Understanding Tourists Repurchase Intention on Airbnb from the Perspective of Low Carbon and Extended Expectation-Confirmation Model

Understanding Tourists Repurchase Intention on Airbnb from the Perspective of Low Carbon and Extended Expectation-Confirmation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Understanding Tourists Repurchase Intention on Airbnb from the Perspective of Low Carbon and Extended Expectation-Confirmation Model Liming Wang, Jinhao Hu, Bei Wang, HongBin Chen, Ning Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4653327/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Airbnb is a popular and low-carbon platform for tourism in the sharing economy. This paper utilized the extended expectation-confirmation model (ECM) and collected 330 valid data through a questionnaire survey to analyze the psychological behavior of tourists during their Airbnb experience. The results demonstrate that the service quality, the concept of low-carbon service, friendly communication with the host, and platform security significantly impact consumers' satisfaction. Highly satisfied consumers are more likely to continue using Airbnb with low-carbon consciousness and engage in electronic word-of-mouth communication. Therefore, ensuring customer satisfaction and low-carbon consciousness are critical to win on the Airbnb platform and attracting more hosts and guests. This study contributes to the existing literature on Airbnb and offers practical implications for the platform and hosts to attract more guests and generate positive word-of-mouth. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Environmental social sciences/Energy and society Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios Airbnb sharing economy repurchase intention expectation-confirmation model Figures Figure 1 Figure 2 1. Introduction Sharing has been a societal norm, and the term "sharing economy" encompasses a diverse array of consumption practices and organizational model (Dogru et al., 2019 ), including but not limited to sharing, renting, lending, bartering, swapping, borrowing, trading, exchanging, gifting, and purchasing second-hand or new goods. This economy has seen significant growth, with platforms facilitating it playing a more prominent role each year. Airbnb stands as a prominent platform within the sharing economy, with a corporate vision and mission dedicated to establishing a secure and trustworthy global community for travelers, while also providing solutions for homeowners with vacant residences. The platform has garnered significant scholarly and practical attention in recent years. Airbnb has not only changed traditional business models and brought about significant socio-economic benefits, but its advocacy and impact on a green lifestyle, as well as its promotion of low-carbon living and sustainable development, are also increasingly recognized by more and more citizens. Nowadays, Airbnb boasts over six million listings, encompassing entire homes, shared rooms, and private spaces, surpassing the combined listings of the top three hotel chains globally. In addition, several studies have affirmed the positive role of the sharing economy in curbing carbon emissions (Zhu et al., 2018 ). Airbnb's sharing economy has not only completely transformed traditional business models and brought significant benefits to the socio-economy, but it has also played an important role in promoting a green lifestyle, low-carbon living, and sustainable development, and these impacts are gradually being recognized by the general public. As one of the representatives of the sharing economy, the success of Airbnb reflects the strengthening of public awareness of environmental protection and a reflection on overconsumption behavior. The integration of Airbnb into discussions on green emission reduction demonstrates the potential of the sharing economy in promoting sustainable tourism. As the platform continues to evolve, it offers an opportunity to lead in the creation of a more environmentally friendly tourism industry that not only minimizes its impact on the planet but also actively promotes global ecological awareness and a sense of responsibility. The literature confirms that researchers have conducted studies on the platform's pricing strategy (Xie et al., 2017 ), trust mechanisms (Liang et al., 2018 ), and perceived value (Chen et al., 2018). Within the sharing economy, consumer satisfaction positively affects their likelihood to reuse services (Ali et al., 2024). Bitner also posited that satisfied consumers are likely to engage in favorable word-of-mouth (WOM) (Bitner et al., 1990). This study aims to determine the primary drivers of consumer repurchase intentions, encompassing service quality, the concept of low carbon service, social interaction, and perceived security. By examining the psychological behaviors of tourists during their interactions with Airbnb, the paper proposes strategies to elevate host service quality, enhance the distinctiveness of listings, foster social engagement between hosts and guests, and bolster consumer perceptions of security(Fisher, 1957). Enhancements in these areas are anticipated to increase customer satisfaction, thereby promoting positive electronic word-of-mouth (WOM) regarding the platform(Lin et al., 2024 ). This study aims to extend the Expectation Confirmation Model (ECM) to the context of Airbnb, assessing the influence of perceived service quality, the concept of low carbon, social interaction, and perceived security on guests' experiences. The specific contributions of the expectation-confirmation model are as follows:(1) the impact of Airbnb's perceived service quality on confirmation, low-carbon and satisfaction; (2) an investigation into the factors that influence consumer satisfaction; and (3) an exploration of the relationship between consumer confirmation, satisfaction, eWoM communication, and repurchase intentions on Airbnb. Our study enhanced the understanding of determinants that shape the visitor experience within the Airbnb ecosystem. Subsequently, we performed comparative analyses of satisfaction factors before and after re-ordering to assess their impact on consumer behavior in relation to platform engagement. The paper then offers strategic recommendations to Airbnb for refining its hospitality approach, with the goal of enhancing customer satisfaction. Implementing these strategies could potentially increase guest retention and stimulate positive word-of-mouth effects. 2. Literature review The foundation of ECM is the Expectation Confirmation Theory (ECT), which has been widely used to study consumer behavior (Lin et al., 2009 ). ECT indicates that consumer satisfaction with products or services has a decisive effect on the intention to repurchase (Anderson et al., 1995; Lin et al., 2005 ). According to the expectation confirmation theory, consumers have a process of repurchase intentions (Oliver, 1980 ). Firstly, consumers will form an initial expectation for products or services before buying. Secondly, after a period of experience, consumers assess the performance against the initial expectation. Thirdly, consumers trigger their satisfaction judgment based on the performance confirmation. Finally, a satisfied consumer will generate a repurchase intention. The present study utilizes ECM as the theoretical model for the following reasons. Bhattacherjee applied the ECT theory into the information system (IS) and proposed the ECM model. In the same manner, ECM tries to predict the continuous usage intention of IS system (Bhattacherjee, 2001 ). The modified model emphases on the confirmation of expectation of the IS system will impact consumers' continuance intention and electronic word-of-mouth communication. Previous studies have shown that ECM is a valid theoretical model for studying visitors' experiences. The relationship between confirmation, perceived uniqueness, support conditions, satisfaction and relationship between continuous use behaviors to get support condition is full intents and important premises that support the use of behavior (Cheng, 2014 ). The behavioral habits, perception uniqueness, previous habits and the use of information systems based on network learning habituation is associated with satisfaction and subsequent behavior, when obtaining satisfaction and prior behavior the information system continues to be used strongly (Lee, 2010 ). Strong predictors, high intensity and high habituation will enhance the continuous use behavior. Stone and Baker-Eveleth studied perceived uniqueness in the context of the use of electronic books, the relationship between characteristics, validation, satisfaction, and continued use intent, getting the desired recognition affects the uniqueness and satisfaction of ebooks. The satisfaction and the concept of low-carbon of using an ebook influences its continued use. The results were also confirmed by many researches (Lee et al., 2011). Second, ECM has been used in multiple scenarios such as e-learning and Web services. In particular, the context of this study (the sharing economy) is also appropriate for the application of ECM. In summary, it is evident that the Expectation Confirmation Model (ECM) possesses effectiveness and applicability across various domains and contexts, particularly on sharing economy platforms such as Airbnb (Maria et al., 2024). ECM provides robust theoretical support for understanding and predicting user behavior. By utilizing ECM, researchers can analyze users' expectations, actual experiences, satisfaction levels, and the resulting word-of-mouth communication and repurchase intentions (Mohammad, 2024 ). This analysis aids platforms in optimizing their service strategies, enhancing user experience, and ultimately, strengthening user loyalty and market competitiveness. So we will expect the validation model to be applied to Airbnb. 