Providing high-quality products has become an important strategy for service firms to gain a competitive advantage (Konuk, 2019). This study adopts Zeithaml’s (1988) definition of perceived quality. Perceived quality is defined as consumers’ judgment of the overall excellence or superiority of a product (Zeithaml, 1988, p. 3). Before obtaining a service, customers create expectations based on their personal needs, past experiences, third-party recommendations, and service provider advertising. After purchasing and using the service, customers compare the expected quality with the quality they receive (Lopes et al., 2019).

For the service industry, especially the tourism industry, understanding customer perceptions of destination quality and improving customers’ perceived value are critical. Perceived value is a dynamic construct based on pre-purchase experience and perceived experience during and after use (Sánchez et al., 2006, p. 1). Consumer-perceived value is essential in the tourism industry, and enhancing customer-perceived value is an important means to maintaining a sustainable competitive advantage (Hanaysha, 2018; Chen et al., 2019; Itani et al., 2019). Additionally, perceived value is a critical factor for consumers to make choices (Konuk, 2019).

Babin et al. (1994) divided perceived value into two dimensions: hedonic and utilitarian. Sweeney and Soutar (2001) divided perceived value into four dimensions (emotional, social, quality, and economic). Bradley and Sparks (2011) argued that perceived value is determined by multiple factors, and that consumers predict and obtain value through many sources. This study draws on Currás-Pérez, Jeong-Tae Bae, and Meeprom’s three-dimensional classification and uses three dimensions: emotional, social, and economic value. Emotional value is the utility gained from a feeling or emotional state generated by a product. Social value (enhanced social self-concept) is derived from the utility of a product’s ability to improve its social self-concept. Economic value is the utility gained from the product because of the reduced short- and long-term costs.

Monroe (1990, p. 46) defined perceived value as “the trade-off between the perceived quality or benefit that consumers have in a product and the perceived sacrifice of the price they pay;” The perceived quality of special events can directly affect social, economic, and emotional value (Meeprom and Silanoi, 2020). Suttikun and Meeprom (2021) show a positive relationship between customers’ perceived quality and perceived value when customers purchase souvenirs. Widodo and Maylina (2022) conclude that the perceived value of airline passengers in all dimensions is directly influenced by perceived quality. Therefore, this study assumes that the perception of perceived value dimension affects perceived quality and proposes the following hypothesis:

Perceived risk theory is first introduced to consumer scenarios by Bauer (1960). According to Bauer (1960), consumers face corresponding risks in making behavioral decisions because purchases produce outcomes that consumers cannot fully determine and anticipate; thus, they are likely to have unpleasant consequences. Scholars have different understandings of the definition of perceived risk. Some scholars believe that perceived risk is a feeling of uncertainty in the product purchase process, because consumers cannot predict the outcome of their purchases and consequences (Derbaix, 1983). Some scholars believe that perceived risk should be composed of performance, financial, time, social, and security and privacy risks (Lee, 2009). This study adopts Bauer’s (1960) definition of perceived risk, which states that “the perception of uncertainty arising from the traveler’s inability to determine the outcome of his or her decision and the resulting adverse consequences in the product selection process.”

With the advancement of transportation and contact methods, tourism has been booming in recent years, and many travel and trading platforms have emerged. These developments, which have allowed travelers to purchase tickets and specialties offline and directly online, have increased the complexity of perceived risks arising from the shopping process. As you can see, the new environment and channels have also created new perceived risks for travelers. In recent studies, the concept of perceived risk is often considered an explanatory variable that influences customer behavior (Holbrook, 1994). To understand this behavior, perceived value and risk also frequently appear together in relevant studies (Chen et al., 2017; Şen Küpeli and Özer, 2020).

