1. Introduction
The growing popularity of the Internet has promoted the development of the world economy. For example, the emergence of homestay platforms such as Airbnb has given the homestay industry an unprecedented boost [1]. By 2020, there were 3 million homestay properties in mainland China, and the number of hosts had risen to nearly 400,000—an increase of 16.5% since 2019 [2]. It is thus clear that homestay has become an attractive accommodation option for many tourists domestically and internationally. However, as standards in China’s homestay industry lag behind those in its hotel industry, and most homestays are privately owned, some problems have emerged along with this market prosperity. Hosts’ concerns include wasted resources, cleanliness issues, and housing damage [3,4,5,6,7,8]. Tourists are worried about privacy violations, safety in unfamiliar places, misleading advertising, and health [9,10,11,12,13,14,15,16,17]. In addition, these concerns have never been more pressing in the post-Covid-19 era, when the accommodation industry suffered heavy losses in 2020 and on, and the growth of the global economy was slowed down by COVID-19, as proclaimed by World Health Organization (2020) [18].
Currently, perceived risk is gaining unprecedented attention from tourism practitioners, visitors, and the government. Although studies have charted the devastating influence of the pandemic on tourism subsectors such as accommodation, airlines, and travel agencies [19,20], the emotional and attitudinal changes, i.e., guests’ emotional solidarity and their loyalty towards homestay in this context, remain unclear. A better understanding of these will play vital role in the sustainable development [21,22] for the homestay industry during the post-pandemic era. The concept of emotional solidarity was introduced to describe people’s feeling of closeness or “togetherness” [23], and has received popular application in sociology, anthropology, social psychology, and other disciplines, including recent tourism research, with a focus on the relationship between residents and tourists. However, while perceived risk has been widely acknowledged to have a profound influence on a series of factors related to tourists’ emotional states [24] and decision making, as well as their loyalty [25], very limited attention has been paid to the current emotional state of tourists’ emotional solidarity with their hosts in the homestay industry so far, not to speak of the dynamic mechanism existing among their perceived risk, their emotional solidarity, and their loyalty towards homestay industry [26].
To fill these gaps, the present study aims to explore the relationships among tourists’ emotional solidarity with their hosts, tourists’ perceived risk, and tourists’ loyalty towards homestay industry in the post-pandemic era. More specifically, this study aims to explore whether and how tourists’ perceived risk relating to homestays affects their emotional solidarity with hosts and their customer loyalty in the post-pandemic era. The results can provide insights for local governments, investors, homestay hosts, and other stakeholders. The sustainable development of the homestay industry requires a better understanding of the emotional bond and mutual understanding between hosts and tourists. The results may also help to improve homestay experiences in cities and regions similar to our study site, and thus enhance the sustainability of this industry.
2. Literature Review
2.1. Emotional Solidarity
2.1.1. Implication of Emotional Solidarity
The concept of emotional solidarity was introduced by Durkheim [27] several decades ago, and was initially used in sociological research. Hammarstrom [28] defined emotional solidarity as the degree of positive emotions that people feel for each other. It can enhance an individual sense of collective identity by strengthening the feeling of being “we together” [27,29] as part of a group, and it is characterized by a high degree of contact and perceived intimacy. In recent years, the concept has been increasingly applied in social psychology and anthropology to gain a better understanding of the relationships between and among groups, including religious members [30], prison inmates [31], residents of different cities and regions [32,33,34], and even different generations of family members [35]. The concept of emotional solidarity was introduced to tourism studies by Woosnam in 2009 [32] to explore the relationship between residents and tourists. Woosnam proposed that the degree of residents’ shared beliefs, shared behavior, and interaction could significantly predict their emotional solidarity with tourists, offering a novel theoretical framework for examining emotional solidarity between residents and tourists [32]. In 2010, Woosnam and Norman [36] developed and validated a scale of emotional solidarity with three dimensions: feeling welcome, emotional closeness, and sympathetic understanding. “Feeling welcome” refers to local residents’ pride in tourists’ visiting or tourists’ feeling that they are welcomed by residents. “Emotional closeness” refers to the likelihood of local residents’ making friends with tourists or of tourists’ feeling very close to residents. “Sympathetic understanding” refers to the degree to which residents and tourists feel that they understand each other and have much in common. The robustness of this scale has been empirically substantiated in many studies [1,32,36,37,38,39].
2.1.2. Emotional Solidarity in the Homestay Industry
Whereas hotels are normally found in commercial centers, homestays are usually located in residential areas, which may offer greater opportunities for interaction with locals [40]. Vodeb et al. [41] illustrate the importance of residents’ participation in property renting, the result also confirms that the more residents are informed and involved in tourism, the more they will enhance their perception of tourism impacts and support development of tourism. Emotional solidarity has rarely been considered in studies of tourism accommodation, except for a few exceptions. Residents’ emotional solidarity with visitors to Airbnb lodgings was found to enhance their sense of safety and their support for the presence of Airbnb hosts in their neighborhoods [1]. In another study, residents’ emotional solidarity with tourists was found to be greater if they had prior experience of being Airbnb guests [38]. Juric et al. [42] also found that emotional solidarity mediated the indirect relationship between tourists’ personality traits (e.g., extraversion, agreeableness, conscientiousness, neuroticism and openness to new experience) and their intention to stay in nonmonetary peer-to-peer accommodation. These findings regarding the role of emotional solidarity in related contexts offer important insights into the host-guest relationship in the homestay industry.