3. Expectation-confirmation model 3.1. Satisfaction Cardozo introduced "consumer satisfaction" into the field of marketing, which he considered as the core of marketing (Cardozo, 1965 ). Satisfaction is "the emotional attitude of a consumer by comparing the perceived effect of a product with its expectation" (Oliver, 1980 ; Xusen et al., 2023 ). Since there are many platforms in the market that can provide tourist lodging, how to improve consumers' satisfaction and willingness to repurchase will be the key to Airbnb's success (Jones, 1996 ; Nakamura et al., 2024 ). The consumers’ satisfaction degree represents Airbnb's reputation and image compared to the competition platform (Sthapit et al., 2019; Wikhamn, 2019 ). The researchers have confirmed that in the context of the sharing economy, consumers’ satisfaction will positively influence consumers’ aspiration to choose again (Anderson et al., 1993; Do et al., 2024). Moreover, Bitner believed that satisfied consumers would generate positive word-of-mouth communication (Bitner, 1990 ). However, there is still lack of evidence on how consumers’ satisfaction with their previous experience impacts their intention to repurchase on Airbnb and spread electronic word of mouth. According to the theory proposed by Gountas and Gountas, which satisfaction can contribute to the future use of specific services (Gountas et al., 2007), we assume that: H1 Satisfaction has a positive effect on eWoM. H2 Satisfaction has a positive effect on repurchase intention. 3.2. Low carbon Carbon emission reduction is a long-term strategic task with a high priority for China and the world(Song et al., 2021 ; Yang et al., 2024 ).In recent years, several studies have affirmed the positive role of the sharing economy in curbing carbon emissions(Maria et al., 2024; Zhu et al., 2024 ).The sharing economy can reduce carbon through improving efficiency(Asghari et al., 2020).With the rapid development of sharing platforms, there is a growing interest in restraining carbon emissions through the sharing economy(Ding et al., 2024 ; Peng et al., 2022 ).Therefore, the demand for a low-carbon among consumers has a positive impact on satisfaction. Over the past decade, Airbnb hosts have brought a low-carbon and environmentally friendly local travel and accommodation experience to travelers from all over the world in an unprecedented way (Liu et al., 2017). Since the launch of the new travel platform Trips in November 2016, Airbnb has demonstrated that the platform has gone beyond home sharing and integrated accommodation experiences, travel experiences, and cultural experiences to create a customer-oriented travel experience that is low-carbon and environmentally friendly for travelers. In fact, because Chinese people like to showcase their low-carbon and environmentally friendly philosophy (Guo et al., 2024 ; Maria et al., 2024), they are more inclined to choose products with low-carbon symbolic significance to build a low-carbon and environmentally friendly self-image and social image, thereby showcasing their commitment to environmental protection (Berger et al., 2007). Airbnb can meet the low carbon needs of consumers. Therefore, we assume that: H3 Low carbon has a positive effect on satisfaction. 3.3. Social interaction Social interaction refers to the communication among people in community. It is a social activity in which people convey information and exchange ideas via certain media in order to achieve a certain purpose. The influence of social interaction on consumption behavior is a heavily studied area (Lin et al., 2024 ; Manchanda et al., 2015 ; Zhang et al., 2015 ). Tourists can experience the sense of belonging when chatting with local Airbnb hosts or enjoying a cup of tea in the real family kitchen of Airbnb, rather than staying in a traditional hotel and being served by uniformed employees. In addition, if a host tends to share useful information about location with tourists, such as nearby store discounts, local food, travel strategies, etc., it will have a positive influence on tourists’ satisfaction and promote them to stay on Airbnb again (Ali, 2024 ; Bosman et al., 2023 ). Then we assume that: H4 Social value has a positive on satisfaction. 3.4. Perceived security In the era of the Internet, security has become a critical issue (Pavlou et al., 2007 ), which is usually described as a different concept related to "some kind of protection" (Colobran, 2016 ). Because most of Airbnb’s listings are private rooms, consumers may worry about the security. In this study, we define perceived security as the expectation of the consumer’s reliability on the certification of the platform, and then reveal the relationship between perceived security and the attitude towards consumers' repurchase on Airbnb. Most studies related to perceived security come from the Technology Acceptance Model (TAM), whose initial purpose was to explain the widely accepted determinants of IS systems (Raziuddin et al., 2024 ; Wulan et al., 2024 ). As we know, perceived security has previously been empirically studied in the context of B2C, with the result that a higher level of perceived security leads to greater intention to purchase products (Davied et al., 2024). As a result, the platform effectively captures the consumer's initial level of trust and is able to achieve transactions and maintain market competitiveness (Sönmez et al., 2024 ). Security has been a major concern for tourist during the booking process. If Airbnb's listings and hosts are professionally and reliably certified, it will greatly enhance consumers' trust in the platform and the host. For example, in China, hosts can increase consumer perceived security through Sesame Credit certification. Chang & Chen (2009) researched perceived security as a significant predictor of consumers’ satisfaction on the site, and consumers with higher perceived security tend to continue to use their site (Hong et al., 2024 ). Therefore, we assume that: H5 Perceived security has a positive on satisfaction. 3.5. Confirmation Confirmation is a process of comparing experiences with expectations after usage. Previous studies have found that confirmation positively affects satisfaction Bhattacherjee, 2001 ; Ding et al., 2024 ). When consumers’ experiences meet their expectations, confirmation will lead to satisfaction. On the contrary, if the experience is lower than expectation, the consumer will generate dissatisfaction. This relationship can also be used in Airbnb context. Bookings compare their experiences with their expectations, and if their expectations are confirmed, they will be satisfied with Airbnb. So we assume that: H6 Confirmation has a positive effect on satisfaction. Festinger's cognitive dissonance theory believes that if users are inconsistent with their perceived usefulness in the actual use process, a dissonance will be generated in their mind (Festinger, 1957 ). Then rational users generally reduce dissonance and make it more realistic by trying to change their perceived usefulness. In other words, confirmation will help improve the perceived usefulness of users. Therefore, we apply cognitive dissonance theory to relevant beliefs on Airbnb usage, including the concept of low-carbon service, social interaction and perceived security (Mohammad, 2024 ), where users constantly change their expectations to conform to reality. Then we assume that: H7 Confirmation has a positive effect on uniqueness. H8 Confirmation has a positive effect on social value. H9 Confirmation has a positive effect on perceived security. 3.6. Perceived service quality Perceived service quality is different between consumer expectation and perceived performance (Parasuraman et al., 1988 ). Parasuram proposed the SERVQUAL model, which divides the perceived service quality into five levels: physical facilities, reliability, responsiveness, security, and emotional input(Parasuraman et al., 1988 ). The better these five dimensions, the higher the consumer’s expectations are confirmed. In other researches, SERVQUAL provides better diagnostic information compared with SERVPERF when measuring perceived service quality indicators (Juma et al., 2024 ; Zeithaml, 1966 ). This model has been widely used in the service industry to understand the target consumers’ perception of service demand and thus to improve service. Perceived service quality is a key performance indicator in the process of consumer behavior. Previous researches have also found that perceived service quality has a significant impact on confirmation (Lai, 2004 ) and satisfaction (Caruana, 2002 ; Kuo et al., 2009 ; Petek et al., 2024). Understanding consumers’ market demand needs to capture the dynamic expectations of change. In this study, we will directly use the perceived service quality to measure the perceived service quality of consumers’ Airbnb usage. We assume that: H10: Perceived service quality has a positive effect on confirmation. H11: Perceived service quality has a positive effect on satisfaction. 