The Role of Trust in Mitigating Perceived Threat,Fear,and Travel Avoidance after a Pandemic Outbreak: A Multigroup Analysis

Abstract

Public trust plays a vital role in a public health crisis. Drawing on trust and protection motivation theories, the study explores how people impose trust, fear and perceived threat in postpandemic travel decisions. Employing a quota sampling approach, the research collected from an online survey of 1208 respondents across regions in mainland China. Findings suggest that public trust in tourists significantly mitigates fear, perceived threat and postpandemic travel avoidance. Although trust in government can decrease travel fear, it inhibits travel intentions in a postpandemic era. The study also detects group differences by examining sociodemographic and pandemic region factors, providing practical implications on building tourists’ trust in postpandemic travel.

Keywords

pandemic fear trust threat travel avoidance multigroup analysis

Introduction

The 2019 novel coronavirus (COVID-19) outbreak is an unprecedented health and economic crisis. As of November 2, 2020, the COVID-19 pandemic has caused 1,192,644 deaths among more than 45.9 million confirmed cases in over 210 countries (World Health Organization 2020). Given the rapid spread of COVID-19, most countries have taken strict measures (e.g., travel ban, quarantines, and social distancing) to control population mobility and limit transmission. During the COVID-19 outbreak, tourists suffered a series of unexpected events and were exposed to high infection risks. For instance, due to sudden border closures, international tourists were prohibited from entering destinations, with little advance notice (Kiernan and DeVita 2020). Tourists’ travel plans were severely disrupted owning to unpredictable flight and hotel cancelations. In cruise travel, the close environment and contact between passengers and crew has caused hundreds of COVID-19 confirmed cases among the passengers (Mallapaty 2020). Thus, there is no doubt that tourism is regarded as a high-uncertain and risk activity in this period of COVID-19, which has evoked a wide range of fears in the public regarding traveling.

Public fear during the public health crisis may lead to significant changes in human behaviors, which causes long-term negative impacts on tourism (Bali, Stewart, and Pate 2016). For instance, the outbreak of SARS caused more than 8.6 million outbound trips cancelation in the following year in China. After more than three years of the Ebola epidemic, international tourist arrivals were still 50% less than the arrivals before the outbreak (World Travel and Tourism Council 2018). Currently, the transmissibility, the number of patients, and deaths and duration of the COVID-19 outbreak has surpassed most of the recent infectious diseases. According to the World Tourism Organization (UNWTO 2020), the COVID-19 pandemic may lead to a decline of 20% to 30% international tourist arrivals, resulting in a US$300 to US$450 billion loss in the tourism industry. To revive and improve the resilience of the tourism industry, adopting effective strategies to encourage travel has become particularly crucial in post–COVID-19.

Pandemic travel fear arises from the high potential risks of being infected by a pandemic disease through travel activities. As a defensive emotional response to a threat, tourists who are experiencing pandemic fear tend to avoid travel to reduce dangerous situations (Fennell 2017; Zheng, Luo, and Ritchie 2021). According to protection motivation theory (Rogers 1975), tourists are motivated to adopt protection measures when they perceive severity and susceptibility toward a health threat (J. Wang et al. 2019). While the majority literature has explored the impacts of health risk perception on travel behaviors (e.g., Jonas et al. 2011), research on how to mitigate public’s travel fear and perceived threat in postpandemic travel is lacking.

Trust refers to individuals’ subjective belief that an exchange partner is reliable and competent to deal with a threat (L. Wang et al. 2014). Given the uncertainty and potential risks in a public health crisis, studies argue that trust plays a critical role influencing public’s protection decisions (e.g., Weerd et al. 2011; Smith 2006). In tourism contexts, trust can increase tourist satisfaction, commitment, and revisit intentions toward a destinations (Su, Hsu, and Marshall 2014; Cheng et al. 2017). Although research explored the impacts of trust in tourists’ behaviors in several contexts, such as medical travel (e.g., Abubakar and Ilkan 2016), e-commerce shopping (e.g., Kim, Chung, and Lee 2011), and destination choices (e.g., Artigas et al. 2017), the discussion on public trust in postpandemic travel and its effects on travel behaviors remains sparse.

In a pandemic context, literature on public trust is from three perspectives: (1) government (e.g., Freimuth et al. 2014), (2) media (e.g., Tai and Sun 2007), and (3) other individuals (e.g., S.-F. Fong and Lo 2015). Since governments in tourism destinations are responsible for travel regulations and help guide tourism sectors to manage the public health crisis, tourists’ perceived trust in government may significantly affect their travel risk assessment (L. H. N. Fong, Law, and Ye 2020; Nunkoo, Ramkissoon, and Gursoy 2012). In addition, as media is an important information source for tourists to grasp the situation of the destinations, public’s trust in media may significantly impact their knowledge and understanding about the travel risk (Weerd et al. 2011). Further, travel behaviors put tourists in close contact with other people, which increases risks of being exposed to the pandemic (Gössling, Scott, and Hall 2020). Given the mobility of tourists, the level of public trust in other tourists’ health (i.e., noninfectious) may be linked to their perceived threat of postpandemic traveling. However, tourism research mainly applies tourist trust toward a tourism destination as an universal scale (J. Liu et al. 2019), which fails to identify how different stakeholders impact tourists’ health safety concern in postpandemic travel.

Moreover, most studies assume that tourists perceive trust and respond to a threat as a homogenous group, which fails to identify sociodemographic (e.g., gender and age) and regional background (e.g., Sadique et al. 2007; Jiang et al. 2009) differences in postpandemic travel. Although public’s different responses to a pandemic crisis (e.g., H1N1) have been investigated in western countries, empirical studies in an eastern country remain sparse (e.g., Freimuth et al. 2014). Therefore, it is necessary to identify the heterogeneity factors in impacting public trust, risk assessment, and travel behaviors in a collectivist context.