2.2. Antecedents of Emotional Solidarity: Perceived Risk
2.2.1. Concept of Perceived Risk
The concept of perceived risk was extended by Raymond and Baur [43] from psychology to business and marketing studies addressing consumers’ cognitive psychology and behavior [33,44]. In 1992, the British Royal Society defined perceived risk as “people’s beliefs, attitudes, judgments and emotions about risks and benefits, as well as cultural and social tendencies in a broader sense” [45]. The concept was initially used to study the risk perceptions of consumers when making purchase decisions. When such decisions are made, consumers cannot be sure whether the results will be consistent with their expectations, which gives rise to uncertainty [46].
2.2.2. Perceived Risk in Tourism Research
Research on tourists’ perceived risk emerged in the 1990s and is now relatively mature [47,48,49,50,51]. Reisinger and Mavondo compared research results pertaining to local and international tourists in Australia and found that travel anxiety was closely related to perceived travel risks [52]. In another study, perceived risk was found to significantly affect tourists’ destination choice [50]. In a study of the COVID-19 pandemic, perceived risk was also found to moderate the relationship between customers’ engagement and revisit intentions [53]. Terrorist incidents such as 9/11 and public health emergencies such as the SARS and COVID-19 outbreaks have intensified research attention to tourists’ perceived risk. The tourism market is highly sensitive to health and safety related issues [20,54,55]. Risk plays a particular role in tourism consumption because of the typical intangibility, non-storability, and uncertainty of tourism products [48].
Tourists’ perceived risk may vary between countries and cultures. Lepp et al. [52] identified seven dimensions of tourists’ perceived risk: health concerns, political instability, terrorism, unfamiliar food, cultural barriers, divergent political and religious beliefs, and crime. All of these dimensions were found to be closely related to tourists’ anxiety about traveling, with higher levels of perceived risk associated with increased anxiety among tourists and lower levels of perceived risk associated with reduced anxiety among tourists. Joo et al. [33] highlighted the importance of perceived risk in the context of extreme risk situations such as COVID-19 in reducing residents’ willingness to welcome tourists, emotional closeness to tourists, and sympathetic understanding of tourists. However, as the tourism industry moves into the post-pandemic era, an up-to-date understanding of perceived risks from the homestay visitor’s perspective is still lacking. Seeking to fill this research gap, we hypothesize as follows:
Homestay tourists’ perceived risk negatively and significantly influences (a) the extent to which they feel welcomed by hosts, (b) their sympathetic understanding of hosts, and (c) their emotional closeness to hosts.
2.3. Outcome of Tourists’ Emotional Solidarity with Homestay Hosts: Customer Loyalty
2.3.1. Customer Loyalty in Tourism Research
“Customer loyalty” [56] refers to consumers’ commitment to repeatedly purchasing products or services from a certain brand or company [56]. Customer loyalty is regarded as crucial to effective marketing management [57] and has become one of the most widely researched topics in the marketing field [58]. Customer loyalty is often measured by repurchase intention and word of mouth (WOM) [59]. Repurchase intention measures customers’ willingness to purchase more products or services from the same company. WOM measures customers’ willingness to publicly express positive opinions about a brand or company [59]. Customer loyalty has also attracted attention from tourism and hospitality scholars [60,61,62,63], who have addressed the issue in relation to hotels [64], tourism types [62], Airbnb [63], tourism suppliers [64], and catering enterprises [65]. Furthermore, tourism scholars have extended the concept to destination loyalty [25], which encompasses tourists’ willingness to recommend and intention to revisit destinations [66].
2.3.2. Perceived Risk and Customer Loyalty in Tourism
Under normal circumstances, if the homestay industry in a city or region has a good reputation, it can be expected to receive repeat customers. However, COVID-19 brought great challenges for potential homestay tourists [33]. Under China’s quarantine policy, effective from January 2020, even a suspected case was required to undergo 14 days of isolation [67]. In 2020, when the risk of coronavirus infection was highest, many people in China were required to stay at home due to lockdown policies. At the time of writing, the pandemic is under control in China and the domestic tourism market is recovering rapidly [2]. Nonetheless, even in the post-pandemic era, exposing oneself to an unfamiliar environment may still be perceived as risky. The private nature of homestay accommodation [68] means that homestays are more geographically scattered than are hotels, which tend to be clustered in popular areas. The use of homestay accommodation may therefore expose tourists to more unpredictable environments. This may trigger concerns and uncertainty about safety, increasing tourists’ perceived risk and reducing their loyalty to the homestay industry in tourist destinations. The direct and indirect negative influence of perceived risk on customer loyalty has been demonstrated in a variety of disciplines, including marketing [65], hospitality [64], and applied sociology [69]. However, the impact of homestay-related perceived risk on tourists’ loyalty to the homestay industry has received very limited academic attention. Based on these considerations, we propose the following hypothesis:
Homestay tourists’ perceived risk has a negative and significant influence on homestay customer loyalty.
