Understanding Key Antecedents of Consumer Loyalty toward Sharing-Economy Platforms: The Case of Airbnb

Abstract

Amidst collaborative consumption and developments in information and communication technology, the sharing economy has attracted worldwide attention, being considered sustainable consumption as it shares time, resources, and materials with others. However, because sharing-economy platforms offer nearly homogeneous assets to traditional business firms, enhancing consumer loyalty presents a huge challenge. This study provides a theoretical view for understanding the mechanisms behind user loyalty in the sharing economy. It identifies consumer satisfaction and trust in Airbnb as the key antecedents of consumer loyalty. Moreover, this study investigates the different effects of economic, hedonic, and symbolic benefits on consumers’ decision-making processes. A structural equation modeling method was used to check the research hypotheses based on a sample of 317 Airbnb consumers in South Korea. The analysis results reveal that in the case of Airbnb, consumer loyalty is jointly shaped by consumer satisfaction and trust, with entertainment and recognition significantly influencing both consumer satisfaction and trust. Moreover, money savings and exploration are not significantly related to consumers’ decision-making processes. Although social benefits significantly influence trust in Airbnb, these have no significant effect on consumer satisfaction. The findings provide theoretical and practical implications and future research direction.

1. Introduction

Given the proliferation of collaborative consumption, the sharing economy has become a major business trend. Recently, the sharing economy has received significant attention as a means of sharing unused resources with others. The sharing economy refers to an alternative social phenomenon where unused or idle resources are shared with others. The sharing economy differentiates itself from the traditional business model in its non-ownership of assets and its access to idle resources [1]. The sharing economy is regarded as sustainable consumption by transforming the waste and production-consumption regimes [2]. Individuals, as suppliers, can provide short-term rentals of their own assets, such as rooms and vehicles that would otherwise be idle and unused. Consumers can benefit from the sharing economy by renting assets at a lower cost [3]. Some firms in the sharing economy attempt to share time, resources, and materials without any profit goals [4]. Furthermore, the sharing economy is highly associated with information and communication technology (ICT), such as smartphones and mobile networks, as ICT helps consumers easily access, reserve, and pay for these services or products [5]. As a result of these benefits, sharing-economy platforms have become a huge challenge for traditional business firms. Airbnb is the most major hospitality firm in the sharing economy. It differentiates itself from the traditional hotel accommodations in terms of the access to idle and unused spaces. Several works on Airbnb showed that accommodations in Airbnb are significantly less resource intensive than the traditional accommodations, creating positive impacts on the environment [6,7]. Moreover, Airbnb aims to become environmentally sustainable by promoting a more efficient use of existing resources [8]. According to the research by Airbnb, accommodations in Airbnb consume 63% less energy and 12% less water, and produce 32% less waste than traditional hotels in North America [9]. Therefore, understanding what drives consumer loyalty toward Airbnb is likely to help platform growth in a sustainable manner.

In the service management and hospitality domains, several studies have shed some light on the key determinants that influence consumer loyalty in various contexts [10,11]. Consumer satisfaction is widely regarded as a vital predictor of consumer loyalty [11,12]. In the sharing economy, consumer satisfaction plays an important role in improving consumer loyalty, as satisfied consumers are more likely than unsatisfied ones to increase their spending and recommend such platforms to others. Most previous studies on the sharing economy have concentrated mainly on affective-based responses such as consumer satisfaction or attitude [10,11,12]. However, affective-based responses alone are not able to fully capture the mechanisms underlying user loyalty toward the sharing economy. Many studies have verified the salient role of trust in consumer decision-making processes in online transactions [13,14]. Specifically, trust is a key enabler of consumer loyalty toward Airbnb. Indeed, the lack of trust in a service provider is a huge barrier to forming relationships with sharing-economy platforms and is a main source of consumers’ hesitation to use such a platform [15,16]. Registering for a shared-economy service requires detailed personal information. For example, in the case of Airbnb, the hosts’ profiles contain more personal information, such as demographics, location data, staying records, and social connections, than their services. However, some sharing-economy platforms can potentially exercise malicious collection and use of consumers’ personal information. Moreover, sharing-economy firms can illegally gather and sell consumers’ personal data to third parties without their permission. Thus, trust is one of the most influential determinants of consumer loyalty toward Airbnb. In this vein, this study attempts to identify the role of trust with regard to consumer loyalty toward Airbnb.