Hansen et al. (2018) consider perceived risk as a decisive antecedent in end-user decision-making. Tzavlopoulos et al. (2019) argue that a high level of perceived quality leads to higher satisfaction and perceived value, which reduce perceived risk and ultimately influence consumers’ adoption of positive consumption behaviors. Tzavlopoulos et al. (2019) also verify that an increase in perceived risk leads to a decrease in perceived value from an e-commerce perspective. Uslu and Karabulut (2018) show that in Fethiye (TR), destination perceived risk has a negative impact on travelers’ perceived value. In addition, consumers’ perceived risk in their choice of consumption channel may also inhibit their perceived value of the product or service (Broekhuizen and Jager, 2004). In most studies, on the issue of perceived risk on perceived value, it has been agreed that perceived risk has a negative impact on perceived value (Tzavlopoulos et al., 2019; Li et al., 2020; Xie et al., 2021). Therefore, this study assumes that the perception of perceived value dimension affects perceived risk and proposes the following hypothesis:

Existing literature suggests that interactions between customers affect their evaluation of the service experience (Wu, 2008) and their product choice (Murphy, 2001). Georgi and Mink (2013, p. 11) considered social interaction as “any type of personal or group social interaction that customers encounter when acquiring and consuming goods and services.” Sunitiyoso et al. (2011) found that social interaction is the most direct interaction that occurs through informative communication between travelers about their travel experiences (choices and their outcomes) or intentions (communication here encompasses all possible social media, as well as face-to-face, phone, video, and SMS communication). The enjoyment gained from such interactions increases consumers’ loyalty and sense of belonging to their travel destination (Koh et al., 2003). It also enhances the interactivity between users and increases perceived value, which enhances the intention to continue using the product (Zhang et al., 2017). This study follows Georgi and Mink’s conceptual definition of social interactions.

Zhang et al. (2017) show that social interactions in social network services (SNS) have a positive impact on perceived value. Social interactions between viewers and anchors also affect viewers’ (consumers’) perceived value (Chen and Lin, 2018). Hussein et al. (2018) conclude that the hospitality industry can effectively increase perceived value and thus retain customers by improving their social interactions. The above studies have also suggested that social interactions play an important role in the dynamics of consumer behavior. Therefore, this study assumes that the perception of perceived value dimension affects social interaction quality and proposes the following hypothesis:

Owing to the diversity of customer needs, fierce competition, and rapid technological development, many service companies try to deliver the best customer value to gain a competitive edge (Woodruff, 1997). Many researchers agree that creating the best customer value is a critical goal for any company. Delivering the best customer value can be regarded as an important factor for the success of hospitality companies (travel industry; Yrjölä et al., 2019; An and Han, 2020; Fitriasari, 2020). Zeithaml (1988. p. 3) defined customer perceived value as the overall evaluation of the benefits customers perceive from the product or service weighed against the costs, removing the fact that the greater the difference between benefits and costs, the greater the customer perceived value.

Perceived value, the most important measure of a company’s competitive advantage, is an important predictor and key determinant of customer satisfaction and loyalty (Lee et al., 2007). Customer satisfaction is usually studied as a unidimensional construct that measures overall satisfaction with the service organization, which is the result of a combined judgment of all interactions with the organization (El-Adly, 2018).

Matsuoka et al. (2017) find that an increase in travelers’ perceived value of a destination increases satisfaction. Lu and Wang (2020) find that, from the travel perspective, an increase in travelers’ perceived value increases customer satisfaction to some extent. Using Daegu city tourist destinations as an example, Peng and Jiang (2022) find that perceived value has a significant positive effect on customer satisfaction. Moreover, in the COVID-19 context, most existing tourism studies on perceived value are conducted in a single dimension, and research on multidimensionality is limited; we explore multidimensional perceived value. Therefore, this study assumes that the perception of perceived value dimension affects customer satisfaction and proposes the following hypothesis:

Satisfying customers is the goal of marketing (Thakur, 2019) because it can bring long-term benefits, such as positive reputation and customer loyalty, to the product (Torabi and Bélanger, 2021; Lin et al., 2022). Customer satisfaction is the overall evaluation of a product obtained by the customer through its performance to date (Johnson and Fornell, 1991). Satisfaction stems from the comparison between consumers’ expectations of a product’s performance and its actual performance (Oliver, 1980). This is a follow-up study based on Oliver’s (1980)definition of satisfaction.

