gao (2008) also considered health risk as an important factor for travel decisions and some developed a scale to measure health and medical risk and associated it to foreign cuisine and hygiene problems (Becken & Hughey, 2013; Boshoff, 2002; Cohen & Avieli, 2004; Dolnicar, 2005; Dowling & Staelin, 1994; Jonas et al., 2011; Lepp & Gibson, 2003; Rittichainuwat & Chakraborty, 2009; Schroeder & Pennington-Gray, 2015; Zhang et al., 2014).

However, the measurement of health risk has been mostly addressed in general terms and the incorporation of tourist risk perception due to pandemic like covid-19 has not been operationalized and measured in these scales. Due to its profound impact on tourism, covid-19 pandemic risk perception needs to be measured as a new dimension in perceived risk perception of tourism.

Methodology

To study the risk perception of the tourists for the country they intend to visit, following steps were followed;

an extensive literature review was conducted on risk perception, it’s dimensions and available scales,

interviews were conducted from the subject area specialists as well as academicians, and

interviews were conducted from potential tourists and tour guides.

Four academic experts from tourism marketing and two experts of epidemic/pandemic diseases were interviewed about their understanding of perceived Covid 19 risk regarding tourism. The four academic experts were university professors with specialization in tourism marketing teaching in universities at USA, Pakistan, and China. Online interviews were conducted with average time of 63 minutes. The two epidemic/pandemic disease experts are working in the USA, having 8–10 years of professional experience in practicing at hospitals. The average interview time was 45 minutes.

The first author interviewed 4 tourist guides with an experience of multiple international destinations and 12 tourists with frequent traveling history were interviewed on their understanding of overall risk perception and Covid 19 risk. The tourists had a traveling history of visiting different countries for recreation; academic and business purposes, that is, (1) Asian Countries, (2) Australia, (3) Canada, (4) East Asian Countries, and (5) European countries. Lastly, tour guides were also interviewed to seek their opinion on pandemic risk perception that accompanied tourists in aforementioned destinations.

An online appointment was booked with tour guides and tourists to conduct interviews. The tour guides were shortlisted based on the data available on their websites. Tourists were further requested to share a list of tourists they accompanied or would accompany in the near future after the pandemic ends. The tourists were randomly selected based on their availability and consent to be interviewed via Google Meet.

A pool of items was derived from open ended interviews that were combined with the items derived from the literature of pandemic risk. Content analysis of 80 items were conducted by PhD specialists in which they were requested to rate each item on a 7-point Likert scale of (1) extremely irrelevant to (7) extremely relevant. This process reduced items to 30 items including, (Q1) “Probability of Covid 19 pandemic outbreak” (Q5) “Infected through service providers” (Q14) “Might not survive if get infected” (Q14) “shortage of precautionary measures such as masks, etc.” (Q17) “might get symptoms quickly” (Q19) “afraid of being quarantined” (Q21) “currency might cause infection” (Q24) “might not rely on sources of information provided on social media” (Q28) “Afraid that infection would put me life at risk” (Q30), trip would end up on anxiety.”

To run EFA, the items were measured on a 7-point scale ranging from (1) Not likely to (7) extremely likely. The data were collected from 250 individuals who intend to visit the countries. This was done as per the recommendations of Fuchs (2006) who suggested conducting a study to determine the risk perception of tourists who were planning to visit a destination. The study also recommended having a different version of questionnaire with minimal overlapping among and between variables and dimensions. Therefore, a different dimension of risk, that is, Covid 19 risk perception of tourists was checked with a sample size of 300 and 250 complete responses were obtained with a response rate of 83% which was very high. Principal Component Analysis (PCA) with Varimax rotation reduced the items to 8. Items derived for Covid 19 risk perception of tourists from PCA process included “prior to my trip, I am worried that I might get infected during journey, “during shopping,” “by interacting with service providers,” “without appearance of symptoms,” “through currency,” “through food,” and “might not rely on source of information provided on media.” The ages ranged from 20 to 60 and above with 71% of the responses were from the tourists with age range of 20 to 45. Among 250 responses, 64% were male and 36% were females. The reason of high response rate from males were the societal mind-set in Pakistan in which most of the females are not allowed to visit foreign countries alone for the purpose of tourism or recreation thus they are accompanied by the family or spouse which ultimately decreases the number of female tourists. The income range of 52% of the respondents was “same as average income” as middle class is high in numbers.

