How will multifaceted trust impact domestic travel during the COVID-19 pandemic and subjective well-being? A comparison between Korea and the US.

This study examines the impact of extended tourist trust constructs on domestic travel experiences, subjective well-being, and future travel intention in the pandemic. Data was obtained through a survey conducted on 1181 Korean and American domestic tourists. The results show that policy trust and destination trust have positive effects on travel frequency and satisfaction. Moreover, interactional trust positively impacted travel satisfaction. While both travel frequency and travel satisfaction have positive impacts on subjective well-being of travelers, travel satisfaction has a stronger impact on subjective well-being than travel frequency. However, certain relationships were influenced by national backgrounds (U·S vs. Korea).

Introduction

The coronavirus disease (COVID-19) pandemic has brought about considerable economic challenges to entire industries throughout the world. In particular, tourism and hospitality are among the hardest hit industries. A report by UNWTO on COVID-19-related travel restrictions stated that international tourist arrivals would decline by 58% to 78% this year, which would consequently place 100 to 120 million tourism-related jobs at risk. Furthermore, the recovery of the tourism and hospitality industry is expected to be slower due to diminished vaccine efficacy toward new variants (Estrin, 2021). Thus, travelers would be reluctant to travel for health reasons for a long time (OECD, 2020). Governments and destinations need to adopt new practices to attract tourists in the pandemic age.

To understand which factors promote travel in times of a pandemic, there has been a recent growth of tourism studies that analyze travel decision-making factors, such as health risk perception (e.g., Neuburger & Egger, 2021; Shin, Nicolau, Kang, Sharma, & Lee, 2022; Teeroovengadum, Seetanah, Bindah, Pooloo, & Veerasawmy, 2021), sense of control (e.g., Abraham, Bremser, Carreno, Crowley-Cyr, & Moreno, 2020; Peluso & Pichierri, 2021), and previous travel experience (e.g., Rasoolimanesh, Seyfi, Rastegar, & Hall, 2021; Shin et al., 2022). While some research focuses on the role of trust in influencing travels (Hassan & Soliman, 2021; Rastegar, Seyfi, & Rasoolimanesh, 2021), a multifaceted nature of trust has not been explored and investigated. Thus, this study focuses on the impact of different types of trust on travel experiences in the pandemic.

In tourism, building trust is regarded as a critical factor for the success of destinations and tourism and hospitality businesses (Crotts, Coppage, & Andibo, 2001). Most existing research focuses on trust in business-to-consumer exchange processes such as tourist trust in destinations (e.g., Choi, Law, & Heo, 2016; Wang, Law, Hung, & Guillet, 2014). However, additional trust constructs would help to provide better insight into travel experiences. For example, policy trust could be a main priority of tourists when selecting a destination; if tourists have less trust for a government's travel or safety policies, then they are less likely to travel (Nunkoo, Ramkissoon, & Gursoy, 2012). In addition, building interactional trust with other tourists at destinations or hospitality properties would be crucial, especially during the pandemic since COVID-19 primarily spreads from person to person. If prospective tourists do not think that other tourists follow the safety precautions to reduce the spread of COVID-19, then they are less likely to travel or visit hospitality properties.

This study attempts to provide a comprehensive investigation of the impact of trust on travel experiences by proposing multi-constructs of trust in terms of macro (e.g., policy trust), meso (e.g., destination trust), and micro (e.g., interactional trust) levels. In addition, unlike most research that examines the impact of trust on either travel behavior (e.g., Ouyang, Gursoy, & Sharma, 2017; Seetanah & Sannassee, 2015) or travel satisfaction (e.g., Martínez & Del Bosque, 2013), this study proposes an integrated model and compare the impact of trust constructs on both quantity (travel frequency) and quality (travel satisfaction) of experiential factors. Such approach provides more nuanced insights into the impact of trust on travel experiences.

Another critical construct that needs to be considered is tourists' subjective well-being. While a body of hospitality and tourism research has examined the impact of travel experiences on subjective well-being, most research only focused on how travel frequency enhances the subjective well-being of disadvantaged groups (e.g., senior tourists, patients, etc.) (e.g., Kim, Woo, & Uysal, 2015; Lee & Tideswell, 2005). Given that reduced traveling during the pandemic could be a major cause of poor psychological health (Yang & Ma, 2020), this study examines how travel frequency and satisfaction affect an individual's subjective well-being during a pandemic. Moreover, considering the positive impact of subjective well-being on travel behavior (Wang, Liu, Huang, & Chen, 2020), this study explores the underlying mechanisms of the relationship between past travel experiences during a pandemic and future travel intention after a pandemic by focusing on the mediating role of subjective well-being.

Altogether, the current study aims to answer the following research questions: (1) how do policy, destination, and interactional trust influence travel frequency and satisfaction during a pandemic? (2) How do travel experiences influence subjective well-being during a pandemic? (3) How is future travel behavior determined by subjective well-being and previous travel experiences? and (4) How does subjective well-being mediate the relationship between past travel experiences during a pandemic and future travel behavior after a pandemic? And lastly, (5) How do national backgrounds (South Korea vs. U.S.) moderate the travel process during a pandemic? Based on these research questions, the purpose of this study is to understand the impact of tourist trust on travel experiences, subjective well-being, and future travel intention. Data was primary obtained through an extensive survey conducted on 1181 Korean and American domestic tourists.

This study will provide deeper empirical insights into the impact of the COVID-19 pandemic on domestic travel processes in both South Korea and the U.S. The proposed comprehensive framework of tourist trust constructs contributes to the development of trust theory by empirically examining the role of policy, destination, and interactional trust not only in promoting travel participation but also in enhancing travel satisfaction. This dual approach provides more nuanced insights into the impact of trust on travel quantity and quality. Importantly, the moderating effects of national backgrounds will provide destination marketers in South Korea and the U.S. with practical implications on travel policy and destination management to deal with the repercussions of the pandemic.

