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Article

The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal

by
Shiqi Zhang
1,
Tianwei Sun
2 and
Yuan Lu
2,*
1
HNU-ASU Joint International Tourism College, Hainan University, Haikou 570228, China
2
School of Economics, Hainan University, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1323; https://doi.org/10.3390/su15021323
Submission received: 5 December 2022 / Revised: 28 December 2022 / Accepted: 5 January 2023 / Published: 10 January 2023
(This article belongs to the Special Issue Current Trends in Tourism under COVID-19 and Future Implications)
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Abstract

:
The COVID-19 health crisis has had unprecedented impacts on the global tourism industry, creating a sense of insecurity among tourists about destinations. Thus, rebuilding tourists’ confidence in the tourism industry is the biggest challenge faced by policymakers in the new normal. The tourism industry needs innovative solutions for sustainable recovery, but limited literature is available on the tourism policies necessary for sustainable and resilient recovery in the new normal. This study investigated the impact of COVID-19 and risk perception on the recovery of tourism. Moreover, this study also explored the mediating role of attitudes toward tourism policies between COVID-19, risk perceptions, and tourism recovery. Data collected from 1437 tourists through an online survey were analyzed using PLS-SEM and descriptive statistics. The results showed that a large majority of the tourists still felt unsafe and insecure about tourism destinations. COVID-19 risk perceptions were found to be negatively associated with tourism recovery in the new normal. Risk perceptions had a significant positive impact on transportation selection behavior (β = 0.725, p < 0.01), as did avoiding overcrowded places (β = 0.692, p < 0.01). Transportation selection behaviors also had a statistically significant negative impact on the recovery of tourism (β = −0.220, p < 0.01). The findings showed that attitudes toward tourism policies mediated the effect between COVID-19 and tourism intentions. This study has important policy implications for the sustainable recovery of the tourism industry and for making it resilient against future crises.

1. Introduction

The COVID-19 pandemic has devastated the world economy and also caused unprecedented upheavals in the world tourism industry. Tourist mobility decreased by 80%, and millions of workers from around the world became unemployed during COVID-19 [1,2]. Moreover, the tourism sector might not recover fully until 2023, and the world economy may face a loss of USD 3–8 trillion due to the COVID-19 pandemic’s effects on the tourism industry [3]. COVID-19 has changed the tourism sector through different public health measures (border closures, quarantines, COVID-19 testing, social distancing, and mandatory face masks). In the new normal, the sustainability of the tourism sector is dependent on health and safety perceptions of tourism destinations [4,5]. The risk perceptions of tourists are considered one of the primary determinants of decision making and tourism intentions [6]. Real risk and risk perceptions are different from each other: Real risks are usually characterized by uncertainty about the potential effects of an activity and the likelihood of the outcomes in question [7]. On the other hand, an individual’s perceptions of risks are based on their own judgments, attitudes, experiences, and feelings. These perceptions are affected by different sociocultural and contextual factors [8]. Thus, despite the presence of minimal real risks, perceived risks can influence potential tourists’ tourism-related behaviors and intentions [9]. Kozak et al. [10] described risk as a primary concern for tourists.
Travel risk perceptions and tourist flows have been extensively discussed in tourism research for the survival and sustainability of the tourism sector in the new normal [11]. Tourists’ risk perceptions are influenced by a number of factors, such as age, gender, cognitive traits, and previous learning [4]. Similarly, tourist flow is a multifaceted, dynamic system that is influenced by a number of elements related to tourists and tourism destinations [12]. Therefore, it is very important for tourism marketers to understand all of these factors in order to devise effective marketing policies for the sustainable recovery of the tourism industry in the new normal. A major part of the prior literature has explored the factors related to destinations, but the factors affecting potential tourists’ intentions for tourism in the new normal have been insufficiently discussed [13,14]. COVID-19 has significantly affected human mobility as well as leisure activities, and, therefore, previous suppositions in regard to travel risks have limitations in the new normal [5,15,16]. This indicates that information about tourism from previous studies needs to be further looked into in light of COVID-19 and the new normal market conditions.
Undoubtedly, potential tourists may perceive different risks, limiting their travel and leisure activities in the new normal because the COVID-19 pandemic is not over yet. The future of the tourism sector looks uncertain amid an ongoing pandemic and recovery from it, and the sustainability of the industry is dependent on the recovery measures taken at tourism destinations [2]. The tourism industry is in a recovery phase, and it is very important that tourism marketers advertise and publicize these measures and efforts to inform and decrease the risks perceived by individuals for tourism recovery amid the COVID-19 pandemic. The role of social media in travel- and leisure-related decision making has been widely discussed in the prior literature and can be effectively used for advertising tourism recovery policies in addition to affecting tourism risk perceptions [13,17]. The tourism industry is ready to use social media because it has mostly depended on a place’s reputation, consumer opinions, the spread of information, and good word-of-mouth advertising. The tourism industry is a good fit for social media platforms because it has always relied heavily on word-of-mouth marketing, customer feedback, the reputation of a destination, and the spread of information [18]. Therefore, the impact of risk perceptions and attitudes toward tourism policies should be discussed for the purpose of the recovery of tourism in the new normal.
A number of previous studies have discussed the interplay between travel risk perceptions and tourism intentions [19,20,21]; however, none of these studies explored the role of attitudes toward tourism policies in risk perceptions and travel intentions in the new normal. Additionally, how the behaviors and attitudes of individuals toward transportation, health and safety measures, and the overcrowding of tourists at destinations affect their tourism intentions in the new normal needs to be examined, especially in emerging markets. Matiza and Kruger [22] contend that it is vital to investigate COVID-19-linked perceived risk and travel behaviors in various locations and nations for the purposes of post-crisis communication and commercial promotion. Moreover, the outbreak has increased the importance of effective travel policies in affecting tourists’ intentions in the new normal.
To fill in the gap in the literature, this study aims to investigate the interplay among perceived risks, attitudes toward tourism policies, and the tourism intentions of individuals in the new normal. How do perceived risks influence tourists’ intentions? How do perceived risks affect individuals’ behaviors toward transportation, hygiene, and safety in the new normal? Additionally, how do transportation, hygiene, and safety behaviors affect tourists’ intentions? Another valid question was whether attitudes toward tourism policies mediate the relationship between COVID-19 or perceived risks and tourism intentions. This research will help tourism authorities, tourism promotion agencies, and related businesses better understand how tourists make travel decisions in the new normal in the world.