Given the importance of trust in health crises (Weerd et al. 2011; Freimuth et al. 2014), this research aims to provide insights into understanding tourists’ trust and reactions toward a public health crisis. Since COVID-19 has evoked much more distress and uncertainty than any other disaster or epidemic, research on investigate what psychological factors inhibit people from traveling in a postpandemic era is vital. By applying trust and protection motivation theories, the study establishes a theoretical model to explore the role of trust in eliminating public fear, perceived threat and travel avoidance in a postpandemic period. The study also contributes to knowledge by investigating the moderating role of demographic and regional factors in the link between trust and postpandemic travel behaviors. The research questions include (1) Does public trust in government, media and tourists influence postpandemic travel? (2) Does trust mitigate the public’s fear and perceived threat in postpandemic travel? (3) How does the public’s trust, fear and perceived threat impact their travel avoidance in a postpandemic period? (4) Are there differences among people in their trust, fear, and perceived threat in postpandemic travel?

From a practical perspective, this research may provide important implications for tourism destinations to gain tourists’ trust and enhance their willingness to travel post COVID-19. Since China is one of the countries that experienced and controlled the COVID-19 outbreak, a study based on an investigation of Chinese tourists can provide meaningful insights and recommendations for tourism recovery for many other nations.

Literature Review

Pandemic Fear

A pandemic is an infectious disease occurring across several countries and continents, which is capable of affecting a high proportion of the population in a short period of time (French, Mykhalovskiy, and Lamothe 2018). Because of globalization and increasing mobility, pandemic outbreaks have aroused greater public health challenges in recent decades. Although not all pandemic disease is fatal to humans, several pandemics such as HIV/AIDS (more than 32 million deaths), H1N1 (more than 151,700 deaths), and COVID-19 (139,515 as of 18 April 2020) have become the main health threat to modern society (UNAIDS 2019; World Health Organization 2020, 2010). The widespread transmission, high contagion potential and mortality rate can evoke fear, panic, suspicion and even stigma among the public, which can significantly threaten people’s mental health and daily lives (Strong 1990). Since the public can access various pandemic-related information from media and social network sites, pandemic fear is further fueled by some misleading or exaggerated news (Ioannidis 2020; Harper et al. 2020). Studies found that the number of people who psychologically suffered from pandemic or epidemic disease numbered more than just the infected patients (Reardon 2015). However, literature on pandemic fear generally focused on special groups such as health care workers (e.g., Gershon et al. 2010), patients (e.g., Bristow 2010), or children (e.g., Remmerswaal and Muris 2011), overlooking the general public’s pandemic fear and its induced behaviors.

Fear is a defensive response that motivates individuals to adopt actions against dangerous situations (Ohman 1993). Tourism studies revealed that travel fear is closely linked to tourists’ perceived risks, which may emerge prior, during, or after the trip. Generally, tourists may adopt various strategies such as taking precautions, companionship, learning experiences, or choosing familiar to reduce or remove travel fear (Fennell 2017). If travel fear is too intense, tourists may also choose to avoid travel activities by canceling the trip (Korstanje 2011). In a public health crisis, tourists’ fear of pandemic infection during traveling may evoke a sense of insecurity, which inhibits their intention to travel in the postpandemic period. Since safety and health risk factors are the most important concerns in travel decisions (Dolnicar 2005), several studies have addressed that tourists would exhibit more caution in making travel decisions in the postpandemic period (Cahyanto et al. 2016; Novelli et al. 2018). Specifically, tourists may choose to avoid high-risk destinations (e.g., pandemic infected areas) or tourism types (e.g., cruise and international trips) (Wen, Huimin, and Kavanaugh 2005). Given the positive relationship between fear and threat avoidance (e.g., Andrade and Cohen 2007; Reisinger and Mavondo 2005), we assume that tourists’ travel avoidance can be significantly triggered by their pandemic fear. Thus, we proposed the following hypothesis:

Hypothesis 1: Public’s fear significantly increases travel avoidance.

Perceived Threat in Postpandemic Travel

Perceived threat refers to individuals’ cognition about a danger or harm, which can be determined by two dimensions: threat severity and threat susceptibility (Witte 1992). Threat severity is defined as how serious people think a threat can be, while threat susceptibility is the extent to which people feel they are vulnerable to a particular threat (Menard, Bott, and Crossler 2017). Based on protection motivation theory (Floyd, Prentice-Dunn, and Rogers 2000), people’s protective behaviors can be triggered by their cognitive evaluation of a specific threat. Since the protection motivation model explains the cognitive process of people’s self-protection, it has been widely applied in epidemic and other public health studies. Studies found people’s perceived threat of the epidemic disease can predict prevention actions, such as social distancing (Williams et al. 2015), vaccination (R. Liu et al. 2016), and risk reduction (Dinoff and Kowalski 1999). Moreover, research revealed that people may change to a healthy lifestyle (e.g., organic food and quitting smoking) and actively prevent diseases (e.g., skin cancer prevention) by increasing their perceived health threat (e.g., Scarpa and Thiene 2011; Babazadeh et al. 2017).

In tourism contexts, threat appraisals were applied to investigated tourists’ protective behaviors toward travel risks, such as postdisasters (e.g., Rittichainuwat, Nelson, and Rahmafitria 2018), terrorism (e.g., Bowen, Fidgeon, and Page 2014), and climate change (e.g., Chen et al. 2020). Studies confirmed that protection motivation can be triggered by the perceptions of threat vulnerability and severity, which prevents tourists from traveling to a destination or engaging in risky travel behaviors (J. Wang et al. 2019; Lu and Wei 2019; Zheng, Luo, and Ritchie 2021). Although infectious disease can result in high risks to tourists, there is little discussion on how people assess an epidemic or pandemic threat in postpandemic travel decisions. In this research, tourists’ perceived threat may include their perceived possibility of the pandemic-infection occurrence (i.e., threat susceptibility) and the severe consequences of being infected by infectious diseases (i.e., threat severity) in postpandemic travel. When tourists’ perceived threat of postpandemic travel is high, they may protect themselves by avoiding traveling after the outbreak. Consequently, we proposed the following hypothesis:

Hypothesis 2: Public’s threat severity significantly increases travel avoidance.

Hypothesis 3: Public’s threat susceptibility significantly increases travel avoidance.