Literature review

Tourist trust structures during the pandemic

Previous research puts forward three viewpoints toward understanding trust: social, psychological and economic. From a social perspective, trust indicates a consensus in society; without trust in the established social laws, society cannot operate effectively. From a psychological perspective, trust is required when making subjective decisions based on uncertainty; individuals try to trust themselves or others to minimize uncertainty and anxiety during decision-making. Lastly, from an economic perspective, trust is a mental attitude toward traders; individuals need to trust their traders' reputation, capability, and previous trading experiences (Tang & Jang, 2008). Most scholars adopt the psychological and economic perspective to emphasize the cognitive role of trust in customer decision-making processes. For example, Rousseau, Sitkin, Burt, and Camerer (1998) define trust as a ‘psychological state to accept vulnerability based on positive expectation [from the] behavior of another.’ Moorman, Zaltman, and Deshpande (1992) refer to trust as ‘a willingness to rely on a trader in whom one has confidence.’ These points implicitly or explicitly regard trust as the confidence or belief that consumers have toward others; consumers constantly face uncertainty and risk in their decisions since they have to make a purchase decision before experiencing the product or service. This aspect illustrates how customers need to trust others in their decisions.

Hospitality and tourism research only began to give more scholarly attention to trust in the late 1990s (Wang et al., 2014). Adopting the view of the social exchange theory that trust is essential for building relationships between service providers and customers (Cropanzano & Mitchell, 2005; Gwinner, Gremler, & Bitner, 1998), most existing hospitality and tourism research focused on trust in business-to-tourist exchange processes (Wang et al., 2014) such as trust in online information (e.g., Tang & Jang, 2008), trust in employees (e.g., Kim, Kim, & Kim, 2009), and trust in destinations (Choi et al., 2016). Recently, a growing number of studies have focused on trust in the context of the sharing economy (e.g., trust in host, peer-to-peer platforms, etc.) (Cheng, Fu, Sun, Bilgihan, & Okumus, 2019). These studies mostly examined how building trust between tourists and travel/hospitality service providers influence travel behavior.

Given that building trust is a dynamic process, anything or anybody can be the object of trust (Tang & Jang, 2008). Wang et al. (2014) proposed that further constructs of trust such as institutional trust, public policy trust, and interactional trust need to be considered in hospitality and tourism research. Building on this proposition, the current study argues that trust is a multi-layered concept encompassing micro- (e.g., trust in employees or other customers, etc.), meso- (e.g., trust in destination, organizations, etc.), and macro-level processes (e.g., trust in countries, industries, policies, etc.) (Grayson, Johnson, & Chen, 2008; Rousseau et al., 1998). Generally, most hospitality and tourism research focused on micro- or meso-levels of trust by examining individuals' perceptions of trust in other people (e.g., other tourists, employees, residents, etc.) or destinations (e.g., agency, authorities, etc.) (Liu, Wang, Fang, & Zhang, 2019; Wang et al., 2014). Therefore, this study proposes three constructs of trust in terms of different trust levels—policy trust, destination trust, and interactional trust—to provide a more in-depth perspective.

While policy trust has become a key issue for researchers in broader disciplines, it has not been adequately studied in the field of hospitality and tourism (Nunkoo et al., 2012). It refers to the belief that a policy system or policies will produce preferred outcomes (Miller & Listhaug, 1990). According to the institutional theory, policy trust is determined by the economic and policy performance of governments or institutions (Nunkoo et al., 2012). In this regard, it is dependent upon how people evaluate their government's ability to implement effective policies (Nunkoo, 2015). In general, individuals' policy trust could influence their behavioral responses (Ouyang et al., 2017). Some studies (e.g., Park & Blenkinsopp, 2011; Rudolph & Evans, 2005) found that if people distrust government policies, they are less likely to support and follow them. This explains why people are less likely to travel if they distrust their government's policy responses to COVID-19. Thus, the following hypothesis is suggested.

Policy trust positively affects travel frequency during a pandemic.

Destination trust is an indicator of competence trust that a tourist believes in a destination's ability and capability to provide its advertised services (Martínez & Del Bosque, 2013). Most previous research only focused on the positive impact of destination trust on tourist behavior or revisit intention (Abubakar & Ilkan, 2016; Kim et al., 2009). For example, Abubakar and Ilkan (2016) examined the impact of destination trust on customers' visit intention in the context of medical tourism. They argued that building trust and mitigating prospective medical tourists' concerns regarding the quality of medical services is key to a successful business. Importantly, trust in a destination's safety management determines perceived risk of prospective travelers and their subsequent decision-making behaviors (Eitzinger & Wiedemann, 2008; Teeroovengadum et al., 2021). Shin and Kang (2020) found that hotel customers are less likely to visit a hotel when they don't trust the hotel's safety measures for COVID-19. In this regard, travel during the pandemic would be dependent upon how much travelers trust the COVID-19 response of the destinations' managements. The following hypothesis is proposed.

Destination trust positively affects travel frequency during a pandemic.

Interactional trust refers to an expectancy held by an individual that another individual could be reliable in terms of the latter's behavior and statements (Wu & Chang, 2006). In hospitality and tourism contexts, interactional trust is built through relationships between tourists, residents, and service providers (Liu et al., 2019). While most previous research focused on interactional trust in the relationships between travelers and service providers (Nunkoo & Gursoy, 2016), this study focuses on interactional trust between travelers. Interactional trust between tourists could be an important inhibitor or promoter of travel during the pandemic since it is primarily transmitted from person to person. The risk of infection prompts everyone to follow health practices to protect themselves and others (Centers for Disease Control and Prevention, 2020; Teeroovengadum et al., 2021). Given that people believe that others will behave as they do and abide by safety measures (Lewis & Weigert, 1985), interactional trust could be a significant determinant of travel behavior during a pandemic. The following hypothesis is suggested.