Tourist satisfaction affects travel intention. As an important tool for evaluating experiences, it profoundly influences destination choice, consumption of products and services, future travel intentions, and recommendations to others (Shafiee et al., 2016). Boulding et al. (1993) defined use intention as an individual’s will and belief in expressing a specific future behavior after consumers form value for an object.

Bayih and Singh (2020) explore the issue of intra-country travel and find satisfaction to be an important antecedent of travel intention. He and Luo (2020) show that the higher the satisfaction of travelers during snow and ice travel, the higher their intention to travel. Satisfaction with VR travel positively effects travel intention (An et al., 2021). The above studies provide evidence that satisfaction is a strong indicator of action intention, repeat purchase, and recommendation of a product or service. Therefore, traveler satisfaction plays a prominent role in travel intention, so this paper proposes the following hypothesis:

The verification of the mediating effect of perceived value and customer satisfaction is important in this study. In this study, the direct effect between perceived quality and travel intention is not verified; although, previous studies find a direct effect (Hou et al., 2021; Wasaya et al., 2021; Hashish et al., 2022). Research on the mediating effect of perceived value and customer satisfaction on the relationship between perceived quality and travel intention during the COVID-19 pandemic is insufficient. Therefore, this study verifies the serial mediating effect of perceived value and customer satisfaction on the relationship between perceived quality and travel intention.

Perceived quality drives the perceived value of customers; if customers invest less time, money, and effort than they receive in perceived quality, customers receive a higher perceived value (Hapsari et al., 2016). In other words, the higher the quality of perception, the higher is the perceived value of the customer. Keshavarz and Jamshidi (2018) validate the mediating role of perceived value in perceived quality and customer behavior from the perspective of the hospitality industry. Meanwhile, Cempena et al. (2021) validate the role of perceived value as a mediating variable between perceived quality and tourist behavior from the perspective of the Gianyar Regency tourist attraction in Bali, Indonesia. Gómez-Carmona et al. (2022) also verify the mediating role of perceived value between service quality and tourist behavior from the perspective of health services. The above studies have examined the mediating role of perceived value, perceived quality, and consumer behavior. This study further explores the mediating role of perceived value between perceived quality and travelers’ satisfaction, based on prior research.

Jeong and Kim (2019) concluded that good perceived value contributes to customer loyalty and retention. Khawaja et al. (2021) verify the mediating role of customer satisfaction in private universities in Lebanon between customer-perceived quality and behavioral intentions. Rahardja et al. (2021) confirm the mediating role of satisfaction between perceived value and customers’ intention to act in the context of business activities in social media. The finding that satisfaction plays a mediating role in perceived value and consumer behavior is also supported by the above-mentioned studies. However, in the COVID-19 context, research on the serial multiple mediation model of perceived value and customer satisfaction in the relationship between perceived quality and travel intention is insufficient. Therefore, this study examines the multiple mediating roles of perceived value and customer satisfaction in the relationship between perceived quality and travel intention and proposes the following hypothesis:

Fear of COVID-19 is a negative emotional state of depression and anxiety due to the awareness of possible outcomes associated with COVID-19, such as infection with coronavirus (Ahorsu et al., 2020). Fear of COVID-19 increases anxiety about going out, and thus significantly decreases consumer behavioral intentions (Apaolaza et al., 2022). Even if they are satisfied with their destination, travelers will reduce their travel intentions under the influence of fear of COVID-19 (Rehman et al., 2021). Several tourism marketing scholars have explored the moderating role of fear of COVID-19 in the relationship between attitudes (satisfaction, engagement) and behavior (Liu H. et al., 2021; Rather, 2021; Akhrani et al., 2022). However, most studies using fear of COVID-19 as a moderating variable have also focused on the mental health domain (Gasparro et al., 2020; Dymecka et al., 2021; Soto-Sanz et al., 2021). This study builds on the fear of COVID-19 definition by Ahorsu et al. (2020).