Public Trust in Pandemic Crisis

Trust is defined as individuals’ belief in the altruism of another party, or the expectation that the other will perform an important action despite uncertain circumstances (Mayer, Davis, and Schoorman 1995). Sociologists argued that trust can be evoked by reciprocal and continual social exchanges, which contributes to ongoing social relations and individuals’ supportive behaviors (Wilson and Eckel 2011). According to the trust and confidence model, trust plays an essential role in people’s response to a threat by impacting their perceived risks and related benefits (Siegrist, Earle, and Gutscher 2003). Studies found that people tend to rely on trust to reduce complexity when they have limit knowledge to make risk assessment and decisions (Siegrist and Zingg 2014). Given the high uncertainty in a public health emergency, public trust is a particularly important factor in predicting people’s perceived risk, risk-preventive, and compliant behaviors (Blair, Morse, and Tsai 2017; S.-F. Fong and Lo 2015).

Current studies on tourists’ trust have been divided into two main dimensions, including institutional trust (e.g., government and tourism enterprise) and interpersonal trust (e.g., tourists, residents, and guides) (J. Liu et al. 2019). However, it is argued that trust is a multidimensional concept that may be different in various tourism contexts (L. Wang et al. 2014). Although trust has been regarded as an important factor in tourists’ satisfaction (e.g., H. Han and Hyun 2015), loyalty (e.g., Su, Hsu, and Swanson 2017), commitment (e.g, Eastlick, Lotz, and Warrington 2006), and travel behaviors (e.g., Su, Hsu, and Marshall 2014), seldom research has explored in a pandemic context. Generally, public trust in a public health crisis mainly focuses on three perspectives, including trust in government (e.g., Freimuth et al. 2014; Weerd et al. 2011), the media (e.g., Tai and Sun 2007; Wallis and Nerlich 2005), and individuals (e.g., neighbors and experts) (e.g., S.-F. Fong and Lo 2015). As travel safety in postpandemic depend on the credibility of key stakeholders to prevent and control the infectious disease, it is believed that tourists’ trust in government, media, and tourists will significantly impact their travel decisions.

First, since government is responsible for responding and managing a health crisis, public’s trust in government affects their perceived efficacy in coping with the threat (E. Fong and Chang 2011). People’s trust in government may determine their fear and risk perception toward a pandemic outbreak, which further encourages public support and participation in government-recommended actions (B. F. Liu and Mehta 2020). Abundant literature has highlighted the positive relationship between trust in government and public compliance with policies and restrictions, such as taxpaying (Scholz and Lubell 1998), environment protection (Jin and Shriar 2013), organic purchases (Carfora et al. 2019), and community development (Ouyang, Gursoy, and Sharma 2017; Nunkoo and Ramkissoon 2012). Tourism studies certified that destinations’ reputation is linked to public trust in government, which impacts tourists’ risk assessment and travel decisions (e.g., Artigas et al. 2017). Thus, trust in destinations’ government may encourage tourists’ compliance with public recommendations, which lower their perceived threat and fear toward postpandemic travel. Consequently, we proposed the following hypotheses:

Hypothesis 4: Public’s trust in government significantly impacts travel avoidance (a), fear (b), threat severity (c), and threat susceptibility (d) toward postpandemic travel.

Second, public trust in media is crucial in shaping public’s reactions toward a health crisis (Taha, Matheson, and Anisman 2013; Prati, Pietrantoni, and Zani 2011). During epidemic (or pandemic) outbreaks, media (e.g., social media and traditional mainstream media) play a dominant role in transmitting health messages (Choi et al. 2018). Since the media may provide biased, contradictory, or exaggerated information to grasp public attention, people perceive media credibility differently (Tsfati and Ariely 2014). Studies found that when people regard media reports as trustworthy and transparent, they may perceive risks and make decisions in accordance to the media portrayal (Taha, Matheson, and Anisman 2013). Conversely, people may become more skeptical and reluctant to adopt recommended health behaviors if they mistrust the media (Klemm, Das, and Hartmann 2016). Tourism research revealed that tourists mainly apply media information to evaluate health safety and select tourism destinations (e.g., Țuclea, Vrânceanu, and Năstase 2020). In a postpandemic recovery, the media is an important marketing strategy for destinations to increase tourism demand (Bhati et al. 2020). Therefore, tourists who trust in media are more willing to accept information and travel suggestions, and can reduce or eliminate their negative concerns toward travel. Consequently, we proposed the following hypotheses:

Hypothesis 5: Public’s trust in media significantly impacts travel avoidance (a), fear (b), threat severity (c), and threat susceptibility (d) toward postpandemic travel.

Third, infectious disease can cause mistrust in others, which exacerbates public fear and lack of engagement with the community (E. Fong and Chang 2011). Socialists argued that social relations among community members (e.g., residents and group members) may collapse if the community is under stress (Park and Burgess 2019). In epidemic outbreaks, people’s fear of infection or death can further trigger stigmatization and discrimination among special groups of people, intensifying interpersonal mistrust and social conflicts (Strong 1990). In contrast, trust in other people in the community can increase the community’s collective efficacy in dealing with problems, which encourages people’s cooperation in achieving common interests (Welch et al. 2005). In a pandemic context, tourists are under health threat as they may get infected by contacting with asymptomatic individuals during traveling (Hall, Scott, and Gössling 2020). Compared with residents, tourists who travel from other places may cause much higher pandemic transmission risks in destinations (Gössling, Scott, and Hall 2020). Therefore, it is argued that tourists’ trust in other tourists can significantly influence their sense of security and travel motivation. Consequently, we proposed the following hypotheses:

Hypothesis 6: Public’s trust in media significantly impacts travel avoidance (a), fear (b), threat severity (c), and threat susceptibility (d) toward postpandemic travel.