Designing the Scale

The questionnaire designed for study 2 included six questions that measured the overall risk perception. The sixth question in overall risk perception was Covid 19 risk which had highest factor loading in pandemic risk perception scale of tourists. Another reason for adding the question of pandemic risk in overall risk perception was its impact on tourism. As due to the lockdown and pandemic outbreak, tourists had to cancel the bookings ranging from airline tickets to hotel bookings which had a huge impact on tourism, tour guide companies, traveling companies, etc. Additionally, 22 items were adopted from the work of Fuchs (2006) to measure the risk perception of tourists on 6 dimensions including human induced risk (5 items), financial Risk (4 items), service quality (5 items), socio-psychological risk (3 items), risk of natural disaster and car accidents (2 items), and risk of food safety, and weather (3 items) were added. Moreover, 8 items of tourist destination Covid 19 risk perception scale were derived from the theory, and EFA were also added in the main study for validation (convergence & divergence). A 7-point Likert scale ranging from “strongly disagree” to “strongly agree” was used to get the responses of tourists. Since the items derived from the work Fuchs (2006) measured overall risk perception and six dimensions of risk perception.

All the items were modified to measure the risk perception of the destination tourists intend to visit by responding to their call for adapting it in future studies. The content and face validity of the scale was assessed from the subject area specialist prior to dissemination of questionnaire. For instance, a question about “XXX is a safe country for tourists” was adapted as “Country X is safe for tourists,” “I worried before my trip about crime in XXX” was modified as “I worried before my trip about crime in country X.” likewise, question about financial risk “I worried that trip to XXX would involve unexpected extra expenses” was changed into “I worried that trip to Country X would involve unexpected extra expenses.” Based on these, the questions of Covid 19 risk perception of tourists were developed which include “Prior to my trip, I am worried about that there might be any chances of Covid 19 at Country X,” “Prior to my trip, I am worried that I might get infected during the journey,” and “I am concerned I might get infectious without appearance of symptoms.” Most of the questions of Covid 19 risk perception were phrased as statements to assess the responses of the tourists prior to the trip. Table 1 below shows demographical composition of sample.

Table 1.

Age distribution, Gender, Perceived Income, Country Intend to Visit.

Age group	Frequency	Percentage
18–28	432	76.5
29–39	58	10.3
40–49	53	9.5
50–59	17	3.0
60 above	5	.9
Total	565	100.0
Gender	Frequency	Percentage
Male	348	61.6
Female	217	38.4
Total	565	100.0
Perceived Income	Frequency	Percentage
Much below average income	90	15.9
Below Average Income	75	13.3
Same as Average Income	240	42.5
Above average income	134	23.7
Much above Average Income	12	2.1
Don’t want to tell	14	2.5
Total	565	100.0
Country Intend to visit	Frequency	Percentage
USA/Canada	167	29.6
Europe	225	39.8
South America	10	1.8
Africa	5	.9
Asia	155	27.4
Missing	3	.5
Total	565	100.0

The target population of the main study was also the international tourists who had a travel history and intended to visit other countries in the near future as their plans had been postponed due to the Covid 19 outbreak. Tourists were given choice of different countries such as USA, Canada, Europe, South America, Africa, and Asia. The locations were grouped into six categories based on the data provided by traveling companies in which frequency of visitation was given.

In the end of February, hotel managers were contacted through landlines, on which they were briefed about the purpose of the research and asked for the permission to conduct online interviews through Zoom which was approved after much delay as hotels had to follow their standard operating procedures (SOPs) to reach out to the tourists. As a result of this, 79 interviews were completed online till third week of March by using online and face to face interviews as few tourists were available in March because it had not spread much in Pakistan and there was not any lockdown at that time. After a partial lockdown was announced by the Government in last week of March, traveling companies were contacted and data of tour guides and travelers registered with companies to travel to their countries after lockdown was received. Among 25 companies, the data of 780 tourists was shared who were emailed to seek their consent and convenience time for interview. After preparing the list, interviews were conducted online with 486 respondents who had travel history and had plan to visit one of the countries mentioned in six categories of the country.

Among 565 respondents, 348 were male which accounted for 62% and 217 were females which was 38% of the responses. Moreover, 76% of the respondents were from the age range of 18–28, 10% were from age range of 29–39 however only 1% of the respondents were above 60. The largest category of income was 42% which was “same as average income” followed by 23% of “above average income.”