Previous studies have shown that age affects intention (Na et al., 2021; Watanabe et al., 2021; Herzallah et al., 2022); and in this study, age was included as a control variable (Hien et al., 2021; Klaver et al., 2021; Saari et al., 2021). Research on the moderating role of COVID-19 fear in the relationship between satisfaction and travel intention is lacking; therefore, this study proposes the seventh hypothesis:

This study constructs the research model described above (see Figure 1).

The items measured for each construct in this study were adapted from previous research and included perceived value (emotional, social, and economic), perceived quality, perceived risk, social interaction, satisfaction, and travel intention. A 5-point Likert scale was used in this study, with 1 indicating strong disagreement and 5 indicating strong agreement. These scales were initially written in English, and after consulting experts and making corrections, the questionnaire was finally translated into Chinese and distributed to the respondents.

Perceived quality was measured using the scale of Chi et al. (2019). For perceived risk, the scales of Wang et al. (2019) and Şen Küpeli and Özer (2020) were used. For perceived value, the scales of Peng et al. (2019) and Meeprom and Silanoi (2020) were used. For socialization, the scales of Lee et al. (2019) and Jasrotia et al. (2022) were used, and the scales of Konuk (2019), Yan et al. (2021), and Liu P. et al. (2021) were used for satisfaction. Chi et al.’s (2019) scale was used for travel intention. The scale developed by Ahorsu et al. (2020) was used to assess the fear of COVID-19.

This study used an online cross-sectional survey. Convenience sampling was conducted based on the topic of this study. Given that the purpose was to analyze the travel intentions of Chinese travelers in the context of the COVID-19 pandemic, participants had to meet the following requirements: (1) be at least 18 years of age and (2) need to have traveled in the last 3 years.

The proposed hypothesis is tested using a questionnaire. Fifty Chinese respondents with a history of traveling in 3 years after the outbreak of the COVID-19 pandemic were first screened as pre-test respondents. Inappropriate questions were then removed, and a final questionnaire was created. The survey was conducted from 1 August to 20 August 2022, and questionnaires were distributed and collected through a questionnaire company (wenjuanxing). A total of 518 questionnaires were returned; 15 invalid questionnaires were removed and 503 were finally used in this study. The demographic characteristics of the participants were as follows.

The results showed that the proportion of female respondents (51.49%) was higher than that of male respondents (48.51%). In addition, the age distribution of respondents was relatively balanced, with respondents aged 18–40 (53.08%) years and over 41 (46.92%) years. Approximately 7.8% of the respondents were in the 18–20 age group, 23.5% were in the 21–30 age group, 21.5% were in the 31–40 age group, 21.9% were in the 41–50 age group, 24.7% were in the 51–60 age group, and 0.6% were aged 61 and above. Most respondents had a college degree or higher (61.83%). The income distribution of the respondents is as follows: monthly income of 3,000 RMB and below (16.90%), 3,001–4,000 (7.55%), 4,001–5,000 (14.51%), 5,001–6,000 (16.90%), 6,001–7,000 (11.73%), and 7,001 and above (32.41%). The number of married respondents (72.17%) was higher than that of unmarried respondents (27.04%), while others accounted for 0.79% of the respondents. In addition, the occupational distribution of the respondents was as follows: students (19.09%), enterprise staff (26.44%), self-employed (10.34%), civil servants (18.69%), housewives (2.58%), freelancers (15.31%), retirees (3.18%), and others (4.37%). Of these, 354 (70.38%) had traveled within the last year, 121 (24.06%) within 2 years, and 28 (5.56%) between 2 and 3 years.