Moderating Effects of Demographic and Regional Factors

Although sociodemographic factors including gender, age, and income are widely employed as key moderating variables in tourist and consumer research, the application in postpandemic travel is lacking (e.g., Huang and van der Veen 2019; Wang, Qu, and Hsu 2016). In tourism crisis context, studies found that female tourists are more sensitive in assessing travel risk than male (Amir, Ismail, and See 2015), who often adopt avoidance strategies to deal with their fear in traveling (Brown and Osman 2017). In parallel, research found that elderly and high income tourists tend to choose avoid travel when they perceive threat in traveling (Garg 2015). Considering the situational differences in epidemic-affected areas, literature also revealed that there were regional variations in terms of people’s precautionary behaviors and risk perceptions (Sadique et al. 2007; Jiang et al. 2009). Moreover, public health research revealed that people’s trust is different among various groups, countries and infectious diseases. When a pandemic crisis occurs, urban and high-income residents perceive higher trust and are more supportive to government actions (Paek et al. 2008). However, seldom research has explored how individual groups evaluate and react differently in postpandemic travel. Thus, we assume that demographic and regional factors can moderate the structural relationships between trust, fear, perceived threat, and travel avoidance. Consequently, we proposed the following hypotheses:

Hypothesis 7: Gender (a), age (b), income (c), and pandemic-affected region (d) moderates the relationships among trust, fear, perceived threat and travel avoidance.

Figure 1 illustrates the conceptual model of this research.

Figure 1.

Hypotheses for the research model.

Methodology

Measurement

The scales of fear, threat severity, threat susceptibility, trust, and travel avoidance were all chosen and adapted from related studies that were statistically validated by quantitative research. The question-statements were measured on five-point Likert scales, ranging from 1 = not at all to 5 = very much for the intensity of each emotion item, or from 1 = strongly disagree to 5 = strongly agree for other measurements. The items referring to respondents’ trust in government, media, and tourists were selected from trust measurement in government (Habibov and Afandi 2015; Baek and Jung 2015), media (Habibov and Afandi 2015; Baek and Jung 2015), and the interpersonal (Komiak and Benbasat 2006), respectively. Respondents were asked to indicate their feelings when thinking traveling after the pandemic (i.e., the period after lifting travel bans). Three fear-related emotions (i.e., frightened, nervous, anxious) were adopted from a fear scale (Block and Keller 1995). To measure respondents’ perceived threat in postpandemic travel, two constructs including threat severity and susceptibility were chosen from items that have been applied in epidemic studies (Masser et al. 2011; Becker 1974; Witte 1996). To investigate respondents’ postpandemic travel intentions, two items from the worry behaviors inventory were adapted to measure respondents’ travel avoidance (Mahoney et al. 2018; Mahoney et al. 2016). Moreover, the study took account of respondents’ sociodemographic information including gender, age, birthplace, current living locations, education, and income level in the survey.

Following a review of the earlier literature and experts’ suggestions, a panel discussion among six tourism academics and undergraduate students was conducted to discuss and ensure the validity of the instruments. Further, the appropriateness of the designed survey was checked with a pilot test using 100 samples. Since all original items were adopted in English, the survey was developed using the translation and back-translation approach aiming for closest semantic equivalence (Behling and Law 2000). First, the survey was translated from English into Chinese by two independent translators. Second, any inconsistencies between the two versions of translations were discussed and revised by a panel discussion among six native Chinese speakers. Finally, the Chinese survey was back-translated by a third person to check the consistency.

Data Collection

Data were collected through a large online survey platform (www.wenjuanxing.com) targeting adult Chinese residents who had experienced the COVID-19 outbreak in China. The validity of Wenjuanxing data for quantitative research has been supported in many studies (e.g., Y. Liu, Yao, and Fan 2020; S. Wang, Hung, and Li 2018). Although online surveys may suffer from some limitations in sample representativeness and low response rates, they provide accessible data given the limitations imposed by quarantine policies during the period of COVID-19. Additionally, they offer critical advantages including geographical reach, cost-effectiveness and high time efficiency. A two-stage survey was conducted from March 1 to 23, 2020. The investigation period started approximately two months after the commencement of the COVID-19 quarantine policy in China (January 23, 2020, in Wuhan) and ended two days before the removal of the national quarantine policy (except in Wuhan city). First, a pilot test was initially launched among 100 respondents who were undergraduate students in China. Second, the formal investigation was conducted by a quota sampling approach. The quota was set based on sociodemographic characteristics (e.g, age and gender) from the population census (China National Bureau of Statistics 2010), the geographical distribution, and the provinces with COVID-19 confirmed cases. A screening question was applied to ensure that all the respondents were Chinese citizens who were currently staying in China during the COVID-19 outbreak (from January 23 to March 23, 2020). In total, 1,235 responses were recorded, of which 1,208 responses were retained as valid data (i.e., excluding incomplete, fast response, and pattern answers) for analysis.

Data Analysis

SPSS (version 23) was employed for descriptive analysis and a common method bias check. Partial least squares structural equation modeling (PLS-SEM) was applied to examine the proposed hypotheses regarding the relationships between trust, perceived threat, fear, travel avoidance, and travel with caution constructs. PLS-SEM has several advantages in predictive research: (1) fewer requirements in sample size and residual distributions; (2) advanced with complex models with higher-order constructs; and (3) minimizes parameter estimation bias (Hair et al. 2016). Since this study aims to test a set of predictive relationships rather than verifying a theory, the study chose the PLS-SEM approach using a SmartPLS (version 3.2.9) software package. The model was assessed following a two-step procedure (i.e., measurement model and structural model). As suggested by Hair et al. (2016), the research ran the standard PLS algorithm and evaluated the significance level of path coefficient by bootstrapping (5,000 subsamples). Further, independent sample t-tests and multigroup analysis (MGA) was conducted to compare individual differences and evaluate the moderators on the proposed structural relationships.

Results

Profile of Respondents

The sample consisted of 49.2% females and 50.8% males. Overall, 44.2% were young respondents who were younger than 34 years while 47.9% were aged between 35 and 49 years. Most of the respondents were well educated, with 77% having received a college or university degree. The sample covered most geographic areas of China. Respondents were from provinces located in the north (11.3%), northeast (8.1%), east (20%), south (16.1%), southwest (10.3%), northwest (7%), and the middle (27.2%) of China. Moreover, 8.7% respondents were from Hubei province, which had more than 60,000 confirmed COVID-19 cases and 36.8% were from other provinces (i.e., Guangdong, Zhejiang, Hunan, and Henan) that had more than 1,000 confirmed COVID-19 cases. Furthermore, 25.6% were from 11 provinces (municipalities) with confirmed COVID-19 cases between 300 and 999 (e.g., Anhui, Jiangxi, and Shandong) and 29% respondents were from provinces where the confirmed COVID-19 cases were below 299 (e.g., Guangxi, Shanxi, and Yunnan) at the time of the research.