2. Literature Review and Hypotheses Development

Governments worldwide imposed different non-pharmaceutical measures to curb the spread of the pandemic according to their own visions [23]. Peoples’ domestic and international mobility was restricted in order to reduce the risk of COVID-19 transmission from person to person in the absence of pharmaceutical interventions [24]. This had an impact on peoples’ social lives as well as leisure activities all over the world [25]. Tourists prefer comprehensive tourism packages, security, and safety when they travel to popular destinations. The non-pharmaceutical measures imposed worldwide to control the pandemic also created panic, anxiety, and stress among individuals, creating concerns about their safety and security at tourist destinations [26]. As a result, tourists began to avoid risk and limited their travel to congested areas [27]. Therefore, the following hypothesis can be made:
Hypothesis H1: 
COVID-19 negatively influences the tourism intentions of individuals in the new normal.
The perception of risk is based on a subjective assessment associated with a threatening situation and its severity [28,29]. As a result, risk perceptions in tourism are strongly linked to evaluations of situations used to make travel decisions, as well as the purchasing and consumption of travel products [30]. Risk perception is the starting point for the evaluation of a crisis’ impact on the tourism industry [31]. Natural disasters, health concerns, hygiene, and diseases affect perceptions of risk when traveling to popular destinations [32,33]. Thus, following hypotheses are given below:
Hypothesis H2: 
COVID-19 positively affects individuals’ perceptions of risk in the new normal.
Hypothesis H3: 
Risk perceptions negatively impact the tourism intentions of individuals in the new normal.
Individuals’ transportation behaviors are influenced by their COVID-19 risk perceptions. The majority of individuals avoid using public transportation and visiting crowded tourism destinations. COVID-19 decreased the use of public transport, lowered shared mobility [34], and increased the use of private transportation [35]. The perception of risk has a significant impact on tourists’ decision-making processes regarding transportation patterns. This is one of the primary reasons for people changing their travel habits during COVID-19 [36]. Although changing transportation patterns is very difficult, especially in public and crowded areas of a country, the availability of different transportation options allows tourists to make efficient decisions regarding modes of travel toward their destinations [27]. Using public transportation increases the risk of contracting COVID-19 [37]. The following hypothesis is proposed regarding transportation patterns:
Hypothesis H4: 
In the new normal, risk perceptions have a significant impact on transportation behaviors.
COVID-19 risk perceptions also affect tourist behaviors towards crowded destinations, as do hygiene, safety, and security at tourism destinations. Individuals that perceive high levels of risk in crowded places are less likely to visit crowded destinations because COVID-19 is not over yet. One of the most important defenses against COVID-19 virus infection is consistent and appropriate hand cleanliness. Similarly, an individual who perceives a higher level of risk due to COVID-19 is more likely to adopt hygienic behaviors. Savadori and Lauriola [38] described risk perception as being one of the most important determinants of protective behaviors in individuals. They further explained that hygiene and cleaning were prompted by a negative attitude toward the pandemic. Nazneen et al. [39] also described risk perception as an important factor in determining the hygiene and safety behaviors of individuals during the pandemic. Abdelrahman [40] also pronounced that COVID-19 risk perceptions affect hygienic behaviors and visits to crowded places. Moreover, people are more likely to become infected during the pandemic in densely populated megacities that are much more internationalized and have a lot of connections with other places around the world. This is because of the high infection rates in these places. The pandemic trend, policies put in place, lockdown time, and features of other cities are all different, which could lead to different and complicated effects on tourism behaviors around the world. Therefore, this study also hypothesizes the following:
Hypothesis H5: 
In the new normal, risk perceptions have a significant impact on the touristic behaviors of individuals toward crowded destinations.
Hypothesis H6: 
Individuals’ hygiene and safety behaviors are significantly influenced by their risk perceptions.
Khadaroo and Seetanah [41] contend that the transportation infrastructure at touristic destinations is a factor in their attractiveness to tourists. Improved transportation infrastructure not only saves time but also reduces costs of transportation. COVID-19 has affected every sector of the economy worldwide, and transportation is no exception. In particular, COVID-19 badly impacted global public transport ridership and service provision [42]. Public transport vehicles are narrowly spaced and can be a source of COVID-19 transmission [43]. Tian et al. [44] also described people’s decreased use of public transport during COVID-19. During a pandemic, it is important to know how tourists see risks in order to understand how they make decisions, predict future tourism needs, and come up with the best recovery plans. Therefore, this study hypothesizes the following:
Hypothesis H7: 
Avoidance behaviors on public transport negatively affects the tourism intentions of individuals in the new normal.
Eichelberger and Heigl [45], in addition to Sung et al. [46], have also pointed out the change in travelers’ preferences towards tourism destinations in the new normal. Tourists avoid overcrowded tourist destinations due to the contagious nature of the COVID-19 pandemic [47]. This research hypothesizes the following:
Hypothesis H8: 
Avoidance of crowded destinations is expected to have a negative impact on tourism intentions in the new normal.
Jovanovi et al. [48] described that health and hygiene are ways to keep the public healthy, make tourists feel safer, and make tourism destinations more competitive. COVID-19 has revamped the tourism and hospitality industries. Konak [49] explained that perceptions of hygiene and safety increase travel anxiety in regard to the pandemic, which is likely to affect the tourism intentions of individuals. Health and hygiene are also important for keeping the public healthy and making tourists feel safer [50]. This study hypothesizes the following:
Hypothesis H9: 
Individuals’ hygiene and safety behaviors have a significant impact on their tourism intentions.
Tourism marketers are also adopting different measures (discounts, insurance, free COVID-19 testing, free mask distribution, and the utilization of social media for publicizing marketing) to encourage individuals, revive the tourism industry, and minimize the impacts of COVID-19 on tourism. Thus, the profound effect of COVID-19 has revamped and transformed the tourism industry worldwide [51], in addition to forcing tourism stakeholders to change their marketing strategies. Therefore, we assume the following;
Hypothesis H10: 
COVID-19 significantly affects attitudes toward tourism policies in the new normal.
Hypothesis H11: 
Risk perceptions significantly influence attitudes toward tourism policies in the new normal.
COVID-19 has created significant perceptions of health risks among individuals at tourism destinations. The likelihood of contracting COVID-19 and its associated expenditures are the biggest concerns for travelers during the pandemic [52]; therefore, insurance, free mask distribution, and free quarantine in the case of COVID-19 infection can be effective strategies with which to revive the tourism industry in the new normal. A price discount is a very common way to induce people to buy something, giving them something extra that makes them want to make a purchase immediately [53]. Yusnita et al. [52] described the fact that discounts have a significant relationship with buying behaviors. Similarly, Alkatiri et al. [54] stated that advertising policies have positive effects on the buying attitudes of individuals. Tourist attitudes have received increased attention in tourism academia since the 1970s [55]. Prior research has examined what products or services people like, what makes them buy them, and how they act around others [56]. Tourists’ positive attitudes increase the possibility of purchasing or visiting and also influence tourists’ intentions. Huh [57] investigated the effects of attitude, including its mediating effect on behavioral intentions. Zhu and Deng [58] also examined the mediating effect of attitude toward tourism intentions and reported a positive relationship between attitude and tourism intentions. In light of the above discussion, this study assumes the following:
Hypothesis H12: 
Attitudes toward tourism policies significantly affect tourism intentions in the new normal.
Hypothesis H13: 
Attitudes toward tourism policies significantly affect transportation behaviors in the new normal.
Hypothesis H14: 
Attitudes toward tourism policies significantly affect behavior toward overcrowded places in the new normal.
Hypothesis H15: 
Attitudes toward tourism policies significantly affect hygiene and safety in the new normal.
Hypothesis H16: 
Attitudes toward tourism policies mediate a positive role between COVID-19 and tourism intentions in the new normal.
Hypothesis H17: 
Attitudes toward tourism policies mediate a positive relationship between risk perceptions and tourism intentions in the new normal.
Similarly, the other hypotheses (H16c–H16e) that were tested in this study were the mediating relationship of attitude toward tourism policies with the avoidance of public transport, the avoidance of crowded destinations, and hygiene as well as safety behaviors. The conceptual framework is shown in Figure 1.

