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Article

A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits

by
Jungho Suh
1,
Cevat Tosun
1,
Thomas Eck
2,* and
Soyoung An
3,*
1
Department of Management, School of Business, The George Washington University, Funger Hall, 2201 G Street NW, Washington, DC 20052, USA
2
Department of Hospitality and Tourism Management, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
3
Department of Sport Industry Studies, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(1), 223; https://doi.org/10.3390/su15010223
Submission received: 18 October 2022 / Revised: 13 December 2022 / Accepted: 19 December 2022 / Published: 23 December 2022
(This article belongs to the Special Issue Sustainable Innovation in Tourism: Practice and Prediction)
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Abstract

:
Guest value priorities in relation to online peer-to-peer accommodation are an underexamined area. This study examined social and economic benefits among Airbnb guests. The relationships between guests’ benefit priorities were tested in relation to satisfaction and behavioral intention. A total of 693 Airbnb guests were recruited from the U.S. and China. A framework to examine how cross-cultural differences moderate the associations between constructs was employed to examine the influences of the two cultures, one characterized by collectivism (China) and the other by individualism (U.S.). Confirmatory factory analysis and partial least-squares structural equation modeling (PLS-SEM) were used to test variable relationships. PLS-SEM analysis indicated that social and economic benefits both significantly influenced satisfaction and behavioral intention (satisfaction also influenced behavioral intention). Multigroup analysis was employed to test a framework examining cultural differences. It was found that social and economic benefits influenced behavioral intention differently for Chinese and U.S. Airbnb guests. The results suggest the importance of social and economic benefits in a peer-to-peer accommodation setting, as well as the need to understand cultural differences in the sharing economy.

1. Introduction

Airbnb has become a disruptive innovation in the tourism and hospitality industry [1,2,3]. The theory of disruptive innovation defines disruption as a process by which a small company with fewer resources can effectively challenge established businesses [1]. In this regard, marketers and decision makers in the traditional lodging industry have realized the crucial impact of Airbnb on their businesses [2,3,4,5,6]. Not surprisingly, many tourism and hospitality researchers have begun to pay attention to the Airbnb phenomenon. As a result, numerous studies have explored the role of Airbnb and its influence on the tourism and hospitality industry [2,3,5,7,8,9,10].
International companies in the sharing economy face difficulties and opportunities when they seek to enter into new markets. Technology-oriented companies which plan to expand globally and launch their products in markets with different cultural priorities need to gain a better understanding of the uniqueness of local markets. Improved understanding of the different values prioritized by users of different cultures is important in order to succeed in such markets. Maintaining company identity and consistency in product features internationally enables sharing economy companies to achieve cost reductions in marketing activities across countries. Conversely, it is necessary for such companies to localize marketing strategies through the realization of local culture and value priorities [11,12]. However, there is still a lack of best practice among the world’s most influential technology companies, including U.S. sharing economy start-ups that have successfully established in the Chinese market [13]. There are many issues that companies entering new markets need to contend with. Kirby et al. [13] discussed the issue of the future of foreign companies that try to enter the Chinese market, highlighting challenges such as their ability to adapt to the local market environment, overcoming China’s unique regulatory conditions, and domestic competitors. Airbnb’s recent decision to fold its operation in China in 2022 [14] highlights the challenges associated with entry, expansion, and survival in the Chinese market. Given this occurrence, a better understanding of what tourists from markets with different cultural priorities desire is an issue of interest to researchers. Further study of this issue may help to explain what sharing companies can do to be successful and to better meet the needs of consumers that have different value priorities, particularly in distinctive cultural markets, or of tourists from those markets who travel elsewhere. An enhanced understanding of value priorities may benefit such companies in employing technology and innovation as they seek to meet the needs of different types of tourist consumers.
Numerous prior studies have taken an exploratory approach and investigated marketing constructs [2,3,5,7,9,15,16,17], but relatively few empirical studies regarding Airbnb have attempted to examine Airbnb guests’ experiences in cross-cultural settings in light of the mediating effects of individualism and collectivism. As Hofstede et al. [18] suggested, social and economic benefits are the distinctive value priorities that differentiate consumers from different cultures based on their individualism/collectivism classification. In particular, social benefits can motivate customers to pursue behaviors that are appreciated by friends and family and offer a chance to relate to others, which can be theoretically explained by the individualism/collectivism distinction. Economic benefits serve the purpose of motivating consumers to pursue monetary and nonmonetary rewards that compensate them with material incentives such as store credits, free upgrades, or discounts. In view of the lack of focus on a cross-cultural perspective in the existing literature, our study aimed to shed light on this crucial issue of cross-cultural differences in the social and economic benefits gained by American and Chinese Airbnb guests by examining the effects of individualism and collectivism. Specifically, the research objectives were to compare the differing effects of Airbnb guests’ value priorities in terms of social and economic benefits on their satisfaction and behavioral intentions, and to apply the individualism/collectivism framework described by Hofstede et al. [18] to examine whether the benefits were different based on cultural orientation. This study addressed the following research questions: Do Airbnb guests’ value priorities influence their satisfaction and behavioral intentions? Is there a difference between Airbnb guests’ social and economic benefits (value priorities) based on cultural orientation? This allowed the researchers to compare the differing effects of Airbnb guests’ value priorities in terms of social and economic benefits on their satisfaction and behavioral intentions, while also testing the individualism/collectivism framework described by Hofstede et al. [18] to examine whether the benefits differed by cultural orientation. Consequently, this study addressed a gap in the literature through an empirical cross-cultural examination of Airbnb guests, enabling the application of the individualism/collectivism framework in order to better understand the benefits sought by guests and their value priorities.