The data were analyzed by structural equation modeling using AMOS 25. This study set up a measurement and structural model using ML estimation methods to verify the normality of the underlying assumptions of ML estimation. The results showed that the skewness of the variables ranged from 0.688 to −1.319, and the kurtosis ranged from −1.165 to 2.035, indicating that the basic assumptions of skewness ±3 and kurtosis ±7 were satisfied, and the sample was normal (Kline, 2015).

Harman’s (1967) one-factor test was used to test for common method bias in the data. The total variance extracted by one factor was 36.812%, which is lower than 50%. Therefore, there was no obvious common-method bias.

In addition, all dimension indicators (nine dimensions, 27 questions) were included in the one-factor confirmatory factor analysis. When there is common method bias, the model value of the one-factor confirmatory analysis is higher than that of the multi-factor model. The one-factor validation model’s values (χ²/df = 16.668, GFI = 0.542, AGFI = 0.466, CFI = 0.486, SRMR = 0.1207, and RMSEA = 0.177) were much lower than those of the multi-factor model (χ²/df = 1.882, GFI = 0.926, AGFI = 0.903, CFI = 0.974, SRMR = 0.0294, and RMSEA = 0.042), further indicating that there was no obvious common method bias in this study (Table 1).

The difference between the one-factor model and multi-factor model’s Chi-square test was compared and reached a significant level (p < 0.05). The Chi-square of the two models was significantly different, indicating that there was no severe common method bias in this study (Mossholder et al., 1998).

Cronbach’s alpha was used to assess the internal consistency of the scale, and a value of above 0.7 was considered sufficient (Bagozzi and Yi, 1988). Cronbach’s α of all items exceeded the criterion of 0.8 from 0.831 to 0.945, indicating that this study had sufficient reliability.

Hair et al. (2010) proposed that models should be evaluated on the basis of convergent and discriminant validity. Convergent validity should be evaluated using the CR and AVE values for each construct. As shown in Table 2, the CR of all the constructs was between 0.835 and 0.945, all of which met the criterion of 0.7, and the AVE of all the constructs also met the criterion of 0.5 between 0.630 and 0.852 (Fornell and Larcker, 1981). In addition, Hair et al. (2011) suggested that the factor loadings for each item should be greater than 0.60 to establish convergent validity, while the factor loadings for all the constructs in this study were between 0.724 and 0.963, which also met the criteria. Therefore, the measurement model used in this study had an acceptable level of convergent validity.

In addition, multicollinearity test was also performed. Tolerances ranged from 0.774 to 1, all greater than 0.2. VIF ranged from 1 to 1.292, all of which were less than 5, thus ruling out the possibility of multicollinearity (Hair et al., 2010).

The Heterotrait Monotrait Ratio (HTMT) has been shown to yield greater specificity and sensitivity (97–99%) than Fornell and Larcker’s (1981) criterion (20.82%; Ab Hamid et al., 2017). Therefore, HTMT criteria were used in the study. The HTMT method specifically assesses discriminant validity by comparing the HTMT values between two constructs, in which the HTMT value between the two factors should be less than 0.85. As shown in Table 3, the results of HTMT ratio ranged from 0.106 to 0.711, indicating that all constructs were independent of each other. Therefore, all constructs in this study had sufficient discriminant validity.

The model fit of this study was as follows: χ² = 518.621, χ²/df = 2.026(<3), RMR = 0.038(<0.05), RMSEA = 0.047(<0.05), which met the criteria. In addition, GFI = 0.924, AGFI = 0.904, NFI = 0.943, RFI = 0.933, IFI = 0.970, TLI = 0.965, and CFI = 0.970 were all above the standard of 0.9. Therefore, this theoretical model setting was acceptable.