2.3.3. Customer Loyalty in Homestay
Emotional solidarity between tourists and residents has been found to lead to many positive tourism outcomes, such as enhanced destination loyalty among tourists [25], positive attitude towards tourism development among residents [34] and greater perceived safety among tourists and residents [39]. Loyalty has received particular attention in tourism research [70]. Early research on loyalty was mostly centered on brand loyalty. However, Uncles et al. [71] emphasized that loyalty is a human trait rather than an inherent feature of a brand.
A recent study confirmed the positive link between residents’ emotional solidarity with tourists and their support for tourism during the COVID-19 pandemic [33]. It is necessary to explore whether tourists’ emotional solidarity has similarly positive effects on their loyalty. Distinct from research on destination loyalty in general, this study uses WOM and repurchase intention to measure customers’ loyalty in the context of the homestay industry. Tourists who feel warmly welcomed by their hosts may be more likely to engage in positive online WOM through reviews and evaluations, and their affection for their hosts may increase their sense of identification with their hosts. Feeling close to homestay operators or even forging friendships with them will contribute to tourists’ positive WOM and strengthen their revisit intention, as they look forward to experiencing similarly warm hospitality in the future. Therefore, when looking specifically at homestay tourism, do the three dimensions of emotional solidarity (feeling welcome, sympathetic understanding and emotional closeness) affect tourists’ attitudes toward the homestay industry, including their willingness to recommend or repurchase? The answer to this question may serve as a useful reference for the recovery and sustainable development of the homestay industry in Guangzhou and similar cities around the world in the post-pandemic era. Based on the literature review, we hypothesize as follows:
Homestay tourists’ (a) sense of feeling welcomed by hosts, (b) sympathetic understanding of hosts, and (c) emotional closeness to hosts significantly enhance their customer loyalty.
This study hypothesizes that homestay tourists’ perceived risk negatively influences the extent to which they feel welcomed by hosts, their emotional closeness to hosts, and their sympathetic understanding of hosts (H1a, H1b, H1c), all of which enhance their loyalty to the homestay industry (H3a, H3b, H3c). A prior study indicated that the three dimensions of residents’ emotional solidarity partially mediated the relationship between residents’ perceived risk and their support for the tourism industry on Jeju Island, Korea [33]. In another study, these dimensions fully mediated the relationship between residents’ sincere social interactions with tourists and tourists’ environmentally responsible behavior in Xiamen, Fujian province, China [72]. Along these lines, we hypothesize as follows:
Homestay tourists’ (a) sense of feeling welcomed by hosts, (b) sympathetic understanding of hosts, and (c) emotional closeness to hosts mediate the relationship between perceived risk and customer loyalty.
The research model is shown in Figure 1.
3. Methodology
3.1. Study Site
Guangzhou was chosen as the study site for the following three reasons. First, Guangzhou, also known as Canton, is the capital city of China’s Guangdong province, with a history of more than 2200 years. Once a major terminus of the maritime Silk Road in ancient China, the city continues to serve as a major port and international transportation hub in southern China. With its unique cuisine (Lingnan/Cantonese, dim sum, etc.), comfortable climate, and wide array of tourist attractions, Guangzhou receives many domestic and overseas tourists every year. The number of visitors reached 16.24 million on the National Day holiday in 2019 [73], and even in the pandemic year of 2020 this figure was only slightly lower, at 14.07 million [73]. The city’s popularity with tourists has led to the rapid development of its homestay industry.
Second, Guangzhou is one of China’s main destinations for the domestic and even international convention and exhibition industry, with a history of hosting many large-scale events, such as fairs and expositions. It hosted the Asian Games in 2010. The city’s accommodation industry has benefited from these events, especially the China Import and Export Fair.
Third, Guangzhou was one of the first homestay destinations in China and has many registered homestays [74], many of which feature special architectural styles. Guangzhou municipality intends to increase its investment in the local homestay industry and has launched a development plan for 2018 to 2035 [74] that includes further regulating and optimizing three homestay industry areas with specific themes, the details of which can be seen in Figure 2 which drawn by the authors according to the “Development Plan of Homestay in Guangzhou from 2018 to 2035” [74]. These plans for the local homestay industry demand scholarly insights into tourists’ perceived risk in the post-pandemic era.
3.2. Instruments
A three-part survey was developed. Part 1 contained two filtering questions: “Are you a tourist?” (to exclude local residents) and “Do you have any recent experience of homestay services in Guangzhou (in the last three months)?”. Part 2 contained items measuring the four focal constructs, namely perceived risk, emotional solidarity, WOM, and repurchase intention. Part 3 collected demographic information, namely age, gender, education, monthly income, and place of origin.