2. Conceptual Framework and Hypotheses

2.1. Social and Economic Benefits as Determinants of Satisfaction and Behavioral Intention in an Airbnb Accommodation

Many researchers have found that customer satisfaction results in favorable post-consumption evaluations such as favorable word of mouth and repeated purchases, thereby fostering stronger customer loyalty and increases in sales and profits [9,15,19,20]. Accordingly, it is crucial to better understand the determinants that lead to customer satisfaction and behavioral intention to use Airbnb accommodation in different settings. As the existing literature suggests [9,15,20], customer satisfaction is defined as customers’ overall evaluations in the context of Airbnb rental home guest experiences.
Previous studies on customer satisfaction and behavioral intention have suggested that customers are influenced by various factors relevant to their needs and wants [15]. Similarly, travelers who are satisfied with joining sharing economy platforms seek out socialization opportunities. Researchers have confirmed that the sharing economy is related to social exchange theory, as examining relationships between guest satisfaction and intention to use sharing economy platforms such as Airbnb are critical to understanding guest behaviors [15].
A significant relationship between satisfaction and behavioral intention has been verified in the literature. Priporas et al. [9] examined the effect of service quality and customer satisfaction on loyalty in an Airbnb setting, and a positive relationship between satisfaction and loyalty was found. More recently, An et al. [21] also examined service quality, perceived value, satisfaction, and revisit intention among Airbnb guests in the United States. They found that satisfaction was a significant predictor of revisit intention. In the same vein, other studies [22,23,24] also support a positive and direct relationship between those two variables.
Prior research related to Airbnb has explored satisfaction and behavioral intention with a specific focus or in different settings: guest experiences [21], risk perception [25], and website perception [26] have been previously studied. None of these prior studies tested social benefits or economic benefits as the antecedent variables of satisfaction or behavioral intention, which indicates that further research about these variables is necessary. It would be useful to further research these variables in an Airbnb setting in relation to social and economic benefits.
Social exchange theory in tourism studies has identified that individual perceptions of the social and economic rewards of tourism are based on human interactions that involve cost–benefit analysis to maximize rewards [27]. Tourists will continue to engage in social exchange if the exchange is likely to generate social and economic tourism benefits. As Tussyadiah [15] suggested, behavioral intention in the context of sharing economy platforms is caused by satisfaction and received benefits. Therefore, it can be suggested that the impacts of social and economic benefits gained via an Airbnb rental home experience can lead to satisfaction and behavioral intention. This study fills a gap in the literature by examining the specific relationships between variables, as informed by the relevant literature and outlined below.
H1. 
Social benefits have a positive effect on Airbnb guests’ satisfaction.
H2. 
Social benefits have a positive effect on Airbnb guests’ behavioral intentions.
H3. 
Economic benefits have a positive effect on Airbnb guests’ satisfaction.
H4. 
Economic benefits have a positive effect on Airbnb guests’ behavioral intentions.
H5. 
Satisfaction has a positive effect on behavioral intention.