3. Materials and Methods

3.1. Data Collection and Questionnaire Design

Data were collected through a well-designed data collection instrument from 23 May to 30 June 2022, through an online survey. The link to the online survey was shared on different tourism-related pages on Facebook, Twitter, WeChat, and Instagram. Researchers also obtained email addresses from LinkedIn with which they could share the survey link with respondents. Moreover, the survey link was also shared with the researchers’ contacts on WhatsApp. Respondents could complete the online survey on their phones, laptops, and computers. The respondents were informed about the purpose of the study, and after obtaining their consent to participate the link directed them towards the data collection instrument. Moreover, the respondents had the choice to leave the survey at any time. At the end of the survey, data were collected from 1858 respondents, but the data were used for an analysis of only those respondents who had previously traveled domestically or internationally. Moreover, the data of respondents who mentioned their intentions to tour in the “new normal” were also used for an analysis in this study. Respondents that did not intending to tour in the future were excluded from the data analysis. As a result, the data from 1437 respondents were used for further analyses.
A well-designed data collection instrument comprising different sections was used to collect data from the tourists. Using Churchill’s [59] work as a guide, the measurement items in this study were taken from or changed from those in previous tourism research studies. The first section of the data collection contained questions related to the backgrounds of the tourists. The second section of the data collection instrument contained queries related to the tourists’ perceptions of risk. The third section of the questionnaire consisted of questions aimed at measuring the behaviors of tourists toward public transport, crowded tourist destinations, hygiene, and safety. The fourth section of the questionnaire comprised statements aimed at measuring the tourists’ responses toward tourism policies. The last part of the survey instrument consisted of questions aimed at measuring the intentions of tourists toward touring in the new normal. Except for those in Section 1, all of the questions were asked on a 5-point Likert scale. The questionnaire was pretested with 30 tourists before the final data collection survey to ensure the reliability of the survey instruments. The preliminary study assisted in adjusting some questionnaire statements for better understanding based on the responses of the preliminary study tourists.

