What Makes Airbnb Experiences Enjoyable? The Effects of Environmental Stimuli on Perceived Enjoyment and Repurchase Intention

Abstract

While scholarly inquiry into Airbnb has experienced an exponential growth in the tourism literature, the role of the environmental stimuli of Airbnb experiences in creating consumer enjoyment is underexplored. Adopting the Mehrabian-Russell model, this study proposes a conceptual framework to investigate the linkages between three well-documented experiential attributes of Airbnb accommodations, perceived enjoyment, and repurchase intention. The results of two separate empirical studies with recent Airbnb users consistently show significant influences of authenticity and home benefits on perceived enjoyment, which subsequently drive repurchase intention, while social interaction was not found significant. The results of the model comparison and mediation analysis also converge across the two studies, providing strong support for the mediating role of perceived enjoyment as a mechanism that underlies the relationships between the environmental stimuli and repurchase intention. The findings provide important insights into what Airbnb attributes contribute to positive emotional responses and subsequently forming favorable behavioral consequences.

Keywords

perceived enjoyment authenticity home benefits social interaction Airbnb

Introduction

In recent years, a new platform-based model utilizing the concept of the sharing economy, also known as collaborative consumption (Belk 2014; Wirtz et al. 2019), has entered the tourism and hospitality marketplace (So, Oh, and Min 2018; Zervas, Proserpio, and Byers 2017). Peer-to-peer business platforms such as Airbnb are described as “disruptive innovations” (Guttentag 2015) owing to their innovative Internet-based business model, which enables people to offer their spare rooms or unoccupied houses in exchange for economic benefits (Tussyadiah and Zach 2017). Since its introduction in 2008, Airbnb has grown from operating with three airbeds in an apartment in San Francisco to becoming the world’s largest accommodation platform with 4.5 million rooms in 81,000 cities, generating more than $41 billion for Airbnb hosts and processing more than 300 million check-ins (Airbnb 2018). Fortune magazine further suggests that Airbnb’s own annual revenues will reach $10 billion by 2020 (Fortune 2015). The continued success of Airbnb lies, to a large extent, on consumers’ wide recognition that Airbnb provides an effective way to experience the destination in an authentic way (Mody and Hanks 2020; Tussyadiah and Pesonen 2016). The company also highlights its uniqueness by saying that “wherever you go, don’t go, live there even if it’s just for a night” (Medium 2017). This message evokes a sense of belonging in visitors that drives them to live like locals who live there (Roelofsen 2018).

The importance of Airbnb attributes has resulted in a significant body of literature examining what factors differentiate Airbnb from other accommodation options and how Airbnb offers unique experiences (Guttentag 2015; Tussyadiah and Zach 2017). Scholars suggest that Airbnb expands destinations’ tourism activities (Tussyadiah and Pesonen 2016), reduces visitors’ accommodation costs (Abrate and Viglia 2019), provides household amenities and extra space (Yang, Tan, and Li 2019), allows for interactions with the local community (Abrate and Viglia 2019), and offers authenticity (Guttentag et al. 2018) and novelty (Guttentag 2016). Other researchers have recently highlighted that the experiential features are integral parts of the offerings from peer-to-peer lodging (Ert, Fleischer, and Magen 2016; Y. Yang, Tan, and Li 2019). Specifically, previous research suggests that social closeness (or social distance on the other end of the spectrum) with the host affects customer loyalty in peer-to-peer accommodations (So, Xie, and Wu 2019). Social relationships and a sense of community are what visitors seek in an Airbnb accommodation unlike what they look for from traditional hotels (Tussyadiah and Zach 2017). A recent study also describes that enriching the social bond between Airbnb hosts and potential guests could potentially create memorable experiences and boost their revenues, a recommendation that is applicable to other peer-to-peer platforms that involve interactions of seller and consumer such as car- and meal-sharing sites (García et al. 2019). Living in a residence allows guests to have a more “local” experience by living more like a local resident, interacting with the host or neighbors, and possibly staying in a “nontouristy” area, because Airbnb accommodations are often more scattered than traditional accommodations (Guttentag 2015). In addition, Airbnb accommodations provide a “homelike” environment where visitors can use household amenities such as a washing machine and a fully equipped kitchen (Nowak et al. 2015; So, Oh, and Min 2018). The existing literature suggests that Airbnb offers a new value proposition that appeal to many consumers (Guttentag et al. 2018).

Despite the extensive literature examining Airbnb attributes (Liang 2015; Visser, Erasmus, and Miller 2017), few studies have investigated the environmental aspects of Airbnb accommodations with respect to their physical, contextual, and social attributes, which have been consistently documented as critical to consumption experiences in other research settings such as hotels (Jani and Han 2015; I. Y. Lin 2009), restaurants (W. G. Kim and Moon 2009; I. Y. Lin and Mattila 2010), and retail stores (Hussain and Ali 2015). Such environmental stimuli also conceptually and fundamentally characterize Airbnb experiences. From a physical perspective, through characteristics similar to those of a home environment, Airbnb accommodations make visitors feel as if they were staying at home (Guttentag 2016). Such home benefits constitute the main physical product that guests receive from Airbnb (Nowak et al. 2015). Contextually, Airbnb’s slogan of “live like a local” indicates that the features of Airbnb accommodations represent a socially constructed interpretation connected with what is genuine and real (Visser, Erasmus, and Miller 2017). From a social point of view, Airbnb is positioning itself as a leading brand in the community, focusing on connecting people with travel experiences (Sung, Kim, and Lee 2018). Therefore, scholarly investigations are required to provide an integrative examination of these environmental factors and their roles in influencing emotional evaluative outcomes as well as subsequent purchase-relevant behaviors.

The Mehrabian–Russell model (Mehrabian and Russell 1974) posits that environmental stimuli affect an individual’s emotional state, which in turn influences approach responses. Thus, emotional state is an important outcome of environment stimuli and results in responses such as behavioral intention. One emotional state of particular significance is perceived enjoyment, which has been found to play a critical role in forming consumers’ attitude toward Airbnb (Guttentag 2015). Research suggests that perceived enjoyment is an affective reaction that plays a mediating role between the stimuli and behavioral intention (Hew et al. 2018). Although prior research has documented an empirical link between perceived enjoyment and attitude and/or loyalty (Hamari, Sjöklint, and Ukkonen 2016; Yang and Ahn 2016), the precise experiential factors that trigger a sense of enjoyment are not yet fully understood. Therefore, despite enjoyment’s theoretical significance, scholars have not given much attention to factors that enrich consumers’ evaluations of Airbnb attributes that eventually lead to perceived enjoyment. Understanding these factors is important, as research has shown that perceived experiential enjoyment increases the likelihood of consumers demonstrating positive attitudes when choosing accommodations (Lu, Zhou, and Wang 2009) and heightens users’ satisfaction with their purchase (Shiau and Luo 2013). The attitude construct, as an overall evaluation of a target object or behavior, has been conceptualized as having cognitive and affective components (Oskamp and Schultz 2005). Research has demonstrated that the extent to which attitudes are consistent with cognitions or affect varies according to the behavior under consideration (Lawton, Conner, and McEachan 2009). For the purpose of this study, rather than examining overall attitude, we focus on perceived enjoyment; this characteristic includes only individuals’ affective feelings, which drives the performance of an action (Z. Liu and Park 2015).