Measurement Model

To check for common method bias, the study tested two estimations including a Harman’s single factor test and a nonresponse bias test (Podsakoff et al. 2003). The first factor explained 23.97% of the variance, which was far below the threshold of 50%. Further, the t-test results did not reflect any significant differences between early and late respondents at the 0.01 level (Armstrong and Overton 1977). Thus, common method bias was not a major issue in this research on the basis of the analysis.

The reliability and validity of the reflective measure constructs were assessed and are shown in Tables 1 and 2. All outer loadings were statistically significant and greater than the 0.6 cutoff level (Hair et al. 2016). Cronbach’s α and composite reliability (CR) values all exceeded the threshold of 0.7 (Hair 2010), and the lowest average variance extracted (AVE) value was above 0.5. Thus, internal consistency reliability and convergent validity were supported (Henseler, Ringle, and Sinkovics 2009). All the square root of AVE scores were above the latent variable correlations, which confirmed the convergent validity (Fornell and Larcker 1981). Further, the heterotrait-monotrait ratio (HTMT) of correlations was applied to ensure the discriminant validity. The highest value of HTMT inference was 0.57, which was below the acceptable value of 0.85 (Henseler, Ringle, and Sarstedt 2015).

Table 1.

Measurement Model for Constructs.

	Mean	Standard Deviation	Outer Loading
Trust
Trust in government (Cronbach’s α = 0.73, CR = 0.84, AVE = 0.64)
Sincere in its attempts to control the outbreak	4.23	0.85	0.88
Can be trusted to make sensible decisions for further prevention	4.20	0.80	0.80
Does an efficient job in control and prevention	4.19	0.86	0.71
Trust in media (Cronbach’s α = 0.82, CR = 0.87, AVE = 0.70)
Feel trustworthy about the information	3.95	0.80	0.67
Feel secure using the information	3.72	0.98	0.88
Feel comfortable using the information	3.69	0.98	0.92
Trust in tourists (Cronbach’s α = 0.84, CR = 0.91, AVE = 0.76)
Feel confident that tourists are not infectious	2.87	1.06	0.89
Feel tourists are trustworthy if they are traveling	2.90	1.06	0.89
Believe tourists have high integrity if they are traveling	3.01	1.09	0.84
Fear (Cronbach’s α = 0.85, CR = 0.91, AVE = 0.77)
Frightened	2.87	1.15	0.87
Nervous	2.88	1.14	0.89
Anxious	2.74	1.19	0.88

Perceived threat
Threat severity (Cronbach’s α = 0.70, CR = 0.87, AVE = 0.77)
Infection consequence for tourists	4.13	0.90	0.83
Health threat to tourists	3.52	0.95	0.91
Threat susceptibility (Cronbach’s α = 0.86, CR = 0.91, AVE = 0.78)
Risk of being infected	3.53	1.00	0.88
Possibility of contact with infected tourists	3.49	1.09	0.90
Possibility of contact with infected people (nontourists)	3.43	1.07	0.87
Travel avoidance (Cronbach’s α = 0.72, CR = 0.88, AVE = 0.78)
Avoid traveling in the post–COVID-19 period	3.93	0.88	0.90
Delay making decisions about traveling in the post–COVID-19 period	4.26	0.83	0.87

Note: CR = composite reliability. AVE = average variance extracted.

Table 2.

Assessment of Discriminant Validity: Heterotrait-Monotrait Ratio (HTMT).

	1	2	3	4	5	6	7
1. Trust in government	–
2. Trust in media	0.386	–
3. Trust in tourists	0.236	0.367	–
4. Fear	−0.119	−0.097	−0.246	–
5. Threat severity	−0.041	−0.120	−0.352	0.415	–
6. Threat susceptibility	−0.059	−0.071	−0.401	0.444	0.566	–
7. Travel avoidance	−0.064	−0.053	−0.267	0.267	0.378	0.366	–

Table 3.

Structural Model Assessment.

Hypotheses		β	p Value	SE
Hypothesis 1	Fear → travel avoidance	0.089	0.004**	0.031	S
Hypothesis 2	Threat severity → travel avoidance	0.204	<0.001	0.040	S
Hypothesis 3	Threat susceptibility → travel avoidance	0.166	<0.001	0.037	S
Hypothesis 4a	Trust in government → travel avoidance	0.128	<0.001	0.033	S
Hypothesis 4b	Trust in government → fear	−0.069	0.042*	0.034	S
Hypothesis 4c	Trust in government → threat severity	0.051	0.167	0.037	N
Hypothesis 4d	Trust in government → threat susceptibility	0.012	0.753	0.037	N
Hypothesis 5a	Trust in media → travel avoidance	−0.008	0.822	0.035	N
Hypothesis 5b	Trust in media → fear	0.016	0.654	0.036	N
Hypothesis 5c	Trust in media → threat severity	−0.005	0.885	0.034	N
Hypothesis 5d	Trust in media → threat susceptibility	0.082	0.072	0.045	N
Hypothesis 6a	Trust in tourists → travel avoidance	−0.134	<0.001	0.031	S
Hypothesis 6b	Trust in tourist → fear	−0.236	<0.001	0.033	S
Hypothesis 6c	Trust in tourists → threat severity	−0.359	<0.001	0.028	S
Hypothesis 6d	Trust in tourists → threat susceptibility	−0.434	<0.001	0.031	S

Note: β = Standardized Regression Weight. SE = standardized error; S = support; N = not support.

p <0.01. ^*p <0.05.