Results and Analysis

The overall risk perception of scale was modified with addition of a new dimension. There was an addition of Q6 regarding Covid 19 outbreak which had not been studied in the previous studies. The reason behind adding the item of Covid 19 risk was the financial, cognitive, and social loss it has caused to the global tourism industry with the chances of reoccurring in a second wave in the near future. As already altered by Fuchs (2006), a consistent scale was used to measure an overall risk perception of the tourists. Table 2 depicted the altered scale of the overall risk perception, mean values, standard deviation, and reliability respectively. The highest mean for Q6 = 4.79 measured the risk of Covid 19 outbreak during visit. The value of Cronbach alpha also predicted that the alpha value would not be improved if the item was removed. Thus, this signifies the need of Covid 19 risk outbreak in the scale of overall risk perception. The second highest value was of Q2 = 4.72 which measured the “friends’ concern on safety during the tour” with S. D = 1.86. Whereas, lowest mean was recorded for Q3 = 3.72, (measuring the perception of the country as more dangerous than the other). The alpha reliability for the scale of overall risk perception was .591 and after deleting Q1 which measured the perception of tourists regarding safety of the country they intend to visit increased with the value of .759.

Table 2.

Questions Measuring Overall Risk Perception and Cronbach Alpha Results (N = 565).

Questions		Mean	SD	Alpha if item deleted
Q1	My destination is a safe country for tourists.	4.58	1.88	.759
Q2	I thought that my friends would worry about my safety while I am on tour.	4.76	1.86	.507
Q3	Prior to your trip, did you view your destination as more dangerous than other places around the world?	3.72	1.72	.536
Q4	Considering your experience in visited destination so far, how would you rate your destination in terms of risk?	4.30	1.36	.463
Q5	To what extent do your friends or relatives see your visited destination as a risky place to visit?	4.26	1.67	.434
Q6	Prior to your trip, you are worried about any chances of Covid 19 at your destination.	4.79	1.66	.469

Table 3 projected the validation results of seven factors, six were adapted from the work Fuchs (2006), and one was the extension of the scale of tourism destination risk by adding Covid 19 risk at destination developed in the present study. Each type of risk was measured by different questions. Table 3 projected the reliability of all the seven types of risks. The values of reliability showed an acceptable inter-item consistency with minimum value of a = .677 for financial risk and maximum value of a = .855 for Covid 19 risk. The values of “alpha if item deleted” showed no improvement in alpha value by deleting any item. Thus, all 18 items were retained at this stage to measure six types of risks. However, to measure seventh type of risk, Covid 19 destination risk, seven questions were retained after EFA/CFA with the threshold value of 0.6 measuring; risk of Covid 19 outbreak, weak coping mechanism, mental health, vulnerability (getting infectious through air, during journey), media information. The items with weak loadings or cross loadings were deleted, ending up on a 7 item Likert scale to measure the Covid 19 destination risk.

Table 3.

Correlation Between Overall Risk Perception and Risk Types (N = 565).

	1	2	3	4	5	6	7	8
1. Overall risk perception	1
2. Human induces risk	.388	1
3.Finacial risk	.242	.403	1
4. Service quality risk	.510	.631	.321	1
5. Socio psychological risk	.329	.455	.321	.594	1
6. Natural disaster risk	.239	.623	.308	.489	.373	1
7. Food safety risk	.348	.538	.451	.541	.424	.565	1
8. Covid-19 risk	.476	.362	.547	.545	.486	.239	.621	1

Correlation is significant at the .01 level (two-tailed).

Factor 1 “human induced risk” consisted of political unrest, terrorism, crowding, and crime with eigenvalue of 9.857 and % variance was explained 32. 858, with alpha value of .758. The factor loadings for political unrest was .775, terrorism had value of .646, crime had relatively low loadings of .599 which can be eliminated from further analysis, while crowded had factor loading of .712.

Factor 2 “financial risk” explained by items of extra expenses at destination, expansiveness, incidental expenses at home and impact on financial situation. The items were loaded with eigenvalue 3.004% and 10.013% of variance was explained. The Cronbach alpha was counted as .677 which was in minimum threshold value. The factor loadings for extra expenses were highest 0.694 however second of more expensive was 0.425 which may be deleted from further analysis. The factor loading for impact on financial situation was low 0.571 which can be retained for further analysis as this is closer to 0.6 however; the value for incidental expenses was 0.614. Table 4 below shows the results of factor analysis.