Based on two separate empirical studies with recent Airbnb users, this research assesses the relative importance of key environmental stimuli of Airbnb experiences and investigates the role of perceived enjoyment in consumers’ evaluation of those experiences. The objectives of this investigation are threefold. First, by adopting the Mehrabian–Russell (M-R) model as a conceptual framework, this study investigates three dimensions, that is, physical, contextual, and social components of environmental stimuli of Airbnb experiences. Second, this study examines the role of perceived enjoyment as an outcome of Airbnb attributes and as a driver of behavioral intention, providing a formal analysis of the relationships between the Airbnb attributes, enjoyment, and behavioral intention by using a widely adopted theoretical framework. Third, the consistent results generated from the two studies contribute meaningfully to the literature by testing the effects of three well-documented attributes of Airbnb accommodations on emotional and behavioral outcomes. This research contributes to the literature by theorizing and empirically testing and subsequently validating a conceptual model that encapsulates key Airbnb atmospherics as the primary environmental stimuli influencing Airbnb customers’ perceived enjoyment through the well-established theoretical framework of M-R model. This research also extends existing research on Airbnb by investigating the role of Airbnb’s environmental attributes and specifically determine which Airbnb attributes drive enjoyment and repurchase intention. In testing the mediating role of perceived enjoyment, this study shows that authenticity and home benefits significantly drive enjoyment through which repurchase intention is also further enhanced. This research extends the current body of literature on Airbnb by uncovering the linkages between the defining characteristics of Airbnb experiences, perceived enjoyment, and customer’s ultimate evaluations of Airbnb experiences. The results also provide an important basis for future research on consumers’ evaluations of Airbnb attributes and factors that lead to perceived enjoyment.

Theoretical Background

Mehrabian–Russell Model

To understand how consumers respond to Airbnb attributes, we rely on the M-R model (Mehrabian and Russell 1974), which has been extensively adopted in prior research in hospitality and tourism contexts (e.g., A. Chen, Peng, and Hung 2015; M. J. Kim, Lee, and Jung 2019; Manthiou et al. 2017; C. Y. Wang, Miao, and Mattila 2015). Drawing on the environmental psychology literature (e.g., Cassidy 2013), marketing scholars have conceptualized the service environment as consisting of physical, social, and contextual elements, which influence the individual within that setting and her or his behavior (Tombs and McColl-Kennedy 2003). The stimulus–organism–response (S-O-R) framework explains how environmental stimuli can alter an individual’s internal states, thereby shaping her or his behavioral responses (Jang and Namkung 2009). Using the fundamental S-O-R framework, the M-R environmental model explains emotional states that result from environmental stimuli and ultimately inform approach or avoidance responses (Mehrabian and Russell 1974). Within the S-O-R framework, stimuli comprise the physical environment (Eroglu, Machleit, and Davis 2003). The “home” is the place in an Airbnb experience, representing an environmental stimulus that is considered to be one of the most significant features of the total stimulus; the environment provides a setting in which a tangible product or service is consumed (Sherman, Mathur, and Smith 1997). Furthermore, scholars in services marketing have argued that the environment consists of social and contextual elements (Chhabra 2008; H. Liu et al. 2016). Social elements often manifest through social interaction, which helps create an overall atmosphere as well as customers’ unique experiences (Tombs and McColl-Kennedy 2003). These social elements of the environment also significantly influence behavior (H. Liu et al. 2016). In addition, the environment is a constructed interpretation of related stimuli (Jiang et al. 2017). Therefore, in tourism, contextual aspects include locally grounded traditions and lifestyles to be connected to a space. Collectively, these features contribute to a genuine image and real experiences in a destination (Chhabra 2008). The M-R model also describes the role of emotions elicited by different stimulus settings in influencing behavioral intention. Various environmental stimuli have been examined to understand their impacts on individuals’ behavior when influenced by emotional states (J. Lee 2014).

The Airbnb literature suggests that pleasure is derived from attributes or activities (Hamari, Sjöklint, and Ukkonen 2016). In theorizing the S-O-R linkage, research shows that pleasure is a powerful determinant of approach–avoidance behaviors in many service settings, such as hotels and restaurants (Jani and Han 2015; I. Y. Lin and Mattila 2010). Further, tourism and hospitality scholars have extensively adopted the M-R model to study tourists’ intentions to stay at hotels (I. Y. Lin 2009), to visit a destination (Lu, Zhou, and Wang 2009), and to spread positive word of mouth (Ha and Im 2012), thus highlighting the relevance of the theoretical model. Several researchers have also conceptualized stimulus factors as antecedents to the theoretical components of this model to predict tourists’ behavioral intentions (Jani and Han 2015; H. Liu et al. 2016). As this study focuses on the examination of the effects of stimuli on emotional and behavioral responses of Airbnb users, we adopted this model as a relevant guiding conceptual framework. In adopting the M-R model, behavioral intentions has been recognized as a surrogate indicator of actual behavior influenced by emotions in prior empirical research (S. S. Jang and Namkung 2009; Manthiou et al. 2017; Hung, Peng, and Chen 2019; J. Y. Park et al. 2019), and was therefore considered as a theoretically and empirically relevant dependent variable for this study.

Perceived Enjoyment

Pleasure or enjoyment refers to the degree to which an individual feels delight and satisfaction in a preferred environment (Hamari, Sjöklint, and Ukkonen 2016). In proposing the general psychoevolutionary theory of emotion, Plutchik (1980) offered a broad evolutionary foundation to conceptualize the domain of emotion in animals and humans. Plutchik (1980) suggested that emotions are fundamentally characterized as either pleasant or unpleasant. These polar categories can be further subclassified into eight primary human emotions: fear, anger, joy, sadness, acceptance, disgust, anticipation, and surprise (Martin et al. 2008). As an emotional dimension, pleasure plays an important role in capturing individuals’ perceptions of physical environments (Mehrabian and Russell 1974). Perceived enjoyment has consistently been considered a qualitative factor conveying individuals’ sense of pleasure, depress, disgust, or hate associated with a particular act (Z. Liu and Park 2015; Triandis 1980). As self-determination theory suggests (Deci and Ryan 2008), intrinsic motivations emerge from the intrinsic value or enjoyment for a task or activity (Hamari, Sjöklint, and Ukkonen 2016). Similarly, consumers seek to derive pleasure or enjoyment from the consumption of a product or service (Hirschman and Holbrook 1982; Ozturk et al. 2016).

Diverse environments induce emotional states (Mehrabian and Russell 1974). For example, in retail settings, empirical research has found a positive association between store attributes and consumers’ emotional states of enjoyment (Wong et al. 2012). In addition, as enjoyment is derived from environmental stimuli and activities, it originates from an individual’s innate interest in the activities themselves (Cerasoli and Ford 2014). For instance, enjoyment plays a critical role in building a positive attitude and behavioral intention in a peer-to-peer setting (Hamari, Sjöklint, and Ukkonen 2016). Consumers choose to use Airbnb because of the enjoyment driven by the activities themselves at the Airbnb accommodation, which highlights the role of enjoyment in consumers’ evaluation of Airbnb (So, Oh, and Min 2018).

Consumers who use sharing economy services are attracted to experiences that are interesting and engaging (Raghunathan and Corfman 2006). They expect positive surprises during their travel (Tung and Ritchie 2011), especially as Airbnb provides the experience of entertainment and escapism (Mody and Hanks 2020). Furthermore, enjoyment leads to the desire to engage in the activity (Lu, Zhou, and Wang 2009) and has a positive effect on satisfaction and intention to use peer-to-peer lodging (Tussyadiah 2016). Thus, we hypothesize that

Hypothesis 1: Perceived enjoyment is positively related to repurchase intention.