2.2. Airbnb Experiences and Cultural Value Priorities

Although basic human desires and needs are similar throughout the world, the causes of consumer satisfaction vary according to culture [28]. The crucial challenge that sharing economy start-ups in the international travel industry have faced is understanding different international tourism business and developing new marketing plans suitable for local markets by considering factors such as cultures, values, and quality of life. The catalyst for cross-cultural investigation and comparisons of Airbnb guests is the natural assumption that the way travelers respond to the rental home experience depends on culture-driven differences in social and economic values [29].
Building on prior work, Hofstede et al. [18] proposed an individualism/collectivism classification, which has been regarded as a distinctive differentiator to compare entire countries based on the relationships individual people have with the group with which they identify. Individualism refers to the extent to which consumers in a culture value individual activity more than group behaviors, whereas in a regional market with a collectivistic culture, there is a strong sense of community and consumers have a high expectation that their group will value harmonious interdependence. Additionally, individualistic cultures often have a less controlled social structure related to group norms, and consumers from individualist cultures are more inclined to not conform to social norms, showing more concern with independent decision making [30]. Consumers from collectivistic cultures tend to express higher degrees of group behavior and value promotion of their status quo [31]. Despite criticism of the framework [32,33], the classification strongly attributes higher individualism to U.S. culture and higher collectivism to Chinese culture. Furthermore, according to the framework developed by Hofstede et al. [18], the Individualism Index scores for the U.S. and China were 91 and 20, respectively. Therefore, this study considers China a collectivistic society, whereas the U.S. is considered an individualistic society.
The Hofstede et al. [18] framework to understand cultural values and priorities could be useful for better understanding travelers’ Airbnb selections. Hofstede’s individualism/collectivism classification has been pervasively adopted in many marketing studies for comparative analysis between different cultures [29,34,35,36]. Yen and Tang [37] found that social benefits motivate travelers to pursue activities that appeal to friends and family significant others and give them chances to be relatable to friends within the setting of electronic word-of-mouth (eWOM) motivations in the context of hotel experiences. In the same vein, Tussyadiah [15] emphasized that a traveler’s social benefits gained through Airbnb experiences and involving the desire for socialization and sense of belonging can include meeting new people in a local community [38,39]. Therefore, we anticipate moderating effects of the individualism/collectivism cultural framework on the relationships of social benefits with satisfaction and behavioral intention. To the best of our knowledge, no other cross-cultural Airbnb studies have directly compared groups from different cultures, so this research will be a useful addition to the literature. Based upon this literature review, the following hypotheses (see Figure 1) were developed to guide the scientific inquiry:
H6. 
The relationship between social benefits and satisfaction is different between Chinese Airbnb guests and U.S. Airbnb guests.
H7. 
The relationship between social benefits and behavioral intention is different between Chinese Airbnb guests and U.S. Airbnb guests.
H8. 
The relationship between economic benefits and satisfaction is different between Chinese Airbnb guests and U.S. Airbnb guests.
H9. 
The relationship between economic benefits and behavioral intention is different between Chinese Airbnb guests and U.S. Airbnb guests.

3. Research Methods

3.1. Measurements

The purpose of this study was to examine the relationships between the differing effects of Airbnb guests’ value priorities in terms of social and economic benefits on their satisfaction and behavioral intentions. Investigating such complex relationships in a comparative manner seems to be challenging, given the absence of similar studies and related research instruments on the issue. Therefore, surveys were utilized as the main data collection method. In order to design a questionnaire informed by the literature on the relationships between the different variables under investigation, several open-ended interviews were carried out with Airbnb owners and customers. From these, a draft questionnaire was developed and a pilot study was completed. The pilot study participants consisted of 40 people with prior purchasing experiences of Airbnb services, 15 owners of Airbnb businesses, and 10 academics. After considering the results of this pilot study, the survey was further enhanced by improving wording, omitting some statements, and adding new items where appropriate. In brief, conducting a number of key-informant interviews, followed by the administration and implementation of a quantitative questionnaire pilot study, was used to confirm both the content and construct validity of instruments employed for this research.
Social benefits and economic benefits as the antecedent variables were measured based on studies regarding travelers’ experiences in the sharing economy [15,39]. Guest satisfaction was operationally defined according to three items (e.g., “I am happy with my decision to stay at Airbnb”) adopted from the instruments used in previous studies [9,20]. A scale utilized by Casaló et al. [40] and Tussyadiah [15] was used to evaluate behavioral intention (e.g., “I intend to revisit Airbnb in the next 2 years”). In the current study, we assessed all scale items on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). In addition, all the measurement items originated from English-language works, but the online survey for Chinese Airbnb guests was designed in Mandarin Chinese. One college professor and two research associates with Chinese as their first language reviewed survey items and ensured language adequacy and fluency. The measurement items and their corresponding scales are summarized in Table 1 (below).

3.2. Data Collection, Sampling, and Analysis

The online survey questionnaire distribution targeted adults (aged 18 years or older) residing in the U.S. and China between January and July of 2018, and who had used Airbnb when traveling in the past. Among the collected 795 responses, participants who did not use Airbnb during their trips were excluded through a screening question and incomplete responses were removed. The remaining 693 responses were used for analysis. The final sample consisted of 362 Airbnb guests from the U.S. and 331 from China. Amazon Mechanical Turk (MTurk) was used to recruit participants in the U.S. and Sojump was used to recruit participants in China. Both platforms have been increasingly adopted to collect samples in recent studies [3,5,15,41,42,43,44,45,46].
Survey data were analyzed using partial least-squares structural equation modeling (PLS-SEM), which in recent years has been used increasingly in tourism research [47,48,49,50,51]. PLS-SEM has been widely used for confirming theories and has been recommended for use in multigroup analysis, compared with conventional SEM [52]. In particular, to test the moderating effects of individualism on satisfaction and behavioral intention, this study applied the product indicator approach, a commonly used approach to create the interaction term in regression-based analyses in PLS-SEM [53]. The software SmartPLS 3.27 was applied to analyze the measurement model and the structural model [54]. Statistical power analysis was completed to assess what an appropriate sample for this study would be. Using G*Power and employing sets suggested by prior researchers [53], a minimum estimated sample size was arrived at. The results showed that through a parameter effect size of 0.15, 5% significance level, and power of 0.90, the minimum required sample size for this study would be 108 participants. Our sample size of 693 respondents was thus deemed acceptable to undertake the analysis.