The constructs were based on the literature, with some minor adjustments to better suit the context of the homestay industry. Perceived risk was measured following Joo et al. [33]; example items are “Guangzhou homestay hosts make me feel more at risk” and “Guangzhou homestay hosts make it inconvenient for me to engage in outdoor activities”. Woosnam and Norman’s [36] three-dimensional scale with 10 items was used to measure tourists’ emotional solidarity. The dimensions were as follows: feeling welcome (e.g., “I am proud to be welcomed as a visitor to Guangzhou”), sympathetic understanding of hosts (e.g., “I understand Guangzhou’s homestay operators”), and emotional closeness to hosts (e.g., “I feel close to homestay operators I have met in Guangzhou”). Customer loyalty was captured by four items from a WOM scale (e.g., “I will say positive things about Guangzhou homestay accommodation”) and three items from a repurchase intention scale (e.g., “I will keep using Guangzhou homestay accommodation”) adapted from Maxim and Netemeyer [69]. A 7-point Likert scale was used for all the items, with 1 indicating strongly support/disagree and 7 indicating strongly support/agree.
The scales were translated from English. To ensure that they were comprehensible to the respondents, the first author translated the items into Chinese, followed by consultation with two other translation experts. To increase the clarity, readability, and face validity of the measurement, the items were then checked by 10 persons who were either Master’s or Ph.D. students with relevant research experience or faculty members in hospitality and tourism at the authors’ universities. Their feedback led to some minor changes to further clarify the items. A pilot study was then conducted with 50 tourists with relevant and recent homestay experience via Airbnb from March 7 to 9, 2021. Airbnb is the largest and most popular homestay platform in the world. All four constructs yielded satisfactory internal consistency (Cronbach’s α > 0.70).
3.3. Data Collection
Owing to limited funds and time constraints, the survey was administered online using purposive sampling and snowball sampling [75]. The survey was available from March 15 to 30, 2021, during which time the pandemic situation in Guangzhou remained stable (with no fluctuations to affect tourism) [67]. Administering the survey online was also appropriate because according to an analysis of Airbnb users in 2020 [76], homestay users are mainly “young and middle-aged” (with the majority between 25 and 40 years old) and highly familiar with the Internet. One of the authors had lived in Guangzhou for 15 years and had relevant experience of conducting tourism research. The cultural experience and social network developed in this period provided a good understanding of the research population and relatively easy access to the homestay industry. Eighty-seven homestay hosts in Guangzhou were contacted through purposive sampling, who came from different homestay platforms (including Airbnb, Booking, Tujia, Ctrip, Agoda and Meituan, etc.). These are very popular platforms on Chinese homestay market. Snowball sampling was then conducted via these hosts’ social media (WeChat) groups, after informing them of the research objectives and obtaining their consent. In China, social media platforms such as WeChat are widely used by homestay hosts and guests as an efficient means of communication. Many homestay hosts specifically create WeChat groups for their guests as part of their customer relationship management. The number of people in these groups can range from dozens to hundreds. Many hosts in the same destination also establish or join social media groups exclusively for homestay hosts to share and update business information. Incentives were offered by the researchers to improve the response rate. The respondents were entered into lotteries for digital vouchers or coupons for use at cafés, restaurants, barber shops, or cinemas in China. Of the 520 questionnaires retrieved from 15 to 30 March 2021, three were excluded due to data abnormality and eight were excluded due to missing values. Therefore, 509 usable questionnaires were collected, giving an overall response rate of 97.88%.
3.4. Analysis
SPSS v.25 and SmartPLS v.3 were used in this study. SPSS was used to generate descriptive statistics and perform basic analysis. Reliability was assessed using Cronbach’s alpha [77] and factor loading, and average variance extracted (AVE) values were used to assess convergent validity [78,79]. The hypotheses were tested by calculating path coefficients, p-values, and R2 (to measure the proportion of the variation in the dependent variable that could be explained by each independent variable) using SmartPLS v.3 [80]. PLS structural equation modeling is a robust variance-based analysis technique associated with relatively few identification and estimation problems [78]. It has been widely used in tourism research [72,81]. PLS has no specific requirements for the normality of data distribution or for data size and is thus suitable for exploratory research [81]. Due to the novelty and exploratory nature of our topic, SmartPLS was deemed to be appropriate for this study. After the reliability and validity analysis, a nonparametric bootstrap analysis method was used to test the hypotheses.
4. Results
4.1. Sample Overview
The sample demographics are shown in Table 1. The sample was almost evenly split between men (49.9%) and women (50.1%). Due to the online administration of the survey, slightly more than a quarter of the respondents were between 26 and 30 years old (25.7%), followed by those aged 18–25 (18.1%); only a small proportion were over 60 years old (3.2%). More than half of the respondents held Bachelor’s or postgraduate degrees (67.5%) and the two most common brackets for monthly income were USD463–924 (28.5%) and USD1387 or above (26.6%). Most of the respondents were from outside Guangdong province (85.5%).