Airbnb Attributes

Compared to traditional accommodation offered by hotels, Airbnb has been consistently described as providing unique or novel experiences that many visitors prefer (Liang 2015). In the sharing economy and Airbnb literature, researchers have identified several factors as critical in visitors’ evaluations of Airbnb attributes. Examples include social interaction (Sung, Kim, and Lee 2018), authenticity (Liang 2015), novelty (Guttentag 2016), home benefits (Guttentag et al. 2018), and perceived risk (Liang 2015). Factors concerning the financial aspects of Airbnb, such as price value (Mao and Lyu 2017) or price (D. Wang and Nicolau 2017), also play crucial roles in consumers’ decisions and evaluations of a purchase. Specifically, explanatory variables such as host attributes, site and property attributes, amenities and services, rental rules, and online review ratings have been found to be significantly related to the nuanced relationships between pricing and its determinants (D. Wang and Nicolau 2017). While the theoretical significance of the aforementioned factors is apparent, the constructs of authenticity (Guttentag et al. 2018; Mody and Hanks 2020; So, Oh, and Min 2018; Yeager et al. 2019), home benefits (Camilleri and Neuhofer 2017; Guttentag et al. 2018; Liu and Mattila 2017; S. Park and Tussyadiah 2019; So, Oh, and Min 2018), and social interactions (Moon et al. 2019; Shuqair, Pinto, and Mattila 2019; Sung, Kim, and Lee 2018) have consistently been deemed defining characteristics of peer-to-peer accommodations; these features are therefore noteworthy in forming unique accommodation experiences in peer-to-peer settings (Sthapit and Jiménez-Barreto 2018). Table 1 summarizes key studies on authenticity, home benefits, and social interaction. Each of these factors is further discussed.

Table 1.

A Summary of Key Studies on Authenticity, Home Benefits, and Social Interaction.

Factors	Author	Key Findings
Authenticity	Guttentag et al. (2018)	Local authenticity is considered as a motivating factor to stay at an Airbnb accommodation.
	Lalicic and Weismayer (2017)	Perceived authenticity is an antecedent of consumer satisfaction, which affects consumer loyalty toward Airbnb accommodations.
	Liang (2015); Liang, Choi, and Joppe (2018)	Perceived authenticity increases consumers’ repurchasing intention and perceived value toward Airbnb but decreased their perceived risk toward Airbnb.
	Mao and Lyu (2017)	Unique experience expectation which is developed from authentic local culture influences customer attitude and customer intention to repurchase Airbnb.
	Mody and Hanks (2020)	Airbnb leverages intrapersonal, existential, and brand authenticity in building brand-loyal customers and brand-loving.
	Paulauskaite et al. (2017)	“Living like a local” emerges to be key to the authenticity of Airbnb stays, and experience co-creation is a facilitator of these feelings of authenticity.
	Poon and Huang (2017)	Airbnb guests perceive Airbnb to be better than hotels in providing authentic experiences.

	Shuqair, Pinto, and Mattila (2019)	Service recovery strategies significantly and positively affect authenticity, which influences postfailure loyalty.
	Yeager et al. (2019)	The guest’s pursuit of the authentic elements of a neighborhood raises pride for some residents in their neighborhood.
Home benefits	Camilleri and Neuhofer (2017)	The homelike environment generated by an accommodation’s physical resources is fundamental to the value proposition of Airbnb accommodations.
	Guttentag (2016); Guttentag et al. (2018)	Home benefits are identified as a motivating factor to stay at an Airbnb accommodation.
	Jang et al. (2019)	A homelike environment such as amenities and a large amount of space were the primary reasons leisure travelers chose Airbnb.
	Johnson and Neuhofer (2017)	The Airbnb’s home is seen as the central value proposition. The “Airbnb home” emerged as a dominant operand resource, reflecting the main physical product that guests buy and consume through Airbnb.
	Liu and Mattila (2017)	Airbnb distinguishes itself from traditional hotels by offering guests a “feeling at home” (e.g., belongingness) and an “atypical place to stay” (e.g., uniqueness).
	Nowak et al. 2015	Amenities such as a washing machine and a fully equipped kitchen are key reasons for visitors to choose Airbnb.
	Park and Tussyadiah (2019)	References to efforts of making guests feel at home emerge as a driver to improve the perceived benevolence of the hosts.
	Pera et al. (2019)	When guests and hosts have interacted on emotional and informal elements, which allow the guest to experience a home feeling.

	Tussyadiah (2016)	Amenities significantly influence satisfaction but not repurchase intention.
	Yang et al. (2018)	The central motive for using Airbnb accommodations is the inexpensive price, and the second most important reason is home benefits, such as homeware, kitchen, or free Wi-Fi.
	Zhu et al. (2019)	Home feeling impacts Airbnb guests’ future intentions to return.
Social interaction	Y. Chen and Xie (2017)	The basic functionality as accommodation is the deterministic factor of consumer evaluation, and social interaction is secondary to functionality in determining consumer valuation of Airbnb listings.
	García et al. (2019)	Improving expectations about the guest-host relationship creates a social bond between the guest and the host before the experience.
	Guttentag et al. (2018)	Interactivity is identified as a motivating factor to stay at an Airbnb accommodation.
	Johnson and Neuhofer (2017)	The human resource necessary for Airbnb value cocreation is represented by Airbnb hosts who need to possess the traits of being responsive, friendly, and helpful.
	Lutz and Newlands (2018)	A recurring theme among “shared room” hosts was interaction, which was presented as an essential part of the Airbnb experience. Whereas recurring theme among “entire home” hosts was that interaction was kept to a minimum and usually only in cases of practical need.
	Moon et al. (2019)	Guests had more positive perceptions of the peer-to-peer interactions than they did with hosts. The major determinants of peer-to-peer interactions for guests have perceived credibility of peer online profile and peer-to-peer dyadic trust, while for hosts, they were perceived credibility of peer online profile, peer-to-peer face-to-face interaction reciprocity, peer-to-peer rapport, and peer-to-peer dyadic trust.
	Shuqair, Pinto, and Mattila (2019)	Perceived social interaction affected positive emotions, which in turn affected post-failure loyalty.

	Sung, Kim, and Lee (2018)	Social relationship affects provider attitude and intention to use.
	Pera et al. (2019)	The lack of real personal interaction becomes a powerful discriminator in deciding to leave a negative review.
	Poon and Huang (2017)	The social interactions that emerge from collaborative consumptions are common motives for tourism.
	Priporas et al. (2017)	The social interactions between guests and hosts play a decisive role in shaping overall service quality perceptions.
	Tussyadiah and Pesonen (2016)	The social appeal of collaborative consumption contributes significantly to the increase in travel frequency, confirming that perceived new ways of traveling (i.e., staying with locals) stimulate more travel.
	Wang, Asaad, and Filieri 2019	Social orientation emerges as a motivator of trust. If a sharing economy company stimulates social values, the company will be more trusted by its hosts.

Airbnb’s slogan, “Live like a local,” communicates a strong value statement of authenticity (Visser, Erasmus, and Miller 2017). Authenticity is defined as perceptions of Airbnb consumers’ cognitive recognition of “real” experiences when staying in an Airbnb rental, which can change with evaluators’ perceptions (Grayson and Martinec 2004; Liang, Choi, and Joppe 2018). Authenticity is an essential attribute of Airbnb (So, Oh, and Min 2018). The term “constructive authenticity” identifies that authenticity is a socially constructed interpretation of the nature of what is observed (Jiang et al. 2017). It is connected with what is genuine and real (Beverland, Farrelly, and Quester 2010). Therefore, authenticity with respect to Airbnb accommodations refers to consumers’ cognitive recognition of real experiences of staying in Airbnb accommodations (Liang 2015).

Authenticity in tourism implies features of roughness in remote traditional communities, inconvenience, and danger often related to efforts to achieve authenticity (Kontogeorgopoulos 2003). As such, in their search for enjoyment, tourists sometimes accept staged authenticity as a protective substitute for the original (Cohen 1995). However, lack of authenticity on the trip fails to spoil or detract in any way from the overall enjoyment of the trip (Kontogeorgopoulos 2003). Authentic attributes affect the level of enjoyment (Elghani 2012). Backpackers, for example, commonly express that their travel is driven by a desire to experience authenticity inaccessible to those who travel within the confines of organized mass tourism (Elghani 2012). Drawing on such relevant literature, therefore, we propose the following hypothesis:

Hypothesis 2: Authenticity is positively related to perceived enjoyment.