Table 4.

Validation Results: Factor Analysis Results (Varimax Rotation).

Factor	Loading	Eigenvalues	% of variance explained	Cronbach’s Alpha
Factor 1“Human-induced risks”		9.857	32.858	.758
Political unrest	0.775
Terrorism	0.646
Crime	0.599
Crowded	0.712
Factor 2“Financial”		3.004	10.013	.677
Extra expenses at destination	0.694
More expansive than. . .	0.425
Incidental Expenses at home. . .	0.614
Impact on financial situation	0.571
Factor 3“Service Quality”		1.928	6.427	.757
Country X would not be friendly	0.676
Facilities would not be acceptable	0.681
Hotels unsatisfactory	0.642
Factor 4Socio-psychological		1.658	5.527	.737
Way friends think	0.834
Way family thinks	0.734
Self-image	0.780
Factor 5Natural disaster and car accidents		1.499	4.998	.846
natural disasters	0.725
car accidents	0.804
Factor 6Food safety problem and weather		1.279	4.262	.617
Food safety	0.623
Weather	0.807
Factor 7Covid 19 risk		1.086	3.619	.855
Chances of Covid 19 existence	0.677
Might not able to survive	0.775
Remain depressed	0.780
Infected during journey	0.680
Infected by service providers	0.680
might catch infection quickly	0.625
Anxiety during trip	0.658
Not rely on media information	0.571

Factor 3 “service quality” was explained by three items measuring friendliness, facilities, and hotels. All three items were reverse coded with eigenvalue of 1.928 and 6.427% of variance was explained. The factor loadings were recorded above 0.6 with alpha value of .757. The item of friendliness loaded with a value of 0.676, unacceptability of facilities = 0.681, and unsatisfactory hotels were loaded as 0.642.

Factor 4, “socio-psychological risk” explained by how family and friends think about tour and the self-image of tourist. The Cronbach alpha value showed a good inter-item consistency with an alpha value of .737% and 5.527% of variance was explained. The factor of friends’ and family’s perception was loaded as 0.834 and 0.734, respectively. How self-image was explained with a factor loading of 0.780.

Factor 5 “natural disaster & car accidents” was explained by two items of natural disaster and car accidents with an alpha value of .846. The factor loadings for both items were above 0.7, natural disaster = .725, car accidents = .804, with 4.998% of variance. However, the eigenvalue was recorded as 1.499.

Factor 6 “food safety problem & weather” comprising two items with Cronbach alpha value of .617. The two items factor recorded eigenvalue of 1.279 and 4.262% of variance explained. The item of food safety was loaded as .623 and weather risk was .807 which was higher than food safety.

Factor 7 “Covid 19 risk” consisted of eight items including chances of Covid 19 pandemic at destination, weak coping mechanism (might not able to survive, might catch infection quickly), cognitive risk (remain depressed during visit, anxiety during trip), risk of active/passive infection transmission (getting infection during journey, by service providers), risk of exposure through media. The Cronbach alpha value was counted as .855% and 3.619% of variance was explained with the eigenvalue of 1.086. The factor loading of chances of pandemic at country X was recorded as 0.677, might not be able to survive = 0.775, might catch infection quickly = 0.625, remain depressed = 0.780, anxiety during trip = .658, getting infection during journey = .680, by service providers = .689, and risk of exposure through media was loaded as .571 which may be deleted from further analysis.

Discussion

This study was conducted on tourists who intend to visit various tourism destinations in near future. Since many countries have now eased the lockdown and tourism destinations are now opened for tourists with proper implementation of SOPs such as wearing masks and maintain the desired social distancing is necessary. However, WHO (2020) has warned about second wave of Pandemic which is “most dangerous” thus warned countries that do not rush to ease lockdown. For instance, China eased lockdown and after a month cases soared to 50 due to which they implemented curfew with immediate effect.

Fight operations are partially opened and not all counties have opened their air spaces. New Zealand, for instance, has closed their flights to the foreign countries and country is under control of Army to strictly implement Corona SOPs. This recent development shows among many risks which have been cited in the literature, one of the most challenging is Covid 19 risk.