Interactional trust positively affects travel frequency during a pandemic.

Trust, along with its economic outcomes, usually plays a role in customer satisfaction. A large number of tourism studies have examined the positive impact of destination trust on satisfaction (Martínez & Del Bosque, 2013). Once tourists trust the capacity of destinations in providing expected travel and hospitality services, they are likely to establish a positive destination image, resulting in a satisfying travel experience (Liu et al., 2019). This study argues that the said rule also applies to policy and interactional trust. When tourists highly trust government responses and policies on COVID-19, they are more likely to enjoy their travels with a high degree of safety relief. Additionally, given that travel experiences are highly interactive (Shin, Perdue, & Pandelaere, 2020), tourists would be more satisfied with their travel experiences if they trust that other travelers follow COVID-19 safety measures. Therefore, this study asserts that there is a positive relationship between the three types of trust constructs and satisfaction with travel experiences. The following hypotheses are proposed.

Policy trust positively affects travel satisfaction during a pandemic.

Destination trust positively affects travel satisfaction during a pandemic.

Interactional trust positively affects travel satisfaction during a pandemic.

The impact of travel experiences on subjective well-being and future travel intention

Leisure and travel experiences can play a significant role in enhancing tourists' quality of life and subjective well-being (Neal, Sirgy, & Uysal, 1999). Subjective well-being pertains to the degree to which individuals evaluate their own cognitive (life satisfaction) and affective aspect (happiness) of life (Sirgy, 2010). The bottom-up spillover theory of subjective well-being (Sirgy & Lee, 2006) explains the impact of travel experiences on subjective well-being. It posits that effects related to a consumption experience associated with community, family, work, and travel can generally influence one's well-being or satisfaction with life (Sirgy, Kruger, Lee, & Yu, 2011). Notably, Chen, Huang, and Petrick (2016) argued that travel experiences could improve one's happiness and well-being. Some critical factors (e.g., travel satisfaction, travel reflections, travel frequency, and different phases of travels) should be considered to understand its impact on subjective well-being or quality of life (Neal, Sirgy, & Uysal, 2004).

Travel frequency could influence subjective well-being: a higher frequency leads to a higher subjective well-being. Furthermore, previous studies have reported on the benefits of travel frequency on subjective well-being. For example, Milman (1998) found that travelers' psychological well-being improved after participating in travel activities. In addition, Gilbert and Abdullah (2004) saw a significant improvement in holidaymakers' subjective well-being after holidays. Neal, Uysal, and Sirgy (2007) found that length of trips is related to subjective well-being, with longer trips having a stronger impact on subjective well-being. These results confirm that a higher travel frequency boosts tourists' subjective well-being. The following hypothesis is proposed.

Travel frequency during a pandemic positively affects travelers' subjective well-being.

Subjective well-being is an integrative concept that incorporates satisfaction in sub-domains of life and overall satisfaction with life (McCabe & Johnson, 2013). Moreover, satisfying travel experiences provide opportunities to build social networks, develop positive emotions, and acquire necessary skills, resulting in improved subjective well-being (Wang et al., 2020). A growing number of studies have looked into the theoretical relationship between travel satisfaction and life satisfaction. Initially, Neal et al. (1999) examined the positive impact of satisfaction derived from trip reflections on overall life satisfaction. Building on this research, Sirgy et al. (2011) found that positive and negative memories of travel experiences significantly affect tourists' overall life satisfaction. Chen et al. (2016) explored the mediating role of travel satisfaction on the relationship between holiday recovery experiences and life satisfaction. Nonetheless, most existing research only focuses on the consequences of travel satisfaction, such as destination loyalty (del Bosque & San Martín, 2008) and behavioral intention (e.g., revisit, repurchase, recommendation intention, etc.) (e.g., Baker & Crompton, 2000; Williams & Soutar, 2009). Further empirical research must be conducted to examine the link between travel satisfaction and life-level constructs such as overall happiness and subjective well-being. Accordingly, the following hypothesis is proposed.

Travel satisfaction during a pandemic positively affects travelers' subjective well-being.

This study suggests that tourists' subjective well-being can promote future travel intention. Existing research claim that travelers are likely to travel again if they obtain higher levels of subjective well-being from past travels. For example, Kim et al. (2015) found that elderly tourists intend to travel again if their quality of life was enhanced after previous travels. In addition, Wang et al. (2020) found that subjective well-being mediates the relationship between destination fascination and destination loyalty. Its positive impact on future travel intention will be certainly be strengthened during the pandemic since most people travel less due to travel restrictions and social distancing measures. Once tourists experience higher levels of subjective well-being after traveling during the pandemic, they are likely to travel again to re-gain subjective well-being when the pandemic is over. To add further empirical insight into this relationship, the following hypothesis is postulated.

Travelers' subjective well-being positively affects future travel intention.

Lastly, following the cross-cultural approach adopted by Park, Jun, and Lee (2015), this study also examined the moderating effects of west and eastern culture in the proposed process by focusing on travelers with two different national backgrounds - South Korea and the United States (U·S.) for two reasons. First, the cultural difference between the U.S. and South Korea (Hofstede & Bond, 1984; Kim, Sohn, & Choi, 2011) will influence how they trust and engage in travel in different ways. For example, travelers in South Korea will highly consider interactional trust in travel decision-making and experience due to their collectivist culture; trust about how other travelers behave in destinations will influence their travel participation and experiences. On the other hand, the individualistic culture of the U.S. will make the U.S. travelers be less concerned about how others behave in destinations, resulting in its limited impact on travels.