Structural Model

The proposed structural model was evaluated using the coefficient of determination (R²), path coefficients (β), predictive relevance (Q²) and effect size (f²) (Table 4). The standardized root mean residual (SRMR), which was below the SRMR <0.08 criterion (Henseler, Ringle, and Sarstedt 2015), was acceptable (0.073). The structural model predicted 12.6% of the construct of threat severity, 16.7% of threat susceptibility, 6.5% of fear, and 13.5% of travel avoidance. Using a Blindfolding test (omission distance = 7), results found the smallest Stone-Gaisser’s Q² value for the constructs was 0.048, which was above the minimum requirement (i.e., zero) (Fornell and Cha 1994). The f² effect size indicates the contribution of a predictor toward an endogenous latent variable. Following Cohen’s (2013) criterion, the effect size can be identified as small (f² > 0.02), medium (f² > 0.15), or large (f2 > 0.35). Trust of tourists had a medium size effect on threat susceptibility (f2 = 0.19), small size effect on threat severity (f² = 0.13), fear (f² = 0.05), and travel avoidance (f² = 0.06). Additionally, fear (f² = 0.05) and trust in government (f² = 0.02) had small size effects on predicting travel avoidance.

Table 4.

Multigroup Analysis.

	Gender, β				Generation, β				Income, β				COVID-19 Infection, β
	Male(n=614)	Female(n=591)	Diff.	p Value^a	X/BB(n=669)	Z/Y(n=539)	Diff.	p Value^a	High(n=684)	Low(n=524)	Diff.	p Value^a	High(n=550)	Low(n=658)	Diff.	p Value^a
Fear→TA	0.09	0.09	0.01	0.92	0.12	0.04	0.08	0.20	0.07	0.10	−0.03	0.63	0.09	0.09	0.00	1.00
TSev→TA	0.19	0.21	−0.02	0.81	0.20	0.21	−0.01	0.91	0.18	0.20	−0.02	0.85	0.27	0.14	0.13	0.11
TSus→TA	0.20	0.10	0.09	0.22	0.14	0.22	−0.08	0.29	0.24	0.08	0.16	0.03*	0.15	0.18	−0.03	0.66
TruG→TA	−0.15	−0.09	−0.06	0.34	−0.09	−0.20	0.12	0.09	−0.13	−0.14	0.01	0.87	−0.16	−0.11	−0.05	0.47
TruG→Fear	−0.05	−0.08	0.03	0.64	−0.08	−0.06	−0.02	0.79	−0.15	0.01	−0.16	0.03*	−0.08	−0.08	0.00	0.94
TruG→TSev	0.09	0.00	0.10	0.20	0.07	0.01	0.05	0.47	−0.07	0.17	−0.23	***	0.04	0.04	0.00	0.95
TruG→TSus	0.10	−0.08	0.18	0.01*	0.02	−0.02	0.04	0.61	−0.07	0.07	−0.14	0.06	0.01	−0.03	0.04	0.62
TruM→TA	−0.03	0.02	−0.05	0.46	0.01	−0.04	0.05	0.48	−0.01	0.06	−0.07	0.28	−0.03	0.01	−0.03	0.62
TruM→Fear	−0.02	0.05	−0.07	0.28	0.01	0.02	−0.01	0.84	0.03	0.04	−0.01	0.86	−0.02	0.05	−0.07	0.35
TruM→TSev	−0.01	0.00	−0.01	0.87	−0.03	0.04	−0.07	0.26	0.04	0.00	0.04	0.48	0.01	−0.01	0.02	0.70
TruM→TSus	0.07	0.10	−0.03	0.71	0.07	0.11	−0.04	0.56	0.02	0.23	−0.21	0.02*	0.08	0.11	−0.03	0.77
TruT→TA	−0.07	−0.22	0.15	0.01*	−0.14	−0.12	−0.02	0.80	−0.19	−0.06	−0.14	0.03*	−0.11	−0.16	0.05	0.43
TruT→Fear	−0.27	−0.20	−0.06	0.34	−0.26	−0.21	−0.05	0.43	−0.27	−0.21	−0.06	0.34	−0.23	−0.24	0.01	0.85
TruT→TSev	−0.34	−0.38	0.04	0.48	−0.38	−0.34	−0.05	0.42	−0.39	−0.30	−0.09	0.12	−0.37	−0.35	−0.01	0.84
TruT→TSus	−0.42	−0.45	0.03	0.61	−0.45	−0.42	−0.04	0.57	−0.49	−0.37	−0.13	0.05*	−0.47	−0.41	−0.06	0.36

Note: TA = travel avoidance; TSev = threat severity; TSus = threat susceptibility; TruG = trust in government; TruM = trust in media; TruG = trust in tourists; Diff. = β difference; β = standardized regression weight.

Permutation p value.

***

p<0.001. ^*p <0.05.

Results indicated that fear (β = 0.089, p < 0.01), threat severity (β = 0. 204, p < 0.001), and threat susceptibility (β = 0. 166, p < 0.001) significantly increased respondents’ travel avoidance, which confirmed hypotheses 1, 2, and 3. Trust in government and other tourists significantly impact travel avoidance (β_government = 0. 128, p < 0.001; β_tourists = −0. 134, p < 0.001) and travel fear (β_government = −0. 069, p < 0.05; β_tourists = −0. 236, p < 0.001), supporting hypotheses 4a, 4b, 6a, and 6b. Only trust in tourists had a negative and significant effect on threat severity (β = −0.359, p < 0.001) and threat susceptibility (β = −0. 434, p < 0.001), supporting hypotheses 6c and 6d (see Table 3).

Multigroup Analysis

To detect individual differences in trust, fear, perceived threat, and travel avoidance, a series of independent sample t-tests among the variables of gender (male vs. female), age (generation X/BB vs. Z/Y), income (monthly income above 10,000 RMB vs. below 10,000 RMB) and COVID-19–affected regions (confirmed cases above 1,000 vs. below 1,000) were employed. Significant differences were found in gender, generation and income groups, but not in COVID-19 infection groups. Results found that females scored higher in fear (M_female = 2.91, M_male = 2.76, p < 0.01), threat severity (M_female = 3.89, M_male = 3.77, p < 0.01), threat susceptibility (M_female = 3.55, M_male = 3.42, p < 0.05), and especially travel avoidance (M_female = 4.21, M_male = 3.99, p < 0.01) than males. Regarding generation, younger respondents scored higher in fear (M_Z/Y = 2.92, M_X/BB = 2.76, p < 0.01), threat severity (M_Z/Y = 3.88, M_X/BB = 3.78, p < 0.05), and threat susceptibility (M_Z/Y = 3.58, M_X/BB = 3.41, p < 0.01) than older respondents. However, trust in government, media, and tourists were not significantly different among gender and generation groups. Additionally, the study found that people who had lower incomes expressed lower trust in media (M_{low income} = 3.70, M_{high income} = 3.85, p < 0.01) and tourists (M_{low income} = 2.81, M_{high income} = 3.01, p < 0.001) and scored higher in threat susceptibility (M_{low income} = 3.90, M_{high income} = 3.77, p < 0.01) in postpandemic travel (see Figure 2).

