Attracted to or Locked In? Explaining Consumer Loyalty toward Airbnb

Abstract

In an increasingly complex and dynamic environment, understanding the fundamental mechanisms of customer loyalty toward Airbnb has become very appealing to both researchers and providers in recent years. Most prior studies on Airbnb have focused mainly on dedication-based mechanisms, such as consumer satisfaction or attitude. However, little is currently known about how the theoretical view for adapting dedication-based and constraint-based mechanisms establishes Airbnb consumer loyalty and affects by attributes, such as authentic experience, trust in Airbnb, and social benefits—that is, the vital predictors of affective commitment. It explores social benefits, relative attractiveness, and price fairness as the key antecedents of calculative commitment. This study identifies the relationship using a structural equation modeling method and empirical data collected from 156 Airbnb consumers who use it more than twice. The analysis results show that consumer loyalty toward Airbnb is shaped by dedication- and constraint-based mechanisms. The findings indicate that both affective and calculative commitments significantly affect customer loyalty in the context of Airbnb. Authentic experience, trust in Airbnb, and social benefits significantly affect affective commitment to Airbnb. While both social benefits and relative attractiveness play an important role in enhancing calculative commitment to Airbnb, price fairness is not significantly related to it. Theoretical and practical implications and future research directions are subsequently discussed.

1. Introduction

Due to the high popularity of collaborative consumption, the sharing economy has emerged as a mainstream business model [1,2]. The sharing economy refers to an economic model in which participants share an under-utilized inventory or assets via fee-based sharing. The sharing economy is based on the sharing of unused or idle resources between peers. The sharing economy is regarded as a method of sustainable consumption that enables customers to collaboratively share idle resources rather than purchasing and owning products or services for themselves [1]. Companies have launched a variety of business models in line with the sharing economy’s mode of collaborative consumption. These companies, which include Uber and Airbnb, develop and distribute sharing-economy platforms to encourage individuals who have extra resources to participate in such platforms. For example, a sharing-economy platform for P2P (peer-to-peer) accommodations provides travelers with access to unused rooms [2]. Airbnb is the largest hospitality firm in the sharing economy. It differentiates itself from traditional hotel chains by providing consumers access to idle resources [3,4]. Understanding the mechanisms by which customer loyalty to Airbnb is formed is a necessary part of succeeding among various accommodation types. In this vein, this study examines the key determinants of enhanced customer loyalty toward Airbnb.

In the highly competitive lodging industry, customer loyalty to P2P accommodations helps hospitality firms enhance their profitability and reduce their marketing costs [5,6,7]. A theoretical framework is proposed to elucidate the formation mechanisms of consumer loyalty toward Airbnb. According to the social exchange theory, customers’ long-term relationships with firms are derived from dedication- and constraint-based mechanisms [5]. Dedication-based mechanisms are defined as consumers’ desire to develop and maintain a sustainable relationship with their current service firm; constraint-based mechanisms refer to forces constraining customers to maintain their relationship with a given service firm, strengthening the lock-in effect [6,7,8,9]. Proponents of dedication-based mechanisms use affective commitment or consumer satisfaction to predict and explain customer loyalty in various service contexts. Kim [2] reveals the significant effects of customer satisfaction and trust in Airbnb on consumer’s post-purchase behavior. Most previous studies on Airbnb have concentrated mainly on dedication-based mechanisms such as attitude or consumer satisfaction; however, dedication-based mechanisms alone are not able to fully predict the mechanisms underlying consumer loyalty toward Airbnb. Contemporary studies highlight the salience of constraint-based mechanisms for explaining and forming consumer loyalty behaviors, such as recommendations and revisits [2,6,7]. A constraint-based mechanism can lock customers into the current service firm by increasing their resistance to transfer to alternative options. Studies on service management and hospitality have shown that constraint-based mechanisms can be captured by calculative commitment [10,11]. This study clarifies the role of affective and calculative commitments in shaping consumers’ loyalty toward Airbnb and examines the fundamental determinants of affective and calculative commitments in the context of Airbnb. Besides dedication-based mechanisms, authentic experience, trust in Airbnb, and social benefits are all regarded as vital predictors of affective commitment. Thus, this study identifies the social benefits, relative attractiveness, and price fairness that influence calculative commitment about Airbnb. We suggest that social benefits play a dual role in the two mechanisms. Building mutually trustful and friendly relationships with Airbnb hosts is deemed to be one of the meaningful activities for peer-to-peer accommodations.

This study contributes to previous works on Airbnb in several ways. It applies the dedication- and constraint-based mechanisms by exploring numerous sets of determinants. Its proposed research model provides a theoretical lens that explains how these two distinctive mechanisms affect consumer loyalty toward Airbnb. A PLS (partial least square) method was used to analyze the data of a sample of 156 Airbnb consumers who used the service more than twice. The analysis results offer several insights that can help service managers and hosts in Airbnb effectively facilitate dedication-based and constraint-based factors through an in-depth understanding of the specific roles of both mechanisms.