Next, the different management styles in coping with the pandemic between the two countries may cause differences in travel patterns. Specifically, South Korea has successfully dealt with COVID-19 by encouraging voluntary public cooperation for social distancing measures without implementing lockdowns or travel restrictions until 2021 (Fisher & Choe, 2020). On the other hand, the US was one of the worst-hit countries, with steadily increasing positive cases during the pandemic. Some of the most affected states in the US (e.g., New York, California, New Jersey, etc.) have imposed statewide lockdowns to reduce the spread of infection. Thus, the difference in COVID-19 management styles and the different pace of the pandemic evolution may influence how citizens in both countries engage in domestic travels and how they trust especially government COVID-19 policies in a different way. Considering them, this study proposes that the hypothesized paths would be moderated by the cultural difference (South Korea vs. the U.S.). The following hypothesis is postulated.

National backgrounds (South Korea vs. the U.S.) have a moderating impact on the hypothesized paths.

Fig. 1 presents the research model of this study. This model explains the relationships between tourist trust, travel experiences, subjective well-being, and future travel intention.

Fig. 1

Travel process during COVID-19.

Method

Sample and data collection

The target population of the study was tourists from South Korea and the United States of America (US). Considering the different situations of COVID-19 evolution in South Korea and the USA and their cultural differences, it was assumed that studying both countries would help provide a more comprehensive representation of the impact of COVID-19 on travel globally. A self-administered survey was conducted by distributing copies of the questionnaire to the Korean tourist sample in the third quarter of 2020. A leading South Korean research company (Macromill Embrain - www.embrain.com) that specializes in panel surveys assisted with the data collection. Given that the company provides rigorous data collection services based on more than 3 million panelists as of December 2020, a large number of published research used the company for data collections (e.g., Kim, Lee, & Jung, 2020; Susanto, Chang, & Ha, 2016). Each participant who successfully completed the survey was financially rewarded (i.e., approximately $2 per participant).

Amazon Mechanical Turk users were invited for data collection from American tourists in the same period. Amazon Mechanical Turk is a reliable and valid source of data in terms of data quality and generalizability, and it is increasingly used within tourism and broader marketing research (Cheung, Burns, Sinclair, & Sliter, 2017; Wong, Newton, & Newton, 2014). Participants who successfully completed the survey were financially rewarded (i.e., $1 per participant).

To limit the sample to those who traveled domestically within South Korea and USA since the first major COVID-19 outbreaks in February 2020, a screening question was included in the beginning of both surveys. Given international travel restrictions during the pandemic, this study decided to focus on domestic travel. In Korea, 680 tourists participated in the survey and a total of 664 responses were used after removing insufficient responses (i.e., those who failed to answer the attention check questions correctly). In the US, 542 tourists participated in the survey and a total of 517 responses were used. Insufficient responses were identified through the same method.

Survey instruments

The survey instruments were developed by adopting and slightly modifying existing measures to fit into the pandemic context. All measures were based on five-point Likert scales (1 = ‘strongly disagree’ and 5 = ‘strongly agree’). The instrument was initially developed in English for the USA sample. After several cross-reviews by two bilingual translators, all items were translated into Korean. Four authors of the current study reviewed and confirmed all the items. Lastly, two versions of survey items in English and Korean were developed.

In terms of trust measures, both destination and interactional trust items (four items each) were adopted from existing scales (Choi et al., 2016; Liu et al., 2019). To measure policy trust, four items were developed after modifying existing scales (e.g., Grimmelikhuijsen & Knies, 2017; Nunkoo et al., 2012).

Travel frequency was measured using two items adopted from existing scales (Jang, Bai, Hu, and Wu; Li and Cai, 2012). In terms of satisfaction, following Baker and Crompton (2000) that the perception model performs better than the disconfirmation model in terms of model fit, four items capturing perceived satisfaction were adopted from existing scales (Eid, El-Kassrawy, & Agag, 2019). Specific item descriptions are provided in Appendix I.

Lastly, subjective well-being was measured using the four items (Kim, Lee, Uysal, Kim, & Ahn, 2015; Su, Swanson, & Chen, 2016) which captured both subjective and affective aspects of subjective well-being. Furthermore, three items were used to measure future travel intention (Meng & Cui, 2020). Six professors and four Ph.D. researchers in tourism reviewed each item and verified their consistency and clarity to enhance the face validity of all items.

Pre-test

Although this study proposes three constructs of trust based on previous studies, it is the first one to examine the three trust constructs in a single model. Therefore, a pre-test was conducted on a panel sample of 150 Korean domestic tourists to check the validity and reliability of the structure. Exploratory factor analysis (EFA) with varimax rotation was conducted to identify the trust dimensions and Cronbach's alpha values was analyzed to examine the reliability of each dimension.

The skewness and kurtosis of all items were reviewed to check whether the examined data follow the normality assumption. All items' skewness and kurtosis values were between −1 and 1; therefore, the data appeared to meet the normality assumption (Hair, Black, Babin, Anderson, & Tatham, 1998). In addition, the Kaiser–Meyer–Olkin (KMO) adequacy test and Bartlett's test of sphericity were conducted to assess the appropriateness of the factor analysis and sample size (n = 150). The KMO and sphericity values were 0.872 and 1277.18 (p < .001), respectively. These results indicate that the factor analysis was appropriate (Tabachnick & Fidell, 2001).

The factor analysis yielded three dimensions of trust (policy, destination, and interactional trust) with eigenvalues greater than 1. The three dimensions explained 76.9% of the total variance. All items' factor loadings were above 0.5 and each dimension was reliable with their Cronbach's alpha (α) values higher than 0.8 (policy trust α = 0.95; destination trust α = 0.90; interactional trust α = 0.91) (Hair et al., 1998). The EFA results indicated that the proposed constructs of trust are acceptable in terms of construct validity and reliability.