While visitors who experience authentic attributes of a travel event seem to have a higher degree of satisfaction (Jensen and Lindberg 2000), to date no consistent explanation exists of the relationship between authenticity and repurchase intention. In the general tourism literature, authenticity is found to be an essential factor of destination choices (Ramkissoon and Uysal 2011). In the Airbnb literature, while some researchers found that authenticity did not explain behavioral intention when other Airbnb motivations and constraint factors were considered (So, Oh, and Min 2018), others reported that authenticity plays an important role in the consumer’s process of repurchasing Airbnb accommodation (Liang 2015; Mao and Lyu 2017). Similarly, empirical research has indicated that perceived authenticity is a significant predictor of customer satisfaction with Airbnb accommodations (Birinci, Berezina, and Cobanoglu 2018). Scholars have also found that perceived authenticity serves as an antecedent of customer satisfaction, which in turn predicts customer loyalty toward Airbnb accommodations (Lalicic and Weismayer 2017). The more enriched and engaged experiences visitors have, the more likely they develop a pleasurable attitude that may eventually result in a higher repurchase intention (Tussyadiah and Pesonen 2016). Airbnb advertises peer-to-to-peer accommodations as potentially providing more authentic experiences when staying within neighborhoods, and the visitor’s pursuit of the authentic elements of a neighborhood increases pride for some residents in their neighborhood (Yeager et al. 2019). Recent research indicates that Airbnb leverages brand, existential, and intrapersonal authenticity in creating brand-loving and brand-loyal customers (Mody and Hanks 2020). Therefore, on this basis, the following hypothesis is proposed:

Hypothesis 3: Authenticity is positively related to repurchase intention.

Another relevant attribute is home benefits, which refers to the functional attributes of a home including “household amenities,” a “homely feel,” and “large space” (Guttentag 2016). Airbnb accommodations make visitors feel as if they were staying at home owing to homelike characteristics such as household amenities and ample space (Guttentag 2016). In particular, amenities such as a washing machine and a fully equipped kitchen are key reasons for visitors to choose Airbnb (Nowak et al. 2015). Tangible and intangible features, such as music, lighting, and scenic beauty of a physical place, play as environmental cues that impact customer feelings (Teng 2011). The homelike environment created by an accommodation’s physical resources, coupled with the accommodation’s location, are central to the value proposition of an Airbnb setting (Camilleri and Neuhofer 2017). In a boutique accommodation, comments from guests indicated that the features of the homelike setting made them feel as follows: “Delightful, homely ambience; feel like you’re in someone’s home; more like house guests”; “We felt at home and very comfortable”; and “It felt like home to home really” (Mcintosh and Siggs 2005, p. 78). Similarly, Airbnb customers pointed out the need to find a quiet place for relaxation and avoid ‘tourist locations’ to enjoy their experience more fully (Camilleri and Neuhofer 2017). Initial research addressed the association between enhanced residential or more homelike environments and an improved emotional statement (Chaudhury et al. 2017). Airbnb accommodations offer functional attributes that allow visitors to enjoy a homelike feel (Guttentag et al. 2018). Therefore, the following hypothesis is proposed:

Hypothesis 4: Home benefits is positively related to perceived enjoyment.

In addition to affecting perceived enjoyment, home benefits could conceivably influence repurchase intention. The ‘Airbnb home’ is seen as the central value proposition (Camilleri and Neuhofer 2017). Described as a “home away from home,” Airbnb encompasses elements of the traditional home, such as a bedroom and kitchen, along with tourist amenities (Johnson and Neuhofer 2017). Guests have indicated their need to find a quiet place for relaxation, which has been a recurrent narrative when choosing an Airbnb stay (Camilleri and Neuhofer 2017). Theoretically, as physical stimuli influence human behavior (Kotler 1973), the physical property is found to be one of the most important attributes affecting travelers’ accommodation decisions, and it creates value for guests during their stay (Countryman and Jang 2006). In a hotel setting, physical aspects such as the lobby and other public spaces have been found to affect purchase decisions (Ali and Amin 2014; Dedeoğlu, Küçükergin, and Balıkçıoğlu 2015). In the Airbnb context, home benefits help customers feel as though they are staying in a home environment, which promotes their Airbnb selection decisions (Guttentag 2016). Scholars suggest that efforts of making guests feel at home could be a driver to enhance the perceived benevolence of the hosts (S. Park and Tussyadiah 2019). This homelike feeling has been found to affect Airbnb guests’ future visit intentions (So, Oh, and Min 2018; Zhu et al. 2019). A recent study also showed that a homelike environment, including amenities and a large amount of space, were major reasons why leisure travelers purchase Airbnb (J. Jang et al. 2019). On this basis, the following hypothesis is proposed:

Hypothesis 5: Home benefits is positively related to repurchase intention.

In addition to authenticity and home benefits, social interaction has been widely cited as an important Airbnb attribute. Unlike home benefits, which represent attributes of a home environment (Guttentag 2016), social interaction refers to guests interacting with the host and local people and obtaining insiders’ tips on local attractions (Poon and Huang 2017). Peer-to-peer accommodations differ from traditional hotels by offering additional benefits, chiefly connectedness and social interactions, to hosts and guests (Y. Chen and Xie 2017). Social interaction plays a decisive role in shaping overall service quality perceptions (Pera et al. 2019). Visitors have described the opportunity for social interaction and cultural exchange with local residents, together with cost savings, as key advantages of lodging rentals (D. Wang and Nicolau 2017). Some guests may be interested in developing meaningful social interactions with their hosts (Priporas et al. 2017). However, a recurring theme when renting an “entire home” is that interaction is kept to a minimum and tends to occur solely in cases of practical need (Lutz and Newlands 2018). Y. Chen and Xie (2017) noted that the basic functionality of an accommodation, which Airbnb and hotels have in common, is a determining factor in consumers’ evaluations. Accordingly, social interaction may be secondary to functionality in determining consumers’ valuations of Airbnb listings. Nevertheless, Airbnb is successfully positioning itself as a leading brand focusing on connecting people with unique accommodation experiences by providing unexpected pleasure (Sung, Kim, and Lee 2018). Travelers use peer-to-peer accommodations to fulfill “the desire for social relationships with the local community and meaningful interaction with the host” (Tussyadiah and Pesonen 2016, p. 1031). Interacting with hosts and local residents provides tourists the enjoyment of staying at accommodations where they can immerse themselves in local culture within a comfortable, relaxing atmosphere (Elghani 2012). A recent study also suggested that perceived social interaction elicited positive emotions, which then affected post-failure loyalty (Shuqair, Pinto, and Mattila 2019). The following hypothesis is therefore proposed:

Hypothesis 6: Social interaction is positively related to perceived enjoyment.

Research findings on the relationship between social interaction and repurchase intention are inconsistent. On the one hand, research has found that social interaction has no influence on satisfaction and repurchase intention (Lutz and Newlands 2018; Tussyadiah 2016). Other research shows that face-to-face interaction is one of the main contributors to visitors’ satisfaction and repurchase intention (Moon et al. 2019). In fact, the social appeal of collaborative consumption contributes significantly to the increase in travel frequency (Tussyadiah 2016). Scholars suggest that Airbnb hosts, who need to possess the traits of being responsive, friendly, and helpful, are the human resource necessary for Airbnb value cocreation (Johnson and Neuhofer 2017), and the social interactions between guests and hosts play a critical role in shaping overall service quality perceptions (Priporas et al. 2017). Furthermore, activities and chatting with local residents are deemed to be a natural approach to improving travelers’ attachment to the place (Ouyang, Gursoy, and Sharma 2017). Social interaction is of paramount importance to Airbnb accommodations as it critically affects guests’ experiences and level of satisfaction (Sung, Kim, and Lee 2018) and the host–guest relationship is a statistically significant predictor of consumers’ satisfaction with their Airbnb stay. Thus, the following hypothesis is proposed:

Hypothesis 7: Social interaction is positively related to repurchase intention.