4. Results

4.1. Profile of Respondents

Table 2 shows the general characteristics of the respondents. A total of 331 Chinese participants responded to the survey. Female respondents (59.2%) outnumbered male respondents (40.8%). Most respondents were in the group aged 27–35 (57.4%) and the average age for the Chinese respondents was 30.7 years old. In terms of employment and education level, the majority of respondents were working full-time (90.9%) and were college graduates (81.3%). The most common annual household income range of respondents was RMB 200,000 or more (30.6%). In the U.S.-based sample, a total of 362 respondents participated in the survey. Male respondents (55.2%) outnumbered female respondents (44.8%). Most respondents were in the group aged 27–35 (48.1%) and the average age for the U.S. respondents was 33.1 years old. In terms of employment and education level, the majority of respondents were working full-time (77.9%) and were college graduates (50.0%). The most common annual household income range of respondents was USD 50,000 to 99,999 (42.5%).

4.2. Measurement Model

With the purpose of evaluating internal consistency, construct validity, convergent validity, and discriminant validity, the study conducted confirmatory factor analysis (CFA) as shown in Table 3. The results of the CFA showed that the composite reliability of each construct (0.826 to 0.900) was higher than the recommended threshold value of 0.70, and Cronbach’s alpha values for the constructs ranged between 0.721 and 0.851, also indicating acceptable or good levels of reliability [55,56]. This suggested sufficient internal consistency of the measurements. Furthermore, all average variance extracted (AVE) values met the threshold value of 0.50, indicating an acceptable convergent validity [55]. Discriminant validity was indicated by the square roots of the AVE for each factor being greater than the correlations between that factor and other factors. Moreover, the maximum shared variance (MSV) was lower than the AVE for all factors, and the heterotrait–monotrait (HTMT) ratio of correlations between two constructs was below 0.90 [57], demonstrating discriminant validity [55,56]. The discriminant validity test results are shown in Table 4.

4.3. Testing Hypotheses

PLS-SEM was used to test the proposed hypotheses regarding relationships among social benefits (SB), economic benefits (EB), guest satisfaction (SAT), and behavioral intention (BI) for the entire group, as presented in Figure 2. The endogenous variables’ variance accounting for R2 was as follows: satisfaction (27.6%) and behavioral intention (50.2%). We used a bootstrapping technique for evaluating the path relationships and t-statistics, and a bootstrapping sampling process of 2000 was employed to assess the significant main and moderating effects of data analysis [47].
The findings indicated that the relationships between social benefits and satisfaction (β = 0.335, t-value = 8.164, p < 0.001), social benefits and behavioral intention (β = 0.156, t-value = 4.602, p < 0.001), economic benefits and satisfaction (β = 0.319, t-value = 7.704, p < 0.001), economic benefits and behavioral intention (β = 0.186, t-value = 4.321, p < 0.001), and satisfaction and behavioral intention (β = 0.524, t-value = 11.321, p < 0.001) were all significant. Thus, H1, H2 H3, H4, and H5, as displayed in Figure 2, were supported.