3.2. Statistical Methods

The study used different descriptive statistics for the data analysis that resulted from a cross-sectional survey of the tourists. Moreover, the research also utilized partial least-squares structural equation modeling (PLS-SEM) to test the hypotheses developed in the above section. Prior literature on PLS-SEM describes 100 as the minimum sample size with which to apply this statistical method for unbiased results [60]. Furthermore, both the “ten times rule” and G*Power, indicated by Hair et al. [61], reflected the adequacy of this study’s sample size for PLS-SEM. This paper mostly used the method of analysis suggested by Hair et al. [61]. There are two steps to the PLS method: a measurement model and a structural model [62]. This being the case, the measurement model and the structural measurement model need to be set up in order to test the hypothesis.

4. Results

4.1. Backgrounds of the Tourists

Table 1 presents the different socioeconomic characteristics of the sampled tourists. More than half of the tourists who took part were between the ages of 30 and 60, with nearly one-fourth being under the age of 30. The number of male tourists participating in this study was slightly larger than that of female tourists. More than two-fifths of the tourists had more than 12 years of education. The majority of the tourists participating in this research were Europeans. More married tourists participated in this survey than unmarried tourists. The percentage of tourists in the high-income group was higher compared to that of the low- and medium-income groups. The number of domestic tourists participating in this survey was greater than those who traveled abroad for tourism purposes.

4.2. Descriptive Statistics

Table 2 shows the descriptive statistics of all of the constructs, as well as their items, considered in this study. The results show that COVID-19 has severely affected the lifestyles and primary income sources of tourists. Moreover, tourists perceived higher travel risks in the new normal. A large majority of the tourists also perceived a risk of infection at tourism destinations. Moreover, tourists were also concerned about the enforcement of precautionary measures at tourism destinations. The findings also suggested a shift in tourist transportation behavior in the new normal. Because of the ongoing COVID-19 crisis, the majority of the tourists avoid public transportation and shared rides. Similarly, the results revealed that tourists are afraid of visiting overcrowded places in the new normal and have restricted their outdoor mobility to only purchasing necessary items. A large majority of the tourists indicated a shift in their hygiene and safety behaviors after COVID-19. Similarly, a large majority of the tourists mentioned that tourism policies (discounts, free insurance, and quarantines in the case of COVID-19 contamination) at tourism destinations attract them to tourism in the new normal. The majority of the tourist participants intended to travel in the new normal.

4.3. Measurement Model Analysis

Before testing the hypotheses of this research, model fit indices were checked to see the overall fit of the model. All of the index values for the final proposed model confirmed an acceptable fit (GFI = 0.97; CFI = 0.96; χ2/df = 5.17; NFI = 0.95; AGFI = 0.91; and RMSEA = 0.042). All of the goodness-of-fit results support the justification for further analyses. Table 3 shows the results of statistical indices. This study took insights from prior literature (e.g., Sher et al. [63], Sher et al. [64], and Singh and Prasad [65]) to evaluate the measurement model.
Additionally, convergent and discriminant validity were examined to assess the goodness of fit of the measurement model. The composite reliability (CR) and average variance extracted (AVE) were measured to analyze the convergent validity of the measurement model. The CR coefficient should not be less than 0.60 for achieving an adequate convergent validity of latent variables [67,68]. Another study by Henseler et al. [69] showed that a CR coefficient value greater than 0.70 indicates an adequate model for confirmatory purposes. Similarly, Daskalakis and Mantas [70] stated that a CR value greater than or equal to 0.80 describes the adequacy of the model for confirmatory purposes. The CR coefficient value for all of the latent variables is greater than 0.85 in this study and fulfills all of the above-mentioned criteria for further analyses. The AVE was also assessed for confirming the convergent validity of all of the latent variables. The AVE value should be greater than 0.50 for achieving substantial convergent validity [62,71,72]. The AVE values of all of the constructs are greater than the threshold level, indicating the convergent validity of the measurement model in this study.
Moreover, the factor loading of any item should not be less than 0.70 for measuring construct validity, and items of each construct with a factor loading value of less than 0.40 should be considered for elimination. Most of the time, indicators with loadings between 0.40 and 0.70 should not be taken off the scale unless taking them off increases the overall reliability above the suggested threshold value [62,73]. The factor loadings in Table 4 have confirmed the threshold criteria for the inclusion of the items in the corresponding construct. The Cronbach’s alpha value for a latent variable should be greater than the cut-off value of 0.70 [27,74]. The findings indicated that the Cronbach’s alpha value for all of the constructs was greater than 0.80, which confirms the internal reliability of the included items in each construct.
Furthermore, Kock [75] reported that a variance inflation factor (VIF) value of more than 3.3 is a sign of pathological collinearity, and it also shows that the model could be tainted by common method bias. As the VIF values shown in Table 4 are less than 3.3, this indicates that the model is free of lateral or pathological collinearity.
The latent variables are required to be distinct from each other [62]. The square root of AVE describes the discriminant validity of a latent variable by comparing its correlation values with all other latent variables. The square root of the AVE of a latent variable must be greater than its correlation scores with all other latent variables [76]. The results shown in Table 5′s diagonal confirmed discriminant validity: the greater the value compared to the correlation values with other constructs, the greater the variance explained by the construct with its own measure compared to the other measures [73]. Moreover, the results regarding the heterotrait–monotrait ratio (HMR) were also tested to check the discriminant validity [73]. The fact that HMR is less than 0.90 confirms its discriminant validity [70].