As the preceding literature review suggests, the conceptual model of this study is based on the stimulus–organism–response (S-O-R) paradigm (Mehrabian and Russell 1974) and its marketing applications. In the S-O-R framework, “stimulus” represents physical, social, and contextual environmental attributes (H. Liu et al. 2016; Tombs and McColl-Kennedy 2003). Environmental stimuli influence an individual’s emotional state, which in turn affects approach or avoidance responses (Mehrabian and Russell 1974). In particular, pleasure is derived from attributes or activities and is a powerful determinant of approach-avoidance behaviors in many service settings (Hamari, Sjöklint, and Ukkonen 2016; Jang and Namkung 2009). The M-R model has been widely adopted in hospitality and tourism research to examine place attachment (Jiang et al. 2017) and brand loyalty (A. Chen, Peng, and Hung 2015), as well as consumer evaluation and intention to stay at a hotel (Dedeoğlu, Küçükergin, and Balıkçıoğlu 2015), making it suitable for this investigation. As this review of literature shows, Airbnb attributes such as authenticity, home benefits, and social interaction influence repurchase intention directly, as well as indirectly through perceived enjoyment. The following hypothesis is proposed and Figure 1 presents the conceptual model.

Hypothesis 8: Perceived enjoyment partially mediates the effect of authenticity, home benefits, and social interaction on repurchase intention.

Figure 1.

Proposed conceptual model.

Methods and Results

Data Collection

To examine the environmental stimuli of Airbnb experiences, this study focuses on individuals who had stayed at an Airbnb accommodation in the last 12 months. Airbnb has spread through the United States and Europe much more quickly than in Asia, Africa, and beyond (Forbes 2017). With an annual growth rate of more than 100%, Airbnb now has more than 4 million listings, with the United States being its largest market (Boston Hospitality Review 2017). As such, the United States was selected as the geographical context for this study. We employed a survey questionnaire to measure the three Airbnb attributes, perceived enjoyment, and repurchase intention. To ensure clarity of the instructions and questions, we pretested the survey instrument with two university staff members who were native English speakers. We made minor modifications to the wording of several items to avoid ambiguity. To achieve generalizability, as well as for cross-validation, we conducted two separate studies to test the conceptual model. Such a multistudy approach has been adopted in recent tourism and hospitality studies to achieve methodological rigor and enhance the validity of the findings (Guchait et al. 2016; Tussyadiah et al. 2018), and therefore was considered necessary for this investigation. Specifically, Study 1 was designed to empirically analyze the theoretical relationships between the constructs. Study 2 was intended to replicate the model with a different sample to improve external validity.

Study 1

Procedure

To provide an initial evaluation of the model, we used the Qualtrics consumer panel to access the required research data through an online survey. Qualtrics is one of the largest online research companies in the United States. Its panels consist of more than 95 million people around the world (Frye et al. 2019). Panel providers recruit respondents, record their personal information (e.g., e-mail address and profession), and offer a database of subjects to Qualtrics for online survey distribution (Chang and Busser 2020). For the purpose of this study, only individuals who had traveled domestically or internationally and stayed at an Airbnb property within the past 12 months were eligible to participate. Over a four-week data collection period, 258 respondents completed the survey. We eliminated 35 cases owing to incomplete responses, leaving 223 usable cases. The sample size was acceptable given the complexity of the proposed model (Fabrigar, Porter, and Norris 2010; Hair et al. 2006). To further test the adequacy of the sample size, we followed MacCallum, Browne, and Sugawara (1996) to compute the minimum sample size required for the current study. Based on the degrees of freedom of the final model of 55, alpha level of .05, desired statistical power of .80, and the null and alternative root mean square error of approximation (RMSEA) of .05 and .08, respectively (Preacher and Coffman 2006), this computation yielded a minimum sample size of 199. Therefore, our final sample of 223 cases exceeded the required minimum sample size.

Survey instrument

The survey instrument was constructed using measurement items generated from the literature, which ensured the validity and reliability of the adopted scales. Specifically, to capture the experiential attributes of Airbnb accommodations, four items were adopted from Guttentag et al. (2018) to measure authenticity, three items from Guttentag (2016) measured home benefits, and three items from Stors and Kagermeier (2015) and Tussyadiah (2015) assessed social interaction. Finally, three items measuring perceived enjoyment were adapted from Venkatesh, Thong, and Xu (2012), and four items were borrowed from Jeong, Oh, and Gregoire (2003) to measure repurchase intention. A 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree) was employed for all items.

Results

Of the 223 respondents, 66.4 percent were male, and 70.3 percent were between age 30 and 60 with 3.9 percent over age 60 and 25.8 percent under age 30. In terms of annual income, 9.6 percent reported under $20,000, 59.8 percent from $20,000 to $100,000, and 30.6 percent over $100,000.

We compared early and late respondents to examine potential nonresponse bias on the individual measurement items and the demographic variables (Armstrong and Overton 1977). The results revealed no significant differences between early and late responses with regard to respondent characteristics and all measured items, suggesting that nonresponse bias is unlikely to be a problem in our study.

Measurement model

To assess the measurement model, we first conducted a confirmatory factor analysis (CFA) on the sample data (n = 223) using AMOS 21 with maximum likelihood estimation. Given that this estimation method assumes multivariate normality of the data, such an assumption was evaluated prior to interpreting the analysis results (Byrne 2016). The results showed that the critical ratio for Mardia’s (1970) normalized estimate of multivariate kurtosis was greater than 5 (Bentler and Wu 2005), suggesting that a multivariate normal distribution was not evident. Therefore, we used bootstrapping to produce parameter estimates in subsequent analyses (Byrne 2016). The initial measurement model showed a marginal fit due to several problematic items with loadings below the minimum acceptable level of 0.60 (Hair et al. 2006). Following the guidelines that Byrne (2016) and Kline (2015) suggest, the wording and results of these items were reviewed and ultimately considered candidates for removal (i.e., authenticity: “Airbnb tends to provide an opportunity to stay in a less standardized accommodation environment”; social interaction: “Airbnb offers guests opportunities to interact more with other guests”; home benefits: “Airbnb offers spacious accommodations like homes”; repurchase intention: “I will use Airbnb in the near future” and “I would like to invest more time in learning about Airbnb, as I would like to stay there on some of my future trips”). After each item was removed, the measurement model was reestimated. The final measurement model resulted in good model fit (χ² = 91.18, p < .05, df = 55, χ²/df = 1.66, comparative fit index (CFI) = 0.97, normed fit index (NFI) = 0.93, Tucker-Lewis index (TLI) = 0.96, root mean square error of approximation (RMSEA) = 0.05 [LO 90 = 0.03, HI 90 = 0.07, and PCLOSE = 0.34]). While two constructs had two measurement items, the remaining constructs contained in the overall CFA were measured using three measurement items, making the measurement model overidentified (df_M > 0), a necessary condition for deriving a unique set of estimates for the parameters (see Kaplan 2008 and Kline 2015). Table 2 presents the results.

Table 2.

Results of the Measurement Model (Study 1).

