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

doi:10.21203/rs.3.rs-4653327/v1

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

https://doi.org/10.21203/rs.3.rs-4653327/v1

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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

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.

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.

3.1. 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:

Satisfaction has a positive effect on eWoM.

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:

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:

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:

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:

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:

Confirmation has a positive effect on uniqueness.

Confirmation has a positive effect on social value.

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.

4.1. 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).

5.1. 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.

6.1. 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.

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.

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.

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No competing interests reported.

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

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Abstract

Figures

1. Introduction

2. Literature review

3. Expectation-confirmation model

3.1. Satisfaction

3.2. Low carbon

3.3. Social interaction

3.4. Perceived security

3.5. Confirmation

3.6. Perceived service quality

4. Methodology

4.1. Data collection

4.2. Scale design

4.3. Data analysis

5. Data analysis and result

5.1. The measurement model

5.2. The structural model

6. Discussion

6.1. Theoretical implications

6.2. Practical implications

7. Conclusion and prospects

Declarations

Competing interests

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

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