4.4. Multigroup Analysis: Moderating Effects of the Cross-Cultural Frameworks

In order to test the moderating effects of cultural frameworks using a multigroup PLS analysis, H6, H7, H8, and H9 were assessed (see Table 5). In a multigroup analysis, researchers are mainly concerned with ensuring measurement invariance prior to group-specific parameter comparisons. According to Hair et al. [58], “Establishing measurement invariance, researchers can be confident that group differences in model estimates do not result from the distinctive content and/or meanings of the latent variables across groups” (p.135). Thus, a measurement invariance of composite models (MICOM) procedure for a multigroup PLS analysis was developed by Henseler et al. [59]. According to Hair et al. [5], the MICOM procedure includes the following steps: (1) configural invariance, (2) compositional invariance, and (3) equality of composite mean values and variances. The authors also discussed partial measurement invariance, which is verified if configural invariance and compositional invariance are confirmed. Comparing the path coefficients in a multigroup analysis can be conducted when partial measurement invariance is verified for all latent variables in the PLS path model.
The current study confirmed that both the PLS path models and the data treatment used in both groups were identical, which was required for the establishment of configural invariance (Step 1 of the MICOM). Next, configural invariance was confirmed, as our group-specific model estimations were dependent on the identical algorithm settings as well. To conduct the MICOM procedure, 1000 permutations were analyzed. A statistical test to establish compositional invariance (Step 2 of the MICOM procedure) is used to evaluate whether the composite scores differ significantly across groups. To do so, the procedure calculates the correlation (c) between the composite scores Y(1) and Y(2) accordingly: c = cor(Y(1), Y(2)). The correlation comparison between the composite scores of the Chinese group and the U.S. group with the 5% quantile showed that the quantile was smaller than (or equal to) the correlation for all of the latent variables. Moreover, it was substantiated by p-values higher than 0.05, indicating that the correlation was not significantly lower than 1 [58]. As shown in Table 5, the establishment of compositional invariance was confirmed for all multi-item constructs in the model. Accordingly, analysis proceeded to the comparison of the standardized path coefficients across groups using a multigroup analysis.
Comparisons of the explained variance (R2) presented differences between the U.S. and Chinese Airbnb guest groups [60]. It was found that more variance was explained for both satisfaction (5.2% more) and behavioral intention (15.6% more) in the U.S. Airbnb guest group compared to the Chinese Airbnb guest group. Moreover, the findings showed that social and economic benefits had significant, positive effects on satisfaction and behavioral intention in both groups, as previously described. As displayed in Table 6, the difference between the coefficients of the other two paths showed significant differences between the two groups related to behavioral intention, but not in relation to satisfaction (H6 and H8 were rejected). Thus, H7 and H9 were confirmed, as the relationship between social benefits and behavioral intention and the relationship between economic benefits and behavioral intention were confirmed to be different between the two groups of Airbnb guests. The effect of social benefits on behavioral intention was stronger in the Chinese Airbnb guest group than in the U.S. Airbnb guest group (βcn = 0.261 > βus = 0.077). Conversely, the difference in magnitude of the coefficients between economic benefits and behavioral intention (βcn = 0.089 < βus = 0.241) was greater in the U.S. Airbnb guest group than in the Chinese Airbnb guest group.

4.5. Mediating Effects

The test for mediating effects was conducted to examine whether satisfaction mediated between social benefits and behavioral intention, and between economic benefits and behavioral intention. As shown in Table 7, social benefits had significantly positive indirect effects on behavioral intention (β = 0.175, t-value = 6.629, p < 0.001). Moreover, economic benefits had significant and positive indirect effects on behavioral intention (β = 0.168, t-value = 6.429, p < 0.001).

5. Discussion, Implication, and Limitations

5.1. Discussion

This study addressed two research questions: Do Airbnb guests’ value priorities influence their satisfaction and behavioral intentions? Is there a difference between Airbnb guests’ social and economic benefits (value priorities) based on cultural orientation? Based on this study, both research questions can be answered affirmatively. This study’s investigation revealed that both social benefits and economic benefits showed significant positive effects on both satisfaction and behavioral intention among Airbnb guests, and that satisfaction in turn also influenced behavioral intention. That is, the more Airbnb guests perceived staying at Airbnb as socially and economically beneficial, the more they were satisfied with their stays at Airbnb.
Tussyadiah [15] identified that social benefits can influence future behavioral intention. In that study, the researcher found a negative relationship between social benefits and behavioral intention. Our study demonstrated a positive relationship between social benefits and behavioral intention. As social benefits increase, it would be expected that the behavioral intention to reuse Airbnb would also increase, based on the findings of this study. Furthermore, in relation to these variables, there was a significant difference between Chinese and U.S. Airbnb guests. This may indicate that the relationship between these variables is perceived differently based on one’s cultural background. Economic benefits had a significant influence on behavioral intention in the sharing economy study by Hamari et al. [61]. Our research study added to the literature by confirming these findings, and there was a significant difference between Chinese and U.S. Airbnb guests in this study as well. Prior researchers have also demonstrated the strong positive relationship between satisfaction and behavioral intention [8,15]. Möhlmann [39] found that satisfaction with an Airbnb accommodation positively influenced likelihood of reusing the accommodation. The current study further confirmed that satisfaction has a positive influence on behavioral intention among Airbnb users.
Social and economic benefits have been previously noted for their importance in a tourism context [62]. This study provides further empirical support for this notion in the context of Airbnb use. While the relationship between benefits and satisfaction did not show a difference based on cultural background, the results indicated that the relationship between benefits and behavioral intention was different based on Airbnb guests’ cultural backgrounds. The current study found that Chinese Airbnb guests showed a higher impact of social benefits on behavioral intention while U.S. Airbnb guests showed a higher impact of economic benefits on behavioral intention. These findings support the individualism/collectivism classification described by Hofstede et al. [18], which attributes higher individualism to U.S. culture and higher collectivism to Chinese culture. These results demonstrated that cultural differences clearly exist, and also suggest that it is necessary to understand the culture of Airbnb guests in order not only to understand peer-to-peer accommodations, but also to encourage sustainable growth in the hospitality industry. While American guests put more weight on economic benefits, Chinese customers focus more on social aspects such as valuing communication or connectivity with local residents. This information can be used as a basis for the hospitality industry to provide customized services for specific markets in the future, and adds to the recent literature demonstrating cross-cultural differences in the Airbnb setting [63].