2. Literature Review

2.1. Theoretical Background

Airbnb’s disruptive and innovative business model is so significant that the company’s value has been estimated at $30 billion—greater than that of Hilton ($23.3 billion) or Hyatt Hotels Corp ($6.8 billion), two of the world’s largest hotel corporations. Zervas et al. [1] showed that a 10% increase in Airbnb listings decreased local hotel room revenue by 0.37%. Although Airbnb provides a service which is nearly identical to that of traditional hotel chains, it provides a unique and authentic experience. In particular, since Airbnb lodgings are often located in residential areas, consumers enjoy local restaurants and culture within short walking distance. Guttentag [3] noted that Airbnb appeals to its customers by providing a community-focused experience, and nearly two-thirds of its guests use it as a hotel substitute.

Given Airbnb’s popularity and its role as a key player in the accommodation industry, some researchers have explored the mechanisms explaining Airbnb customers’ post-purchasing behaviors, such as customer loyalty and satisfaction [2,9]. Some studies have investigated the effects of each dimension of Airbnb service quality—such as the host, facility, web responsiveness, and web efficiency—on customer satisfaction. Another study investigated Airbnb customers’ behavioral intentions and these intentions’ effects on loyalty by factoring in service quality and hospitality in P2P accommodation settings [12]. Lee and Kim [9] argues that hedonic and utilitarian values influence customers’ loyalty, especially in the case of Airbnb. This study also contributes to Airbnb marketers in fulfilling users’ expectations about the company. Mody et al. [13] identify how the inter-relationships built between customers, Airbnb hosts, and locals provide customers with the feeling of an authentic experience and thus influence their brand loyalty. Lalicic and Weismayer [12] found that customers seek authentic experiences when choosing to stay at an Airbnb accommodation, and that Airbnb consumers are more concerned about the authenticity of their experience than the price of their accommodation because they desire a similar experience to locals. Perceived authenticity has been identified as a key antecedent that influences Airbnb consumers’ repurchasing behaviors. The concept of price fairness in the sharing economy can be justified with reasonable consciousness. The sharing economy awakes shared norms, which in turn drive customers to be concerned about whether the price is fair or unfair [14]. Consumers are willing to book Airbnb accommodations based on their subjective perception of the prices and add value to the sharing economy, and are often focused on the non-financial benefits or “social benefits” of Airbnb [15]. In this context, social benefits refer to the emotional benefits to the consumer—e.g., the experience they receive from their personal connection with the service and service providers [16]. The Airbnb platform can foster social benefits because it provides an opportunity for consumers to develop an intimate relationship with local individuals and places. Sharing economy platforms, such as Airbnb have also been found to have significant economic and financial benefits, which motivate tourists to participate in the sharing economy. Participants in sharing economy platforms have indicated that social benefits motivate their behaviors more than economic and financial benefits, and studies have shown that perceived social benefits attract most customers to Airbnb [16]. Building social equity and identity upon Airbnb can help engage customers in further transactions and lead them to develop brand loyalty [17]. The research indicates that the desire for a sense of community and authenticity can be attributed to motivating customers to choose Airbnb.

2.2. Dedication-Based and Constraint-Based Mechanisms

The concept of these two mechanisms came about through social exchange theory [5,6]. Whereas constraint-based mechanisms dictate that customers may maintain relationships because they feel that they have to stay in a given business relationship for any number of reasons (a rational cost/benefit analysis, because the cost of switching is too high, because there are no alternatives, and so on), dedication-based mechanisms dictate that consumers maintain these relationships because they want to stay in the relationship [18]. While a dedication-based mechanism may be translated into affective commitment, a constraint-based mechanism may be translated into calculative commitment [19]. Affective commitment is defined as “the desire to continue a relationship because of positive affect toward the partner” [20]. Thus, whereas buyers who are calculatedly committed must dedicate themselves to a seller because they need to, buyers who are affectively committed are motivated to continue their relationship with the seller because they want to.

Commitment is an essential part of continuing relationships between parties. Commitment is either calculative or affective and plays a critical role in enhancing hotel customers’ loyalty. Calculative commitment refers to customers who engage with a service supplier due to economic motivations [11]. Affective commitments reflect customers’ emotional devotion to a supplier, contributing to a long-term relationship. Both types of commitment play a fairly important role in the hotel industry [20]. Dedication-based mechanisms such as affective commitment and consumer satisfaction have been used to predict and explain customer loyalty in various service contexts. The marketing literature suggests that overall customer satisfaction, affective commitment, and calculative commitment can predict customer retention [19]. Bendapudi and Berry [5] suggest that constraints (i.e., motivations for calculative commitment) and dedication (i.e., motivations for affective commitment) only determine the stability of these relationships. Odekerken-Schröder and Bloemer [21] empirically assessed the role of constraints and dedication as drivers of commitment and measured how and to what extent these two drivers fit into the established relationships between overall service quality, satisfaction, trust, and commitment in a healthcare context. Several works found that relationship commitments have an especially strong impact on users’ behavior in mobile contexts [7,22]. Li et al. [23] developed a commitment-based research model to elucidate users’ continued use of web sites. They find that users’ intention to continue using a service are shaped by both affective and calculative commitments. Zhou et al. [7] shows that calculative commitments are important predictors of users’ intent to continue using a service in the context of social media and the digital marketplace. Bilgihan and Bujisic [11] found that calculative commitment can develop customer loyalty in the context of online hotel booking and state that users with high switching costs and a lack of alternatives are likely to stay with their current hotel booking service provider.