Past literature indicate that most studied types of risk during 1972 to 1999 are physical (induced by human behaviors, physical dangers), financial (financial burden, expensive, threats of theft or stolen cases), and social/psychological risk (cultural acceptability, racism, perception of friends and family, self-image) (Jacoby & Kaplan, 1972; Roehl & Fesenmaier, 1992; Sönmez & Graefe, 1998a; Tsaur et al., 1997). With the change in time, the studies on types of risks expanded to natural disasters, safety concerns, cultural risks, geographic differences, and socio-political difficulties at international destinations (Floyd et al., 2004; Fuchs & Reichel, 2006; Reichel et al., 2007; Wu et al., 2001).

The contribution of present study is addition of a new risk dimension. Though, Fuchs and Reichel (2006) has studied the risk of epidemic disease at tourism destination but that was measured by only one item under the category of physical risk according to theoretical construct. The present study, however, has focused on studying Covid 19 risk perception as one of the emerging risks which will be considered by tourists in the near future before selecting any tourism destination. Second reason behind studying the construct of Covid 19 risk is increased awareness among people regarding the Covid 19 pandemic outbreak which has hit global tourism. Thirdly, previous work on pandemic or epidemic is limited to health care or medical studies which focused to measure economic effects, health effects, the detection of disease, and response (Smith, 2006). Thus most of the scales available on measuring the pandemic risk perception were measuring the cognitive risk, coping mechanism, response toward SARS, knowledge about Ebola and its treatment, or change in lifestyle during Ebola while studying aforementioned in terms of medical, health, buying behaviors, lifestyles, and hospital employees (Abdulkareem et al., 2021; Bagnoli et al. 2007; De Zwart et al., 2009; Obenauer et al., 2018)

In our study, the focus has been given to conceptualization and operationalization of Covid 19 risk perception which is an addition to measure tourist destination risk. The eight item scale comprehensively covers Covid 19 risk perception of tourists which includes categories such as chances of Covid 19 at destination, weak coping mechanism (might not able to survive, might catch infection quickly), cognitive risk (remain depressed during visit, anxiety during trip), risk of active/passive infection transmission (getting infection during journey, by service providers), and risk of exposure through media. Thus, this study is beyond the work of Fuchs and Reichel (2006), who have proposed six dimensions of tourist risk perception about destination. Another difference is that present study provides a scale to measure tourist’s destination risk perception for different destinations. Moreover, the present study has also validated the scale so tourist risk perception proposed by Fuchs and Reichel (2006).

Theoretical and Managerial Implications

The result of the present study is aligned with numerous past studies which demonstrate destination risk as a multidimensional construct. Since the focus of the study was operationalization and measurement of another dimension which is Covid 19 risk perception, which is like other types of risk, consists of several questions to identify its correlation with overall risk perception and other risk index. The findings of the study suggest that Covid 19 risk will be an important consideration for future travel and hospitality industry has to adopt a proactive approach. The hotels will have to develop this confidence in customers that everything they do is redesigned in wake of the Covid 19 pandemic to ensure risk free stay of travellers. The aviation industry has to ensure travel without any fear of being infected and there should be zero tolerance for this in SOPs. Moreover, the study offers multiple implications for various services related industries where consumer interaction with service provider is high and marketers are interested to predict buying behavior. For instance, studies on risk perception will help researchers and marketers to predict online shopping behavior for high involvement products such as branded phones, electronic products (ACs, refrigerators, etc.). Additionally, risk perception may also be studied on cross-platform buying behavior to predict the critical factors for consumers of emerging markets after COVID-19. Likewise, studies may also be conducted to know the role of risk perception to evaluate quality-value relationships in services sector which will help managers to improve consumer-service related touch-points.

Limitations and Future Research

The attempt to incorporate the Covid 19 risk into the tourist risk perception scale needs to be empirically tested in various conceptual and theoretical studies for the purpose of additional validation. Apart from that, a more diversified tourist sample for the empirical studies will aid in the improvement of the reliability and validity of the questionnaire.

The operationalization of the Covid 19 risk dimension addition to the tourist risk perception leads to various questions. Risk perceptions vary according to the personality types, overall surroundings, and the native culture of the tourists. Hence, future researches could examine the differences in the tourist risk perception including Covid 19 risk in different ethnic groups as well as across different personality types. Furthermore, tourist risk perceptions have a great impact on the interests (both tourist and tourist destination institutions). For that purpose, new studies could be conducted on the effect of tourist risk perceptions during and after Covid 19 pandemic on the independent and co existing interests of the all the stake holders in the tourism and hospitality industry.