4.4. Structural Model Analysis

R2, which describes the explained variance portion, was measured to assess the predictive accuracy of the structural model. The R2 results of all of the hypotheses constructed in the model were greater than 0.50 (Table 6), except for H6, which has a R2 of less than 0.50. Following Wetzels et al. [77], the non-parametric bootstrapping method was applied to test the relationships of the latent variables hypothesized from H1 to H15. Among all 15 hypotheses, 2, H9 and H15, were not supported and rejected. The findings revealed that COVID-19 has a significant negative impact on tourism intentions and a significant positive impact on risk perceptions because its t-value is greater than the threshold value (2.32). The findings also suggested that risk perceptions have a significant influence on tourism intentions in the new normal (β = −0.772, p = 0.01). There was a significant positive impact of risk perceptions on transportation behavior (β = 0.725, p < 0.01), avoiding overcrowded places (β = 0.692, p < 0.01), and hygiene as well as safety behaviors (β = 0.568, p < 0.01). Furthermore, in the new normal, public transportation behaviors had a negative and significant impact on tourism (β =-0.220, p < 0.01), and avoiding overcrowded places had a significant and negative impact on tourism (β = −0.402, p < 0.01). The results also depicted that hygiene and safety behaviors positively impact tourism in the new normal, but this relationship was statistically insignificant. COVID-19 (β = 0.602, p < 0.01) and risk perceptions (β = 0.592, p < 0.01) were also positively associated with attitudes toward tourism policies. The direct impact of moderating variable attitudes toward tourism policies on transportation behaviors and overcrowded tourist places was also significant and negative.
The effect size was measured by using the f2 value. A value of f2 < 0.02 describes a small effect, whereas 0.15 depicts a medium effect, and > 0.35 a large effect size [78]. The findings show that tourism intentions (f2 = 0.384) in the case of hypothesis H8 have a large effect size, while in all remaining cases the effect size of variables was medium because the f2 was less than 0.15 but greater than 0.02. The predictive relevance of all of the hypotheses was also confirmed by estimating the Q2; a value greater than zero [79] ensured the predictive relevance of all of the constructs.
The findings regarding the mediating effect of attitudes toward tourism policies are presented in Table 7. The results show that attitudes toward tourism policies mediate the effect of COVID-19 on tourism intensions (β = 0.302, p < 0.01). Similarly, attitudes toward tourism policies mediated the effect of risk perceptions on tourism intentions (β = 0.434, p < 0.01), transportation behavior s(β = -0.339, p < 0.01), behaviors toward overcrowded places (β = −0.216, p < 0.01), and hygiene as well as safety (β = 0.109, p < 0.01).