4. Methodology 4.1. Data collection A survey which explores factors affecting Airbnb was obtained from a Chinese online questionnaire company (WJX.com) in Apr. 2021. We confirm that informed consent was obtained from all subjects and/or their legal guardian(s) and all methods were performed in accordance with the relevant guidelines and regulations. Two screening questions were developed to recruit qualified respondents: (1) have you known about Airbnb? (2) have you used Airbnb before? The questionnaire was set up based on an interview and literature review in the pilot period to improve the validity of the content and the credibility of the questionnaire. A total of 330 valid surveys were obtained. Table 1 provided a summary of the demographic of the sample. There are 183 women, about 47.9% between 30 and 39 years old. The vast majority of respondents received a bachelor's degree (83.9%). More than a third of respondents earn between 5,000 and 8,000 a month. Table 1 Demographic characteristics of the respondents (n = 330). Variable Specification Frequency Percent Gender Male 147 44.5 Female 183 55.5 Age 20 or less 8 2.4 21–29 127 38.5 30–39 158 47.9 40–49 29 8.8 Above 50 8 2.4 Education High school graduate 21 6.4 College 277 83.9 Postgraduate degree 32 9.7 Income Under ¥3000 24 7.3 ¥3000-¥5000 56 17.0 ¥5000-¥8000 117 35.5 ¥8000-¥15000 108 32.7 ¥15000-¥20000 21 6.4 ¥20000 and above 4 1.2 4.2. Scale design The survey contains three parts. The first part investigates the demographics of the tourists who have purchased on Airbnb. The second part of measures (1) perceived service quality toward confirmation and satisfaction. (2) customers’ satisfaction. The last part assesses consumer intention to purchase on Airbnb again and eWoM spreading. A total of 33 attributes were rated by respondents on a 7-point Likert-type scale ranging from “strongly disagree − (1)” to “strongly agree (7)”. The theoretical model of this study is based on the extension of ECT to build the ECM of Airbnb continuance (Fig. 1). Most of the measurement items in this study are adapted from previous studies with slight modifications. Specifically, the measurements of LC and SI are extracted from Rasoolimanesh (2016), and those of PQ are extracted from Varma (2016) and Rasoolimanesh (2016). Confirmation is measured by scales from Bhattacherjee. As most studies related to PS are conceptual, the measurement items are created according to the key descriptive terms of PS in the existing literature such as “secure” and “protect”. The items from Varma (2016) and Sullivan & Dan ( 2018 ) are used to measure consumers’ eWoM spreading and repurchase intentions, and satisfaction are measured by using items from Jiang (Jiang et al., 2017 ). 4.3. Data analysis A structural equation model (SEM) with partial least squares (PLS) for hypotheses is the available statistical analysis techniques to interpret data from experimentation. In this study, we used Smart PLS 2.0 because it can examine the hypotheses in the model (Barclay et al., 1995 ) without requiring a large sample size (Gefen et al., 2000 ). The PLS Algorithm examines the relationships between constructs and path analysis to evaluate the measurement model (Hair et al., 2011 ). The bootstrapping technique is used to detect the significance and reliability of the model paths, we use 0.05 significance level as a statistical criterion (Fisher, 1992 ). 5. Data analysis and result 5.1. The measurement model In order to determine the accuracy of measurement model, the study was tested for reliability, convergent validity, and discriminant validity. We tested the internal consistency of the mode, the Cronbach’ alpha for all constructs was higher than 0.6 (Table 1 ), indicating strong internal consistency (Robinson et al., 1991; Wang et al., 2018). However, research shows that Cronbach’ alpha often underestimates the true reliability. So, composite reliability becomes more rigorous evaluation standard (Gefen et al., 2000 ). Table 1 indicates that composite reliability values exceeded the minimum of 0.70 (0.78–0.81) (Barclay et al., 1995 ; Hu et al., 2004 ). Through good data support, it is concluded that the expectation-confirmation model has good applicability in the satisfaction survey of Airbnb platform. It can better understand the repurchase intention of tourists on Airbnb and electronic word-of-mouth communication. In order to assess the convergent validity, in Table 1 , all the average variance extracted (AVE) scores are above 0.50(Hu et al., 2004 ). And the item loadings were almost higher than 0.7(Hair et al., 2011 ), ensuring convergent validity. Discriminant validity was evaluated by comparing the correlations between the square roots of AVE and the other constructs (Fornell et al., 1982). Table 2 shows that the square roots of AVE (on-diagonal values) are higher than the correlations among the constructs (off-diagonal values), and the correlation between the two random variables is lower than 0.85 (Kline, 2011 ). Therefore, reliability, convergence effectiveness, and discriminant validity of this measurement model were guarantees. 5.2. The structural model Table 3 , Table 4 and Fig. 2 demonstrated the results of the structural model, which indicated all hypotheses were supported. Specifically, consumers’ satisfaction with Airbnb's occupancy experience was significantly affected by the concept of low-carbon service (b = 0.23, p < 0.001), social interaction (b = 0.10, p < 0.05), and perceived security (b = 0.19, p < 0.001). Thus, hypotheses 3, 4 and 5 were supported. Confirmation was found to significantly affect satisfaction, low carbon, social interaction and perceived security. The path coefficients from confirmation to these four factors were 0.25 (p < 0.001), 0.41 (p < 0.001), 0.34 (p < 0.001) and 0.33 (p < 0.001), respectively, so hypotheses 6, 7, 8 and 9 were supported. Perceived service quality exerted significant impacts on confirmation (b = 0.45, p < 0.001) and consumers’ satisfaction (b = 0.24, p < 0.001) with an Airbnb stay, supporting hypotheses 10 and 11. Overall, the model accounts for 30.5% and 21.9% of the variance in repurchase intention and eWoM, respectively. It was supporting hypotheses 1 and 2. Table 2 Assessment of the measurement model. Note: AVE- average variance explained Constructs and related measurement Item Loadings Cronbach’s alpha Composite reliability AVE Perceived Service Quality 0.61 0.79 0.56 SQ1 0.78 SQ2 0.76 SQ3 0.70 Low Carbon 0.60 0.79 0.56 LC1 0.75 LC2 0.70 LC3 0.78 Social Interaction 0.60 0.79 0.56 SI1 0.77 SI2 0.81 SI3 0.64 Perceived Security 0.60 0.79 0.56 PS1 0.78 PS2 0.71 PS3 0.75 Confirmation 0.61 0.79 0.56 CON1 0.77 CON2 0.67 CON3 0.79 Satisfaction 0.62 0.80 0.57 SAT1 0.79 SAT2 0.73 SAT3 0.75 Repurchase Intention 0.65 0.81 0.59 RI1 0.78 RI2 0.67 RI3 0.83 eWoM 0.63 0.80 0.57 eWoM1 0.73 eWoM2 0.75 eWoM3 0.79 Table 3 Discriminant validity. CON eWoM PQ RI SAT PS SI LC CON 0.746 eWoM 0.308 0.757 PQ 0.453 0.438 0.748 RI 0.474 0.439 0.432 0.765 SAT 0.549 0.468 0.564 0.552 0.756 PS 0.332 0.393 0.403 0.371 0.487 0.746 SI 0.335 0.234 0.358 0.320 0.388 0.267 0.742 LC 0.411 0.488 0.415 0.435 0.537 0.381 0.284 0.745 Note: CON - confirmation, PQ - perceived service quality, RI - repurchase intension, SAT - satisfaction, PS - perceived security, SI - social interaction, LC - low carbon Table 4 Results of the structural model and hypotheses tests. Hypotheses Path coefficients P value Supported H1 0.47 0.000 Yes H2 0.55 0.000 Yes H3 0.23 0.000 Yes H4 0.10 0.032 Yes H5 0.19 0.000 Yes H6 0.25 0.000 Yes H7 0.41 0.000 Yes H8 0.34 0.000 Yes H9 0.33 0.000 Yes H10 0.45 0.000 Yes H11 0.24 0.000 Yes Note: all path coefficients are significant at the 0.05 level. 6. Discussion 6.1. Theoretical implications The main contribution of this research is the extension of the expectation-confirmation model (ECM) within the context of the sharing economy, specifically pertaining to Airbnb. The paper retains the core architecture of the ECM, which is the most representative research model for continuous intention. By analyzing consumer perceptions of Airbnb's perceived quality, low carbon, social interaction, and perceived security, the paper predicts consumer repurchase intentions and electronic word-of-mouth communication on the platform. Notably, previous studies have not simultaneously examined all these aspects (Maria et al., 2024). For the extension of the ECM model, we will expect to confirm that the universality of the model is applied to the general information system environment to the specific environment of Airbnb's intended platform. Specifically, perceived security has a positive impact on consumer satisfaction, contradicting the results of the original model, perceived security has no effect on satisfaction with usage. Chinese tourists are very sensitive to the perception of travel safety, and this sensitivity has a clear influence on future travel decisions. Another important aspect of extended ECM is the perceived impact of service quality on confirmation and satisfaction. In the lodging market, consumers can have many similar homestay options, which means that if Airbnb's quality of service does not satisfy consumers, they may easily move to other platforms (Mohammad, 2024 ; Zhou et al., 2001). In addition, we also studied the positive impact of consumers' perceptions of Airbnb's perceived quality of service, low carbon, social interaction, and perceived security on their future use of Airbnb and electronic word-of-mouth communication to enrich the literature on the sharing economy. 