In the case of Airbnb, its perceived benefits are considered the main motivational factors in increasing consumer satisfaction and trust. Several studies in the fields of marketing and hospitality have proven that perceived benefits are multidimensional and can be measured in terms of three types of benefits [17,18]. Mimouni-Chaabane and Volle [17] conceptualized perceived benefits in three distinct dimensions: Economic, hedonic, and symbolic benefits. Economic benefits refer to the economic benefits or utility derived from products or services performed by the sharing economy. This study regarded monetary savings as the key element of the sharing-economy platforms’ monetary benefits [12]. Hedonic benefits relate to the consumers’ emotional experience derived from using the sharing-economy platforms, such as entertainment and pleasure. This study measured the hedonic benefits of exploration and entertainment. For example, Airbnb helps consumers find new accommodation and join unique regional trips, leading to memorable and enjoyable experiences. Symbolic benefits refer to the extrinsic benefits of collaborative consumption. They relate to consumers’ needs for social approval, personal expression, and self-esteem [19]. In the sharing economy, symbolic benefits can be captured by recognition and the social benefits derived from using sharing-economy platforms. This study investigates the different effects of these three types of perceived benefits on consumers’ decision-making processes in the context of Airbnb.

The next section covers the theoretical background and research model. Following this, Section 3 describes the research methodology and information on survey participants. The results of the structural equation modeling are presented in Section 4. After that, the theoretical and practical implications of this study are discussed. Moreover, Section 5 provides the study’s limitations and outlines directions for further research.

2. Literature Review and Hypotheses

2.1. Theoretical Background

Some researchers noted that the sharing economy becomes sustainable consumption [1,3,6,7]. Based on this concept, a number of companies have launched a variety of business models with collaborative consumption. These companies, such as Uber and Airbnb, develop and distribute sharing-economy platforms to encourage individuals who have extra resources to participate in such platforms. These sharing-economy platforms have transformed from owning products or services to sharing idle ones. For examples, a sharing-economy platform for peer-to-peer accommodations encourages the access and use of unused rooms for travelers. Moreover, these platforms are significantly changing consumers’ travel and staying patterns and the range of local trips and activities [12]. Several studies have focused on consumers’ motivations to use sharing-economy platforms in a variety of contexts [9,20,21]. Sung et al. [20] identified economic benefits, sustainability, enjoyment, social relationships, and network effects as key motivations in the sharing economy. They found significant effects of enjoyment and networking on consumers’ attitude toward the sharing economy. Zhu et al. [21] integrated value-based adoption model into a technology-acceptance model to examine consumers’ usage motivations about a sharing-economy platform. They regarded function, emotional, and social values, as well as learning and risk costs as the main factors affecting perceived value. Liang et al. [22] investigated the effects of perceived authenticity, risk, and value, as well as price sensitivity on consumers’ repurchase intention in the Airbnb business. They found that perceived risk negatively influences repurchase intention. The findings imply that consumers hesitate to use Airbnb because they cannot trust peer-to-peer accommodations. Mao and Lyu [5] integrated the theory of planned behavior into prospect theory in the Airbnb environment and found that perceived risk also negatively influences consumer attitude toward Airbnb. In summary, although trust is an important element in peer-to-peer transactions, these previous works on the sharing economy did not provide an integrative view of consumer satisfaction and trust. Indeed, lack of trust in Airbnb is a huge barrier to developing and maintaining a long-term relationship with Airbnb. Möhlmann [13] clarified the effects of the determinants of satisfaction with a sharing-economy platform and the likelihood of reusing the same platform. They found that utility, trust, cost savings, and familiarity are significant contributing factors to the success of a sharing-economy platform. In particular, trust had a significant influence on the likelihood of consumers choosing the same platform. As such, this study investigates the impact of consumer satisfaction and trust on their loyalty toward Airbnb.