Figure 2.

Mean comparison between the groups.

To identify the boundary conditions of the proposed model, the study applied multigroup permutation tests to examine the moderating effects of gender, age, income, and COVID-19 infection area. Results revealed that the path between trust in government and threat susceptibility was significantly different in gender (β_male = 0.1 vs. β_female = −0.08, p < 0.01). The negative effects of trust in media on travel avoidance is higher among female than male respondents (β_male = −0.07 vs. β_female = −0.22, p < 0.01). For the high-income group, the path effect between threat susceptibility and travel avoidance was significantly greater than the low-income group (β_{high income} = 0.24 vs. β_{low income} = 0.08, p < 0.05). However, the path effects of trust in government on fear (β_{high income} = −0.15 vs. β_{low income} = 0.01, p < 0.05) and threat severity (β_{high income} = −0.07 vs. β_{low income} = 0.17, p < 0.01) were less in the high-income group than the low-income group. Trust in media has stronger effects on threat susceptibility in higher income group (β_{high income} = 0.02 vs. β_{low income} = 0.23, p < 0.05). Moreover, the impacts of trust in tourists on travel avoidance group (β_{high income} = −0.19 vs. β_{low income} = −0.06, p < 0.05) and threat susceptibility (β_{high income} = −0.49 vs. β_{low income} = −0.37, p < 0.05) are stronger among high-income groups than low-income respondents. Thus, hypotheses 7a and 7c were supported.

Discussion and Implications

COVID-19 has brought global travel to a standstill, and this has caused an unprecedented level of economic recession and public mental stress. To revive tourism destinations and accelerate tourism recovery, it is vital to understand how to eliminate tourists’ negative concerns and encourage travel intentions after the pandemic outbreak. By addressing the significant research gap in investigating tourists’ trust, fear, and perceived threat in postpandemic travel, this study offers important contributions to health-related tourism crisis research. Moreover, this research provides insights by identifying individual differences in public trust, risk assessment, and postpandemic travel decisions.

Theoretical Implications

The study is the one of the first academic attempts to investigate tourists’ fear in postpandemic travel. Earlier research mainly explored tourists’ travel in postdisaster destinations by examining their perceived risks (Chew and Jahari 2014; Rittichainuwat and Chakraborty 2009; Cossens and Gin 1995), neglecting emotional consequences generated by public health crises. Findings revealed that fear significantly increased tourists’ travel avoidance in postpandemics, yet the effect was lower compared with threat and trust. Moreover, the study detected individual differences in fear toward postpandemic travel. In addition to segmentation studies of tourist’s fear (Dolnicar 2005), this research identified that people who were young and female (Generation Z/Y) evoked much higher intensity of fear than senior male tourists (Generation X/BB). However, results found that there was no significant difference among respondents who had different levels of income and pandemic infection experiences (i.e., pandemic infection situation of living place). In line with the confirmed relationship between anxiety and travel intention (Reisinger and Mavondo 2005), the study detected that tourists’ fear evoked by a pandemic outbreak positively increases travel avoidance intention. The research empirically certified travel fear as a constraint in postpandemic travel, which supports previous studies regarding intrapersonal tourism constraints (Gilbert and Hudson 2000; Fennell 2017).

The research also contributes to the protection motivation model by testing the effects of perceived threat on travel behaviors in a postpandemic context. By comparing multiple groups, the study revealed that people with various gender, age, and income levels perceive threat severity and susceptibility in postpandemic travel differently, whereas pandemic infection does not impact their assessment of threat. Additionally, this study initially reveals that the effect of threat susceptibility on travel avoidance is higher among high-income respondents than low-income respondents, which provides empirical evidence of the moderating role of sociodemographics (Cui et al. 2016). Since protection motivation theory has been seldom employed in health-related tourism studies (Ritchie and Jiang 2019), the research indicated the importance of understanding tourists’ assessment of pandemic threats in tourism. The hypothesis concerning perceived threat and travel avoidance was confirmed, which supports prior studies in perceived threat and tourists’ protective behaviors (J. Wang et al. 2019). However, the results are in contrast to Fisher et al.’s (2018) research, which argued that tourists’ handwashing in cruise ships was not related to their perceived threat of norovirus. We assume that the dissimilarity is mainly due to contextual differences between these two studies. COVID-19 is a global pandemic threatening all human beings and people’s perceived threat is higher than that for most infectious diseases (e.g., norovirus). This can have much greater impacts on their protective behaviors. Thus, the research highlights the importance of distinguishing threat stimuli in protection motivation studies.

More importantly, the research is the first to introduce the concept and examines the role of public trust in postpandemic travel. Despite the abundant literature on tourists’ trust in tourism and hospitality sectors (L. Wang et al. 2014), scant research has probed into a health crisis context. The study confirms that trust in government and tourists significantly influence tourists’ trust risk perception and postpandemic travel intentions. Instead of assessing people’s single dimension of trust in an epidemic crisis (Lee 2009; Blair, Morse, and Tsai 2017), the study suggests that the public’s trust in postpandemic travel depends on various stakeholders. Travel after a pandemic outbreak not only refers to personal protective behaviors, but it also relies on interpersonal and institutional efficacy in disease control and prevention. Thus, the context of tourism offers a new perspective for understanding the public’s trust in a postpandemic era. Results found that people perceive much higher trust in government than media and tourists, and that there are no significant differences among groups in gender, age, and pandemic infection experience. Previous studies found that people who experienced hardship during the epidemic demonstrated less trust in government (Blair, Morse, and Tsai 2017; Lee 2009). Although China, especially Hubei province, has experienced difficult times during COVID-19, the study confirms that residents expressed high trust in the government’s efficacy in pandemic control. However, people’s interpersonal trust remains low, which adds new insights into people’s trust in health crises in a collectivist society. In addition, results confirm the mitigatory effect of trust on tourists’ fear and perceived threat. This hypothesis supports existing sociology studies on citizens’ trust and fear of terrorism (e.g., Ramon et al. 2019), which is confirmed for the first time in a health tourism context. Consistent with other tourism research, the study also highlights that tourists’ trust can be an essential antecedent of travel intention (Wang et al. 2014). Although trust in tourists can decrease travel avoidance, the research finds that tourists who trust in government tend to avoid travel in the postpandemic. The results are in line with public compliance with policies in public health studies (Weerd et al. 2011; Q. Han et al. 2020). Since government recommend the public to reduce traveling during the pandemic, tourists may be more cautious in postpandemic travel decisions. Additionally, the study reveals that the effect of trust in tourists on travel avoidance is stronger among female than male respondents, further highlighting the necessity of segmentation.