3. Research Model

The proposed theoretical framework is shown in Figure 1. The proposed framework suggests that consumer loyalty is greatly influenced by both affective and calculative commitments. This study identifies authentic experience, trust in Airbnb, social benefits, relative alternatives, and price fairness as key components of affective and calculative commitments.

3.1. Consumer Loyalty

Creating loyalty is essential to the sustainability of the sharing economy and has thus become a main focus of firms seeking long-term relationships with customers. To better understand consumers’ loyalty, we need to understand their behavioral intentions. These intentions reflect customers’ loyalty to a service provider or their potential shift to other providers, and influence other behavior intentions, including repurchase intention, complaining behavior, willingness to pay more, intention to switch providers, word-of-mouth advertising, and sensitivity to price of services [24]. Oliver [25] also defines customer loyalty as “a deeply held commitment to re-buy or re-patronize a preferred product or service consistently in future”. Consumer loyalty is regarded as the goal in the service industry because loyal customers bring numerous benefits to a firm [4,8]. The central idea of these definitions is that consumer loyalty is connected to devoted behavior to a certain product, service, or brand.

Given the importance of the retention of customers in the sharing economy, customer loyalty has been tested in various service contexts [4,8]. The benefits of increasing consumer loyalty are an increase in customers’ expenditures, a reduction in marketing costs, and the continuance of customer 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 advertising. Thus, understanding the mechanisms behind consumer loyalty benefits service firms such as Airbnb by giving them the data to increase the use of their sharing-economy platform by encouraging customers to share their experiences on social network services.

3.2. Dedication-Based Mechanism

3.2.1. Affective Commitment

Affective commitments, or dedication-based relationships characterizing a long-term relationship and its maintenance, are based in psychological attachments which originate in feelings of identification, loyalty, and affiliation [5]. Allen and Meyer [26] describe affective commitment as an inclination toward commitment to a relationship or a partner based on emotional aspects of service. Such studies posit that affective commitment is the main source of dedication-based mechanisms [7]. Several studies have shown that affective commitment impacts customer loyalty through expert interviews and experimental studies [22,27]. Mittila [20] investigated the role of affective commitment in strengthening customer loyalty when identifying customers’ decision-making processes regarding hotel bookings, and found that customers’ long-lasting relationship behaviors still depend on the positive feelings they derive from a favorable service experience; that study therefore concluded that a frequent-guest program was a sure way to boost hotel guest loyalty. Liu and Mattila [28] showed that firms can enhance positive relationships with their customers by exploring the relationship between affective commitment and customers’ willingness to give feedback. Kim and Min [29] also explored the important role played by affective commitment in the case of social networking sites. Still, other studies emphasize the role of affective commitment in determining loyalty to Airbnb [2]. Dellaert [30] also states that affective commitment is very important in leading in the sharing economy. Affective commitments, such as customer’s emotional attachments to a service provider, become even more significant in the sharing economy, where customer loyalty plays a key role in motivating customers to continue using the service. The above studies lead us to the following hypothesis:

H1: 

Affective commitment positively influences consumer loyalty.

3.2.2. Authentic Experience

Customers’ perception of an authentic experience involves a connection to local communities and is enhanced by staying in somebody’s home as opposed to a hotel [31]. The authentic experience is a significant variable regarding the perceived values of socialization and is a unique experience which can affect the corporations’ brand [31,32]. Social interaction between hosts and guests can be presumed to be the “authentic” experiences that tourists long for [32]. To foster authentic experiences in the sharing economy, virtually connected technologies have encouraged online communities to better engage with customers and foster interactions among them. Kim and Cho [33] state that participants in the sharing economy are willing to use a service that offers unique and attractive experiences as customers interact with each other to share their experiences, passions, and impressions for authentic experience. Yannopoulou et al. [34] found that Airbnb guests want to experience a city and living like a local, and look for “meaningful life enrichment, human contact, access and authenticity”. Poon and Huang [35] propose that an authentic local experience is part of P2P accommodation’s special charm and identify authentic experience as one of the strongest forces that drive consumers to use Airbnb. Authenticity is a key factor motivating consumers to choose Airbnb. Some studies identify authenticity as a key antecedent enhancing Airbnb consumers’ repurchasing behaviors [36]. Airbnb accommodations provide a unique and authentic experience because they offer household benefits not typically available in hotels. Airbnb consumers want to a focus on a new type of travel that seeks authenticity in the local culture. As Airbnb focuses on providing an authentic experience, our study seeks to determine whether and how authentic experience forms consumers’ perception of loyalty in the sharing economy. The above literature helps us lead to our next hypothesis:

H1a: 

Authentic experience positively influences consumer loyalty.