6.2. Practical implications The study improves Airbnb platform construction and host improvements by understanding consumer behaviors on Airbnb. Gradually, sharing as an alternative to consumption, in this context, the lodging industry can be a representative of the emerging trends in the sharing economy (Belk, 2014 ). Hotel managers can gain a deeper understanding of the success and competitive advantages of the Airbnb service model in the context of sharing economy, and optimize their strategies to create a brilliant tourism accommodation industry. From a practical perspective, the results of this study hold significant implications. The main factors driving customers to repurchase on Airbnb include service quality, low carbon, social interaction, and perceived security. Therefore, the Airbnb platform and its hosts should prioritize the improvement of these factors while also providing personalized services to enhance customer satisfaction. Fortunately, Airbnb is currently developing a host college that offers tailor-made online and offline training courses to its hosts, which can help them improve their hospitality. Provision of high-quality service is crucial in pleasing customers. With respect to low carbon, hosts can offer a combination of local styles for the guests to choose from. In terms of social interaction, Airbnb hosts should prioritize communication with guests to create a sense of belonging. For perceived security, the Airbnb platform should strictly review the qualifications and reliability of listings, which can instill assurance among users. By addressing these aspects, Airbnb can satisfy its consumers, garner positive repurchase intent, and generate positive word-of-mouth communication. In addition to the suggestions previously mentioned, Airbnb hosts must also maintain high-quality infrastructure and amenities, such as comfortable and updated facilities, cleanliness, and top-notch equipment. Additionally, providing low-carbon services can establish a friendly relationship between tourists and nature. Examples of such services may include pick-up and drop-off services, local transportation, or recommendations for local attractions and specialties. In cases where unique cultural practices exist in the host's area, it is pertinent for the host to explain these to the guests in a clear manner. By doing so, hosts can establish a loyal customer base and encourage repurchases by generating enhanced customer satisfaction. 7. Conclusion and prospects This study adopts a systematic approach to understand and predict Airbnb's repurchase intentions and electronic word-of-mouth communication. The paper extends the ECM model by adding other factors specific to Airbnb's unique background. The research confirms that confirmation, low carbon, social interaction, perceived security, and satisfaction are crucial influencers of Airbnb's continued use intentions and electronic word-of-mouth communication. The study further emphasizes that providing high-quality service is crucial for maintaining customer loyalty, while also pointing out that consumers will consider factors of green emission reduction when making purchases. By strengthening the appeal of their services with environmental concepts, companies can attract and retain more consumers who focus on a sustainable lifestyle. Therefore, the quality of the host's service, the concept of low carbon, the social interaction between the host and the tourists, and the perceived security of the consumer all play significant roles in predicting Airbnb's future stay and electronic word-of-mouth communication. Although this study in addressing concerns regarding the satisfaction of home-stay experience in the sharing economy, there remain certain areas that can be improved and further developed. One potential avenue of research may involve the implementation of additional models or techniques beyond expectation confirmation model, which may further enhance the accuracy and effectiveness of this research. Additionally, further investigation into other relevant factors and their potential influence on consumer satisfaction could also provide deeper insights into this topic. Finally, different regions may offer different home stay experiences, this study lacks a broad analysis of consumer satisfaction with home stay experience in China's home stay market. In the future, incorporating data from diverse sources and regions may offer a more comprehensive understanding of consumer satisfaction in the sharing economy. Declarations Competing interests The authors declare no competing interests. Ethical approval The ethical application for this research was approved by the Academic Committee of Zhejiang Gongshang University. All research was performed in accordance with relevant guidelines and regulations. Informed consent All online questionnaire survey participants provided informed consent. Author Contribution Liming Wang and Jinhao Hu designed the research study. Liming Wang, Jinhao Hu and Bei Wang collected the data. Liming Wang and Bei Wang performed the data analysis. Jinhao Hu and Ning Xu prepared figures and tables. HongBin Chen and Ning Xu wrote the initial draft of the manuscript. Liming Wang and HongBin Chen reviewed and edited the manuscript for intellectual content. All authors contributed to the interpretation of the results and approved the final manuscript. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Ali, M. A. 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Renewable Energy. 2024, 227, 120538. Zhu, G.; Li, H.;Li, Z. Enhancing the development of sharing economy to mitigate the carbon emission: a case study of online ride-hailing development in China. Natural Hazards. 2018, 91(2),611–633. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4653327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":345202301,"identity":"d280114d-d1ed-4aa6-ab8d-b423556c0220","order_by":0,"name":"Liming Wang","email":"","orcid":"","institution":"Hangzhou Dianzi University Information Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Liming","middleName":"","lastName":"Wang","suffix":""},{"id":345202302,"identity":"c07682bb-3fab-4395-ad83-1847b3d3f521","order_by":1,"name":"Jinhao Hu","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Jinhao","middleName":"","lastName":"Hu","suffix":""},{"id":345202303,"identity":"7cf689ac-afa8-448d-ba30-51c21e1eca75","order_by":2,"name":"Bei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAwkw9V+On4EhASJ0gDgtzMaSDaRqSTSAqySkxVy6+djnyja2BOPzBx6//NnGIMd3I4HxcwEeLZZzjiXPPNvGk2d24ECaNW8bg7HkjQRm6Rn4HHYjx5ixsU2i2OxgQ5oxYxtD4oYbCWzMPHi15H8GajFI3NzMkGYIdFg9EVpymIFaEhI3sDEkPwA6LMGAkBagX4wZG84dMJY4w5DGzHNOwnDmmYfN0vi0AEPsMWND2QE5/v4zyR9/lNnI8x1PPvgZnxYwYGQDkTxpwDgCRRNjAyENQPAHRLAf/kCE0lEwCkbBKBiBAADWVk531TLRnQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Gongshang University Hangzhou College of Commerce","correspondingAuthor":true,"prefix":"","firstName":"Bei","middleName":"","lastName":"Wang","suffix":""},{"id":345202304,"identity":"1bfff950-12b0-4a59-8c38-bbe522f34bec","order_by":3,"name":"HongBin Chen","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"HongBin","middleName":"","lastName":"Chen","suffix":""},{"id":345202305,"identity":"0a84cecb-c558-404f-8bb2-1f8f4e6c57ff","order_by":4,"name":"Ning Xu","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-06-28 08:40:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4653327/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4653327/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63378223,"identity":"7ee63922-e43c-415c-8450-ff60d363e296","added_by":"auto","created_at":"2024-08-27 13:15:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104459,"visible":true,"origin":"","legend":"\u003cp\u003eAn expectation-confirmation model of repurchase intention on Airbnb\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4653327/v1/7196deac74aa1bf138101fa8.png"},{"id":63378222,"identity":"47672ddc-f08f-477b-b9fe-2879e00361bb","added_by":"auto","created_at":"2024-08-27 13:15:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112710,"visible":true,"origin":"","legend":"\u003cp\u003eThe path diagram\u003c/p\u003e\n\u003cp\u003e*p\u0026lt;0.05,**p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4653327/v1/6971f0586be66e6a21f37c31.png"},{"id":64266825,"identity":"e875fbf6-9963-493c-aa4d-f12a72dc3b91","added_by":"auto","created_at":"2024-09-11 04:22:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":883498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4653327/v1/f48a62e9-6745-42a4-80d0-34cee334f85b.pdf"},{"id":63378886,"identity":"146d653a-660d-49d0-9d22-1557b8fde4f8","added_by":"auto","created_at":"2024-08-27 13:23:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16080,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-4653327/v1/e424ab776d65eeb8b83afb8b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding Tourists Repurchase Intention on Airbnb from the Perspective of Low Carbon and Extended Expectation-Confirmation Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSharing has been a societal norm, and the term \"sharing economy\" encompasses a diverse array of consumption practices and organizational model (Dogru et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), including but not limited to sharing, renting, lending, bartering, swapping, borrowing, trading, exchanging, gifting, and purchasing second-hand or new goods. This economy has seen significant growth, with platforms facilitating it playing a more prominent role each year. Airbnb stands as a prominent platform within the sharing economy, with a corporate vision and mission dedicated to establishing a secure and trustworthy global community for travelers, while also providing solutions for homeowners with vacant residences. The platform has garnered significant scholarly and practical attention in recent years. Airbnb has not only changed traditional business models and brought about significant socio-economic benefits, but its advocacy and impact on a green lifestyle, as well as its promotion of low-carbon living and sustainable development, are also increasingly recognized by more and more citizens. Nowadays, Airbnb boasts over six million listings, encompassing entire homes, shared rooms, and private spaces, surpassing the combined listings of the top three hotel chains globally.