Result

Demographics of the sample

Among the 664-respondent Korea sample, 360 (54.2%) were male and 304 were female. Respondents were almost equally distributed in terms of age. Most of them had bachelor's degrees (68.5%) and were white-collar workers (32.1%). In terms of monthly income, 20.9% earned $2000 to $2999 and 20.6% earned $3000 to $3999. Among the 517-respondent USA sample, 296 (57.3%) were male and a majority of them were in their 20s (42.7%). Similar with the Korea sample, most respondents had bachelor's degrees (63.6%) and were white-collar workers (32.5%). In terms of monthly income, 21.7% earned $4000 to $4999, 20.5% earned $3000 to $3999, and 15.7% earned 2000 to $2999.

Testing the structural model

To check for common method bias, Harman's single-factor analysis was conducted using EFA. The total variance for a single factor was 36.11%, which is less than 50%; therefore, the data are unaffected by common method bias (Chang, Witteloostuijn, & Eden, 2010). Normality assumption was then tested for all samples. Skewness and kurtosis values of most items were within absolute value 1 and some values were slightly off from the absolute value 1, indicating that the data appeared to have a normal distribution (Cain, Zhang, & Yuan, 2017). The results of the descriptive data analysis are updated in Appendix II.

Following a two-step approach (Anderson & Gerbing, 1988), CFA was conducted to test the dimensional structures of the proposed model. The model had an acceptable model fit: χ2 = 814.873, df = 254, χ2/df = 3.208, p < .001, goodness-of-fit index (GFI) = 0.953, comparative fit index (CFI) = 0.980, Tucker–Lewis index (TLI) = 0.975, incremental fit index (IFI) = 0.941, root mean square error of approximation (RMSEA) = 0.039 (Schumacker & Lomax, 2004). All items had standardized factor loading values greater than 0.6 and critical ratio (CR) values ranged from 23.31 to 49.22 (p < .001). In terms of reliability, the seven constructs had values higher than 0.9 (Hair et al., 1998).

Convergent validity was verified by calculating the average variance extracted (AVE) values and composite reliability values. Since all the AVE values of the constructs were greater than 0.5, convergent validity was confirmed. AVE values were also compared with the squared correlation values to assess discriminant validity. Since all the AVE values were greater than the squared correlation values, discriminant validity was confirmed (Bagozzi, Yi, & Nassen, 1998) (Table 1, Table 2 ).

Table 1

Confirmatory factor analysis, descriptive analysis, and reliability.

Variables and measurement items	Standardized factor loading
Policy trust (composite reliability = 0.94, α = 0.947)
From the outbreak of COVID-19 up until now,
I have trusted the Korean/American government's policies on COVID-19.	0.895
The Korean/American government's policies on COVID-19 have been reliable.	0.915
I have trusted that the Korean/American government's policies can effectively address COVID-19.	0.900
Overall, I have believed in the effectiveness of COVID-19 policies in South Korea/the US.	0.907
Destination trust (composite reliability = 0.91, α = 0.945)
In terms of destination (e.g., attractions, hotels, resorts, etc.) management in dealing with COVID-19,
I have trusted the destinations at all times.	0.812
I have considered the destinations to have a high level of integrity.	0.836
I have believed that the destinations do everything that they can to fulfill the promises they make.	0.786
Overall, I have believed in the reliability of the destinations.	0.880
Interactional trust (composite reliability = 0.91, α = 0.908)
In terms of other travelers during the COVID-19 pandemic,
I have trusted that they would avoid close contact and maintain physical distancing.	0.832
I have trusted that they would protect themselves and others by wearing a mask or face shield in public places.	0.818
I have believed that they would follow the essential safety precautions.	0.855
Overall, I have trusted in other tourists.	0.868
Travel frequency (composite reliability = 0.90, α = 0.889)
From the outbreak of COVID-19 up until now,
How much have you traveled within South Korea/ the US?	0.960
How frequently have you traveled within South Korea/the US?	0.874
Travel satisfaction (composite reliability = 0.92, α = 0.890)
From the outbreak of COVID-19 up until now,
My overall evaluation of my travel experiences is positive.	0.821
I feel good about my travel experiences.	0.811
I am satisfied with the travel experiences.	0.806
I am pleased with my travel experiences.	0.836
Subjective well-being (composite reliability = 0.93, α = 0.903)
In general, I consider myself a very happy person.	0.869
Compared to most of my peers, I consider myself to be happier.	0.816
I generally very enjoy my life.	0.851
In general, I feel good about my life.	0.822
Domestic travel intention (composite reliability = 0.92, α = 0.879)When the pandemic is over,
I am planning to travel around South Korea/the US within the next 12 months.	0.870
I would like to travel around South Korea/the US within the next 12 months.	0.805
I intend to make time and save money to travel around South Korea/the US within the next 12 months.	0.851

Table 2

Correlation matrix, descriptive analysis, and average variance extracted.

Variable	1	2	3	4	5	6	7	AVE
1	1.00							0.799
2	0.59	1.00						0.718
3	0.53	0.74	1.00					0.716
4	0.42	0.40	0.33	1.00				0.817
5	0.31	0.50	0.49	0.43	1.00			0.737
6	0.38	0.42	0.42	0.36	0.23	1.00		0.766
7	0.30	0.28	0.28	0.42	0.25	0.30	1.00	0.783
Mean	3.49	3.32	3.40	2.71	3.56	3.67	3.89
SD	0.98	0.81	0.87	0.90	0.73	0.74	0.73

Note. 1. Policy trust, 2. Destination trust, 3. Interactional trust, 4. Travel frequency, 5. Travel satisfaction, 6. Subjective well-being, and 7. Future travel intention.

The proposed hypotheses were tested using Structural Equation Modeling (SEM) for two reasons. First, SEM is a useful multivariate method used to test hypotheses regarding the influences among interacting variables (Smelser & Baltes, 2001). Second, SEM has advantages over traditional multivariate analyses, such as its explicit assessment of measurement errors, efficient estimation of latent variables, and effective evaluation of the fit of the data. Thus, we assumed that SEM is the optimal method to test the proposed hypotheses (Urbano, 2013).