The results of the path analysis revealed that all the hypotheses presupposed in this study were valid, and that there was a positive relationship between PQ and PVEM (H1-1: β = 0.537, t = 10.924, p < 0.001), PVEC (H1-2: β = 0.447, t = 8.935, p < 0.001), and PVSO (H1-3: β = 0.618, t = 12.583, p < 0.001). There were also negative relationships between PR and PVEM (H2-1: β = −0.096, t = −2.098, p < 0.05), PVEC (H2-2: β = −0.101, t = −2.134, p < 0.05), and PVSO (H2-3: β = −0.185, t = −4.271, p < 0.001). Positive relationships existed between SI and PVEM (H3-1: β = 0.122, t = 2.790, p < 0.01), PVEC (H3-2: β = 0.155, t = 3.412, p < 0.001), and PVSO (H3-3: β = 0.111, t = 2.715, p < 0.01). A positive relationship was found between perceived value (PVEM, PVEC, and PVSO) and SAT (H4-1: β = 0.324, t = 7.710, p < 0.001; H4-2: β = 0.203, t = 5.110, p < 0.001; H4-3: β = 0.420, t = 9.174, p < 0.001). There was also a positive relationship between the SAT and TI (H5: β = 0.704, t = 18.952, p < 0.001).

A phantom model (see Table 4) was used in this study to analyze the results of the indirect effects and statistical significance. PVEM and SAT (p < 0.000), PVEC and SAT (p < 0.001), and PVSO and SAT were all mediated by PQ and TI (p < 0.000). There was no 0 in the 95% CI, again validating the existence of a mediating effect.

Thus, PV and SAT not only play a direct role in influencing PQ to TI but also indirectly influence TI through their mediating effects on PV and SAT. Therefore, by improving the perception of destination quality, travelers’ perceived value and satisfaction can be further improved, thereby increasing their travel intentions.

This study argues that the hypothesis is supported if the change in the Chi-square (χ2) difference between the constrained and free models is above the cardinality standard threshold (Stone and Hollenbeck, 1989). When analyzing the effect of satisfaction on travel intentions, the analysis result showed that the constrained model was χ² = 917.240 (df = 512), while the freedom model was χ² = 921.841 (df = 513). The change in the difference in the constrained model for one degree of freedom, Δχ² (1), was 4.601 (p = 0.032). The results were considered statistically significant, thus supporting H7 (see Table 4).

Tests for control variables were also conducted. Age was nominal scale, so this study classified 1–3 (18–40 years old) as 1 (MZ) and 4–6 (41+ years old) as 2 (non-MZ). Age had a statistically significant negative effect on TI (β = −0.093, t = −2.760, p < 0.01; see Table 4). Thus, the larger the Age, the lower the travel intention was. Age was also divided into two groups: MZ and non-MZ generation, with the MZ generation used as a reference group for the dummy variable under the FOC moderating effect. The results show that in the high FOC group, the non-MZ generation has a lower travel intention than the MZ generation. Therefore, we can assume that the older the age, the higher the fear of FOC, eventually leading to a lower travel intention.

This study analyzes travelers’ travel intentions during the COVID-19 pandemic. It examines the structural relationships between perceived quality, perceived risk, social interaction, perceived value, satisfaction, and travel intention. First, the results reveal a significant positive relationship between all three dimensions of PQ and PV, with PQ having the most significant effect on PVSO. Our results are consistent with that of Choi and Kim (2013), Meeprom and Silanoi (2020), and Li and Shang (2020). Second, a significant negative relationship exists between the three dimensions of PR and PV, with PR having the most significant negative effect on PVSO. These findings are consistent with that of Şen Küpeli and Özer (2020), Li et al. (2020), and Xie et al. (2021). Third, a significant positive relationship exists between the three dimensions of SI and PV, with SI having the most significant effect on PVEC. Our results are consistent with that of Liu Y. et al. (2021), Li et al. (2021), and Zhang et al. (2022). Fourth, a significant positive relationship exists between all three dimensions of PV and SAT, with PVSO having the most significant effect on SAT. These findings are consistent with that of Choi and Kim (2013) and Rasoolimanesh et al. (2020). However, our results are slightly different from that of Prebensen and Xie (2017), whose results show that perceived economic value is insignificant. Finally, SAT has a positive impact on TI, which is consistent with the findings of He and Luo (2020), Pai et al. (2020), and Abbasi et al. (2021).