2.2. Research Model

Figure 1 illustrates the proposed theoretical framework for investigating the key factors of consumer loyalty in the Airbnb business. Consumer loyalty is predicted based on two main factors: Consumer satisfaction and trust. The theoretical framework was developed by integrating consumer satisfaction and trust in Airbnb. Moreover, this study identified the key drivers of consumer satisfaction and trust in Airbnb to be monetary savings, exploration, entertainment, recognition, and social benefits.

2.2.1. Consumer Loyalty

Consumer loyalty is regarded as the ultimate goal in the service industry because loyal consumers bring numerous benefits to a firm [10,11]. The benefits of consumer loyalty include an increase in consumers’ expenditures, reduction of marketing costs, and continuance of consumer visits. In the sharing economy, consumer loyalty is also a key element to the success of a sharing-economy platform, as it leads to shared consumption and positive word-of-mouth. Thus, understanding the mechanisms behind consumer loyalty can help service firms such as Airbnb and Uber by increasing the frequency of use of a sharing-economy platform and encouraging consumers to share their experiences on social network services.

2.2.2. Consumer Satisfaction

Consumer satisfaction is defined as an ex-post and overall evaluation based on a consumer’s experience of a target service [23]. According to the expectation-disconfirmation model, consumer experience induces a positive feeling (satisfaction), indifference, or a negative feeling by comparing the actual consumer experience of a service with his/her expectation [24]. Consumers are likely to be satisfied if their actual experience is more entertaining and pleasant than their expectation [11,13]. Dissatisfied consumers tend to have lower intention of revisiting or repurchasing the same service as compared with satisfied consumers. Moreover, they may spread negative word-of-mouth and share their negative experiences on social-networking sites [12,15]. Several studies on service management and marketing have demonstrated the vital role of consumer satisfaction in enhancing consumer loyalty, such as in spreading positive word-of-mouth and in consumers’ repurchasing intention [10,12,15,25]. In the case of Airbnb, consumer satisfaction plays an important role in improving the level of consumers’ continued usage and recommendation of Airbnb [12,15]. Satisfactory experiences are an essential condition for the development and enhancement of consumer loyalty. Thus, consumer satisfaction is expected to be a significant factor in improving the level of consumers’ loyalty toward Airbnb.

Hypothesis 1 (H1).

Consumer satisfaction positively influences consumer loyalty.

2.2.3. Trust

Trust in Airbnb is defined as the belief that Airbnb is honest, reliable, and competent [14]. Airbnb plays an important role in connecting consumers with hosts. Trust is related to a social and relational construct that stems from interpersonal relationships. Trust is the major determinant of consumer loyalty in a variety of sharing-economy platforms. According to the social exchange theory, trust helps mitigate perceived risks of opportunism [13] because trusting behaviors attract and maintain commitment in host-consumer relational contexts. In the case of Airbnb, trust is a key success factor in developing consumer loyalty, as it provides a commercial-sharing-service platform between hosts and consumers [15]. Airbnb attempts to build and maintain long-term relationships between hosts and consumers from the perspective of social exchange. Moreover, trust mitigates perceived risks such as personal information abuse and digital discrimination [26]. Trust increases consumers’ expectation of a safe and reliable experience. Thus, it is expected that trust is positively related to consumer loyalty toward Airbnb.

Hypothesis 2 (H2).

Trust positively influences consumer loyalty.

2.2.4. Economic Benefits

Monetary savings refer to the extent to which consumers perceive that using Airbnb provides them with monetary savings when making reservations for their accommodations [3]. Monetary saving is associated with utilitarian benefits, which provide consumers value by achieving their goals effectively. Several works have noted that saving money is regarded as the major motivation for joining innovative products of services [3,17]. Generally, consumers can get their accommodation at a relatively low price as compared with staying at a branded hotel. Previous studies on marketing and service management have verified the significance of economic benefits on consumers’ positive attitude and participation in the sharing economy. Guttentag [27] showed that monetary savings are a critical factor in consumers’ choice of accommodation. Sung et al. [20] noted that the economic benefits of the sharing economy have unique characteristics as compared with the traditional economy. High economic benefits are more likely to develop positive consumer satisfaction and trust when consumers stay in Airbnb accommodations at a reasonable price. Thus, monetary savings from using Airbnb would improve the level of consumer satisfaction and trust in Airbnb.