3.2.3. Trust in Airbnb

Trust refers to variations of psychological inclinations involved interactions between two parties. These interactions happen when one party performs with uncertainty and sometimes expects their trust to be rewarded [37]. While most scholars agree that trust is a psychological state, it can be studied in terms of its cognitive or affective aspects [38]. Johnson and Grayson [38] defined affective trust with the cognitive concept that is solely based on positive emotions. Trust is one of the essential parts of emotional brand relationships. Chiou and Droge [37] identified the relationship between trust and affective commitment by proposing an integrated framework that can predict customer loyalty regarding luxury products. A hotel can employ a mixture of trust and emotional commitment as a powerful marketing tool to develop customer loyalty [39]. Trust is essential to building sustainable business relationships, especially in online businesses. Trust leads to positive experiences and favorable consequences through the reduction of uncertainty about illegal aggregation and information abuses [22]. Affective events theory (AET) dictates that users are motivated perform to given behaviors based on their trust of individual hosts in the sharing economy [40]. Airbnb encourages meaningful interactions between its hosts and guests in order to build a high level of trust in the sharing economy. Because Airbnb represents P2P accommodation, it is important to understand why guests (buyers) build trust in Airbnb and trust in the hosts [41]. Facing a successful competitor like Airbnb, the hotel industry can remain competitive by developing its understanding of trust as well. The prominent appearance of sellers’ photos on these platforms can have a significant impact on guests’ trust and reputation. Studies have also shown that affective-based trust can be a more powerful influence on users than cognitive-based trust when users find their Airbnb host familiar through emotional connections [41]. Airbnb’s key mechanism for facilitating trust between hosts and guests is its public review system, where both parties can post reviews [3]. Thus, as Airbnb customers are likely to build affective commitments when they consider Airbnb as reliable and trustworthy P2P accommodation, we propose our next hypothesis:

H1b: 

Trust in Airbnb positively influences affective commitment.

3.2.4. Social Benefits

Social benefits refer to an expansion of meanings related to belonging and social identity, including identity-seeking behavior, such as getting involved with members of a community and maintaining those relationships [42]. Social benefits have been shown to be significant antecedents of user participation in the sharing economy [43]. Accordingly, motivation for participation describes many different motivations for hosting, including the social benefits of interacting with guests and the satisfaction of being a “good” Airbnb host. Similarly, Barnes and Mattsson [44] employed the theory of reasoned action to examine consumers’ decisions to use ridesharing services in Europe. They found that consumers’ decisions are motivated mainly by perceived social benefits and are mediated by perceived affective commitments, such as usefulness and enjoyment of a given service. Airbnb’s online review system facilitates social benefits and motivates tourists to participate in the sharing economy [45]. Customers’ interaction with their Airbnb host can enhance their sense of social identity by providing a stimulating experience of community. Airbnb hosts can also derive social benefits. This means that consumers concern social benefits when they evaluate the Airbnb listings [46]. Research shows that social benefits and social interactions are major factors motivating consumers to choose Airbnb [47]. Participants of the sharing economy platforms have indicated that social benefits and the experience they enjoy during their stay are more important to them than the economic and financial benefits in the sharing economy platforms [48]. Research indicating that social benefits may have significant and positive influences on affective commitment lead us to our next hypothesis:

H1c: 

Social benefits positively influence affective commitment.

3.3. Constraint-Based Mechanism

3.3.1. Calculative Commitment

Andreassen and Olsen [49] indicate that calculative commitment is closely related to switching costs, perceived barriers to exit, or a lack of real alternatives, and rests on a customer’s cost. Thus, customers’ economic decisions are dictated by their interactions with constraint-based mechanisms and reflect a motivational basis tied to the various costs of leaving a service provider [50]. Bansal et al. [51] assert that, because of consumers’ rational concerns related to the financial costs to continue a relationship, calculative commitments push consumers into maintaining their existing relationships with providers. When switching to a new service provider, customers often face “one-time costs that customers associate with the process of switching from one provider to another”. Some studies have identified several types and facets of switching costs, reflecting both calculative and affective commitments [52].

Calculative commitment plays an important role in influencing reward programs, which aim to increase hotel customers’ loyalty [53]. Mattila [20] investigated the interrelationship between calculative commitment and loyalty commitment in the hotel industry and found that both forms of commitment play important roles in boosting customers’ loyalty by promoting frequent-guest programs. Morgan and Hunt [54] argued that loyalty and commitment can work together to enhance corporate relationships and that these relationships improve when both the service provider and customer have the same values and beliefs. In the context of Airbnb, calculative commitments limit users’ alternatives and cause them to engage in cost analyses that force them to stick to their current service provider. Accordingly, we propose the following hypothesis:

H2: 

Calculative commitment positively influences consumer loyalty.