4.2. Measurement Model
Before testing the hypotheses, a measurement model was established and evaluated with SmartPLS, using Cronbach’s alpha and composite reliability (CR) to test the reliability of the scales. For acceptable reliability, Cronbach’s alpha and CR should be higher than 0.70 [77,80]. As shown in Table 2, Cronbach’s alpha ranged from 0.749 to 0.876 and CR ranged from 0.888 to 0.915 for all of the constructs. These results indicate the reliability of the scales.
Standardized factor loading and AVE values were calculated to test the convergent validity of the scales. The standardized factor loadings of all of the items were greater than 0.60 [78] and the AVE values of all of the constructs were higher than 0.50 [79], indicating that the scales had good convergent validity. As shown in Table 2, the standardized factor loadings of all of the items ranged from 0.794 to 0.907 and the AVE values ranged from 0.667 to 0.799.
To ensure discriminant validity, the related coefficient between the value and other constructs should be less than the square root of the AVE of a specific construct. Table 3 shows that the related coefficients were all less than the square root of the AVE, between 0.816 and 0.894. This shows that each latent variable had good discriminant validity.
The composite mean of perceived risk (2.768) indicates that even in the post-pandemic era, tourists’ perceived risk was not high, as was also reflected in the score for each dimension of emotional solidarity. The composite means obtained for feeling welcome (5.28), sympathetic understanding (5.278), and emotional closeness (5.055) were all higher than 4.0, clearly indicating that the respondents felt a sense of solidarity with their homestay hosts. The mean score for customer loyalty (5.14) was also high.
4.3. Structural Model
R2 is an essential criterion accounting for the explanatory power of the endogenous latent variable [81]. Bootstrapping with 5000 iterations was carried out to test the structural model in this study. As shown in Figure 3 and Table 4, R2 for feeling welcome was 0.293, meaning that the explained variance of this construct reached 29.3%. The R2 values for sympathetic understanding and emotional closeness were 0.317 and 0.247, respectively, indicating that the explanatory power of these constructs reached 31.7% and 24.7%. R2 for customer loyalty was 0.572, indicating that this construct explained 57.2% of the variation.
Figure 3 and Table 4 also show that perceived risk had a significant negative impact on customer loyalty (β = −0.264, p < 0.05), feeling welcome (β = −0.541, p < 0.05), sympathetic understanding (β = −0.563, p < 0.05), and emotional closeness (β = −0.497, p < 0.05). These results support H1a, H1b, H1c and H2. The findings suggest that homestay tourists’ perceived risk has a direct and negative impact on their customer loyalty, and that when their perceived risk is low, they have a stronger sense of feeling welcomed by their homestay hosts and greater sympathetic understanding of and emotional connection with their hosts.
Furthermore, tourists’ sense of feeling welcome, sympathetic understanding, and emotional closeness had significant positive effects on their customer loyalty (β = 0.319, p < 0.05; β = 0.138, p < 0.05; β = 0.245, p < 0.05), supporting H3a, H3b, and H3c. The findings suggest that when tourists feel warmly welcomed by their homestay hosts and establish an emotional connection with and sympathetic understanding of their hosts, they are more likely to spread positive WOM and recommend the homestays to others.
4.4. Mediation Effect
As shown in Table 4, the test of the mediating effect of emotional solidarity indicated that tourists’ perceived risk had an indirect effect on their customer loyalty through their sense of feeling welcome and sympathetic understanding of and emotional connection with the host (β = −0.173, p < 0.05; β = −0.078, p < 0.05; β = −0.122, p < 0.05, respectively; the confidence intervals do not contain 0, indicating an indirect effect). The three dimensions of emotional solidarity between tourists and homestay hosts thus partially mediated the relationship between tourists’ perceived risk and customer loyalty. This finding supports H4a, H4b, and H4c.
5. Conclusions and Discussion
Tourists’ perceptions of various risks in destinations (sparked by major events such as the SARS and COVID-19 outbreaks, the 9/11 terrorist attacks, and natural disasters) have attracted ongoing scholarly interest due to their significance for destination management organizations and other industry practitioners [54,82]. The rapid growth of the Internet has greatly boosted the global development of the homestay industry. However, despite its significance and timeliness, the perceived risk of homestay tourists, especially in the post-pandemic era, requires further attention from tourism researchers. To supplement and enrich the literature, this study took Guangzhou’s homestay industry as the context for exploring the relationship between tourists’ perceived risk, emotional solidarity, and customer loyalty in the post-pandemic era.
The results suggest that tourists neither perceive homestay hosts as a primary source of risk nor feel that hosts are likely to pose an inconvenience to their travel when lodging in homestays. Although it is human nature to avoid risks [33], especially in the face of potential exposure to a deadly virus, the results of this study suggest that homestays pose no additional concerns beyond those normally held by tourists in the post-pandemic era. When this study was conducted, Guangzhou had not recorded any new local cases of COVID-19 for nearly 5 months [67]. When cases were imported from abroad, the public was informed in a timely and transparent manner, which may have been reassuring for tourists. This may have helped to alleviate tourists’ perceived risk, potentially explaining why customer loyalty remained high in the results for H2.