In addition, the results show that the three dimensions of PV and SAT have a significant mediating effect on the relationship between PQ and TI, and that FOC has a significant moderating effect on the relationship between SAT and TI. With the moderation of FOC, AGE has a significantly negative effect on TI, which is in line with the findings of Ayeh et al. (2013), Gemar et al. (2019), and Gu et al. (2021). Thus, the older the age, the lower the travel intention.

This study has four theoretical contributions to the literature.

First, previous studies have not specifically examined social interactions in the tourism field. Particularly, studies focusing on social interactions in the tourism field are limited despite the fact that the field values interaction with tourists. Because the Chinese government has had a zero-COVID policy for a longer period, travel has been extremely restricted during the zero-COVID period. Unlike the situation before zero-COVID, face-to-face interactions are lacking in the context of a newly crowned pneumonia-ridden China. This study not only expands the scope of the research by targeting tourists (or the tourism field), but also contributes theoretically to the expansion of existing research models.

Second, while studies on the effects of perceived value are widely available, most of them focus on one or two dimensions (e.g., utilitarian value and hedonic value). Therefore, based on Sheth et al. (1991), this study divides perceived value into multiple dimensions. It not only analyzes the direct effect of perceived value, but also explores its possible mediating effect, which should have a theoretical significance. In particular, further refining and differentiating perceived value, which is considered an important variable influencing tourists’ choice behavior, not only contributes to a clearer and more hierarchical understanding of actual travelers’ product and service purchasing behaviors and consumption processes, but also helps expand existing research in the field of tourism destinations.

Third, this study makes important methodological contributions to the literature. That is, the measurement items of fear of COVID-19 are validated with Chinese consumers. This study also uses the items developed by Ahorsu et al. (2020), which have been modified and revalidated for the Chinese pandemic situation; thus, contributing to the reliability and validity of the scale. Existing research on fear of COVID-19 has been conducted mainly in the mental health context. The present study not only extends the scope to the field of tourism, but also expands the existing research by adding an analysis on the moderating effect of fear of COVID-19.

Fourth, this study uses age as a control variable affecting travel intention in the context of the COVID-19 pandemic. Respondents are divided by age into the MZ generation (under 40 years) and non-MZ generation (after 40 years) based on the rationale that there are significant differences in product or service selection between the MZ and non-MZ generations. Future studies could expand age’s role as a moderating variable by dividing the subjects by age (MZ vs. non-MZ).

This paper contributes to the enhancement of tourism destinations from five perspectives.

First, the object of this study differs from previous studies in that it analyzes the differences in travel intentions of the MZ and non-MZ travelers during COVID-19, using FOC as a moderating variable. The results show that travel intention decreases as age increases under the moderation of FOC. The findings of this study can be used as a guide for tourist destinations to understand the behavior of travelers of different ages and improve traveler satisfaction and travel intention.

Second, the results show that perceived quality had a positive effect on all three dimensions of perceived value, with a significant effect on perceived social value. Social values reflect how well visitors are recognized, respected, and accepted by others (Caber et al., 2020). The experience of travel matches their social value expectations, emotional value, and economic value, similar to the results of prior studies (Fu et al., 2018; Kim and Thapa, 2018). Tourists consume tourism products to enhance their social image, as purchasing and participating in tourism can convey symbolic meaning to others. In addition, travelers often make travel choices through their own emotional appeals. It is possible that the perceived economic value of the travelers was not as high as that of the first two due to long-term pandemic isolation. To enhance the travel intentions of travelers, travel destinations should prioritize improving the perceived social value of travelers by conducting more interactive entertainment interactions so that travelers can fully interact with their peers, residents, and so on, and gain a sense of accomplishment in communication and collaboration. There is also a need to enhance the perceived emotional value of travelers, which can fully exploit the local characteristics of tourism resources to create a competitive advantage of differentiation and avoid uniformity. Destinations need to focus on enhancing the perceived economic value of travelers, and all aspects of food, lodging, transportation, shopping, and entertainment should be reasonably charged to show a simple style of honest management.