5.2. Theoretical Contributions

Based on literature suggesting that Airbnb guest experiences are established on the basis of different cultural value priorities [15], this study tested the cultural orientation framework developed by Hofstede et al. [18] and provided empirical evidence that it can be successfully implemented to examine culturally different sharing economy markets in the tourism and hospitality industry. This study of Airbnb also applied a cross-cultural approach about this topic, building on recent scholarship [63]. A unique contribution of the current study is that no other direct empirical cross-cultural studies in an Airbnb setting have been undertaken. The direct examination of value priorities in relation to satisfaction and behavioral intention was an area where this study broke new ground, as these specific variable relationships were applied in a new setting. Prior Airbnb studies had not tested social benefits or economic benefits as antecedents of satisfaction or behavioral intention.
Previous research found significant positive effects of social and economic benefits on satisfaction and intention to use sharing economy accommodations [15]. Our study extended this line of inquiry by confirming the cross-cultural framework of Hofstede et al. [18] regarding the effect of social and economic benefits on behavioral intention among Airbnb guests. Identifying this difference between U.S. and Chinese Airbnb guests based upon cultural background demonstrated the theoretical relevance of this framework in this area of study. This framework enabled us to examine differences between tourists from individualistic and collectivistic societies who stayed at Airbnb accommodations. Cross-cultural frameworks have been empirically tested and the findings from this research confirmed cultural differences that had been observed in prior studies [37,64,65]. Although past research has examined the impact of an individualism/collectivism framework on social factors including social benefits [64], not many researchers have specifically addressed how Chinese travelers could be influenced within a cross-cultural framework in the context of Airbnb guest experiences [63]. In short, this study contributes to the construction of a theoretical foundation for generating knowledge relevant to social and economic benefits in cultures characterized by collectivism or individualism from the perspective of the sharing economy in the tourism and hospitality industry.

5.3. Practical Implications

The current study provides practical implications for practitioners in the tourism and hospitality industry who are getting ready to meet the demands of regional and global travel as it returns to normal in the aftermath of the Covid-19 pandemic. Firstly, practitioners should strengthen the economic benefits the offer, especially for current Airbnb customers. In order to do so, a sufficient compensation system should be established for loyal Airbnb customers, which will help to maintain the loyalty of customers. It is surprising that there is currently no compensation system for loyal customers who are willing to stay with Airbnb continuously, which means that guests do not have to remain loyal to Airbnb as there is no direct benefit. This situation will hinder the sustainable growth of the peer-to-peer accommodation industry. In addition, discounting reservation fees to customers who want long-term accommodation or offering additional discounts on incidental accommodation fees when traveling to other areas during long-term stays could be considered. Various promotional strategies can be attempted to attract potential Airbnb customers by providing different types of coupons. In order for the peer-to-peer accommodation industry to grow sustainably in the future, it will have to provide specialized services not provided by the hotel industry, minimizing the economic burden to guests. Peer-to-peer accommodations could also consider offering special support services, such as offering the accumulation of points that can be redeemed for discounts, which loyal customers who like staying at peer-to-peer accommodations can use. This is not a groundbreaking strategy; however, given the situation wherein peer-to-peer accommodations do not provide such economic benefits to sustain current customers and attract new customers, not implementing such a fundamental system can be seen as a limitation of Airbnb currently.
Secondly, based on the results of the current study which showed that social benefits play a significant role in attracting Airbnb customers, practitioners should find ways for customers to experience the local atmosphere and travel like local residents when they stay at an Airbnb. The owners of Airbnb establishments who rent private houses could provide an opportunity for guests to communicate directly with local residents, such as by touring the neighborhood, which could help to increase the accommodation experience for Airbnb guests who seek such social benefits. Sharing this information before guests arrive and ensuring that guests have such information would be a useful practice for peer-to-peer accommodations to embrace. For example, if a person who operates a farm rents a house, they could invite their guests to tour the farm and offer to teach guests how to participate in a farming activity as an experiential learning program on the farm. Moreover, some owners currently offer breakfast for the guests who stay at their homes. By expanding these services, it might be possible to provide special experiences for guests and provide information on local culture or specialties by providing breakfast using local specialties. It also would be useful to decorate such an accommodation to represent local images and authentic characteristics, or to place information such as descriptions of the local area and popular places to visit nearby where guests can interact comfortably with local residents, thereby providing social benefits for guests.
Thirdly, sustainable practices can be enhanced in the peer-to-peer accommodation sector by informing customers and efficiently marketing the sustainable practices and benefits provided by the peer-to-peer accommodation sector. The importance of both social and economic benefits was noted in this empirical study, as the results indicated that both benefits significantly influenced satisfaction and behavioral intention. As a sustainable alternative to conventional accommodations, Airbnb and other similar peer-to-peer accommodations can highlight sustainable benefits such as less energy, resource, and water consumption usage compared with their larger counterparts [66]. By marketing the sustainable benefits of peer-to-peer accommodations, sustainable practices can be related to individual guest experiences due to the social and economic benefits that can be provided to individual guests through such practices. Noting the social and economic benefits of sustainable practices in peer-to-peer accommodations can have a positive outcome for sustainable tourism practice by better educating customers and reaching future customers who seek to engage in sustainable practices. Such benefits of staying at an Airbnb or peer-to-peer accommodation could be strongly promoted through the accommodation’s website or through various social media networks.