5. Discussion

COVID-19 has severely impacted the tourism industry worldwide, and this pandemic is not over yet. The world tourism sector is starting to recover from the pandemic, and tourism stakeholders are in search of different policies that can assist in the recovery of this industry in the new normal. Thus, one of the primary goals of this study is to assess the mediating effect of attitudes toward tourism policies on tourist behaviors and tourism intentions, which were severely impacted by COVID-19. The role of attitudes toward tourism policies in the new normal will greatly contribute to the rehabilitation of the tourism sector. A newly developed scale was used to measure the impact of the pandemic on tourism intentions as well as risk perceptions. The study also looked at the impact of tourists’ perceptions of risk toward transportation, crowded places, and hygiene as well as safety behaviors. The structural equation model’s findings established a link between COVID-19, risk perceptions, and tourism intentions. The pandemic created general fear [80] due to its easy and rapid spread. This rapid and easy transmission of COVID-19, alongside its long incubation period after infection, created fear among tourists and travelers [81,82], which increased the perceived risks in regard to the pandemic. Neuburger and Egger [16] also found high perceived risks among tourists during COVID-19. Due to perceived risks, tourists in the new normal prefer safe and secure leisure activities. Tourists are wary of disasters and health crises because they put their health and safety at risk [83,84]. Thus, health disasters and crises affect tourists’ intentions toward tourist destinations. Kourgiantakis et al. [85] also reported that the pandemic has significantly increased perceived risks and affected the travel intentions of tourists worldwide.
The results of the current study indicated the significant impact of risk perceptions on tourist behaviors (transport behaviors and behaviors towards crowded places, hygiene, and safety) and tourism intentions in the new normal. Risk perceptions do not only affect tourists’ decisions in selecting tourism destinations but also affect tourists’ decisions as to whether they travel or not [30,86,87], which ultimately affects tourists’ intentions [88]. Peri et al. [84] and Peco-Torres et al. [89] discussed the negative impact of various risk perceptions on tourist behaviors and tourism intentions. Chan et al. [90] also explained that the perceived risks of tourists modified the transportation behaviors of people during COVID-19. Thus, the travelers who perceived high levels of risk were less likely to use public transport and more likely to use private or owned transport [91].
According to Vickerman [92], behaviors toward transportation use have changed significantly during the pandemic due to the different levels of perceived risk associated with different modes of transportation. According to the same study, the pandemic has reduced the use of public transport while increasing the use of private transport. According to Pawar et al. [93], approximately 75% of Indian commuters believe public transport is dangerous, which has resulted in a shift from public to private modes. The study also reported that 5% of Indian commuters have shifted from public transport to private cars. A survey conducted in Australia revealed that private modes of transport are more comfortable than public modes, and 42% of respondents referred to the bus as the least comfortable mode during the pandemic [94]. According to De Haas et al. [95], people in the Netherlands prefer to drive rather than take public transport. In Budapest, Hungary, the modal share of public transport has decreased from 43% to 18%, while an unexpected growth from 43% to 65% in car use has been observed in the modal share [37]. Similarly, people perceive a very low risk of viral transmission in private transport modes, such as personal cars, motorcycles, etc., a moderate risk in shared modes, e.g., ridesharing, rikshaws, and autorickshaws, and a very high risk in public transport modes, such as buses [91]. The results of this study contradict those of Liu et al. [96], who contend that a free tourist public transport scheme does not encourage tourists to use public transport. The difference may be due to the fact that the present study was conducted purely in the context of COVID-19, which has changed the world altogether.
Leisure places and tourism destinations play a vital role in human life to maintain quality of life [97], but during the pandemic these places have become very risky and vulnerable to the spread of the virus. Therefore, tourists with high levels of perceived risks are less likely to tour during COVID-19. Bayrsaikhan et al. [98] also reported the negative impact of risk perceptions on selecting crowded destinations for tourism during COVID-19. High levels of perceived risks will require more hygiene and safe destinations in the new normal. The easy and rapid transmission of the virus caused sensitivity among tourists, who preferred highly hygienic and safe tourist destinations [39]. Aydin et al. [99] also noted the increasing concern about hygiene and safety among tourists in Turkey. Therefore, changes in transportation behaviors have negatively impacted tourism intentions. This may imply that avoiding public transport for traveling toward a destination will cause more tourism expenditures (using private or shared transport), which may negatively affect tourism intentions in the new normal.
The fact that attitudes toward tourism policies act as a bridge between risk perceptions and travel plans has important policy implications for bringing the tourism industry back to life and making it more resilient to future crises. The literature on the role of attitudes toward tourism policies in the recovery of the tourism sector is very limited, and the new normal era requires effective tourism policies for attracting tourists and reviving the tourism industry [100]. The tourism policies considered in this study determined that their application and adoption have a significant mediating role between COVID-19 as well as risk perceptions and transportation behaviors, overcrowded destinations, and tourism intentions. For example, effective marketing of tourist destinations may assist in the recovery of tourism after disasters [101], and relevant marketing strategies play an important role in mitigating the perceived risks during COVID-19 [102]. Therefore, the current literature supports offering discounts to motivate tourists to travel toward a particular destination [103]. Promotional marketing strategies were employed to revive tourism by lodging businesses and tourism service providers after many disasters and crises around the globe, such as the 9/11 US terrorist attack, the Bali bombing in 2002, the SARS health crisis in Southeast Asia in 2002–04, a forest fire in Canada, and the Ebola outbreak in 2013–15 in Africa [104,105,106,107,108,109]. Similarly, the pandemic caused fundamental changes in tourism destination communications, and marketers started to use social media for communication during the pandemic [110,111]. Social media is a major source of the dissemination of information concerning the ongoing situation of the pandemic around the world [112], and can be used for marketing in the new normal to revive the tourism industry. Because the perception of a crisis and its magnitude is heavily shaped by media discourses [113,114], communication via social media plays an important role in restoring a destination’s image [115]. Thus, in the new normal, publicizing the benefits associated with visiting a tourist attraction and taking protection measures can positively attract tourists toward visiting a particular destination.
To conclude, this research is not without limitations. Firstly, this study used cross-sectional data for its analyses, and future research is suggested to collect longitudinal data to compare whether travel risk perceptions and attitudes toward tourism policies significantly affect tourism intentions in the new normal. Secondly, the study used an online survey to collect data for this study, and so the respondents may not represent the whole population of world tourists. Future research using face-to-face surveys with other sampling methods is suggested to improve the representativeness of the population. Third, this study was conducted at a time when the world was preparing for the new normal, with some countries lifting or relaxing travel restrictions while others maintained strict travel restrictions. This study ignores cross-country travel restriction differences. It is suggested that cross-country studies take into account the travel restriction differences in the new normal. In addition, this research does not differentiate between domestic and international tourists in measuring risk perceptions and tourism intentions; however, Seyfi et al. [116] suggested that countries follow separate travel restriction policies for domestic and international tourists. This might influence the findings of the study, and future research should consider differences in travel policies when measuring the risk perceptions and tourism intentions of domestic and international tourists.