\u003c/p\u003e \u003cp\u003eIn addition, several studies have affirmed the positive role of the sharing economy in curbing carbon emissions (Zhu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Airbnb's sharing economy has not only completely transformed traditional business models and brought significant benefits to the socio-economy, but it has also played an important role in promoting a green lifestyle, low-carbon living, and sustainable development, and these impacts are gradually being recognized by the general public. As one of the representatives of the sharing economy, the success of Airbnb reflects the strengthening of public awareness of environmental protection and a reflection on overconsumption behavior. The integration of Airbnb into discussions on green emission reduction demonstrates the potential of the sharing economy in promoting sustainable tourism. As the platform continues to evolve, it offers an opportunity to lead in the creation of a more environmentally friendly tourism industry that not only minimizes its impact on the planet but also actively promotes global ecological awareness and a sense of responsibility.\u003c/p\u003e \u003cp\u003eThe literature confirms that researchers have conducted studies on the platform's pricing strategy (Xie et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), trust mechanisms (Liang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and perceived value (Chen et al., 2018). Within the sharing economy, consumer satisfaction positively affects their likelihood to reuse services (Ali et al., 2024). Bitner also posited that satisfied consumers are likely to engage in favorable word-of-mouth (WOM) (Bitner et al., 1990). This study aims to determine the primary drivers of consumer repurchase intentions, encompassing service quality, the concept of low carbon service, social interaction, and perceived security. By examining the psychological behaviors of tourists during their interactions with Airbnb, the paper proposes strategies to elevate host service quality, enhance the distinctiveness of listings, foster social engagement between hosts and guests, and bolster consumer perceptions of security(Fisher, 1957). Enhancements in these areas are anticipated to increase customer satisfaction, thereby promoting positive electronic word-of-mouth (WOM) regarding the platform(Lin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to extend the Expectation Confirmation Model (ECM) to the context of Airbnb, assessing the influence of perceived service quality, the concept of low carbon, social interaction, and perceived security on guests' experiences. The specific contributions of the expectation-confirmation model are as follows:(1) the impact of Airbnb's perceived service quality on confirmation, low-carbon and satisfaction; (2) an investigation into the factors that influence consumer satisfaction; and (3) an exploration of the relationship between consumer confirmation, satisfaction, eWoM communication, and repurchase intentions on Airbnb. Our study enhanced the understanding of determinants that shape the visitor experience within the Airbnb ecosystem. Subsequently, we performed comparative analyses of satisfaction factors before and after re-ordering to assess their impact on consumer behavior in relation to platform engagement. The paper then offers strategic recommendations to Airbnb for refining its hospitality approach, with the goal of enhancing customer satisfaction. Implementing these strategies could potentially increase guest retention and stimulate positive word-of-mouth effects.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe foundation of ECM is the Expectation Confirmation Theory (ECT), which has been widely used to study consumer behavior (Lin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). ECT indicates that consumer satisfaction with products or services has a decisive effect on the intention to repurchase (Anderson et al., 1995; Lin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to the expectation confirmation theory, consumers have a process of repurchase intentions (Oliver, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Firstly, consumers will form an initial expectation for products or services before buying. Secondly, after a period of experience, consumers assess the performance against the initial expectation. Thirdly, consumers trigger their satisfaction judgment based on the performance confirmation. Finally, a satisfied consumer will generate a repurchase intention.\u003c/p\u003e \u003cp\u003eThe present study utilizes ECM as the theoretical model for the following reasons. Bhattacherjee applied the ECT theory into the information system (IS) and proposed the ECM model. In the same manner, ECM tries to predict the continuous usage intention of IS system (Bhattacherjee, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The modified model emphases on the confirmation of expectation of the IS system will impact consumers' continuance intention and electronic word-of-mouth communication. Previous studies have shown that ECM is a valid theoretical model for studying visitors' experiences. The relationship between confirmation, perceived uniqueness, support conditions, satisfaction and relationship between continuous use behaviors to get support condition is full intents and important premises that support the use of behavior (Cheng, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The behavioral habits, perception uniqueness, previous habits and the use of information systems based on network learning habituation is associated with satisfaction and subsequent behavior, when obtaining satisfaction and prior behavior the information system continues to be used strongly (Lee, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Strong predictors, high intensity and high habituation will enhance the continuous use behavior. Stone and Baker-Eveleth studied perceived uniqueness in the context of the use of electronic books, the relationship between characteristics, validation, satisfaction, and continued use intent, getting the desired recognition affects the uniqueness and satisfaction of ebooks. The satisfaction and the concept of low-carbon of using an ebook influences its continued use. The results were also confirmed by many researches (Lee et al., 2011). Second, ECM has been used in multiple scenarios such as e-learning and Web services. In particular, the context of this study (the sharing economy) is also appropriate for the application of ECM.\u003c/p\u003e \u003cp\u003eIn summary, it is evident that the Expectation Confirmation Model (ECM) possesses effectiveness and applicability across various domains and contexts, particularly on sharing economy platforms such as Airbnb (Maria et al., 2024). ECM provides robust theoretical support for understanding and predicting user behavior. By utilizing ECM, researchers can analyze users' expectations, actual experiences, satisfaction levels, and the resulting word-of-mouth communication and repurchase intentions (Mohammad, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This analysis aids platforms in optimizing their service strategies, enhancing user experience, and ultimately, strengthening user loyalty and market competitiveness. So we will expect the validation model to be applied to Airbnb.\u003c/p\u003e"},{"header":"3. Expectation-confirmation model","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Satisfaction\u003c/h2\u003e \u003cp\u003eCardozo introduced \"consumer satisfaction\" into the field of marketing, which he considered as the core of marketing (Cardozo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). Satisfaction is \"the emotional attitude of a consumer by comparing the perceived effect of a product with its expectation\" (Oliver, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Xusen et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since there are many platforms in the market that can provide tourist lodging, how to improve consumers' satisfaction and willingness to repurchase will be the key to Airbnb's success (Jones, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Nakamura et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The consumers\u0026rsquo; satisfaction degree represents Airbnb's reputation and image compared to the competition platform (Sthapit et al., 2019; Wikhamn, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe researchers have confirmed that in the context of the sharing economy, consumers\u0026rsquo; satisfaction will positively influence consumers\u0026rsquo; aspiration to choose again (Anderson et al., 1993; Do et al., 2024). Moreover, Bitner believed that satisfied consumers would generate positive word-of-mouth communication (Bitner, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). However, there is still lack of evidence on how consumers\u0026rsquo; satisfaction with their previous experience impacts their intention to repurchase on Airbnb and spread electronic word of mouth. According to the theory proposed by Gountas and Gountas, which satisfaction can contribute to the future use of specific services (Gountas et al., 2007), we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eSatisfaction has a positive effect on eWoM.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eSatisfaction has a positive effect on repurchase intention.