The following results confirm the acceptability of the model fit: χ2 = 1114.227, df = 263, χ2/df = 4.237, p < .001, GFI = 0.927, CFI = 0.961, IFI = 945, TLI = 0.956, RMSEA = 0.052 (Schumacker & Lomax, 2004). Policy trust (β = 0.222, t = 6.005, p < .001) and destination trust (β = 0.243, t = 4.702, p < .01) were found to have a positive and significant impact on travel frequency. On the other hand, interactional trust did not have a significant impact on travel frequency (β = 0.050, t = 1.041, p = .298). In terms of the impact of trust on travel satisfaction, policy trust (β = 0.155, t = 4.371, p < .001), destination trust (β = 0.246, t = 4.949, p < .001), and interactional trust (β = 0.242, t = 5.215, p < .001) had a positive and significant impact on travel satisfaction. To sum up, Hypothesis 1, Hypothesis 2, Hypothesis 4, Hypothesis 5, Hypothesis 6 were supported, while Hypothesis 3 was rejected.

Travel frequency and travel satisfaction had a positive and significant impact on subjective well-being (β = 0.101, t = 3.250, p < .001 and β = 0.354, t = 10.730, p < .001). Additionally, subjective well-being had a positive and significant impact on future travel intention (β = 0.315, t = 9.790, p < .001). These results support Hypothesis 7, Hypothesis 8, Hypothesis 9 (Table 3 and Fig. 2 ).

Table 3

Regression paths of the structural model (n = 1181).

Hypotheses	Standardized path coefficient (p-value)	Critical ratio (=t-value)	Results
H1	0.222**	6.005	Supported
H2	0.243**	4.702	Supported
H3	0.050(0.298)	1.041	Not supported
H4	0.155***	4.371	Supported
H5	0.246***	4.949	Supported
H6	0.242***	5.215	Supported
H7	0.101***	3.250	Supported
H8	0.354***	10.730	Supported
H9	0.315***	9.790	Supported
Total variance explainedR2 for travel frequency = 0.204R2 for travel satisfaction = 0.316R2 for subjective well-being = 0.153R2 for domestic travel intention = 0.101

Note. ***p < .001, **p < .01.

Fig. 2

Structural path model.

Note. ***p < .001, **p < .01.

Mediation analysis

To further provide empirical insights, the mediating role of subjective well-being was examined by bootstrap method (bootstrap samples = 2000, confidence level = 95%) (Macho & Ledermann, 2011). For the relationship between travel frequency and future travel intention, the results indicated that the partial mediating model (χ2 = 117.682, p < .001; CFI = 0.985, TLI = 0.978, RMSEA = 0.058) was a better fit than the full mediating model (χ2 = 159.476, p < .001; CFI = 0.979, TLI = 0.970, RMSEA = 0.068). The total difference in χ2 was 41.79 (p < .001) and partial mediation effects were confirmed (total effect: β = 0.252, p < .01; direct effect: β = 0.195, p < .01; indirect effect: β = 0.057, p < .05). Regarding the relationship between travel satisfaction and future travel intention, the partial mediating model (χ2 = 155.694, p < .001; CFI = 0.986, TLI = 0.981, RMSEA = 0.049) was a better fit than the full mediating model (χ2 = 271.850, p < .001; CFI = 0.971, TLI = 0.962, RMSEA = 0.068). The total difference in χ2 was 116.156 (p < .001) and partial mediation effects were identified (total effect: β = 0.424, p < .01; direct effect: β = 0.361, p < .01; indirect effect: β = 0.062, p < .05).

Moderating effect of national backgrounds

A multi-group chi-square difference test was conducted to examine the moderating effects of sample nationality on the hypothesized nine paths. Table 4 presents a summary of the results of this test.

Table 4

Moderating effect of national backgrounds.

Model	Chi-Square Statistics	Chi-Square Difference	Chi-Square Sig. Difference	Coefficients (T Values)
				US (n = 517)	Korea (n = 664)
Mb: Baseline Model	1483.913 (df = 526)
M1: Policy trust → Travel frequency	1488.981 (df = 527)	Δx2 = M1 – Mb = 5.07	Yes	0.253 (3.638)***	0.117 (2.408)*
M2: Destination trust → Travel frequency	1492.380 (df = 527)	Δx2 = M2 – Mb = 8.47	Yes	0.376 (4.820)***	0.166 (2.382)*
M3: Interactional trust → Travel frequency	1484.420 (df = 527)	Δx2 = M3 – Mb = 0.51	No	−0.032 (−0.434)	0.043 (0.681)
M4: Policy trust → Travel satisfaction	1483.983 (df = 527)	Δx2 = M4 – Mb = 0.07	No	0.110 (1.686)	0.105 (2.290)*
M5: Destination trust → Travel satisfaction	1512.987 (df = 527)	Δx2 = M5 – Mb = 29.07	Yes	0.524 (6.918)***	0.029 (0.441)
M6: Interactional trust → Travel satisfaction	1491.118 (df = 527)	Δx2 = M6 – Mb = 7.20	Yes	0.099 (1.426)	0.387 (6.305)***
M7: Travel frequency → Subjective well-being	1491.710 (df = 527)	Δx2 = M7 – Mb = 7.80	Yes	0.037 (0.754)	0.155 (3.643)***
M8: Travel satisfaction → Subjective well-being	1484.360 (df = 527)	Δx2 = M8 – Mb = 0.45	No	0.451 (8.276)***	0.308 (7.390)***
M9: Subjective well-being → Future travel intention	1503.661 (df = 527)	Δx2 = M9 – Mb = 19.75	Yes	0.479 (8.937)***	0.247 (6.045)***

Note. ***p < .001, *p < .05.