Managerial Implications

The COVID-19 pandemic has had overwhelming negative impacts on almost every tourist and every sector of the tourism industry. Beyond estimating future tourist demand, it is crucial to understand the psychological factors that will encourage and inhibit tourists to travel after the outbreak. Since China is the first country that was affected and has since recovered from COVID-19, this research can provide important implications for many other countries. Further, as China is the largest outbound travel market, understanding Chinese tourists’ consideration about postpandemic travel can help tourism destinations to establish more efficient marketing campaigns to boost tourism recovery after the pandemic.

The results have shown the importance of alleviating the public’s fear and perceived threat in postpandemic travel. Given the widespread mental stress and panic during COVID-19, tourism agencies and authorities need to highlight travel safety in postpandemic marketing campaigns. As young female tourists may be the largest group who could perceive travel as a threat and choose to avoid travel at the early stage of COVID-19, more supporting service (e.g., emergency consulting and sanitation equipment), for young female tourists needs to be provided to relieve their fear in traveling. Findings also suggest that tourism providers need to demonstrate that they can effectively minimize tourists’ potential infections. Considering the uncertainty of COVID-19 disease, destinations need to strictly control tourism flow even after travel restrictions have been lifted. For instance, scenic spots (e.g., national parks and theme parks) can manage daily tourist numbers by selling online tickets in advance. Tourism providers can offer real-time and transparent information to decrease tourists’ perceived threat of traveling, such as implementing hygiene-related regulations, providing frontline employee health status, and reporting tourist numbers at different scenic spots.

Furthermore, tourists’ trust in government, media, and tourists were shown to be important factors in mitigating their negative concerns toward postpandemic travel. Since tourists may choose follow authorities’ recommendations during the pandemic (e.g., avoid traveling), it is necessary for government to declare travel safety and provide updated travel suggestions in a postpandemic period. For tourism destinations, it is vital to build tourists’ confidence in the local government’s efficacy in control and prevention of future pandemic outbreaks. For instance, tourism authorities can establish a series of policies to regulate the tourism industry’s obligations in dealing with a public health crisis. In the meantime, governments need to be committed to enhancing communication with the public and to protecting tourist rights under pandemic emergencies (e.g., cancelation and refund policies). As media is the main resource for tourists to gain information of tourism destinations, tourism providers can cooperate with official channels (e.g., government-operated social media accounts and research centers) and offer travel safety suggestions via online platforms. Moreover, results revealed that tourism operators need to implement effective regulations to enhance trust among tourists. Since people may become more skeptical and mistrust strangers in postpandemic travel, it is important to promote mutual understanding and avoid tourist conflicts. For example, destinations can popularize basic knowledge of COVID-19, demonstrate precautionary regulations using different languages, and explain cultural differences in pandemic protective behaviors.

Limitations and Future Research

While this research initially investigated the role of public trust in tourists’ postpandemic travel, several limitations need to be addressed and advanced in further research. First, limited by the quarantine COVID-19 regulations in China, the study could only collect data via an online survey. Future studies can implement face-to-face surveys and interviews to further validate and extend the research model. Since this study mainly adopted a quantitative method to test the proposed theoretical framework, future research can apply qualitative or a mixed-method approach to improve the explanations of the model (Khoo-Lattimore, Mura, and Yung 2019). Moreover, further studies can develop specific constructs (e.g., travel avoidance and travel fear) to improve the structural model in postdisaster travel contexts. Second, despite the merits of investigating China, we caution that the study’s results, which were based on a collectivist country, may limit the generalizability of the findings. As trust can be sensitive because of different cultures, political ideologies, and systems, it may be necessary to launch investigations in Western countries to identify people’s differences in trust in postpandemic travel. Third, the research mainly explored the relationship between trust, threat, and fear in tourists’ travel avoidance intentions, and excluded other potential factors (e.g., certainty, coping, and protection motivation) and dependent variables (e.g., actual travel behaviors and protective travel behaviors) that may be relevant to this model. Thus, we suggest that further studies can incorporate other constructs to extend the established model.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the National Natural Science Foundation of China (grant number 41971176) and National Social Science Fund for Art Projects of China (grant number 19ZD26).

ORCID iDs

Danni Zheng

Qiuju Luo

Brent W. Ritchie

Author Biographies

Danni Zheng is an assistant professor in the Department of Tourism at Fudan University, China. She was a postdoctoral research fellow and obtained her PhD degree in School of Business at the University of Queensland, Australia. Her research interests include sociopsychology of tourism, community well-being, crisis tourism management, tourists’ prosocial behaviors, and creative tourism development.

Qiuju Luo is professor and deputy dean of School of Tourism Management, Sun Yat-sen University, China. Researching in event management, social media, and special interest tourism, she has published more than 60 academic articles in major journals. As the popularization of new media in tourism industry, she focuses more on online events besides the offline perspectives and devotes herself to conducting innovative research on social media and its influence in tourism domain.

Brent W. Ritchie is professor and associate dean (research) of Faculty of Business, Economics and Law, The University of Queensland, Australia. His research interests include tourism crisis management, tourist behavior, and residents’ attitudes toward tourism. He is on the editorial board of eight journals including the Tourism Management and Journal of Travel Research.

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