The results of this study also show that tourists’ perceived risk was negatively correlated with the three aspects of emotional solidarity, as predicted in H1. This indicates that the lower tourists’ perceived risk, the more likely they were to feel welcome and to establish positive emotional ties with and a sympathetic understanding of their hosts, and vice versa. A possible reason for this finding is that low perceived risk alleviates tourists’ anxiety about risks during travel, reduces the inconvenience of communication and interaction with their hosts, enhances their understanding of their hosts, and thus strengthens their emotional solidarity with their hosts. This echoes another study [33] that indicated that high perceived risk was manifested in maintaining social distance and wearing masks, which reduced interaction between residents and tourists and generated emotional instability. This in turn led to their reduced emotional solidarity. Our findings point to the same conclusion from the opposite direction (i.e., from the case of low perceived risk).
Finally, the results of this study demonstrate the significantly positive relationship between tourists’ emotional solidarity and their customer loyalty. When tourists feel welcomed by their hosts and establish emotional ties and empathy with their hosts, they are likely to be more loyal to the destination’s overall homestay industry. In the post-pandemic era, the recovery of the homestay industry will rely on the recovery of the overall tourism economy. Homestay tourists who receive more attentive services from their hosts and develop a greater sense of emotional solidarity with their hosts may be more likely to choose homestay accommodation again. Based on their enhanced emotional recognition, the desire to visit again and enjoy similar emotionally gratifying experiences is likely to influence tourists’ decisions regarding future travel. Tourists’ emotional solidarity with hosts has been shown to enhance destination loyalty in general business contexts [25]. In the present study, this relationship was validated in the homestay industry in particular. Emotional solidarity was also found to play a partial mediating role in the relationship between perceived risk and customer loyalty. It therefore appears that tourists’ low perceived risk can further bolster their loyalty through its positive relationship with emotional solidarity. These findings suggest that when homestay tourists’ perceived risk relating to a destination is low, their loyalty to the destination’s homestay is indirectly stimulated by an enhanced sense of feeling welcome and of sympathetic understanding of and emotional closeness to their hosts.
5.1. Theoretical Implications
This study offers several major theoretical contributions.
First, it enriches the existing literature on the emotional solidarity by extending the scope of emotional solidarity theory from tourists in general to the specific context of homestay guests in the post-pandemic era. A very limited number studies of the homestay industry have adopted the concept of emotional solidarity to measure the perceived closeness of guests to their hosts [1,34,42]. Furthermore, although customer loyalty receives much academic attention in the homestay industry [83,84,85], the prediction of customer loyalty from the perspective of emotional solidarity is still lacking. Thus, this study complements the related research of adopting emotional solidarity theory in discussing the relationship between hosts and tourists to explain tourists’ loyalty towards homestay industry. In addition, this study also addresses the much-discussed issue of perceived risk in current post-pandemic era, by highlighting the relationship between tourists’ perceived risk and their emotional closeness with their hosts. This adds a more practical angle to the examination of tourists’ emotional state during pandemic. The findings reveal that tourists’ perceived risk has a negative impact on their emotional solidarity towards hosts and their customer loyalty. All in all, this study affirms the central and salient role of emotional solidarity by linking all three constructs in the context of the post-pandemic era. In addition, our proposed conceptual framework exhibited satisfactory explanatory power in the empirical analysis, which may have considerable theoretical implications for further studies in emotional-solidarity-relevant domains. Viruses such as SARS-CoV-2 are likely to be transmitted through human-to-human contact, prolonging the potential for infection.
5.2. Practical Implications
The results of this study are of practical significance to a range of stakeholders. To revitalize the homestay industry, homestay operators and hosts are highly recommended to take effective measures to reduce tourists’ perceived risk, as this will enhance tourists’ emotional solidarity with hosts and maintain their customer loyalty. Disinfection and cleanliness should be standardized routine practice for operators, including providing fresh linen for each guest, monitoring the health status of employees, being equipped with epidemic prevention materials, offering hygienic products with sterilization function, and ventilating every day. Furthermore, this is also highly suggested to be visualized or even promoted to their guests and other potential markets to free them from excessive anxiety or pressure in relation to homestay accommodation. Additionally, the layout of public space needs to be re-designed to facilitate the social distancing policy while increasing the amenity of the whole environment. Destination governments should provide and release timely information on the pandemic and publish the results of epidemiological investigations of relevant foreign populations (when and where these people have been [67]). Specifically, regulations about virus prevention and sanitation standards need to be issued and monitored by local governments.