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Low carbon\u003c/h2\u003e \u003cp\u003eCarbon emission reduction is a long-term strategic task with a high priority for China and the world(Song et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).In recent years, several studies have affirmed the positive role of the sharing economy in curbing carbon emissions(Maria et al., 2024; Zhu et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).The sharing economy can reduce carbon through improving efficiency(Asghari et al., 2020).With the rapid development of sharing platforms, there is a growing interest in restraining carbon emissions through the sharing economy(Ding et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Therefore, the demand for a low-carbon among consumers has a positive impact on satisfaction.\u003c/p\u003e \u003cp\u003eOver the past decade, Airbnb hosts have brought a low-carbon and environmentally friendly local travel and accommodation experience to travelers from all over the world in an unprecedented way (Liu et al., 2017). Since the launch of the new travel platform Trips in November 2016, Airbnb has demonstrated that the platform has gone beyond home sharing and integrated accommodation experiences, travel experiences, and cultural experiences to create a customer-oriented travel experience that is low-carbon and environmentally friendly for travelers. In fact, because Chinese people like to showcase their low-carbon and environmentally friendly philosophy (Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maria et al., 2024), they are more inclined to choose products with low-carbon symbolic significance to build a low-carbon and environmentally friendly self-image and social image, thereby showcasing their commitment to environmental protection (Berger et al., 2007). Airbnb can meet the low carbon needs of consumers. Therefore, we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eLow carbon has a positive effect on satisfaction.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Social interaction\u003c/h2\u003e \u003cp\u003eSocial interaction refers to the communication among people in community. It is a social activity in which people convey information and exchange ideas via certain media in order to achieve a certain purpose. The influence of social interaction on consumption behavior is a heavily studied area (Lin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Manchanda et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Tourists can experience the sense of belonging when chatting with local Airbnb hosts or enjoying a cup of tea in the real family kitchen of Airbnb, rather than staying in a traditional hotel and being served by uniformed employees. In addition, if a host tends to share useful information about location with tourists, such as nearby store discounts, local food, travel strategies, etc., it will have a positive influence on tourists\u0026rsquo; satisfaction and promote them to stay on Airbnb again (Ali, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bosman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Then we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eSocial value has a positive on satisfaction.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Perceived security\u003c/h2\u003e \u003cp\u003eIn the era of the Internet, security has become a critical issue (Pavlou et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which is usually described as a different concept related to \"some kind of protection\" (Colobran, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Because most of Airbnb\u0026rsquo;s listings are private rooms, consumers may worry about the security. In this study, we define perceived security as the expectation of the consumer\u0026rsquo;s reliability on the certification of the platform, and then reveal the relationship between perceived security and the attitude towards consumers' repurchase on Airbnb.\u003c/p\u003e \u003cp\u003eMost studies related to perceived security come from the Technology Acceptance Model (TAM), whose initial purpose was to explain the widely accepted determinants of IS systems (Raziuddin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wulan et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As we know, perceived security has previously been empirically studied in the context of B2C, with the result that a higher level of perceived security leads to greater intention to purchase products (Davied et al., 2024). As a result, the platform effectively captures the consumer's initial level of trust and is able to achieve transactions and maintain market competitiveness (S\u0026ouml;nmez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Security has been a major concern for tourist during the booking process. If Airbnb's listings and hosts are professionally and reliably certified, it will greatly enhance consumers' trust in the platform and the host. For example, in China, hosts can increase consumer perceived security through Sesame Credit certification. Chang \u0026amp; Chen (2009) researched perceived security as a significant predictor of consumers\u0026rsquo; satisfaction on the site, and consumers with higher perceived security tend to continue to use their site (Hong et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003ePerceived security has a positive on satisfaction.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Confirmation\u003c/h2\u003e \u003cp\u003eConfirmation is a process of comparing experiences with expectations after usage. Previous studies have found that confirmation positively affects satisfaction Bhattacherjee, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When consumers\u0026rsquo; experiences meet their expectations, confirmation will lead to satisfaction. On the contrary, if the experience is lower than expectation, the consumer will generate dissatisfaction. This relationship can also be used in Airbnb context. Bookings compare their experiences with their expectations, and if their expectations are confirmed, they will be satisfied with Airbnb. So we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003cp\u003eConfirmation has a positive effect on satisfaction.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFestinger's cognitive dissonance theory believes that if users are inconsistent with their perceived usefulness in the actual use process, a dissonance will be generated in their mind (Festinger, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Then rational users generally reduce dissonance and make it more realistic by trying to change their perceived usefulness. In other words, confirmation will help improve the perceived usefulness of users. Therefore, we apply cognitive dissonance theory to relevant beliefs on Airbnb usage, including the concept of low-carbon service, social interaction and perceived security (Mohammad, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where users constantly change their expectations to conform to reality. Then we assume that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003eConfirmation has a positive effect on uniqueness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH8\u003c/strong\u003e \u003cp\u003eConfirmation has a positive effect on social value.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH9\u003c/strong\u003e \u003cp\u003eConfirmation has a positive effect on perceived security.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Perceived service quality\u003c/h2\u003e \u003cp\u003ePerceived service quality is different between consumer expectation and perceived performance (Parasuraman et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Parasuram proposed the SERVQUAL model, which divides the perceived service quality into five levels: physical facilities, reliability, responsiveness, security, and emotional input(Parasuraman et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The better these five dimensions, the higher the consumer\u0026rsquo;s expectations are confirmed. In other researches, SERVQUAL provides better diagnostic information compared with SERVPERF when measuring perceived service quality indicators (Juma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zeithaml, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1966\u003c/span\u003e). This model has been widely used in the service industry to understand the target consumers\u0026rsquo; perception of service demand and thus to improve service.\u003c/p\u003e \u003cp\u003ePerceived service quality is a key performance indicator in the process of consumer behavior. Previous researches have also found that perceived service quality has a significant impact on confirmation (Lai, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and satisfaction (Caruana, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kuo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Petek et al., 2024). Understanding consumers\u0026rsquo; market demand needs to capture the dynamic expectations of change. In this study, we will directly use the perceived service quality to measure the perceived service quality of consumers\u0026rsquo; Airbnb usage. We assume that:\u003c/p\u003e \u003cp\u003eH10: Perceived service quality has a positive effect on confirmation.\u003c/p\u003e \u003cp\u003eH11: Perceived service quality has a positive effect on satisfaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Data collection\u003c/h2\u003e \u003cp\u003eA survey which explores factors affecting Airbnb was obtained from a Chinese online questionnaire company (WJX.com) in Apr. 2021. We confirm that informed consent was obtained from all subjects and/or their legal guardian(s) and all methods were performed in accordance with the relevant guidelines and regulations. Two screening questions were developed to recruit qualified respondents: (1) have you known about Airbnb? (2) have you used Airbnb before? The questionnaire was set up based on an interview and literature review in the pilot period to improve the validity of the content and the credibility of the questionnaire. A total of 330 valid surveys were obtained. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provided a summary of the demographic of the sample. There are 183 women, about 47.9% between 30 and 39 years old. The vast majority of respondents received a bachelor's degree (83.9%). More than a third of respondents earn between 5,000 and 8,000 a month.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the respondents (n\u0026thinsp;=\u0026thinsp;330).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder \u0026yen;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;3000-\u0026yen;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;5000-\u0026yen;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;8000-\u0026yen;15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;15000-\u0026yen;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;20000 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Scale design\u003c/h2\u003e \u003cp\u003eThe survey contains three parts. The first part investigates the demographics of the tourists who have purchased on Airbnb. The second part of measures (1) perceived service quality toward confirmation and satisfaction. (2) customers\u0026rsquo; satisfaction. The last part assesses consumer intention to purchase on Airbnb again and eWoM spreading. A total of 33 attributes were rated by respondents on a 7-point Likert-type scale ranging from \u0026ldquo;strongly disagree \u0026minus;\u0026thinsp;(1)\u0026rdquo; to \u0026ldquo;strongly agree (7)\u0026rdquo;. The theoretical model of this study is based on the extension of ECT to build the ECM of Airbnb continuance (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eMost of the measurement items in this study are adapted from previous studies with slight modifications. Specifically, the measurements of LC and SI are extracted from Rasoolimanesh (2016), and those of PQ are extracted from Varma (2016) and Rasoolimanesh (2016). Confirmation is measured by scales from Bhattacherjee. As most studies related to PS are conceptual, the measurement items are created according to the key descriptive terms of PS in the existing literature such as \u0026ldquo;secure\u0026rdquo; and \u0026ldquo;protect\u0026rdquo;. The items from Varma (2016) and Sullivan \u0026amp; Dan (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) are used to measure consumers\u0026rsquo; eWoM spreading and repurchase intentions, and satisfaction are measured by using items from Jiang (Jiang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Data analysis\u003c/h2\u003e \u003cp\u003eA structural equation model (SEM) with partial least squares (PLS) for hypotheses is the available statistical analysis techniques to interpret data from experimentation. In this study, we used Smart PLS 2.0 because it can examine the hypotheses in the model (Barclay et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) without requiring a large sample size (Gefen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The PLS Algorithm examines the relationships between constructs and path analysis to evaluate the measurement model (Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The bootstrapping technique is used to detect the significance and reliability of the model paths, we use 0.05 significance level as a statistical criterion (Fisher, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Data analysis and result","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1. The measurement model\u003c/h2\u003e \u003cp\u003eIn order to determine the accuracy of measurement model, the study was tested for reliability, convergent validity, and discriminant validity. We tested the internal consistency of the mode, the Cronbach\u0026rsquo; alpha for all constructs was higher than 0.6 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating strong internal consistency (Robinson et al., 1991; Wang et al., 2018). However, research shows that Cronbach\u0026rsquo; alpha often underestimates the true reliability. So, composite reliability becomes more rigorous evaluation standard (Gefen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates that composite reliability values exceeded the minimum of 0.70 (0.78\u0026ndash;0.81) (Barclay et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Through good data support, it is concluded that the expectation-confirmation model has good applicability in the satisfaction survey of Airbnb platform. It can better understand the repurchase intention of tourists on Airbnb and electronic word-of-mouth communication.\u003c/p\u003e \u003cp\u003eIn order to assess the convergent validity, in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all the average variance extracted (AVE) scores are above 0.50(Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). And the item loadings were almost higher than 0.7(Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), ensuring convergent validity. Discriminant validity was evaluated by comparing the correlations between the square roots of AVE and the other constructs (Fornell et al., 1982). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the square roots of AVE (on-diagonal values) are higher than the correlations among the constructs (off-diagonal values), and the correlation between the two random variables is lower than 0.85 (Kline, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, reliability, convergence effectiveness, and discriminant validity of this measurement model were guarantees.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2. The structural model\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated the results of the structural model, which indicated all hypotheses were supported. Specifically, consumers\u0026rsquo; satisfaction with Airbnb's occupancy experience was significantly affected by the concept of low-carbon service (b\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social interaction (b\u0026thinsp;=\u0026thinsp;0.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and perceived security (b\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, hypotheses 3, 4 and 5 were supported. Confirmation was found to significantly affect satisfaction, low carbon, social interaction and perceived security. The path coefficients from confirmation to these four factors were 0.25 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.41 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.34 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.33 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, so hypotheses 6, 7, 8 and 9 were supported. Perceived service quality exerted significant impacts on confirmation (b\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and consumers\u0026rsquo; satisfaction (b\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an Airbnb stay, supporting hypotheses 10 and 11. Overall, the model accounts for 30.5% and 21.9% of the variance in repurchase intention and eWoM, respectively. It was supporting hypotheses 1 and 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssessment of the measurement model. Note: AVE- average variance explained\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs and related measurement Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Service Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfirmation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepurchase Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeWoM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeWoM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeWoM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeWoM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeWoM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeWoM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: CON - confirmation, PQ - perceived service quality, RI - repurchase intension, SAT - satisfaction, PS - perceived security, SI - social interaction, LC - low carbon\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the structural model and hypotheses tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: all path coefficients are significant at the 0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Theoretical implications\u003c/h2\u003e \u003cp\u003eThe main contribution of this research is the extension of the expectation-confirmation model (ECM) within the context of the sharing economy, specifically pertaining to Airbnb. The paper retains the core architecture of the ECM, which is the most representative research model for continuous intention. By analyzing consumer perceptions of Airbnb's perceived quality, low carbon, social interaction, and perceived security, the paper predicts consumer repurchase intentions and electronic word-of-mouth communication on the platform. Notably, previous studies have not simultaneously examined all these aspects (Maria et al., 2024). For the extension of the ECM model, we will expect to confirm that the universality of the model is applied to the general information system environment to the specific environment of Airbnb's intended platform.