Hypothesis 3a (H3a).

Monetary savings positively influences consumer satisfaction.

Hypothesis 3b (H3b).

Monetary savings positively influences trust.

2.2.5. Hedonic Benefits

Exploration refers to the degree that consumers’ curiosity is satisfied by enabling them to try new services or products and unique experiences [17]. Exploration is related to hedonic benefits, which offer consumers with enjoyment and emotional value. Staying in new accommodations or experiencing unique regional trips increases consumers’ curiosity about the sharing economy. In the hotel industry, consumers desire to experience unexpected but positive surprises during their travel [12]. In particular, Airbnb provides unique and unusual travel experiences to attract more consumers. Consumers experience unique and delightful activities that they might not have undertaken otherwise. As Airbnb enables consumers to have unexpected but delightful experiences, it helps satisfy consumers’ desire for exploration. Thus, the exploration benefits provided by Airbnb prompt consumers to develop satisfaction and trust in Airbnb.

Hypothesis 4a (H4a).

Exploration positively influences consumer satisfaction.

Hypothesis 4b (H4b).

Exploration positively influences trust.

Entertainment is defined as the degree to which an activity is perceived to be fun and enjoyable, apart from any performance consequences [3,12]. According to the motivation theory, intrinsic motivation can be captured by entertainment. Several works on marketing and service management have shown that intrinsic motivation plays an important role in enhancing consumers’ satisfaction and trust [3,12,21]. Some works on the sharing economy identified consumers’ perception of entertainment as the key element for developing positive attitudes toward the sharing economy. In the case of Airbnb, creating a memorable and enjoyable staying experience can foster a favorable attitude toward Airbnb. Intrinsic motivations related to Airbnb can capture entertainment from a fun and delightful staying experience [3]. Thus, the experience of entertainment and enjoyment provided by Airbnb has a positive effect on consumer satisfaction and trust in Airbnb.

Hypothesis 5a (H5a).

Entertainment positively influences user satisfaction.

Hypothesis 5b (H5b).

Entertainment positively influences trust.

2.2.6. Symbolic Benefits

Recognition is related to symbolic benefits, which are consumers’ needs for self-esteem and social approval [28]. Hosts in Airbnb can build and maintain long-term relationships with consumers by collecting consumer information and preferences [27]. They can differentiate and customize their services or facilities based on consumers’ preferences. Recognition depends on how hosts in Airbnb respond to various consumer needs. When consumers feel that Airbnb hosts treat them better, they experience the benefits of recognition. When consumers have a positive feeling from their accommodation experience through Airbnb, this tends to increase their level of satisfaction and trust in Airbnb. Thus, it is expected that user perception of recognition affects both consumer satisfaction and trust in Airbnb.

Hypothesis 6a (H6a).

Recognition positively influences user satisfaction.

Hypothesis 6b (H6b).

Recognition positively influences trust.

Social benefits refer to the degree to which consumers’ needs for interaction with people in the local area are satisfied [29]. Crompton [30] noted that the desire to develop good relationships with local people is one of the key motivations for a pleasurable and delightful trip. In the case of sharing-economy services, Yang et al. [19] showed that consumers interact with service providers directly and eventually develop social bonds beyond economic exchanges. Such social benefits motivate consumers to maintain long-term relationships with the service providers [29]. When staying in Airbnb accommodations helps consumers build relationships with local citizens, consumer are more likely to be satisfied with Airbnb. Airbnb enhances consumers’ perception of social benefits, such that consumers consider themselves part of a special and exclusive group of people who share values with the Airbnb community. To increase social benefits, service providers try to check ethical issues such as consumer protection and provider misconduct [31]. For these reasons, it is hypothesized that social benefits positively affect consumer satisfaction and trust in Airbnb.

Hypothesis 7a (H7a).

Social benefits positively influences user satisfaction.

Hypothesis 7b (H7b).

Social benefits positively influences trust.