Construct/Item	SL	CR	SR	AVE
Authenticity, adapted from Guttentag et al. (2018)			0.97	0.65
Mean = 5.45, SD = 0.99
1. Airbnb tends to provide an authentic local experience	0.83	NA
2. Airbnb tends to offer a unique, one-of-a-kind experience	0.79	16.70
3. Airbnb tends to offer an accommodation that integrates local cultures	0.71	13.96

Home benefits, adapted from Guttentag (2016)			0.98	0.72
Mean=5.84, SD=1.06
1. Airbnb provides guests with home-like amenities	0.80	NA
2. Airbnb provides a “homely” feel during the stay	0.90	27.09
3. Guests can feel home and relax at Airbnb.	0.78	18.48

Social interaction, adapted from Stors and Kagermeier (2015) and Tussyadiah (2015)			0.91	0.72
Mean=4.09, SD=1.51
1. Airbnb offers guests opportunities to interact more directly with local people	0.93	NA
2. Airbnb offers guests good social opportunities with the host	0.76	3.31

Perceived enjoyment, adapted from Venkatesh, Thong, and Xu (2012)			0.97	0.60
Mean=5.70, SD=0.94
1. Staying at Airbnb is fun	0.84	NA
2. I would enjoy using Airbnb	0.74	14.49
3. Airbnb offers an entertaining accommodation experience	0.74	14.70

Repurchase intention, adapted from Jeong, Oh, and Gregoire (2003)			0.95	0.66
Mean=6.05, SD=0.92
1. Airbnb will be one of the accommodation options I will consider for my next trip	0.78	NA
2. I would recommend Airbnb to others as a viable lodging option	0.84	11.38

Note: Model fit statistics: χ² = 91.18, p < .05, df = 55, χ²/df = 1.66, CFI = 0.97, NFI = 0.93, TLI = 0.96, and RMSEA = 0.05. All estimates were generated using bias-corrected bootstrapping using 5,000 subsamples. SL = standardized loadings; CR = critical ratio; SR = scale reliability; AVE = average variance extracted; CFI = comparative fit index; NFI = normed fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation.

In addition, each scale was tested for reliability and validity. Table 2 indicates that all 13 items had standardized loadings greater than 0.70 (Hair et al. 2006). The bootstrap critical ratios of the indicators were statistically significant at p <.001 and the average amount of variance extracted (AVE) was greater than 0.50 for all constructs (Fornell and Larcker 1981), providing evidence for convergent validity.

Discriminant validity of the model was examined in two methods. The first method was suggested by Fornell and Larcker (1981) to compare the square root of each construct’s AVE with the correlations between constructs. Discriminant validity is established if the square root of the AVE is greater than the correlations. The results show that the square root of each AVE is greater than its inter-construct correlations, suggesting evidence for discriminant validity. The second method was to analyze pairwise two-factor CFA models (Anderson and Gerbing 1988). Each model was examined twice, with one allowing the interconstruct correlation freely estimated and the other constraining the correlation to the value of 1. This analysis resulted in 10 comparisons of the unconstrained and the constrained measurement models. We examined the chi-square difference between the two models to see whether the imposed constraint significantly worsened the model fit. Discriminant validity is established if a significantly higher chi-square value is acquired for the model in which the correlation is constrained to unity (Bagozzi and Yi 1988). Table 3 presents that all combinations showed a significantly higher value (χ² >3.84 at α = 5%) for the constrained model, suggesting additional evidence for discriminant validity (Jöreskog 1971).

Table 3.

Discriminant Validity Analysis from Chi-Square Difference Tests (Study 1).

Comparison		Unconstrained Model		Constrained Model		Chi-square Difference		Discriminant Validity
Comparison		χ²	df	χ²	df	Δχ²	Δdf	Discriminant Validity
AU	HB	15.23	8	81.75	9	66.52	1	Yes
	SI	3.41	4	48.84	5	45.43	1	Yes
	EN	11.72	8	57.96	9	46.24	1	Yes
	RI	3.48	4	73.32	5	69.84	1	Yes
HB	SI	8.70	4	84.75	5	76.05	1	Yes
	EN	25.26	8	101.74	9	76.48	1	Yes
	RI	5.49	4	54.36	5	48.87	1	Yes
SI	EN	10.82	4	53.96	5	43.14	1	Yes
	RI	0.54	1	84.31	2	83.77	1	Yes
EN	RI	2.32	4	73.52	5	71.20	1	Yes

Note: AU, authenticity; HB, home benefits; SI, social interaction; EN, perceived enjoyment; RI, repurchase intention.

Common method variance

Because several variables were assessed via the same method, common method variance could inflate the relations among the constructs (Spector and Brannick 2010). We tested for common method variance in three different ways. First, we conducted a CFA with all 13 items loading onto a single common factor to assess common method variance and whether a single factor could account for all of the variance in the data (e.g., Baldauf et al. 2009; Mossholder, Bennett, and Martin 1998). A chi-square difference test indicated that the measurement model with five latent factors fit the data significantly better than the common factor model (Δχ²= 301.3, df = 11, p < .001). Second, Harman’s one-factor test was conducted through an exploratory factor analysis with unrotated principal components factor analysis. Forcing the analysis to extract one factor, the factor merged accounted for less than 50% of the variance (i.e., 30.21%), thus, no general factor is apparent (Podsakoff et al. 2003). Third, following Podsakoff et al. (2003) and Richardson, Simmering, and Sturman (2009), we included an unmeasured latent method construct in the original CFA and specified factor loadings from the method construct, which has no unique indicators of its own, to all of the substantive items. As the factor loadings did not change by more than 20% from the original estimates, the common method variance was not considered to be sufficient to bias results. Therefore, on the basis of the three different tests, common method variance was not a critical concern.

Structural model

The structural model revealed a good model fit (χ² = 91.18, p < .05, df = 55, χ²/df = 1.66, CFI = 0.97, NFI = 0.93, TLI = 0.96, RMSEA = 0.05 [LO 90 = 0.03, HI 90 = 0.07, and PCLOSE = 0.34]). The path coefficients suggested four paths were significant (i.e., hypothesis 1: perceived enjoyment to repurchase intention; hypothesis 2: authenticity to perceived enjoyment; hypothesis 4: home benefits to perceived enjoyment; hypothesis 5: home benefits to repurchase intention). Table 4 summarizes the results of hypothesis testing.

Table 4.

Standardized Estimates and Results of Hypothesis Testing (Study 1).

Structural Path	Standardized Coefficient	Lower CL	Upper CL	p Values	Conlcusion
Perceived enjoyment → Repurchase intention (hypothesis 1)	0.19*	0.06	0.37	.02	Supported
Authenticity → Perceived enjoyment (hypothesis 2)	0.35***	0.19	0.53	.00	Supported
Authenticity → Repurchase intention (hypothesis 3)	0.06	−0.05	0.21	.44	Not supported
Home benefits → Perceived enjoyment (hypothesis 4)	0.16*	0.03	0.32	.04	supported
Home benefits → Repurchase intention (hypothesis 5)	0.49*	0.27	0.64	.01	Supported
Social interaction → Perceived enjoyment (hypothesis 6)	0.11	−0.02	0.17	.11	Not supported
Social interaction → Repurchase intention (hypothesis 7)	−0.10	−0.14	0.04	.20	Not supported
R²:
Perceived enjoyment: .20
Repurchase intention: .36

Note: Model fit statistics: χ² = 91.18, p < .05, df = 55, χ²/df = 1.66, CFI = 0.97, NFI = 0.93, TLI = 0.96, and RMSEA = 0.05. All estimates were generated using bias-corrected bootstrapping using 5,000 subsamples. CL, 90 percent confidence limit. CFI = comparative fit index; NFI = normed fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation

Significant p < .05; ^**significant p < .01; ^***significant p < .001.