3.3.2. Social Benefits

While numerous studies have identified the relationship between social benefits and affective commitments, studies of the relationships between social benefits and calculative commitments are still scant. Previous research has found that customers’ rational judgments and economic decisions in online travel communities were driven mainly by perceptions of social benefits, providing support, exchanging ideas, and forming relationships [55]. In an increasingly digital marketplace, social bonds not only cause psychological or emotional losses, but also huge economic losses. In other words, it represents a constraint-based commitment through increased social benefits with related partners [7]. Kim et al. [56] argue that perceived benefits from online transactions are beliefs that consumers believe will get better from the electronic commerce transaction. Combined with the unique social attributes of Airbnb, social benefits refer to consumers developing social relationships with hosts, guests, neighbors, etc., through online short rental, and obtaining local people’s sincere suggestions and strategies [57].

Customers’ benefits from social interactions include the formation of a state of calculative commitment for new customers, which is largely considered to be a solid cognitive basis for trust and repeat business. When customers in Facebook communities are highly engaged through word-of-mouth advertising, it is anticipated that they check other customers’ reviews and experiences as part of their rational decision making. This study identified that three types of relationship benefits, including social benefits generated from word-of-mouth, could enhance loyalty behaviors [58]. Tsai and Kang [59] contribute to the above studies that conclude that rational activities like knowledge-seeking intentions are motivated by perceived social benefits in an online professional community. Social benefits require that customers engage with the extent to which continuance commitment can influence customer switching costs, or calculative commitments. Since the perception of social benefits would influence calculative commitment, we propose the following hypothesis:

H2a: 

Social benefits positively influence calculative commitment.

3.3.3. Relative Attractiveness

Relative alternatives are a part of an overall perception based on the customer’s subjective evaluation of the perceived quality of a service or product [49,51]. Customers compare the services and products of potential alternative suppliers based on the assumption that their future choices are a function of their past choices, present experience, and the perceived value of alternative options [49]. Shukla et al. [60] mention that calculative commitment depends on customers’ emotions regarding alternatives to luxury brands and that appealing alternatives are a strong component of calculative commitments. Attractive alternatives reduce the burden of choice that is placed on customers, and therefore attractiveness leads customers to strong calculative commitment towards the brand [51]. Based on the collected data from both Airbnb hosts and guests, Möhlmann [61] examines the significant role of relative attractiveness in developing customer’s evaluation process in P2P accommodation marketplaces. Thereby, these studies indicated that customer loyalty is motivated by both constraint-based and dedication-based mechanisms, which lower the possibility that consumers will switch to relative alternatives, and conclude that these mechanisms ultimately lock customers in to their current service provider. So, we are led to our next hypothesis:

H2b: 

Relative attractiveness positively influences calculative commitment.

3.3.4. Price Fairness

Economic crisis and recession have raised global middle-class consumers’ suspicion of companies’ price-related behavior and make them generate extra cash by exercising a pervasive sharing economy platform, such as Uber and Airbnb, thereby making consumers more sensitive to price-inequality and price fairness [1]. When consumers are very sensitive to the price of their accommodations, they tend to perceive more value in choosing Airbnb [62]. This indicates that consumers in the sharing economy are sensitive to price and that their perception of price fairness is an important factor in determining their calculative commitments. The perception of price fairness is defined as consumers’ evaluation of whether the price they pay for services meets their expectations—i.e., whether they feel the prices are reasonable, acceptable, or legitimate [63]. As consumers’ assessment of price fairness involves measuring a price against a certain standard or reference, when consumers consider price fairness, they subjectively conceptualize price as different from value. This supports the notion that consumers’ overall subjective evaluation of price is based on the relationship between value-related perceptions. Consumers perceive the price of products or services to be high, low, or fair according to their internal standards. Consumers’ perception of price fairness increases the perceived value of the sharing economy [64]. Bolton et al. [25] showed that the overall perception of product price fairness results in favorable feelings toward a specific product or service provider. More specifically, price fairness is perceived by customers who value the links between luxury restaurants and their customers [28]. Accordingly, participants in the sharing economy are more sensitive to price than participants in the conventional economy because, while market conditions are as likely to change favorably as they are to change unfavorably, discounts abound throughout the sharing economy [1]. The effort to maximize profit by charging consumers in the sharing economy causes sensitive issues in terms of price perception because price-fairness recognition is more severe to the issue that is not judged as unfair in the sharing economy. Empirical evidence shows that price fairness would be positively related to calculative commitment and enhance customer loyalty [65,66]. This study expected that price fairness would play an important role in enhancing calculative affective commitment.

H2c: 

Price fairness positively influences calculative commitment.