5.4. Limitations

Like all studies, the current study also had a few limitations. Firstly, while recruiting Chinese and U.S. participants allowed us to apply the individualism and collectivism framework clearly, the scope of the comparison accounts for only two countries, which makes our findings limited. Thus, results can be considered too narrow to be generalizable elsewhere. Researchers could further examine the research model and the robustness of our findings, adopting participants from other countries to explore Hofstede’s individualism/collectivism framework. Secondly, we addressed only one cultural factor, individualism versus collectivism. Future studies could investigate the moderating effects of the other cultural factors described by Hofstede et al. [18], such as femininity/masculinity, power distance, uncertainty avoidance, short-term or long-term orientation, and indulgence versus restraint constructs. Thirdly, the sample of this study was limited to travelers who stayed at Airbnb rental homes in China and the U.S. In order to increase the generalizability of the findings, future studies should consider examining the research model in the context of other peer-to-peer accommodation platforms and settings in other destinations. Lastly, data were collected in 2018, and it may be questioned whether the collected data reflect the current reality well as they were collected before the recent pandemic. However, many places have returned to normal operations and are functioning as they were prior to the beginning of the pandemic. Therefore, as the tourism and hospitality industry revitalizes into the future, the results of this study should prove useful as a marketing tool and to promote future research on this topic.