Testing competing models

Although our literature review suggests for a partial mediation model, whereby the independent variables (i.e., authenticity, home benefits, social interaction) have a direct, as well as an indirect, effect through perceived enjoyment, on the dependent variable (i.e., repurchase intention), a full mediation model is also theoretically plausible, with the effects of the independent variables being fully mediated by perceived enjoyment. Therefore, while the proposed model produced good model fit, we formally compared the two competing models. Testing competing or theoretically rival models could rule out equivalent or even better fitting models (MacCallum and Austin 2000). To determine which model achieves the better model fit, we conducted a chi-square difference test. We compared the full mediation model (χ² = 137.29, df = 58, χ²/df = 2.37, CFI = 0.93, NFI = 0.89, TLI = 0.91, and RMSEA = 0.08 [LO 90 = 0.06, HI 90 = 0.09, and PCLOSE = 0.001) with the partial mediation model (χ² = 91.18, df = 55, χ²/df = 1.65, CFI = 0.97, NFI = 0.93, TLI = 0.96, and RMSEA = 0.05 [LO 90 = 0.03, HI 90 = 0.07, and PCLOSE = 0.34), and the results suggested that the partial mediation model was significantly better than the full mediation model (Δχ²= 46.11, Δdf = 3, p < .001). Therefore, the partial mediation model was substantiated, providing support for hypothesis 8.

Estimation of mediation effects

To formally estimate the indirect effects of the three environmental stimuli constructs on repurchase intention, we used bias-corrected bootstrapping to examine the confidence intervals of the indirect effects. Traditional methods of significance testing for indirect effects assume a normal distribution of the product term in the population (Preacher and Hayes 2004). Such an assumption is normally violated, resulting in biased or unreliable results (MacKinnon, Lockwood, and Williams 2004). A comparison of different methods for testing indirect effects has resulted in a recommendation for the use of asymmetric confidence intervals derived from bootstrapping (MacKinnon 2008; MacKinnon, Lockwood, and Williams 2004). The bias-corrected bootstrap method has been consistently supported as the best method for generating confidence intervals for statistical inference in mediation analysis (MacKinnon, Lockwood, and Williams 2004). Therefore, we used this method for our estimation. Bootstrap results based on 5,000 samples indicated that perceived enjoyment significantly mediated the relationships between authenticity and repurchase intention (90% confidence interval for the indirect effect of perceived enjoyment [0.018, 0.152]), and between home benefits and repurchase intention (0.001, 0.096).

Study 2

Procedure

Following the same data collection procedure adopted in Study 1, we conducted Study 2 through a different consumer panel via Amazon’s Mechanical Turk (MTurk). MTurk is an online crowdsourcing website that connects businesses (i.e., requesters) and potential workers to perform on-demand tasks. In recent years, scholars have used MTurk extensively to collect research data. The data collected through MTurk are as reliable as those gathered via conventional approaches, and participants are more demographically varied than typical samples collected via the Internet (Buhrmester, Kwang, and Gosling 2011). Researchers can use MTurk data to make generalizable and credible inferences (Berinsky, Huber, and Lenz 2012; Buhrmester, Kwang, and Gosling 2011; Levay, Freese, and Druckman 2016). With respect to sampling criteria, only individuals who had traveled domestically or internationally and stayed at an Airbnb property within the past 12 months were qualified to participate in this study. In total, 263 respondents completed the survey. We removed 60 cases because of incomplete responses, leaving 203 usable cases. Considering the complexity of the proposed model, the sample size was deemed acceptable (Fabrigar, Porter, and Norris 2010; Hair et al. 2006). Our final sample also exceeded the required minimum sample size of 199 as estimated in the power analysis.

Results

Of the 203 respondents, 56.9 percent were female and 67.7 percent were between ages 30 and 60 years, with 0.5 percent older than 60 years and 31.8 percent younger than 30 years. In terms of annual income, 11.8 percent of the sample earned under $20,000, 76.8 percent between $20,000 and $100,000, and 11.4 percent more than $100,000.

The analysis of nonresponse bias showed no differences between early and late responses in terms of respondent characteristics and all measured items, indicating that nonresponse bias was not a major concern.

Measurement model

The results of the multivariate normality check showed that the critical ratio for Mardia’s (1970) normalized estimate of multivariate kurtosis was greater than 5 (Bentler and Wu 2005), indicating that the data did not follow a multivariate normal distribution. We therefore used bootstrapping to produce parameter estimates for subsequent analyses (Byrne 2016). The measurement model resulted in good model fit (χ² = 93.89, p < .001, df = 55, χ²/df = 1.71, CFI = 0.97, NFI = 0.93, TLI = 0.95, and RMSEA = 0.06 [LO 90 = 0.04, HI 90 = 0.08, and PCLOSE = 0.22]). Table 5 presents the results.

Table 5.

Results of the Measurement Model (Study 2).

Construct/Item	SL	CR	SR	AVE
Authenticity, adapted from Guttentag et al. (2018).			0.98	0.61
Mean=5.49, SD=0.89
1. Airbnb tends to provide an authentic local experience	0.84	NA
2. Airbnb tends to offer a unique, one-of-a-kind experience	0.78	20.50
3. Airbnb tends to offer an accommodation that integrates local cultures	0.71	14.49
Home benefits, adapted from Guttentag (2016)			0.97	0.62
Mean=5.71, SD=1.01
1. Airbnb provides guests with homelike amenities	0.82	NA
2. Airbnb provides a “homely” feel during the stay	0.76	17.18
3. Guests can feel home and relax at Airbnb.	0.79	16.50
Social interaction, adapted from Stors and Kagermeier (2015) and Tussyadiah (2015)			0.91	0.66
Mean=5.24, SD=1.18
1. Airbnb offers guests opportunities to interact more directly with local people	0.86	NA
2. Airbnb offers guests good social opportunities with the host	0.77	9.95
Perceived enjoyment, adapted from Venkatesh, Thong, and Xu (2012)			0.98	0.60
Mean=5.71, SD=0.98
1. Staying at Airbnb is fun	0.84	NA
2. I would enjoy using Airbnb	0.71	13.58
3. Airbnb offers an entertaining accommodation experience	0.77	15.44
Repurchase intention, adapted from Jeong, Oh, and Gregoire (2003)			0.95	0.64
Mean=5.66, SD=1.09
1. Airbnb will be one of the accommodation options I will consider for my next trip	0.77	NA
2. I would recommend Airbnb to others as a viable lodging option	0.83	18.85

Note: Model fit statistics: χ² = 93.89, p < .05, df = 55, χ²/df = 1.71, CFI = 0.97, NFI = 0.93, TLI = 0.95, and RMSEA = 0.06. All estimates were generated using bias-corrected bootstrapping using 5,000 subsamples. SL = standardized loadings; CR = critical ratio; SR = scale reliability; AVE = average variance extracted; CFI = comparative fit index; NFI = normed fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation.

As Table 5 shows, construct reliability and validity were achieved. All 13 items had standardized loadings greater than 0.70 (Hair et al. 2006). The bootstrap critical ratios of the indicators were statistically significant at p < .001 and all AVEs were greater than 0.50 (Fornell and Larcker 1981), substantiating the convergent validity of the constructs. The square root of the AVE for each factor is greater than any value of the pairwise between-construct correlations, thereby lending support for discriminant validity. Furthermore, Table 6 reveals that all models constraining the pairwise construct correlations to unity resulted in worse model fit (χ² >3.84 at α = 5%) than the models freely estimating the correlations, suggesting additional evidence for discriminant validity (Jöreskog 1971). Following the same analytical procedure adopted in Study 1, we found that common method variance was not a major issue.

Table 6.

Discriminant Validity Analysis from Chi-Square Difference Tests (Study 2).

Comparison		Unconstrained Model		Constrained Model		Chi-Square Difference		Discriminant Validity
Comparison		χ²	df	χ²	df	Δχ²	Δdf	Discriminant Validity
AU	HB	7.95	8	37.76	9	29.82	1	Yes
	SI	1.95	4	27.20	5	25.25	1	Yes
	EN	12.81	8	69.39	9	56.58	1	Yes
	RI	4.64	4	42.47	5	37.83	1	Yes
HB	SI	10.51	4	40.53	5	30.02	1	Yes
	EN	20.57	8	77.62	9	57.04	1	Yes
	RI	10.84	4	39.55	5	28.71	1	Yes
SI	EN	14.16	4	60.83	5	46.67	1	Yes
	RI	.07	1	42.61	2	42.54	1	Yes
EN	RI	9.15	4	66.73	5	57.56	1	Yes

Note: AU, authenticity; HB, home benefits; SI, social interaction; EN, perceived enjoyment; RI, repurchase intention.