3. Research Methodology

3.1. Instrument Development

In this study, a self-administered survey questionnaire was developed based on previously validated measurements. We modified the questionnaire items to fit the case of Airbnb. The survey consisted of three parts as follows: (1) Screening, (2) Airbnb experience and motivations, and (3) demographics. In the screening section, respondents were asked about their Airbnb experience in terms of frequency and the amount of their expenditure at Airbnb. If the respondents had no Airbnb experience, they completed the survey at this point. Otherwise, they moved on to the next section, which concerned their Airbnb experience and motivations. Here, respondents were asked to recall and evaluate their Airbnb experience. Each item, which corresponded to the constructs, was measured on a seven-point Likert scale that ranged from 1 (strongly disagree) to 7 (strongly agree). In the last section, the respondents were asked for personal demographic information such as age, gender, and household income. Before implementation, the questionnaire items were reviewed by three researchers in the service management and marketing fields. They checked and modified some minor problems in wording, content, and question ambiguity. The modified questionnaire was piloted with 35 university students. The reliability of all measurement items were confirmed by verifying the Cronbach’s alpha. All Cronbach’s alpha values exceeded 0.7, which is an acceptable threshold value of reliability. The measurement items and related literature are listed in Appendix A.

3.2. Subjects and Data Collection

The theoretical framework was empirically tested using data collected from the self-administered survey. A cross-sectional survey was conducted, targeting consumers who have accommodation experience with Airbnb. With the cooperation of an online survey agency that has the largest panel base in South Korea, the online link to the questionnaire was delivered to its panels and the survey was advertised on the site of the company. Only after completing all questions in each section of the questionnaire were respondents allowed to move on to the rest of the questionnaire. After eliminating insincere responses and consumers who have no experience with Airbnb, the remaining 317 responses were used for the data analysis.

Table 1. Profile of respondents.

Demographics	Item	Subjects (n = 317)
		Frequency	Percentage
Gender	Male	150	47.3
	Female	167	53.7
Age	Less than 30	126	39.7
	31~40	126	39.7
	More than 40	65	20.6
Staying Frequency at Airbnb Per Year	Once	159	50.2
	2 times	97	30.6
	3 time	33	10.4
	More than 4 times	28	8.8

presents the demographic information of respondents in the final sample, which shows that 47.3% are male and the mean age is 33.09, with a standard deviation of at 8.27.

4. Research Results

AMOS, a covariance-based structural equation modeling tool, was used to test the measurements and structural model. The analysis was carried out in two stages: An evaluation of the convergent validity, reliability, and discriminant validity of the measurement model, and an evaluation of the structural model.

4.1. Measurement Model

Confirmation factor analysis was conducted to assess the convergent validity, reliability, and discriminant validity of the measurement scales. The results showed that Chi-square is significant (x²(271) = 524.281, p < 0.05). This study calculated model fit in terms of three different indices: root mean square error approximation (RMSEA), comparative fit index (CFI), non-normed fit index (NNFI). The three fit indices are recommended based on their stability and insensitivity to sample size [32]. The recommend cut-off values for the fit indices are as follows: RMSEA of 0.08 or lower, CFI of 0.90 or higher, and NNFI of 0.90 or higher. The recommended fit indices were within the recommended cutoff level, with RMSEA at 0.054, CFI at 0.967, and NNFI at 0.960, indicating a good model fit. To verify convergent validity, this study investigated the factor loadings of the measurements, as shown in

Table 2. Scale reliabilities.

Construct	Item	Mean	St. dev.	Factor Loading	CR	AVE
Consumer Loyalty	CLO1	4.21	1.421	0.743	0.726	0.717
	CLO2	3.55	1.549	0.742
	CLO3	4.37	1.319	0.932
	CLO4	4.21	1.382	0.947
Consumer Satisfaction	CSA1	4.49	1.084	0.858	0.889	0.760
	CSA2	4.66	1.095	0.891
	CSA3	4.67	1.091	0.866
Trust	TRU1	3.80	1.313	0.809	0.920	0.823
	TRU2	4.15	1.208	0.903
	TRU3	4.09	1.247	0.955
	TRU4	4.07	1.244	0.953
Monetary Savings	MOS1	4.87	1.079	0.864	0.895	0.791
	MOS2	4.95	1.142	0.960
	MOS3	4.92	1.215	0.839
Exploration	EXP1	5.27	0.956	0.779	0.787	0.606
	EXP 2	4.92	1.199	0.759
	EXP 3	5.08	1.187	0.797
Entertainment	ENT1	4.79	1.052	0.907	0.915	0.808
	ENT2	4.83	1.095	0.908
	ENT3	4.97	1.097	0.881
Recognition	REC1	4.50	1.130	0.855	0.909	0.810
	REC2	4.42	1.110	0.93
	REC3	4.31	1.153	0.913
Social Benefits	SOC1	3.92	1.231	0.786	0.857	0.754
	SOC2	4.36	1.244	0.899
	SOC3	4.29	1.247	0.915