4. Research Methodology

Instrument Development

This study developed a self-administered survey questionnaire. All measurement items were derived from previously validated measurements in marketing, information systems, and hospitality. The measurement items were modified to fit the case of Airbnb. The questionnaire consisted of three parts: (1) customers’ Airbnb usage patterns; (2) customer’s perceptions, attitudes, and loyalty regarding Airbnb; and (3) customers’ demographic information. In the first section, respondents were asked about their usage patterns—how often they used Airbnb and how much they spend on the service. If the respondents did not have experience with Airbnb, they did not go any further in the survey. The next section covered customer’s perceptions, attitudes, and loyalty toward Airbnb. The measurement items were 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 their demographic information, such as their age and gender. Before conducting the survey, the questionnaire items were checked by two professors and two researchers in the marketing, information systems, and hospitality fields. They modified some minor problems in question ambiguity, wording, and content. The modified measurement items were piloted with 30 university students. The reliability of all questionnaires was confirmed by checking the value of Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). All Cronbach’s alpha and CR values exceeded 0.7, which is an acceptable threshold value of reliability. Moreover, all AVE (average variance extracted) values also are more than 0.7. The measurement items and related literature are included in the Appendix A.

5. Research Results

5.1. Subjects and Data Collection

The research model was empirically tested by using data gathered from the self-administered survey. A cross-sectional survey was conducted, targeting consumers who use Airbnb more than twice. With the cooperation of an online survey agency that has a variety of panels in South Korea, the online link to the questionnaire was delivered to its panels on the e-mail. Only after completing all the questions (on every page) were respondents able move on to the next questionnaires. After deleting insincere responses and customers with less than two uses of Airbnb, the remaining 158 responses were used for the analysis.

Table 1. Profile of respondents.

Demographics	Item	Subjects (n = 158)
		Frequency	Percentage
Gender	Male	80	50.6
	Female	78	49.4
Age	Less than 30	76	48.1
	31 ~ 40	56	35.4
	More than 40	26	16.5
Average Income per month	Less than 3 mil. won	49	31.0
	3 mil. won ~ 5 mil. won	46	29.1
	5 mil. won ~ 7 mil. won	32	20.3
	More than 7 mil. won	31	19.6

presents the demographic information of the final respondents. Among the final respondents, 50.6% are male and the mean age is 33.04, with a standard deviation of 8.34. The average staying frequency of Airbnb is 2.87, with a standard deviation of 1.74. They spent an average of 102,300 won a year on Airbnb for accommodation.

The PLS method is used to analyze the measurements and structural model. This method has some benefits in terms of minimal restrictions on sample size and residual distributions. The analysis was conducted in two stages: an evaluation of the convergent validity, reliability, and discriminant validity of the measurement model, and a test of the structural model.

5.2. Measurement Model

Confirmation factor analysis was conducted to check the convergent validity, reliability, and discriminant validity of the measurement items. To verify the convergent validity, this study checked the factor loadings of the measurement items, as shown in

Table 2. Scale reliabilities.

Construct	Item	Mean	St. dev.	Factor Loading	CR	AVE
Customer Loyalty	CUL1	4.601	1.350	0.829	0.926	0.758
	CUL2	4.019	1.486	0.834
	CUL3	4.747	1.185	0.912
	CUL4	4.652	1.268	0.905
Affective Commitment	AFC1	4.886	1.102	0.795	0.937	0.788
	AFC2	4.519	1.316	0.931
	AFC3	4.158	1.491	0.917
	AFC4	4.095	1.483	0.902
Calculative Commitment	CAC1	3.823	1.399	0.882	0.950	0.827
	CAC2	3.601	1.599	0.921
	CAC3	3.203	1.72	0.945
	CAC4	3.203	1.683	0.888
Authentic Experience	AUE1	4.791	1.202	0.857	0.924	0.754
	AUE2	4.987	1.119	0.878
	AUE3	5.158	1.199	0.869
	AUE4	4.873	1.173	0.868
Trust in Airbnb	TRA1	3.962	1.414	0.884	0.963	0.866
	TRA2	4.424	1.213	0.932
	TRA3	4.392	1.242	0.952
	TRA4	4.361	1.244	0.952
Social Benefits	SOB1	4.146	1.257	0.863	0.919	0.791
	SOB2	4.652	1.227	0.883
	SOB3	4.544	1.276	0.921
Relative Attractiveness	REA1	4.968	1.214	0.872	0.890	0.73
	REA2	5.076	1.076	0.835
	REA3	4.728	1.117	0.855
Price Fairness	PRF1	4.525	1.178	0.929	0.960	0.890
	PRF2	4.614	1.205	0.949
	PRF3	4.563	1.198	0.952

. Convergent validity is acceptable if the values of the factor loading exceed 0.70 [67]. In this study, the lowest value of the factor loading was 0.795 (AFC1), indicating a satisfactory level of convergent validity. To determine the reliability of the constructs, the CR and the AVE values were assessed. It is recommended that the CR value exceeds 0.70 and the AVE value exceeds 0.50 [68]. As reported in Table 2, all CR and AVE values had more than the recommended threshold values. Finally, to evaluate the discriminant validity, the AVE values of individual constructs were compared with the shared variances between constructs [68]. As seen from

Table 3. Correlation matrix and discriminant assessment.