In terms of the relationship between trust and travel frequency, national backgrounds moderated the relationship between policy trust and travel frequency. A stronger relationship was observed among the American travelers (β = 0.253, p < .001) than Korean travelers (β = 0.117, p < .05). National backgrounds moderated the impact of destination trust on travel frequency. The relationship was stronger among American travelers (β = 0.376, p < .001) than Korean travelers (β = 0.166, p < .05). As for the relationship between trust and satisfaction, the American group (β = 0.524, p < .001) had a significant coefficient in the relationship between destination trust and travel satisfaction while the Korean group did not. While interactional trust had a positive relationship with travel satisfaction (β = 0.387, p < .001) in the Korean group, no significant relationship was found in the American group.

A significant difference was found in the relationship between travel frequency and subjective well-being in both countries. The Korean group had a significant and positive relationship (β = 0.155, p < .001) whereas the American group showed no significant relationship. National backgrounds also moderated the relationship between subjective well-being and future travel intention, with a stronger relationship observed among American travelers (β = 0.479, p < .001) than Korean travelers (β = 0.247, p < .001) (see Fig. 3 ). Thus, hypothesis 10 was partially supported.

Fig. 3

Moderating effect of national backgrounds.

Note. (U) = USA; (K) = Korea.

Note. ***p < .001, **p < .01.

Conclusion and discussions

Theoretical implications

The study results have several theoretical implications. First, the proposed trust structures could contribute to developing trust theory in hospitality and tourism contexts. While trust has increasingly received academic attention, most research only focuses on trust between tourists and destinations (e.g., Abubakar & Ilkan, 2016; Liu et al., 2019). Following the suggestion to explore other trust constructs in hospitality and tourism (Wang et al., 2014), this study puts forward policy, destination, and interactional trust. In terms of social exchanges between stakeholders, the significant impact of trust on travel experiences indicate that building trust among tourists, governments, and destinations is critical for promoting travel and enhancing travel experiences during the pandemic. The trust framework proposed in this study could serve as a theoretical tool to understand how trust promotes travel and improves travel satisfaction when most prospective tourists are reluctant to travel during such unstable times.

Second, this study adopted a dual approach to examine travel experiences in terms of travel frequency and travel satisfaction. While most previous research only looked into the impact of trust on either travel behavior (e.g., Abubakar & Ilkan, 2016; Ouyang et al., 2017) or travel satisfaction (e.g., Chang, 2014; Martínez & Del Bosque, 2013), this study examined and compared the impact of trust constructs on both experiential factors. For example, policy and destination trust has positive impact on both travel frequency and satisfaction whereas interactional trust only has a positive impact on travel satisfaction. These results demonstrate that trust constructs have different effects on the quantity and quality of travel experiences.

Third, this study provides further empirical evidence on the relationship between travel and subjective well-being. Although a body of hospitality and tourism research has focused on the impact of travel on life satisfaction or quality of life (e.g., Chen et al., 2016; Neal et al., 1999), only a few studies have been conducted on the connection between travel experiences and subjective well-being. The study results support the bottom-up spillover theory of subjective well-being indicating that travel experiences improve individuals' subjective well-being (Sirgy & Lee, 2006). Notably, this study found that travel satisfaction has a stronger impact on subjective well-being than travel frequency. While travel satisfaction is essential for successful destination management due to its positive impact on destination choice, revisit, recommendations, and positive word of mouth (Neal & Gursoy, 2008; Yu & Goulden, 2006); the study results indicate that the travel quality (satisfaction) matters more than travel quantity (frequency) for improving subjective well-being. Given that travel frequency is highly restricted during a pandemic, enhancing travel quality instead of travel frequency would be the key for successful travel design.

Fourth, this study shows that tourists with higher levels of subjective well-being are more likely to travel again. The study results empirically support existing research, which proposed a positive connection between subjective well-being and future travel intention (e.g., Kim et al., 2015; Wang et al., 2020). Importantly, the mediating effects of subjective well-being in the relationship between travel experiences (frequency and satisfaction) and future travel intention indicate that travel could be an antecedent and consequence of subjective well-being simultaneously. Future hospitality and tourism research needs to focus on this connection and explore additional factors that influence future travel behavior in the context of a pandemic.

Lastly, this study found moderating effects of national backgrounds (South Korea vs. U.S.) on some of the hypothesized links. The impact of policy trust on travel frequency was stronger among the American travelers than Korean travelers. This indicates that the American travelers are more sensitive about COVID-19 policies when they decide to travel during the pandemic. The stronger impact of destination trust on travel frequency and satisfaction in the American group indicates that the American travelers care more about a destination's capability to provide a safe environment during the pandemic. The above results may be related to the American travelers' high sensitivity to government policies and destination measures to deal with COVID-19 due to high mortality rates and case numbers in the U.S. On the other hand, the significant impact of interactional trust on travel satisfaction only in the Korean group could be attributed to the cultural differences between U.S. and Korea identified by Hofstede (Hofstede & Bond, 1984). Since the collectivist culture of South Korean society prompts everyone to take responsibility for others, interactional trust toward other travelers would be critical to ensure Korean travelers' satisfaction during a pandemic. Conversely, the individualistic culture in the U.S. explains why American travelers are not overly concerned about how others behave in their travel experiences. Lastly, the difference in the relationship between travel frequency and subjective well-being between the American group and the Korean group may indicate that American travelers have traveled relatively less compared to Korean tourists due to strict travel restrictions and lockdowns, resulting in the negligible impact of travel frequency on the former's subjective well-being.