. Convergent validity is acceptable if factor loading value exceeds 0.60 [33]. In this study, the lowest factor loading was 0.742 (CLO2), which confirmed convergent validity. To check the reliability of the constructs, the composite reliability (CR) and the average variance extracted (AVE) values were calculated. Reliability is acceptable if the CR value exceeds 0.70 and if the AVE value exceeds 0.50 [34]. As shown in Table 2, all CR and AVE values had more than the recommended cutoff values. Finally, to determine the discriminant validity, the shared variances between constructs were compared with the AVE values of individual constructs [34]. As shown in

Table 3. Correlation matrix and discriminant assessment.

	1	2	3	4	5	6	7	8
1. Consumer Loyalty	0.847
2. Consumer Satisfaction	0.797	0.872
3. Trust	0.699	0.652	0.907
4. Monetary Savings	0.370	0.294	0.355	0.889
5. Exploration	0.456	0.482	0.379	0.358	0.778
6. Entertainment	0.735	0.779	0.592	0.433	0.568	0.899
7. Recognition	0.594	0.567	0.545	0.437	0.522	0.603	0.900
8. Social Benefits	0.695	0.605	0.583	0.347	0.51	0.687	0.621	0.869

, the diagonal contains the square root of the AVE values of the reflective items. The square roots of the AVE values were greater than the correlations with other constructs, indicating a satisfactory level of discriminant validity.

4.2. Structural Model and Hypothesis Testing

A structural equation modeling was conducted using AMOS to check the hypothesized relationships among the constructs. The bootstrap resampling method (500 resamples) was used to assess the significance of the path coefficients within the theoretical framework. The analysis results are presented in Figure 2, which shows that Chi-square is significant (x²(277) = 582.024, p < 0.05). The results indicate an excellent fit for our hypothesized model, with root mean square error of approximation (RMSEA) at 0.059, comparative fit index (CFI) at 0.960, and non-normed fit index (NNFI) at 0.953.

Consistent with expectations, consumer satisfaction and trust significantly influenced consumer loyalty, supporting both H1 and H2. The theoretical model accounted for 70.6% of the variance of consumer loyalty. However, contrary to expectations, monetary savings did not significantly affect both consumer satisfaction and trust, failing to support H3a and H3b. Exploration was also not significantly associated with either consumer satisfaction or trust, failing to support H4a and H4b. Consistent with expectations, entertainment was strongly associated with consumer satisfaction and trust repurchase behavior, supporting H5a and H5b. In line with expectations, recognition significantly influenced both consumer satisfaction and trust, supporting H6a and H6b. The effects of social benefits on trust were confirmed, supporting H7b. However, their impact on consumer satisfaction was insignificant, failing to support H7a. The research model explained 66.8% of the variance of consumer satisfaction and 45.6% of the variance of trust. The gender and age, which were considered as control variables, did not significantly affect consumer loyalty.

Table 4. Summary of the results.