	1	2	3	4	5	6	7	8
1. Customer Loyalty	0.871
2. Affective Commitment	0.766	0.888
3. Calculative Commitment	0.592	0.651	0.909
4. Authentic Experience	0.533	0.589	0.325	0.868
5. Trust in Airbnb	0.653	0.657	0.581	0.421	0.931
6. Social Benefits	0.674	0.723	0.53	0.57	0.581	0.889
7. Relative Attractiveness	0.613	0.699	0.379	0.472	0.576	0.530	0.854
8. Price Fairness	0.511	0.558	0.318	0.515	0.460	0.458	0.482	0.943

Note: Diagonal elements are the square root or AVE (average variance extracted).

, the square root values of the AVE were greater than the correlations with other constructs. This result confirms discriminant validity.

5.3. Structural Model and Hypothesis Testing

A structural equation model using PLS was conducted to assess the hypothesized paths among the constructs. The bootstrap resampling method (500 resamples) was used to check the significance of the path coefficients within the research model. The analysis results are presented in Figure 2.

Consistent with expectations, both affective commitment and calculative commitment significantly influenced customer loyalty, supporting both H1 and H2. The two commitments accounted for 59.9% of the variance of customer loyalty. In line with expectations, authentic experience, trust in Airbnb, and social benefits significantly affect affective commitment. Hence, H1a, H1b, and H1c were supported. Moreover, both social benefits and relative attractiveness significantly influence calculative commitment, providing empirical support for hypotheses H2a and H2b. However, price fairness did not significantly influence calculative commitment. Hence, H2b was not supported. The research model explained 64.0% of the variance of affective commitment and 34.1% of the variance of calculative commitment.

Table 4. Summary of the results.

	Cause	Effect	Coefficient	t-Value	Hypothesis
H1	Affective Commitment	Customer Loyalty	0.661	9.661	Supported
H1a	Authentic Experience	Affective Commitment	0.218	3.096	Supported
H1b	Trust in Airbnb	Affective Commitment	0.328	4.579	Supported
H1c	Social Benefits	Affective Commitment	0.409	5.295	Supported
H2	Calculative Commitment	Customer Loyalty	0.159	2.451	Supported
H2a	Social Benefits	Calculative Commitment	0.372	4.325	Supported
H2b	Relative Attractiveness	Calculative Commitment	0.283	3.434	Supported
H2c	Price Fairness	Calculative Commitment	0.017	0.193	Not Supported

and

Table 5. Indirect effects of antecedents on customer loyalty.

Cause	Effect	Coefficient	t-Value	Hypothesis
Authentic Experience	Customer Loyalty	0.144	2.738	Supported
Trust in Airbnb	Customer Loyalty	0.217	4.215	Supported
Social Benefits	Customer Loyalty	0.329	3.4675	Supported
Relative Attractiveness	Customer Loyalty	0.045	2.123	Supported
Price Fairness	Customer Loyalty	0.003	0.188	Not Supported

summarized the analysis results.

6. Discussions and Implications

6.1. Summary of Results

The theoretical model asserts that consumer loyalty regarding Airbnb is determined by both dedication-based and constraint-based mechanisms. The analysis results confirm this assertion. The theoretical model provides a huge amount of explanatory power on consumer loyalty, accounting for 60.0% of its variance. Affective commitment was identified as the main determinant of enhanced customer loyalty toward Airbnb. Consumers who feel emotionally attached to Airbnb tend to use it more often and spread positive messages about the service via word-of-mouth. Calculative commitment also plays a critical role in enhancing customer loyalty to Airbnb. This result implies that greater calculative commitment in Airbnb would increase customers’ lodging expenditures and staying frequency. This study verified that affective and calculative commitment independently develop customer’s loyalty toward Airbnb. By applying both dedication-based and constraint-based mechanisms to the context of Airbnb, this study advances our understanding of the formation mechanisms of Airbnb consumer loyalty.

The findings of the analysis reveal that authentic experience significantly affects affective commitment. Cohen [69] has noted that authentic experience is predominantly perceived as an essential factor that motivates customers to travel to new places and has indicated that customers who experience authentic interactions via Airbnb have are more positive about and satisfied with the service. Trust in Airbnb plays an important role in improving affective commitment in the context of Airbnb. If consumers believe that Airbnb hosts are reliable and trustworthy, they develop a high level of attractive commitment toward Airbnb. The results of the analysis indicate that social benefits are also positively associated with affective commitment. Consumers with high levels of social benefits and special treatments from Airbnb hosts were very emotionally attached to Airbnb, which enhanced their loyalty toward Airbnb. The analysis results show that social benefits also significantly affect calculative commitment. These results imply that social benefits play a dual role in affecting customer loyalty about Airbnb. Good relationships with Airbnb hosts and local people can improve the level of affective commitment as well as calculative commitment. Relative attractiveness significantly influences calculative commitment. In line with our findings, Shukla et al. [60] found that relative attractiveness is an important factor to facilitate consumer’s calculative commitment in the service industry. The results of the analysis reveal that consumers’ perception of price fairness is not significantly associated with their calculative commitments. Thus, price fairness is not the key predictor of explaining customer’s calculative commitment.