Tourism also has a deep rooted connection with the automotive and airline industry, thus affecting the said businesses in the shorter and longer terms, Hence, future studies can test use this questionnaire with relation to the operations of the airlines with special focus on the interdependence of tourist risk perceptions and job uncertainty of the airline and automotive industry human resource. Lastly, with a robust and reliable TRP scale including Covid 19 risk, hospitality and tourism research can be expanded into various experimental and mix method studies.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sajid Bashir

References

Abdulkareem

A. G.

Qader

D. J.

Ismael

N. H.

Othman

(2021). Impact of Quarantine Time in COVID-19 Pandemic Lockdown on Parents and their Children. Journal of Zankoy Sulaimani, 23(1), 1–8.

Ahmed

Q. A.

Memish

Z. A.

(2020). The cancellation of mass gatherings (MGs)? Decision making in the time of COVID-19. Travel Medicine and Infectious Disease, 34, 101631.

Ayittey

F. K.

Ayittey

M. K.

Chiwero

N. B.

Kamasah

J. S.

Dzuvor

(2020). Economic impacts of Wuhan 2019-nCoV on China and the world. Journal of Medical Virology, 92(5), 473–475.

(2005). The subjective factors influence tourists risk perception and implications for tourism crisis management. Journal of Zhejiang Academy, 1, 196–200.

Bagnoli

Lio

Sguanci

(2007). Risk perception in epidemic modeling. Physical Review E, 76(6), 061904.

Baker

Levy

Grewal

(1992). An experimental approach to making retail store environmental decisions. Journal of Retailing, 68(4), 445.

Becken

Hughey

K. F.

(2013). Linking tourism into emergency management structures to enhance disaster risk reduction. Tourism Management, 36, 77–85.

Blake

Sinclair

M. T.

Sugiyarto

(2003). Quantifying the impact of foot and mouth disease on tourism and the UK economy. Tourism Economics, 9(4), 449–465.

Bogoch

I. I.

Watts

Thomas-Bachli

Huber

Kraemer

M. U.

Khan

(2020). Potential for global spread of a novel coronavirus from China. Journal of Travel Medicine, 27(2), taaa011.

10.

Boksberger

P. E.

Bieger

Laesser

(2007). Multidimensional analysis of perceived risk in commercial air travel. Journal of Air Transport Management, 13(2), 90–96.

11.

Boshoff

(2002). Service advertising: An exploratory study of risk perceptions. Journal of Service Research, 4(4), 290–298.

12.

Chen

S.-M.

Chen

J.-H.

(2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert systems with applications, 36(3), 6833–6842.

13.

Cheron

E. J.

Ritchie

J. B.

(1982). Leisure activities and perceived risk. Journal of Leisure Research, 14(2), 139–154.

14.

Chien

P. M.

Sharifpour

Ritchie

B. W.

Watson

(2017). Travelers’ health risk perceptions and protective behavior: A psychological approach. Journal of Travel Research, 56(6), 744–759.

15.

Choi

K. H.

Kim

Leopkey

(2019). Prospective tourists’ risk perceptions and intentions to travel to a mega-sporting event host country with apparent risk. Journal of Sport & Tourism, 23(2-3), 97–114.

16.

Cohen

Avieli

(2004). Food in tourism: Attraction and impediment. Annals of Tourism Research, 31(4), 755–778.

17.

Conchar

M. P.

Zinkhan

G. M.

Peters

Olavarrieta

(2004). An integrated framework for the conceptualization of consumers’ perceived-risk processing. Journal of the Academy of Marketing Science, 32(4), 418–436.

18.

Cui

Liu

Chang

Duan

(2016). An overview of tourism risk perception. Natural Hazards, 82(1), 643–658.

19.

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.

20.

Dolnicar

(2005). Understanding barriers to leisure travel: Tourist fears as a marketing basis. Journal of Vacation Marketing, 11(3), 197–208.

21.

Dowling

G. R.

Staelin

(1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.

22.

Fernandes

(2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy (Working Paper No. WP-1240-E). IESE Business School.

23.

Floyd

M. F.

Gibson

Pennington-Gray

Thapa

(2004). The effect of risk perceptions on intentions to travel in the aftermath of September 11, 2001. Journal of Travel & Tourism Marketing, 15(2–3), 19–38.