6. Conclusions and Policy Recommendations

The COVID-19 pandemic has severely harmed both domestic and international tourism. The literature noted the significant decline in the number of tourists due to strict mobility restrictions imposed to curb the pandemic’s spread worldwide. The rapid and easy transmission of COVID-19 from person to person increased the perceived risks among tourists. This caused a change in tourists’ behaviors in terms of transportation selection, hygiene, and safety. This change in behaviors, coupled with high risk perceptions, negatively affected tourists’ intentions of touring during COVID-19. COVID-19 is not over yet, and people will have to live with it in the new normal. This new normal requires effective tourism policies to direct tourists’ intentions and behaviors toward the revival of the tourism sector.
COVID-19 has a significant impact on tourism intentions as well as on risk perceptions in the new normal. The findings also revealed that tourists’ perceptions of risk influenced their behaviors regarding transportation use and hygiene as well as safety. Tourists’ perceptions of risk and transportation use behaviors during the pandemic are also significantly mediated by their attitudes toward tourism policies. Moreover, attitudes toward tourism policies also mediate between COVID-19, risk perception, and sustainable recovery in the new normal. This implies that the provision of incentives such as insurance and discount packages, coupled with publicizing the benefits, services, and safety measures at a destination, will assist in reviving the tourism industry in the new normal.
Based on the aforementioned theoretical background and the findings of the current study, some practical implications are offered for the stakeholders in the tourism industry. To begin with, policymakers and relevant stakeholders should work to reduce external barriers to travel, such as cost and time, in order to revitalize the tourism industry in the new normal period. Countries with world-famous tourist destinations should relax their PCR requirements and mandatory quarantine for vaccinated tourists to accomplish this. Moreover, vaccinated tourists belonging to a country or region with a low number of COVID-19 cases may also be exempted from mandatory quarantine. Moreover, tourism stakeholders may offer discounted lodging packages and products to frequent travelers to a destination in order to attract tourists for sustainable recovery. As a result, tourists are more likely to travel again and share their experiences with their social circle. This will also have a public relations impact on a specific destination and encourage other tourists to visit this place. Second, tourism authorities, destination residents, and related businesses should ensure the hygiene and safety of tourists’ health by wearing masks whenever they communicate with tourists. Furthermore, they should also publicize their hygienic and safe services on social media. They can make short videos that include compliments and positive reviews by tourists about their vacation destinations, which they can share on social media to inform other potential tourists. This publicity will help reduce tourists’ perceived risks and encourage tourists to travel to tourism destinations. Thus, innovative tourism policies can help revive the industry by creating a sense of safety and security among tourists in the new normal.

Author Contributions

Conceptualization, T.S. and S.Z.; methodology, T.S. and S.Z; software, T.S., and Y.L.; validation, S.Z. and Y.L.; formal analysis, S.Z.; investigation, T.S., S.Z., and Y.L.; resources, T.S.; data curation, T.S., and Y.L.; writing—original draft preparation, T.S., S.Z., and Y.L.; writing—review and editing, S.Z. and Y.L.; visualization, S.Z., and Y.L.; supervision, S.Z. and Y.L.; project administration, S.Z. and Y.L.; funding acquisition, S.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is one of the phased results of the general project of the National Social Science Foundation of China in 2020, “Research on Statistical Accounting and Dynamic Monitoring of Regional Tourism under Multi-source Data Fusion” (project no. 20BTJ031).

Institutional Review Board Statement

The study was approved by the Institutional Review Board of Hainan University, China.