	Cause	Effect	Coefficient	t-Value	Hypothesis
H1	Consumer Satisfaction	Consumer Loyalty	0.619	10.595	Supported
H2	Trust	Consumer Loyalty	0.329	6.970	Supported
H3a	Monetary Savings	Consumer Satisfaction	−0.085	−1.832	Not Supported
H3b	Monetary Savings	Trust	0.062	1.186	Not Supported
H4a	Exploration	Consumer Satisfaction	0.004	0.065	Not Supported
H4b	Exploration	Trust	−0.059	−0.902	Not Supported
H5a	Entertainment	Consumer Satisfaction	0.670	9.352	Supported
H5b	Entertainment	Trust	0.321	4.257	Supported
H6a	Recognition	Consumer Satisfaction	0.145	2.453	Supported
H6b	Recognition	Trust	0.205	3.044	Supported
H7a	Social Benefits	Consumer Satisfaction	0.113	1.759	Not Supported
H7b	Social Benefits	Trust	0.251	3.425	Supported

presents a summary of the analysis results. Moreover, the indirect effects of monetary savings, exploration, entertainment, recognition, and social benefits on consumer loyalty are presented in

Table 5. Indirect effects of antecedents on consumer loyalty.

Cause	Effect	Coefficient	t-Value	Significance
Monetary Savings	Consumer loyalty	−0.032	0.018	No Significance
Exploration	Consumer loyalty	−0.017	0.061	No Significance
Entertainment	Consumer loyalty	0.520	8.225	Significance
Recognition	Consumer loyalty	0.157	4.132	Significance
Social Benefits	Consumer loyalty	0.153	3.430	Significance

. Entertainment and recognition indirectly influenced consumer loyalty through consumer satisfaction and trust. On the other hand, social benefits indirectly affected consumer loyalty via trust.

5. Discussions and Implications

5.1. Summary of Sesults

The theoretical model asserts that consumers’ loyalty to Airbnb is determined by their satisfaction and trust in Airbnb. The analysis results show that consumer loyalty, demonstrated by reuse intention and intention to recommend, is guided by both consumer satisfaction and trust in Airbnb. Consumer satisfaction was identified as the main factor that enhances consumer loyalty in Airbnb. Satisfied consumers tend to stay in accommodations offered through Airbnb, ultimately leading to loyal consumer behaviors. Consumers’ trust in Airbnb also plays a vital role in shaping consumer loyalty to Airbnb. This result implies that greater trust in a sharing-economy platform may increase consumers’ usage frequency and lodging expenditures in the Airbnb business [35]. Both consumer satisfaction and trust in Airbnb explain the considerable variance in consumer loyalty toward Airbnb. By integrating consumer satisfaction with trust in Airbnb, we advance our understanding of consumer loyalty in the context of a sharing-economy platform.

The findings of the analysis indicate that monetary savings does not significantly affect both consumer satisfaction and trust in Airbnb. In line with our results, Lamb [36] verified that money savings is a less important factor to utilize Airbnb. Sung et al. [20] also showed the insignificant role of economic benefits in developing positive attitudes toward the sharing economy. Moreover, contrary to expectations, exploration does not play an important role in improving consumer satisfaction and trust in Airbnb. This is because most of the variance in consumer satisfaction and trust in Airbnb was explained by entertainment and recognition rather than consumers’ perceptions of money savings and exploration. The results of the analysis reveal that consumers’ perception of entertainment is positively related to their satisfaction and trust in Airbnb. As consumers consider the accommodation experience provided by Airbnb as unique and delightful, the levels of their satisfaction and trust in Airbnb increase. Thus, enjoyment is the predominant predictor of consumer satisfaction and trust in Airbnb. Sung et al. [20] showed that enjoyment plays a vital role in forming positive attitudes and intentions about the sharing economy. Tussyadiah [3] also showed that enjoyment is the most important factor to facilitate consumer’s usage about the sharing economy. The results of the analysis indicate that recognition is also positively associated with consumer satisfaction and trust in Airbnb. Consumers with higher levels of perceived recognition and special treatment from Airbnb hosts had higher perceptions of satisfaction and trust, leading to increased loyalty toward Airbnb. Social benefits significantly affect consumer satisfaction with Airbnb, but did not significantly influence trust in Airbnb. In line with our results, Tussyadiah [3] found that social benefits are not significantly related to consumer satisfaction. As social benefits are a special attribute of the sharing-economy platforms, good relationships with local people can improve the level of trust in Airbnb.