\u003c/p\u003e \u003cp\u003eSpecifically, perceived security has a positive impact on consumer satisfaction, contradicting the results of the original model, perceived security has no effect on satisfaction with usage. Chinese tourists are very sensitive to the perception of travel safety, and this sensitivity has a clear influence on future travel decisions. Another important aspect of extended ECM is the perceived impact of service quality on confirmation and satisfaction. In the lodging market, consumers can have many similar homestay options, which means that if Airbnb's quality of service does not satisfy consumers, they may easily move to other platforms (Mohammad, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., 2001). In addition, we also studied the positive impact of consumers' perceptions of Airbnb's perceived quality of service, low carbon, social interaction, and perceived security on their future use of Airbnb and electronic word-of-mouth communication to enrich the literature on the sharing economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Practical implications\u003c/h2\u003e \u003cp\u003eThe study improves Airbnb platform construction and host improvements by understanding consumer behaviors on Airbnb. Gradually, sharing as an alternative to consumption, in this context, the lodging industry can be a representative of the emerging trends in the sharing economy (Belk, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Hotel managers can gain a deeper understanding of the success and competitive advantages of the Airbnb service model in the context of sharing economy, and optimize their strategies to create a brilliant tourism accommodation industry.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, the results of this study hold significant implications. The main factors driving customers to repurchase on Airbnb include service quality, low carbon, social interaction, and perceived security. Therefore, the Airbnb platform and its hosts should prioritize the improvement of these factors while also providing personalized services to enhance customer satisfaction. Fortunately, Airbnb is currently developing a host college that offers tailor-made online and offline training courses to its hosts, which can help them improve their hospitality. Provision of high-quality service is crucial in pleasing customers. With respect to low carbon, hosts can offer a combination of local styles for the guests to choose from. In terms of social interaction, Airbnb hosts should prioritize communication with guests to create a sense of belonging. For perceived security, the Airbnb platform should strictly review the qualifications and reliability of listings, which can instill assurance among users. By addressing these aspects, Airbnb can satisfy its consumers, garner positive repurchase intent, and generate positive word-of-mouth communication.\u003c/p\u003e \u003cp\u003eIn addition to the suggestions previously mentioned, Airbnb hosts must also maintain high-quality infrastructure and amenities, such as comfortable and updated facilities, cleanliness, and top-notch equipment. Additionally, providing low-carbon services can establish a friendly relationship between tourists and nature. Examples of such services may include pick-up and drop-off services, local transportation, or recommendations for local attractions and specialties. In cases where unique cultural practices exist in the host's area, it is pertinent for the host to explain these to the guests in a clear manner. By doing so, hosts can establish a loyal customer base and encourage repurchases by generating enhanced customer satisfaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion and prospects","content":"\u003cp\u003eThis study adopts a systematic approach to understand and predict Airbnb's repurchase intentions and electronic word-of-mouth communication. The paper extends the ECM model by adding other factors specific to Airbnb's unique background. The research confirms that confirmation, low carbon, social interaction, perceived security, and satisfaction are crucial influencers of Airbnb's continued use intentions and electronic word-of-mouth communication. The study further emphasizes that providing high-quality service is crucial for maintaining customer loyalty, while also pointing out that consumers will consider factors of green emission reduction when making purchases. By strengthening the appeal of their services with environmental concepts, companies can attract and retain more consumers who focus on a sustainable lifestyle. Therefore, the quality of the host's service, the concept of low carbon, the social interaction between the host and the tourists, and the perceived security of the consumer all play significant roles in predicting Airbnb's future stay and electronic word-of-mouth communication.\u003c/p\u003e \u003cp\u003eAlthough this study in addressing concerns regarding the satisfaction of home-stay experience in the sharing economy, there remain certain areas that can be improved and further developed. One potential avenue of research may involve the implementation of additional models or techniques beyond expectation confirmation model, which may further enhance the accuracy and effectiveness of this research. Additionally, further investigation into other relevant factors and their potential influence on consumer satisfaction could also provide deeper insights into this topic. Finally, different regions may offer different home stay experiences, this study lacks a broad analysis of consumer satisfaction with home stay experience in China's home stay market. In the future, incorporating data from diverse sources and regions may offer a more comprehensive understanding of consumer satisfaction in the sharing economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThe ethical application for this research was approved by the Academic Committee of Zhejiang Gongshang University. All research was performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003eAll online questionnaire survey participants provided informed consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLiming Wang and Jinhao Hu designed the research study. Liming Wang, Jinhao Hu and Bei Wang collected the data. Liming Wang and Bei Wang performed the data analysis. Jinhao Hu and Ning Xu prepared figures and tables. HongBin Chen and Ning Xu wrote the initial draft of the manuscript. Liming Wang and HongBin Chen reviewed and edited the manuscript for intellectual content. All authors contributed to the interpretation of the results and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli, M. A. Parents\u0026rsquo; view on the effect of online games on social interaction of adolescents with intellectual disability. International Journal of Developmental Disabilities. 2024, 70(3), 530\u0026ndash;535.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson, E. W.; Sullivan, M. W. The antecedents and consequences of customer satisfaction for firms. Marketing Science. 1993, 12(2), 125\u0026ndash;143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsghari, M.; Al-E-Hashem, S. A green delivery-pickup problem for home hemodialysis machines sharing economy in distributing scarce resources. Transportation Research Part E Logistics and Transportation Review. 2020, 134, 101815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarclay, D.; Higgins, C.; Thompson, R. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Studies in Health Technology and Informatics. 1995, 285\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelk, R. You are what you can access: sharing and collaborative consumption online. 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Natural Hazards. 2018, 91(2),611\u0026ndash;633.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Airbnb, sharing economy, repurchase intention, expectation-confirmation model","lastPublishedDoi":"10.21203/rs.3.rs-4653327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4653327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAirbnb is a popular and low-carbon platform for tourism in the sharing economy. This paper utilized the extended expectation-confirmation model (ECM) and collected 330 valid data through a questionnaire survey to analyze the psychological behavior of tourists during their Airbnb experience. The results demonstrate that the service quality, the concept of low-carbon service, friendly communication with the host, and platform security significantly impact consumers' satisfaction. Highly satisfied consumers are more likely to continue using Airbnb with low-carbon consciousness and engage in electronic word-of-mouth communication. Therefore, ensuring customer satisfaction and low-carbon consciousness are critical to win on the Airbnb platform and attracting more hosts and guests. This study contributes to the existing literature on Airbnb and offers practical implications for the platform and hosts to attract more guests and generate positive word-of-mouth.\u003c/p\u003e","manuscriptTitle":"Understanding Tourists Repurchase Intention on Airbnb from the Perspective of Low Carbon and Extended Expectation-Confirmation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 13:15:35","doi":"10.21203/rs.3.rs-4653327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e20a2d8-4a32-43cb-9f4b-f557120fa5c1","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36585878,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":36585879,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":36585880,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society"},{"id":36585881,"name":"Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios"}],"tags":[],"updatedAt":"2024-09-11T04:14:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 13:15:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4653327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4653327","identity":"rs-4653327","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}