Practical implications

While building a trustworthy relationship with tourists is a critical strategy for destinations and hospitality organizations (Wang et al., 2014), trust will be more crucial than ever to the recovery of the tourism industry during and even after the pandemic. In this regard, it is essential to build tourist trust toward government policies, destination measures, and other travelers' behavior in response to COVID-19. Given the significant impact of policy trust on travel frequency, governments need to clearly advertise their COVID-19 policies. Since policy trust is gradually developed over time through active interactions between governments and members of society (Nunkoo et al., 2012), building public trust in COVID-19 policies needs to have a long-term orientation.

Next, destination and hospitality organization managers need to build trust with prospective visitors since they would be reluctant to visit if they feel a health risk at the destination or hospitality properties (hotels, restaurant, resorts, etc.). Destinations should adopt COVID-19 social distancing measures and advertise their responses to reassure visitors and gain their trust. Specifically, they need to employ online communication tools to announce their managerial strategies for protecting visitors against COVID-19. In light of cultural differences, destination and hospitality marketers in the US need to pay particular attention to enhancing levels of destination trust as it strongly affects destination satisfaction. In terms of interactional trust, tourism and hospitality marketers, especially in South Korea, must ensure that their visitors do not only serve their own self-interest, but also consider the welfare of other visitors. To ensure a satisfying travel experience, destinations and hospitality organizations need to take serious measures such as deporting travelers or banning them from visiting the destinations or hospitality properties if they violate safety protocols (e.g., wearing mask, social distancing, etc.).

Lastly, destination managers and government officials should acknowledge that quality, rather than frequency, of travel is more important in times of a pandemic. The study results demonstrate that travel satisfaction has a stronger impact on subjective well-being than travel frequency. Therefore, it is essential to provide memorable and highly satisfying travel experiences to improve travelers' subjective well-being during a pandemic. In other words, highly satisfied visitors are a critical factor for the success of destinations and hospitality properties during and after a pandemic.

Limitations and future research recommendations

Several research limitations need to be mentioned. First, the trust constructs must be re-examined and expanded further. Although this study already proposed three main trust constructs in terms of macro, meso, and micro levels, other trust constructs (e.g., trust in employees, trust in service policy, etc.) could also impact travel experience during a pandemic. In addition, while this study focused on both travel frequency and satisfaction to measure travel experiences during the pandemic, future research needs to focus on additional experiential factors (e.g., experience value, perceived service quality, destination brand love, etc.) to expand our knowledge on how pandemic influences travel experiences.

Second, further research must be done on the relationship between travel experience and future travel intention. Although this study found that subjective well-being mediates the relationship between them, the partial mediating effects indicate that other factors could also come into play. Furthermore, the role of socio-demographic factors (e.g., age, gender, income, etc.) in influencing travel experience and subjective well-being needs to be looked into. In particular, age can be a critical determinant of travel experience during a pandemic given its significant impact on the mortality rate (i.e., older people are more vulnerable to COVID-19) (Centers for Disease Control and Prevention, 2020).

Third, the proposed framework must be explored in different cultural contexts. While this study was conducted in South Korea and the US to provide initial empirical insights into travel patterns and processes during a pandemic, governments' responses and policies to COVID-19 vary across different countries. Additionally, destinations' COVID-19 measures and interactional trust among people may differ depending on national backgrounds. To improve the external validity of the study results, future research must be done to explore the impact of trust constructs on travel experiences and subjective well-being in different cultural contexts.

Lastly, it is important to acknowledge that SEM is basically a correlated-based method that only provides insights into associations instead of causal relationships (Dolnicar, 2020). Since this study used SEM to test cross-sectional survey data, the study results do not establish causality of variable relationships. Thus, future research needs to use experimental or longitudinal analyses to analyze how tourist trusts influence travel experiences and their subsequent impacts on subjective well-being.

Credit author statement

The authors jointly proposed the initial idea of this research. After discussing this idea with each other, specific research design was developed. Dr. Shin and Dr. Kang wrote the first draft of the manuscript. Dr. Park organized data collection and helped revise the manuscript. Dr. Lee has critically reviewed and revised the manuscript. All authors contributed to the finalization of the manuscript.

Disclosure statement

None.

Uncited reference

Jang, Bai, Hu and Wu, 2009

Items	Mean	Std. Deviation	Skewness	Kurtosis
Policy Trust 1	3.50	1.04	−0.980	0.414
Policy Trust 2	3.43	1.07	−0.942	0.313
Policy Trust 3	3.45	1.08	−0.869	0.078
Policy Trust 4	3.47	1.06	−0.851	0.129
Destination Trust 1	3.25	0.94	−0.667	−0.029
Destination Trust 2	3.28	0.93	−0.432	−0.200
Destination Trust 3	3.43	0.91	−0.690	0.253
Destination Trust 4	3.34	0.92	−0.606	0.073
Interactional Trust 1	3.33	1.01	−0.577	−0.298
Interactional Trust 2	3.48	1.00	−0.667	−0.042
Interactional Trust 3	3,41	0.98	−0.669	0.003
Interactional Trust 4	3.35	0.95	−0.600	−0.039
Travel Frequency 1	2.72	0.92	−0.880	−0.623
Travel Frequency 2	2.69	0.98	−0.835	−0.576
Travel Satisfaction 1	3.53	0.81	−1.051	1.092
Travel Satisfaction 2	3.60	0.83	−1.013	1.240
Travel Satisfaction 3	3.61	0.87	−1.125	1.129
Travel Satisfaction 4	3.51	0.88	−0.834	1.101
Subjective Well-Being 1	3.71	0.78	−0.897	1.119
Subjective Well-Being 2	3.61	0.87	−0.844	0.939
Subjective Well-Being 3	3.72	0.82	−0.872	1.204
Subjective Well-Being 4	3.64	0.90	−0.900	0.844
Domestic Travel Intention 1	3.87	0.81	−1.145	1.057
Domestic Travel Intention 2	3.93	0.82	−1.088	1.033
Domestic Travel Intention 3	3.86	0.83	−1.085	1.012