24.

Floyd

M. F.

Pennington-Gray

(2004). Profiling risk perceptions of tourists. Annals of Tourism Research, 31(4), 1051–1054.

25.

Fuchs

(2013). Low versus high sensation-seeking tourists: A study of backpackers’ experience risk perception. International Journal of Tourism Research, 15(1), 81–92.

26.

Fuchs

Reichel

(2006). Tourist destination risk perception: The case of Israel. Journal of Hospitality & Leisure Marketing, 14(2), 83–108.

27.

González-Reverté

Díaz-Luque

Gomis-López

J. M.

Morales-Pérez

(2018). Tourists’ risk perception and the use of mobile devices in beach tourism destinations. Sustainability, 10(2), 413.

28.

Huang

Chuang

S. T.

Lin

Y. R.

(2008). Folk religion and tourist intention avoiding tsunami-affected destinations. Annals of Tourism Research, 35(4), 1074–1078.

29.

Jacoby

Kaplan

L. B.

(1972). The components of perceived risk. ACR Special Volumes. p. 3.

30.

Jonas

Mansfeld

Paz

Potasman

(2011). Determinants of health risk perception among low-risk-taking tourists traveling to developing countries. Journal of Travel Research, 50(1), 87–99.

31.

Karim

Haque

Anis

Ulfy

M. A.

(2020). The movement control order (MCO) for COVID-19 crisis and its impact on tourism and hospitality sector in Malaysia. International Tourism and Hospitality Journal, 3(2), 1–07.

32.

Lepp

Gibson

(2003). Tourist roles, perceived risk and international tourism. Annals of Tourism Research, 30(3), 606–624.

33.

Shen

Zhang

Chan

M. T.

W. K.

(2020). Emerging roles of non-coding RNAs in scoliosis. Cell Proliferation, 53(2), e12736.

34.

Liu

Pennington-Gray

(2015). Bed bugs bite the hospitality industry? A framing analysis of bed bug news coverage. Tourism Management, 48, 33–42.

35.

Liu

Gao

(2008). A cognitive analysis of travel risks based on the conceptual models of risk perception: A case study of inhabitants in Shanghai. Tourism Science, 22, 37–43.

36.

Mao

Liang

Shen

Ghosh

Zhu

L.-R.

Yang

Chen

M.-H.

K-C

Chen

M.-H.

, Chinese Society of IBD; Chinese Elite IBD Union; Chinese IBD Quality Care Evaluation Center Committee. (2020). Implications of COVID-19 for patients with pre-existing digestive diseases. The Lancet Gastroenterology & Hepatology, 5(5), 426–428.

37.

Millán-Oñate

Rodriguez-Morales

A. J.

Camacho-Moreno

Mendoza-Ramírez

Rodríguez-Sabogal

I. A.

Álvarez-Moreno

(2020). A new emerging zoonotic virus of concern: The 2019 novel Coronavirus (COVID-19). Infectio, 24(3). In press.

38.

Mizrachi

Fuchs

(2016). Should we cancel? An examination of risk handling in travel social media before visiting ebola-free destinations. Journal of Hospitality and Tourism Management, 28, 59–65.

39.

Obenauer

Rübsamen

Garsevanidze

Karch

Mikolajczyk

R. T.

(2018). Changes in risk perceptions during the 2014 Ebola virus disease epidemic: Results of two consecutive surveys among the general population in Lower Saxony, Germany. BMC Public Health, 18(1), 1–8.

40.

Page

Song

D. C.

(2012). Assessing the impacts of the global economic crisis and swine flu on inbound tourism demand in the United Kingdom. Journal of Travel Research, 51(2), 142–153.

41.

Park

Reisinger

(2010). Differences in the perceived influence of natural disasters and travel risk on international travel. Tourism Geographies, 12(1), 1–24.

42.

Pueyo

(2020). Coronavirus: Why you must act now. Politicians, community leaders and business leaders: what should you do and when. Medium.

43.

Reichel

Fuchs

Uriely

(2007). Perceived risk and the non-institutionalized tourist role: The case of Israeli student ex-backpackers. Journal of Travel Research, 46(2), 217–226.

44.

Reisinger

Mavondo

(2005). Travel anxiety and intentions to travel internationally: Implications of travel risk perception. Journal of Travel Research, 43(3), 212–225.

45.