Author Contributions

Conceptualization, writing—original draft, methodology, formal analysis: J.S.; writing—review and editing, supervision: C.T.; visualization, writing—review and editing: T.E.; visualization, validation, writing—review and editing: S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research 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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Sustainability 15 00223 g001 550
Figure 1. Research model.
Figure 1. Research model.
Sustainability 15 00223 g001
Sustainability 15 00223 g002 550
Figure 2. The results of SEM with path coefficients. Note: *** p < 0.001. The figures in parentheses are t-values.
Figure 2. The results of SEM with path coefficients. Note: *** p < 0.001. The figures in parentheses are t-values.
Sustainability 15 00223 g002
Table
Table 1. Measurement item sources.
Table 1. Measurement item sources.
MeasurementItemSource
Social benefitsStaying at Airbnb allows me to get insider tips on local attractions.Möhlmann [39];
Tussyadiah [15]
Staying at Airbnb allows me to know people from the local neighborhoods.
Staying at Airbnb allows me to have a more meaningful interaction with locals.
Staying at Airbnb helps me connect with locals.
Economic benefitsStaying at Airbnb saves me money.
Staying at Airbnb helps lower my travel cost.
Staying at Airbnb makes travel more affordable.
Staying at Airbnb benefits me financially.
SatisfactionI am happy with my decision to stay at Airbnb.Han et al., [20];
Priporas et al. [9]
My experience exceeded my expectation.
Overall, I am satisfied with my experience with Airbnb
Behavioral intentionI intend to reuse Airbnb in the next 2 yearsCasaló et al. [40];
Tussyadiah [15]
I plan to reuse Airbnb in the next 2 years
I desire to reuse Airbnb in the next 2 years
Table
Table 2. Demographic profile of respondents (N = 693).
Table 2. Demographic profile of respondents (N = 693).
CountryChina (N = 331)U.S. (N = 362)
CharacteristicsFrequency (n)(%)Frequency (n)(%)
Gender:
Female 19659.216244.8
Male13540.820055.2
Age groups in years:
18–267021.17721.3
27–3519057.417448.1
36–456419.37921.8
46–5572.1195.2
56–6500.0102.8
66+00.030.8
Education
Some high school195.710.3
High school graduate319.4226.1
Some college10.38423.2
College graduate26981.318150.0
Some graduate school00.0154.1
Completed graduate school30.95816.0
Other82.400.0
Income (RMB; USD)
Less than 20,00041.24011.0
20,000 to 49,999226.6287.7
50,000 to 99,9994212.715442.5
100,000 to 149,9997021.182.2
150,000 to 199,9999227.812735.1
200,000 or more10130.551.4
Employment
Working full-time30190.928277.9
Working part-time51.5123.3
Homemaker72.1236.4
Retired00.0328.8
Not working00.030.8
Student185.430.8
Other00.061.7
Table
Table 3. Results of the measurement model confirmatory factory analysis.
Table 3. Results of the measurement model confirmatory factory analysis.
Construct.MeasuresFactor LoadingαRAVE
Social BenefitsStaying at Airbnb … 0.7210.8260.543
SB1… allows me to get insider tips on local attractions.0.744 ***
SB2… allows me to have a more meaningful interaction with locals.0.753 ***
SB3… allows me to get to know people from the local neighborhoods.0.741 ***
SB4… helps me connect with locals.0.709 ***
Economic Benefits 0.8510.9000.691
EB1… saves me money. 0.837 ***
EB2… helps lower my travel cost. 0.857 ***
EB3… makes travel more affordable.0.797 ***
EB4… benefits me financially.0.834 ***
Satisfaction 0.7650.8640.681
SAT1I am happy with my decision to stay at Airbnb.0.869 ***
SAT2My experience exceeded my expectation.0.745 ***
SAT3Overall, I am satisfied with my experience with Airbnb0.855 ***
Behavioral Intention 0.8090.8870.723
BI1I intend to reuse Airbnb in the next 2 years.0.852 ***
BI2I plan to reuse Airbnb in the next 2 years.0.872 ***
BI3I desire to reuse Airbnb in the next 2 years0.827 ***
Note: *** p < 0.001. α = Cronbach’s alpha; R = composite reliability; AVE = average variance extracted; SB = social benefits; EB = economic benefits; SAT = satisfaction; BI = behavioral intention.
Table
Table 4. Discriminant validity test: Fornell–Larcker criterion (below the main diagonal) and heterotrait–monotrait (HTMT) ratio (above the main diagonal).
Table 4. Discriminant validity test: Fornell–Larcker criterion (below the main diagonal) and heterotrait–monotrait (HTMT) ratio (above the main diagonal).
BIEBSATSB
BI0.8510.5340.8440.555
EB0.4440.8320.5040.346
SAT0.6670.4100.8250.551
SB0.4270.2740.4210.737
Note: main diagonal in bold: square root of the AVE. SB = social benefits; EB = economic benefits; SAT = satisfaction; BI = behavioral intention.
Table
Table 5. Step 2 of the measurement invariance test for PLS-MGA.
Table 5. Step 2 of the measurement invariance test for PLS-MGA.
Latent VariablesCorrelation c5% Quantile of the Empirical Distribution of cp-Value Compositional Invariance Established?
BI1.0000.9990.315Yes
EB0.9990.9980.472Yes
SAT0.9990.9980.424Yes
SB1.0000.9920.946Yes
Note: SB = social benefits; EB = economic benefits; SAT = satisfaction; BI = behavioral intention.
Table
Table 6. Comparison of path coefficients between Chinese and U.S. groups.
Table 6. Comparison of path coefficients between Chinese and U.S. groups.
PathChina (A)U.S. (B)t-Value (A-B)p Value (A-B)Difference
H1.SB→SAT0.372 ***0.366 ***0.127n.s.N/A
H2.SB→BI0.261 ***0.077 **3.030<0.01A > B
H3.EB→SAT0.269 ***0.292 ***0.388n.s.N/A
H4.EB→BI0.089 *0.241 ***2.021<0.05A < B
Note: *** p < 0.01; ** p < 0.05; * p < 0.10; n.s. = nonsignificant. R2: variance explained; the Chinese group: satisfaction (23.4%), behavioral intention (40.6%); the U.S. group: satisfaction (28.6%), behavioral intention (56.2%); SB = social benefits; EB = economic benefits; SAT = satisfaction; BI = behavioral intention.
Table
Table 7. Direct and indirect effects of the structural model.
Table 7. Direct and indirect effects of the structural model.
PathDIRECT EFFECTIndirect Effect (Mediating)Total Effect
Structural model
SB→SAT0.335 *** 0.335 ***
SB→BI0.156 ***0.175 ***0.331 ***
EB→SAT0.319 *** 0.319 ***
EB→BI0.186 ***0.168 ***0.354 ***
SAT→BI0.524 *** 0.524 ***
Note: *** p < 0.001. SB = social benefits; EB = economic benefits; SAT = satisfaction; BI = behavioral intention.
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Suh, J.; Tosun, C.; Eck, T.; An, S. A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits. Sustainability 2023, 15, 223. https://doi.org/10.3390/su15010223

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Suh J, Tosun C, Eck T, An S. A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits. Sustainability. 2023; 15(1):223. https://doi.org/10.3390/su15010223

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Suh, Jungho, Cevat Tosun, Thomas Eck, and Soyoung An. 2023. "A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits" Sustainability 15, no. 1: 223. https://doi.org/10.3390/su15010223

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