Moreover, to enhance tourists’ emotional solidarity with homestay hosts, it is necessary to provide a safe, hospitable and comfortable tourism environment. The emotional closeness between tourists and hosts is key to continuous business success, so special attention should be paid to creating and maintaining a positive host-guest relationship. Destination marketing organizations or local government can facilitate a series of professional training programs for hosts in order to upgrade their hospitality services and enable them to provide tourists with high levels of expertise. Hosts or operators should attach great importance to bonding with their guests. One of the potential advantages of homestay industry over other traditional hotel industry comes possibly from more personalized human interaction, resulting in a greater degree of host-guest intimacy. This can be achieved by more local culture promotion to guests, which may enhance sympathetic understanding. An exquisitely designed exhibition of community landscape and individualized travelling activity recommendations can instill guests with a sense of uniqueness. Customized service to meet guests’ different needs and wants can also create a welcoming atmosphere. In the wake of the pandemic, gentle and kind reminders for tourists to adhere to health protocols also serve as a warm gesture of hospitality to show that hosts care for their guests. Taken together, providing tourists with a neat, convenient, comfortable and hospitable homestay environment could help to minimize tourists’ risk perception and thus enhance their emotional solidarity with their hosts.
6. Limitation and Future Research Directions
Although this study was rigorously designed and implemented, there are three limitations that should be considered. First, the proposed model in this study exhibited satisfactory explanatory power in the post-pandemic era by integrating tourists’ perceived risk, emotional solidarity, and customer loyalty, and could be replicated in a wider geographic context in future post-pandemic studies. However, the city of Guangzhou enjoys “alpha” city status (GaWC, 2020) [86] and was ranked among China’s 10 most popular tourism cities in 2020 [87]. Given the high quality and large quantity of homestay accommodation in Guangzhou [74], which itself has a particular urban appeal, the model developed here might not be generalizable to other kinds of destinations, especially small or medium-sized cities, providing more of a countryside homestay style.
Second, due to the pandemic, our sample did not include international tourists. However, as the world recovers from the pandemic and rates of vaccination increase, the international tourism market will eventually reopen. At this point, research could compare the extent to which the emotional solidarity with hosts of tourists from different cultural backgrounds is affected by their perceived risk related to homestay accommodation. Our model would thus provide a useful reference for the sustainable development of the homestay industry in the post-pandemic era.
Third, this study did not attempt to distinguish types of homestay (e.g., traditional versus modern homestays divided by architectural style, countryside versus urban homestays divided by geographical location, riverside versus mountain-view homestays divided by landform [88]). Figure 2 reveals that Guangzhou is planning three homestay areas with distinct architectural styles or themes. Future research on the segmented homestay market in China or other countries is warranted to gain more insights into this industry trend.
Author Contributions
X.Z. and J.T. were involved in the conceptualization, literature review, methodological design, investigation, data analysis, writing and review. All authors have read and agreed to the published version of the manuscript.
Funding
This study received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Figures and Tables
![View Image - Figure 2. Three areas of Guangzhou homestay industry. Source: Drawn by Authors according to “The Development Plan of Homestay in Guangzhou from 2018 to 2035” [74].](https://media.proquest.com/media/hms/THUM/4/Of1sJ?_a=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%3D%3D&_s=%2BoB%2FmqgbzCGDeobxoM24AxpxnJc%3D "View Image - Figure 2. Three areas of Guangzhou homestay industry. Source: Drawn by Authors according to “The Development Plan of Homestay in Guangzhou from 2018 to 2035” [74].")
Enlarge this image.Figure 2. Three areas of Guangzhou homestay industry. Source: Drawn by Authors according to “The Development Plan of Homestay in Guangzhou from 2018 to 2035” [74].
Sample overview.
| Variables | n (%) |
|---|---|
| Gender | |
| Male | 254 (49.9%) |
| Female | 255 (50.1) |
| Age | |
| 18 years or younger | 55 (10.8%) |
| 18–25 years | 92 (18.1%) |
| 26–30 years | 131 (25.7%) |
| 31–40 years | 84 (16.5%) |
| 41–50 years | 76 (14.9%) |
| 51–60 years | 55 (10.8%) |
| 60 years or older | 16 (3.2%) |
| Education | |
| Junior middle school or below | 52 (10.3%) |
| High school | 113 (22.2%) |
| Undergraduate | 267 (52.4%) |
| Postgraduate or above | 77 (15.1%) |
| Monthly income | |
| CNY 3000 (USD 462) or less | 104 (20.4%) |
| CNY 3001 (USD 463) to CNY 6000 (USD 924) | 145 (28.5%) |
| CNY 6001 (USD 771) to CNY 9000 (USD 1386) | 125 (24.5%) |
| CNY 9001 (USD 1387) or more | 135 (26.6%) |
| Place of origin | |
| Guangdong Province | 74 (14.5%) |
| Outside Guangdong Province | 435 (85.5%) |
Results for measurement model.