Rittichainuwat

B. N.

Chakraborty

(2009). Perceived travel risks regarding terrorism and disease: The case of Thailand. Tourism Management, 30(3), 410–418.

46.

Roehl

W. S.

Fesenmaier

D. R.

(1992). Risk perceptions and pleasure travel: An exploratory analysis. Journal of Travel Research, 30(4), 17–26.

47.

Rundmo

T. R.

Moen

B. R. E.

(2006). Risk perception and demand for risk mitigation in transport: A comparison of lay people, politicians and experts. Journal of Risk Research, 9(6), 623–640.

48.

Sarman

Scagnolari

Maggi

(2016). Acceptance of life-threatening hazards among young tourists: A stated choice experiment. Journal of Travel Research, 55(8), 979–992.

49.

Schroeder

Pennington-Gray

(2015). The role of social media in international tourist’s decision making. Journal of Travel Research, 54(5), 584–595.

50.

Shemma

(2014). Tourist destination: Demand-motivating factors in Israel’s domestic tourism. Journal of Tourism Challenges and Trends, 7(2), 66–86.

51.

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.

52.

Sridhar

Brouqui

Fontaine

Perivier

Ruscassier

Gautret

Regner

(2016). Risk perceptions of MSF healthcare workers on the recent Ebola epidemic in West Africa. New Microbes and New Infections, 12, 61–68.

53.

Sönmez

S. F.

Graefe

A. R.

(1998a). Determining future travel behavior from past travel experience and perceptions of risk and safety. Journal of Travel Research, 37(2), 171–177.

54.

Sönmez

S. F.

Graefe

A. R.

(1998b). Influence of terrorism risk on foreign tourism decisions. Annals of tourism research, 25(1), 112–144.

55.

Stephany

Stoehr

Darius

Neuhäuser

Teutloff

Braesemann

(2020). The CoRisk-index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time. Cornell University.

56.

Tian-Que

(2012). Perceived risk in marketing strategy business, economics, financial sciences, and management (pp. 175–178). Springer.

57.

Tsaur

Tzeng

Wang

(1997). The application of AHP and fuzzy MCDM on the evaluation study of tourist risk. Annals of Tourism Research, 24(4), 796–812.

58.

WHO. (2020). Coronavirus disease 2019 (COVID-19): Situation report, 67. World Health Organization.

59.

Wildavsky

Dake

(1990). Theories of risk perception: Who fears what and why? Daedalus, 119(4), 41–60.

60.

Wilson

M. E.

Chen

L. H.

(2020). Travellers give wings to novel coronavirus (2019-nCoV). Oxford University Press.

61.

Wang

(2001). Chinese college students perceive the evaluation of tourism security research. Journal of Guilin Institute of Tourism, 12(3), 62–68.

62.

(2009). Travel safety introduction. China Tourism Press, Beijing, 60–76.

63.

Yang

E. C. L.

Sharif

S. P.

Khoo-Lattimore

(2015). Tourists’ risk perception of risky destinations: The case of Sabah’s Eastern Coast. Tourism and Hospitality Research, 15(3), 206–221.

64.

Yang

H. Y.

Chen

K. H.

(2009). A general equilibrium analysis of the economic impact of a tourism crisis: A case study of the SARS epidemic in Taiwan. Journal of Policy Research in Tourism, Leisure and Events, 1(1), 37–60.

65.

Ying

Wang

Liu

Wen

Goh

(2020). Rethinking game consumption in tourism: A case of the 2019 novel coronavirus pneumonia outbreak in China. Tourism Recreation Research, 46(2), 1–6.

66.

Zhang

Tao

Zhang

Lin

Zhang

(2019). The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, 98, 207–220.

67.

Zhang

H.-L.

Zhang

Cheng

(2014). Predicting residents’ pro-environmental behaviors at tourist sites: The role of awareness of disaster’s consequences, values, and place attachment. Journal of Environmental Psychology, 40, 131–146.

Covid-19 Risk Perception of Travel Destination Development and Validation of a Scale

Abstract

Keywords

Introduction

Literature Review

Covid-19 Pandemic’s Effect on Hospitality and Tourism

Existing Scales on Tourist Risk Perceptions

Need to Develop Covid-19 Risk Perception Scale

Methodology

Designing the Scale

Results and Analysis

Discussion

Theoretical and Managerial Implications

Limitations and Future Research

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References