Informed Consent Statement

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

Data Availability Statement

The data can be obtained from corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 01323 g001 550
Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
Sustainability 15 01323 g001
Table
Table 1. Backgrounds of the tourists.
Table 1. Backgrounds of the tourists.
Tourist CharacteristicsPercentage
Age (Years)
<3023.41
30–6052.25
>6024.34
Gender
Male51.94
Female48.06
Education (Years)
<89.25
8–1230.40
>1260.65
Ethnicity
Europeans55.65
Asians12.65
Africans7.16
Americans8.88
Others15.66
Marital Status
Unmarried23.30
Married63.98
Divorced9.47
Widow3.25
Annual Income ($)
Low (<8000)32.51
Medium (8000–16,000)30.52
High (>16,000)36.97
Tourist Type
Domestic tourists61.06
International tourists38.94
Table
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
Construct/Item ModeMeanSD
COVID-19 pandemic (CP) 3.991.21
I have low immunity against the COVID-19 pandemic (CP1).22.391.03
COVID-19 badly affected my daily mobility (CP2).43.891.23
COVID-19 had a significant impact on my primary source of income (CP3).54.751.35
COVID-19 changed my lifestyle (CP4). 54.891.26
COVID-19 risk perception of tourism (RP) 3.571.98
I will become infected at the tourist destination (RP1).44.151.96
I will become infected while traveling to a tourist destination (RP2).33.261.88
I perceive that public health precautions will not be strictly enforced in tourist areas (RP3).43.861.79
I perceive that I cannot live in isolation if I am contaminated by COVID-19 at a tourism destination (RP4).32.981.87
Transportation behavior (TB) 3.10 2.27
I try not to use public transportation (TB1).44.092.57
I avoid shared mobility (TB2).22.671.90
I avoid using transport that is not regularly disinfected (TB3).22.531.87
Behavior towards overcrowded places (BOP) 4.02 1.53
I try to avoid visits to overcrowded places (BOP1).44.351.46
I try not to interact with the people at overcrowded places (BOP2).33.311.66
I try to visit markets alone for necessary items purchasing (BOP3).44.231.49
I try to visit crowded places only when its absolute necessary (BOP4)44.171.55
Hygiene and safety behavior (HSB) 4.53 0.90
COVID-19 changed my hygiene and safety behaviours (HSB1).54.870.98
I prefer to stay in hotels and restaurants that adhere to social distancing guidelines (HSB2).44.50.67
I prefer tourist places with better public health measures (HSB3).54.881.09
I like to stay alone after COVID-19.33.530.89
Attitude toward tourism policies for reviving tourism industry in new normal (ATTP) 3.89 1.10
In the new normal, I like the idea of getting free health insurance at the destination during the trip (TP1).54.671.66
The fact that there are testing and quarantine facilities at the destination makes me more likely to visit a tourism destination in the new normal (TP2).43.971.13
Discount travel policies entice me to visit a tourist destination in the new normal (TP3).44.161.09
Publicizing the tourist protection measures helps me make a decision about a tourism destination (TP4).44.31.23
Publicizing the benefits associated with the destination visits encourages me to make a decision in the new normal (TP5).33.140.78
Publicizing tourist assistance resources at the destination in the event of an emergency encourages me to visit the destination in the new normal (TP6).43.871.04
I prefer that the tourism destination have immediate and effective communication sources’ available to the tourist in the new normal (TP7).32.910.74
Tourism intentions in the new normal (TI) 3.32 2.56
I have frequently travelled around the country or the world since the beginning of COVID-19 for tourism (TI1).22.491.99
I am likely to travel around the country or the world in the new normal for tourism (TI2).33.322.68
I intend to travel around the country or the world in the new normal for tourism (TI3).44.132.89
Table
Table 3. Goodness of fit indices.
Table 3. Goodness of fit indices.
Goodness of Fit MeasuresStrutcural Model Results
χ2/df5.17
GFI (goodness of fit index)0.97
CFI (compartive fit index)0.96
AGFI (adjusted goodness of fit index)0.93
NFI (normed fit index)0.95
RMSEA (root mean square error of approximation)0.042
Note: All goodness of fit measures are within the threshold limit, as suggested by Putrevu and Lord [66].
Table
Table 4. Constructs and their validity measurements.
Table 4. Constructs and their validity measurements.
Construct/Associated ItemsFactor LoadingCronbach’s AlphaCRAVEVIF
CP 0.8780.9590.8532.12
CP10.902
CP20.921
CP30.876
CP40.832
RP 0.880.9170.7361.78
RP10.874
RP20.882
RP30.854
RP40.921
TB 0.8550.8690.6891.89
TB10.904
TB20.896
TB30.849
BOP 0.8970.9230.7522.43
BOP10.932
BOP20.857
BOP30.903
BOP40.901
HSB 0.9040.8610.6072.76
HSB10.876
HSB20.857
HSB30.846
HSB40.863
ATTP 0.8950.9330.6652.65
ATTP10.921
ATTP20.897
ATTP30.889
ATTP40.900
ATTP50.922
ATTP60.911
ATTP70.899
TI 0.8030.8590.672
TI10.853
TI20.831
TI30.875
Table
Table 5. Discriminant validity.
Table 5. Discriminant validity.
Fornell–Larcker Criterion
CPRPTBBOPHSBATTPTI
CP0.924
RP0.4530.858
TB0.3240.4360.830
BOP0.5480.5340.2310.867
HSB0.6740.3210.4020.4780.779
ATTP0.6340.6630.2090.6870.5730.815
TI0.5010.4320.3290.4420.2630.3560.819
Heterotrait–Monotrait Ratio (HMR)
CPRPTBBOPHSBATTPTI
CP
RP0.440
TB0.6810.476
BOP0.6550.3200.439
HSB0.5360.2130.5670.201
ATTP0.7120.4730.630.4450.295
TI0.6440.5380.4040.5630.2780.333
Table
Table 6. Path coefficients.
Table 6. Path coefficients.
BetaSDt-Valuef2Q2R2Decision
H1CP → TI−0.7680.039−19.845 *0.1170.4770.613Accepted
H2CP → RP0.6330.03418.509 *0.0920.3640.654Accepted
H3RP → TI−0.7720.040−19.397 *0.0700.4970.531Accepted
H4RP → TB0.7250.0888.239 *0.1430.2100.601Accepted
H5RP → BOP0.6920.05412.839 *0.2080.3090.587Accepted
H6RP → HSB0.5680.04213.492 *0.1380.1930.493Accepted
H7TB → TI−0.2200.078−2.810 *0.1430.2010.502Accepted
H8BOP → TI−0.4020.092−4.355 *0.3840.3420.677Accepted
H9HSB → TI0.3210.2991.0740.2340.2530.633Rejected
H10CP → ATTP0.6020.03716.183 *0.2340.3260.667Accepted
H11RP → ATTP0.5920.05810.278 *0.2640.4430.761Accepted
H12ATTP → TI0.5530.02521.858 *0.1680.5620.702Accepted
H13ATTP → TB−0.4230.034−12.441 *0.2120.2290.551Accepted
H14ATTP → BOP−0.2910.057−5.105 *0.2030.3030.611Accepted
H15ATTP → HSB0.3670.2991.2270.1630.3990.559Rejected
Note: t-value ≥ 2.32 considered significant at * p < 0.01 at 5.
Table
Table 7. Mediating effects.
Table 7. Mediating effects.
BetaSDt-Valuep-ValueDecision
H16aCP → ATTP → TI0.3020.0545.6030.000Accepted
H16bRP →ATTP → TI0.4340.0676.4780.000Accepted
H16cRP → ATTP → TB−0.3390.022−15.4090.000Accepted
H16dRP → ATTP → BOP−0.2160.034−6.3530.000Accepted
H16eRP → ATTP → HSB0.1090.0482.2710.000Accepted
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Zhang, S.; Sun, T.; Lu, Y. The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal. Sustainability 2023, 15, 1323. https://doi.org/10.3390/su15021323

AMA Style

Zhang S, Sun T, Lu Y. The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal. Sustainability. 2023; 15(2):1323. https://doi.org/10.3390/su15021323

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Zhang, Shiqi, Tianwei Sun, and Yuan Lu. 2023. "The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal" Sustainability 15, no. 2: 1323. https://doi.org/10.3390/su15021323

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