| Factor and Item | λ | SD |
|---|---|---|
| Perceived risk (Mean = 2.77, α = 0.833, CR = 0.889, AVE = 0.667) | ||
| Guangzhou homestay operators increase my anxiety/stress related to risk | 0.816 | 1.645 |
| Guangzhou homestay operators increase my perceived risk | 0.825 | 1.663 |
| Guangzhou homestay operators increase my inconvenience in outdoor activities | 0.824 | 1.626 |
| Guangzhou homestay operators make me reduce my outdoor activities | 0.800 | 1.628 |
| Welcoming nature (Mean = 5.28, α = 0.857, CR = 0.903, AVE = 0.700) | ||
| I am proud to be welcomed as a visitor to Guangzhou | 0.876 | 1.59 |
| I feel Guangzhou homestay operators appreciate the benefits associated with me (as visitor) coming to the community | 0.794 | 1.72 |
| I feel Guangzhou homestay operators appreciate visitors for the contribution we (as visitors) make to the local economy | 0.853 | 1.48 |
| I treat Guangzhou homestay operators fairly | 0.822 | 1.633 |
| Sympathetic understanding (Mean = 5.28, α = 0.876, CR = 0.915, AVE = 0.729) | ||
| I identify with Guangzhou homestay operators | 0.863 | 1.749 |
| I have a lot in common with Guangzhou homestay operators | 0.870 | 1.663 |
| I understand Guangzhou homestay operators | 0.846 | 1.752 |
| I feel affection towards Guangzhou homestay operators | 0.836 | 1.75 |
| Emotional closeness (Mean = 5.06, α = 0.749, CR = 0.888, AVE = 0.799) | ||
| I feel close to Guangzhou homestay operators I have met in Guangzhou | 0.880 | 1.738 |
| I have made friends with some of Guangzhou homestay operators | 0.907 | 1.651 |
| WOM (Mean = 5.21, α = 0.847, CR = 0.897, AVE = 0.685) | ||
| I will say positive things about Guangzhou homestay | 0.826 | 1.57 |
| I will recommend Guangzhou homestay to someone who seeks my advice | 0.817 | 1.543 |
| I will encourage friends and relatives to stay at Guangzhou homestay | 0.807 | 1.455 |
| I am likely to spread positive word-of-mouth about Guangzhou homestay | 0.860 | 1.519 |
| Repurchase intention (Mean = 5.05, α = 0.843, CR = 0.905, AVE = 0.761) | ||
| I will keep visiting Guangzhou homestay | 0.858 | 1.616 |
| I am proud to tell others that I am a customer of Guangzhou homestay | 0.885 | 1.657 |
| I would definitely recommend Guangzhou homestay to my friends and coworkers | 0.875 | 1.772 |
Construct correlation coefficients and square root of AVE.
| Perceived Risk | Welcoming Nature | Sympathetic Understanding | Emotional Closeness | WOM | Repurchasing Intention | |
|---|---|---|---|---|---|---|
| Perceived risk | 0.816 | |||||
| Welcoming nature | −0.541 | 0.837 | ||||
| Sympathetic understanding | −0.563 | 0.418 | 0.854 | |||
| Emotional closeness | −0.497 | 0.406 | 0.361 | 0.894 | ||
| WOM | −0.554 | 0.521 | 0.451 | 0.482 | 0.828 | |
| Repurchasing intention | −0.555 | 0.563 | 0.433 | 0.488 | 0.518 | 0.872 |
Note: Values on the diagonal line are the square roots of AVE and those off the diagonal line are inter-construct correlation coefficients.
Table 4Results of testing hypotheses.
| Hypothesis | Path | Path Coefficient (β) | STDEV | t | p | Confidence Intervals | Remark |
|---|---|---|---|---|---|---|---|
| H1a | Perceived risk → Feeling Welcome | −0.541 | 0.046 | 11.676 | 0.000 | [−0.629, −0.455] | Supported |
| H1b | Perceived risk → Sympathetic Understanding | −0.563 | 0.044 | 12.775 | 0.002 | [−0.650, −0.481] | Supported |
| H1c | Perceived risk → Emotional Closeness | −0.497 | 0.045 | 10.957 | 0.000 | [−0.582, −0.408] | Supported |
| H2 | Pperceived risk → Customer Loyalty | −0.264 | 0.073 | 3.627 | 0.000 | [−0.413, −0.126] | Supported |
| H3a | Feeling Welcome → Customer Loyalty | 0.319 | 0.042 | 7.693 | 0.000 | [0.238, 0.402] | Supported |
| H3b | Sympathetic Understanding → Customer Loyalty | 0.138 | 0.043 | 3.211 | 0.001 | [0.053, 0.221] | Supported |
| H3c | Emotional Closeness → Customer Loyalty | 0.245 | 0.041 | 5.990 | 0.000 | [0.161, 0.325] | Supported |
| H4a | Perceived risk → Feeling Welcome → Customer Loyalty | −0.173 | 0.029 | 6.052 | 0.000 | [−0.233, −0.120] | Supported |
| H4b | Perceived risk → Sympathetic Understanding → Customer Loyalty | −0.078 | 0.025 | 3.073 | 0.002 | [−0.131, −0.032] | Supported |
| H4c | Perceived risk → Emotional Closeness → Customer Loyalty | −0.122 | 0.023 | 5.307 | 0.000 | [−0.170, −0.081] | Supported |
© 2021 by the authors.