5.2. Implications for Researchers and Practitioners

This study provides several theoretical implications for sustainability and hospitality researchers. First, it develops a theoretical framework for an in-depth understanding of the key antecedents of consumer loyalty in the case of Airbnb. It posits consumer satisfaction and trust in Airbnb as the prevailing factors that lead to loyal consumer behaviors in Airbnb. Overall, this study contributes to the literature on sustainability and hospitality management by verifying the role of consumer satisfaction and trust in Airbnb with regard to consumer loyalty in the sharing economy. In line with the findings of previous works on the sharing economy, this study finds that a higher level of consumer satisfaction and trust in Airbnb elevates the level of consumers’ reuse intention and recommendation of Airbnb. The model provides a theoretical lens for how consumer satisfaction and trust in Airbnb influence consumer loyalty behaviors in Airbnb. If managers or hosts using Airbnb want to attract and retain consumers, they should consider the significant role of consumer satisfaction and trust in Airbnb. To increase the frequency of lodging and expenditures of consumers, practitioners in Airbnb should strive to provide higher levels of consumer satisfaction and foster trust in Airbnb, relative to other accommodations. These elements of Airbnb help consumers practice sustainable consumption by sharing unused or idle rooms. Since consumers using Airbnb produce less waste and consume less water and energy than ones in traditional hotel accommodations [8,9], increasing the frequency of lodging in the Airbnb can accelerate sustainable innovation in the hospitality industry.

Second, this study provides an in-depth understanding of the key factors that influence consumer satisfaction and trust in Airbnb. The theoretical framework identifies monetary savings, exploration, entertainment, recognition, and social benefits as key enablers in delivering consumer satisfaction and promoting trust in Airbnb. The results indicate that money savings are not significantly related to consumer satisfaction and trust in Airbnb. A number of consumers might be primarily driven to Airbnb by unique and authentic consumer experiences rather than economic benefits. The findings show that exploration is not a key driver in enhancing consumer satisfaction and trust in Airbnb. This is because some consumers tend to search for information about lodge status and home amenities. Moreover, this study confirms the predictive power of entertainment in eliciting consumer satisfaction and trust in the sharing economy. The analysis results indicate that when consumers have a memorable and enjoyable stay in an Airbnb lodging (in comparison with alternative modes of accommodation), they are likely to be satisfied with their stay and, thus, build trust in Airbnb. As such, managers and hosts using Airbnb should try to find some local trips or programs, as consumer loyalty relies heavily on delightful and unique experiences. Moreover, the results provide evidence on the significant effects of recognition in improving consumer satisfaction and trust in Airbnb. The special treatment provided by Airbnb hosts is considered part of the overall service, and these benefits increase consumer satisfaction and trust in Airbnb. Thus, Airbnb hosts can differentiate their accommodations from branded hotels by customizing their offerings based on consumer preferences and providing them with special programs. The analysis results show that social benefits are a salient determinant for achieving trust from consumers. Moreover, presenting consumers with an opportunity to develop good relationships with local people facilitates the formation of trust in Airbnb, which in turn leads to an increase in the lodging expenditures and usage frequency of consumers.

5.3. Limitations and Future Works

This study has some limitations that should be addressed in future research. First, the theoretical framework was tested using one sharing-economy platform, Airbnb. Although Airbnb is one of the most successful sharing-economy platforms, some sharing economy platforms encourage customers to share time, resources, and materials without any profit goals. Thus, future research is necessary to reanalyze the hypotheses based on various sharing-economy platforms to improve the validity and generality of the results. Second, although sharing-economic platforms have gained popularity worldwide, this study gathered and analyzed data based on one country. In future research, sample data should include other countries to reduce the effect of cultural attributes. Third, this study did not incorporate the unique characteristics of the sharing-economy platform into the theoretical framework. For example, consumer can experience authentic interaction since Airbnb is often located in residential areas [5]. Thus, future research is needed to include the unique features of the sharing-economy platform into the research model. Last, although this study considered trust in Airbnb as a key facilitator of consumer loyalty, Liang et al. [16] noted that trust can be classified into two types: Trust in hosts and trust in Airbnb. Thus, for a better understanding of the role of trust, further research is needed to analyze differences between the impacts of trust in hosts and trust in Airbnb as key antecedents of consumer loyalty in the sharing economy.