Third, the findings indicate that perceived risk has a negative effect on all three dimensions of perceived value, with perceived risk having the strongest negative effect on perceived social value, followed by perceived economic value and perceived emotional value. Because the current study did not compare PR with multidimensional PV (Tzavlopoulos et al., 2019; Xie et al., 2021), the results of this study could not be compared with those of existing studies. However, the overall results were the same and both showed a negative effect. Travelers’ perceptions of environmental risks reduce their perceived value, and the negative impact is the strongest because social values need to be realized in an overall free and safe environment. Therefore, to achieve the sustainable and healthy development of travel destinations, it is necessary to prevent risks. During the pandemic, it is important to pay attention to safe contact between travelers and service providers and to disinfect and sterilize public facilities in a timely manner to reduce the risk of COVID-19 infection. Simultaneously, the leakage of users’ personal information and additional unspecified charges during the transaction process should be minimized. A good image of tourist destinations should be established, transaction risks reduced, and the overall transaction experience improved to enhance the security management in tourist destinations, thereby motivating travelers’ intention to travel.

Forth, the results show that social interaction has a positive effect on all three dimensions of perceived value, with social interaction having the strongest effect on perceived economic value, followed by perceived emotional value and perceived social value. To enhance the experience of social interaction, travel destinations should not only focus on social business platforms but also strengthen the development of basic applications, especially in the new technological environment using artificial intelligence, big data, cloud computing, and virtual reality technologies to seek breakthroughs for the destination. Thus, destinations can satisfy the demand for interactive experiences in various forms, save time, and promote the sustainable development of tourist places. Additionally, different marketing strategies should be used for different customer groups. Travelers have different motivations and focus on different types of interactive content. Travel destinations can determine users’ interaction motivations based on the content and frequency of user posts on a platform, and can design interactive content around user engagement motivations to enhance social interaction.

Finally, FOC was found to moderate the relationship between SAT and TI. In other words, low FOC implies increased travelers’ satisfaction and travel intention. Therefore, to reduce tourists’ FOC, travel destinations must establish a staff health monitoring system and a daily registration of staff health status. Scenic public health and prevention and control measures should be implemented and cleanliness and hygiene in the scenic area should be maintained. Before important holidays and large events, inspections should be organized to promptly identify prevention and control loopholes and risk points, and rectifications and improvements should be ensured. Tourist places can implement time-sharing reservations using online booking and big data platforms and issue early warning alerts to hot scenic spots during peak tourist periods to reasonably regulate the flow of people. Younger groups are not significantly affected by FOC and can increase publicity to similar groups through channels such as social media and big data platforms.

Although the present study yields many interesting results, it has several limitations. First, all respondents in the survey are Chinese and the object of study is relatively homogeneous. Future studies could compare and analyze findings from other countries. Second, this study is a cross-sectional investigation. The analysis is done in a relatively short period of time. Future research could use a longitudinal approach to confirm causality and assess the consequences of the study’s framework over a long period of time. Third, this study only investigated the mediating effect in the relationship between PQ and TI, without examining the mediating effect in the relationship between PR, SI, and TI. Future studies could expand the research from this perspective. Forth, age is used as the control variable. However, there are significant differences in the characteristics of MZ and non-MZ eras in China. Therefore, future studies should divide age into MZ and non-MZ to draw clear insights on the moderating effect of age. Finally, in this study, fear of COVID-19 is analyzed as a moderating variable. Future research could consider fear of COVID-19 as an independent variable.