6.2. Implications for Researchers and Practitioners

This study offers several theoretical implications for marketing and service management. First, it develops a theoretical framework that examines the effects of both dedication-based and constraint-based factors affecting loyalty in the Airbnb context. The analysis results reveal that consumer loyalty toward Airbnb is influenced by both affective and calculative commitment. This study contributes to previous literature on marketing and service management by extending consideration to both factors, instead of dedication-based factors, which dominate the literature. In particular, this study asserts that customer loyalty is largely explained by affective commitment, implying that affective commitment is more critical than calculative commitment in determining consumer loyalty. In line with our findings, Shukla et al. [60] showed that calculative commitment is less important than affective commitment when customers develop advocacy commitment. Even in Generation Y, calculative commitment negatively affects word-of-mouth on a travel web site [70]. Therefore, Airbnb managers and hosts should strive to enhance positive psychological reactions to their accommodations and services. They should not ignore the constraint-based mechanisms for developing and maintaining a long-term relationship with current customers. To increase customer’s calculative commitment, they can develop some marketing programs, such as loyalty programs, referral bonuses, and frequent customer discounts, which might all be effective means of locking in customers.

Second, the current research tests a framework for understanding the effects of the antecedents of affective commitment in the Airbnb context. The theoretical framework identifies authentic experience, trust in Airbnb, and social benefits as key facilitators in enhancing affective commitment toward Airbnb. The analysis results reveal that authentic experience is significantly associated with affective commitment. The results imply that unique and authentic experiences, such as home-like lodging, involvement with local culture, and individually tailored tours, evoke positive feelings. Since customers increasingly seek unique and memorable lodging experiences [61], service managers in Airbnb should try to explore differentiated accommodations to create new and different lodging experiences for customers. Moreover, they can incentivize Airbnb hosts to develop local and delight programs to encourage greater customer involvement in Airbnb. The results show that trust in Airbnb serves as a critical factor driving consumers’ affective commitments. In the Airbnb context, customers have to provide personal information, including their gender, birth date, etc., when registering. This obviously leaves them vulnerable to privacy concerns, and therefore some consumers are likely to refuse to register on Airbnb. Thus, by increasing trust in the service, hosts and managers can reduce and mitigate customers’ uncertainty about privacy invasions or information abuses. Trust in Airbnb is often fostered by online review systems, which strengthen customers’ psychological attachment to the service. This study shows that social benefits play an important role in developing customer’s affective commitment. In line with our findings, Gwinner et al. [71] also verified that the emotional part of relationships is shaped by social benefits. Because customers interact with their hosts directly, they sometimes establish bonds which transcend mere economic exchange. Such social benefits assist in building a mutual understanding between customers, which might facilitate loyalty.

Lastly, this study sheds light on the impacts of constraint-based mechanisms on consumer loyalty in the context of Airbnb. This study offers an in-depth understanding of the key determinants of calculative commitment about Airbnb. The theoretical framework posits social benefits and relative attractiveness as the key factors of calculative commitment. Consistent with the results of prior studies on marketing and service management, this study confirms the salience of social benefits and relative attractiveness in elucidating calculative commitment regarding Airbnb. Because they prioritize authentic experiences, Airbnb customers are not likely to move their business to well-known hotels. According to the push–pull–mooring framework, the attractive attributes of products or services pull customers to purchase or use the products or services [72]. When consumers perceive that the key characteristics of alternatives are better than their service provider, they are more willing to switch providers. Thus, service managers and marketers should develop unique and local programs that can create a memorable and enjoyable staying experience. Our findings provide preliminary evidence of the significant effects of constraint-based mechanisms on the formation of consumer loyalty toward Airbnb.

6.3. Limitations and Future Works

This study has some limitations that could be addressed by future research. First, this study collected and tested data based on the cross-sectional survey method, and the antecedents of consumer loyalty were examined at static points. To offer more insights for service researchers and managers, it might be valuable to check the dynamic effects of dedication-based and constraint-based mechanisms on customer loyalty toward Airbnb. Further research is necessary to conduct a longitudinal survey method to check the dynamic effects of affective and calculative commitments in forming consumer loyalty toward Airbnb. Second, although Airbnb is popular all over the world, this study was conducted using data from South Korea. Several works on Airbnb have found that cultural attributes play a significant role in the formation of consumer loyalty, so future studies should explore the role of dedication-based and calculative mechanisms on consumer loyalty across a variety of cultures. Last, Yang et al. [41] established the following two trust types: trust in Airbnb and trust in the host. This study just focused on trust in Airbnb because customers consider trust in Airbnb as more important when choosing accommodation. However, since it is meaningful to distinguish trust in Airbnb from trust in hosts, we will investigate the exact roles of the two separate areas of trust in future research.