Structural model

The overall structural model resulted in good fit to the data (χ² = 93.89, p < .05, df = 55, χ²/df = 1.71, CFI = 0.97, NFI = 0.93, TLI = 0.95, and RMSEA = 0.06 [LO 90 = 0.04, HI 90 = 0.08, and PCLOSE = 0.22]). Consistent with the results from Study 1, the path coefficients suggest that four paths were significant (i.e., hypothesis 1: perceived enjoyment to repurchase intention, hypothesis 2: authenticity to perceived enjoyment, hypothesis 4: home benefits to perceived enjoyment, hypothesis 5: home benefits to repurchase intention). Table 7 presents the results.

Table 7.

Standardized Estimates and Results of Hypothesis Testing (Study 2).

Structural Path	Standardized Coefficient	Lower CL	Upper CL	p Value	Conlcusion
Perceived enjoyment → Repurchase intention (hypothesis 1)	0.22*	0.07	0.42	.03	Supported
Authenticity → Perceived enjoyment (hypothesis 2)	0.31*	0.06	0.47	.03	Supported
Authenticity → Repurchase intention (hypothesis 3)	0.14	−0.15	0.41	.41	Not supported
Home benefits → Perceived enjoyment (hypothesis 4)	0.26*	0.04	0.42	.04	supported
Home benefits → Repurchase intention (hypothesis 5)	0.53***	0.27	0.75	.00	Supported
Social interaction → Perceived enjoyment (hypothesis 6)	0.06	−0.11	0.16	.58	Not supported
Social interaction → Repurchase intention (hypothesis 7)	−0.08	−0.21	0.06	.41	Not supported
R²:
Perceived enjoyment: .32
Repurchase intention: .56

Note: Model fit statistics: χ² = 93.89, p < .05, df = 55, χ²/df = 1.71, CFI = 0.97, NFI = 0.93, TLI = 0.95, and RMSEA = 0.06. All estimates were generated using bias-corrected bootstrapping using 5000 subsamples. CL, 90 percent confidence limit. CFI = comparative fit index; NFI = normed fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation

Significant p < .05; ^**significant p < .01; ^***significant p < .001.

Testing competing models

Following the same model testing procedure, we again compared the full mediation model (χ² = 139.16, df = 58, χ²/df = 2.40, CFI = 0.93, NFI = 0.89, TLI = 0.91, and RMSEA = 0.08 [LO 90 = 0.07, HI 90 = 0.10, and PCLOSE = 0.001]) with the partial mediation model (χ² = 93.89, df = 55, χ²/df = 1.71, CFI = 0.97, NFI = 0.93, TLI = 0.95, and RMSEA = 0.06 [LO 90 = 0.04, HI 90 = 0.08, and PCLOSE = 0.22]), and the results suggested that the partial mediation model was significantly better than the full mediation model (Δχ²= 45.27, Δdf = 3, p < .001).

Estimation of mediation effects

To estimate the indirect effects of environmental stimuli on repurchase intention, we again used bias-corrected bootstrapping to obtain the confidence intervals of the indirect effects. Analysis of 5,000 bootstrap samples indicated that perceived enjoyment significantly mediated the relationships between authenticity and repurchase intention (90% confidence interval for the indirect effect of perceived enjoyment [0.011, 0.172]), and between home benefits and repurchase intention (0.013, 0.162). Figure 2 presents the results of the separate empirical studies.

Figure 2.

Results of two studies for final structural model.

Discussion and Implications

Peer-to-peer platforms offer services in many sectors including lodging, transportation, food service, and shopping, all of which are core components of the tourism industry. New and innovative platforms enable visitors to autonomously arrange a tailor-made holiday, providing them opportunities to plan to enjoy local culture in line with relevant trends and preferences. This new business model has also made it more common for people to exchange things such as unused goods, skills, space, money, or peer-to-peer travel and homes through swapping or renting (Andriotis and Agiomirgianakis 2014). Building on prior research that recognizes the important role of perceived enjoyment in Airbnb accommodation, we conducted two separate studies to examine the role of Airbnb’s environmental attributes and specifically determine which Airbnb attributes drive enjoyment and repurchase intention. Results suggest that strong authenticity and home benefits lead to enhanced perceived enjoyment and, in addition, that home benefits were found to determine future repurchase intention. Furthermore, our mediation analyses across the two studies consistently suggested that the partial mediation model was the best-fitting model, which means that authenticity affects repurchase intention only indirectly through perceived enjoyment while home benefits affect repurchase intention both directly and indirectly through perceived enjoyment. Unexpectedly, however, social interaction appeared to have no role in determining perceived enjoyment and repurchase intention.

Theoretical Implications

This study has several theoretical implications. Originating from the S-O-R framework from the experimental psychology and learning literatures (Jacoby 2002), the M-R model conceptualizes causal relationships among emotions elicited from different environment stimuli and their influence on human behaviors in the environment. Drawing on this theoretical framework, we proposed and tested a conceptual model that captures key Airbnb atmospherics as the primary environmental stimuli affecting visitors’ perceived enjoyment. Previous papers in the hospitality and tourism areas have applied the M-R model to examine the role of environmental stimuli in the creation of emotions and consumer behaviors (Hussain and Ali 2015; W. G. Kim and Moon 2009). However, few studies have taken into account the attributes that include social and contextual aspects in the context of Airbnb accommodations. Thus, this study makes a valuable and unique contribution to the Airbnb literature by using a well-established theoretical framework.

While prior research shows inconsistent results for the relationships between Airbnb attributes and repurchase intention (Liang 2015; So, Oh, and Min 2018; Tussyadiah 2016), those studies did not explicitly consider any potential mediators such as perceived value or risks that could potentially uncover significant relationships between Airbnb attributes and repurchase intention. By testing the mediating role of perceived enjoyment, this study presents significant findings through the lens of a proven framework on the theoretical connection between Airbnb attributes and repurchase intention. Study results show that authenticity and home benefits seem to draw enjoyment through which repurchase intention also becomes stronger. From a theoretical perspective, this research extends the current understanding of perceived enjoyment by testing the role of Airbnb attributes and perceived enjoyment in consumer evaluations of Airbnb experiences.

Despite the encouraging results about the role of authenticity and home benefits, social interaction did not significantly influence perceived enjoyment and repurchase intention. This result is against our hypotheses based on the previous studies such as Lutz and Newlands (2018) and Tussyadiah (2016). A plausible explanation is that gradually visitors are renting the whole house rather than sharing with the host; in such a situation, the importance of social interaction between the host and visitors during the stay is likely to diminish. It is also conceivable that, as Airbnb has matured into a frequent choice of accommodation to today’s lodging customers, the authentic value of the host–guest interaction may have gradually faded to the point that it is no longer a novel aspect of Airbnb. Similarly, Airbnb operators are increasingly motivated to run their business like a traditional lodging property, situating themselves away from direct interactions with their guests. Studies are necessary to examine how the importance of Airbnb attributes evolves over time.

This study adds strong evidence to the literature on the role of perceived enjoyment as a mediator between Airbnb stimuli and repurchase intention. Notable was the fact that our two studies in replication provide converging results on the key hypothesized relationships. Both studies confirmed the direct effects of authenticity and home benefits on perceived enjoyment and perceived enjoyment on repurchase intention. In addition, they consistently confirmed no direct effect of authenticity on repurchase intention. Such results coincide with previous research (Liang, Choi, and Joppe 2018), indicating that perceived authenticity plays a critical role in enhancing Airbnb consumers’ repurchase intentions indirectly by reducing perceived risk and increasing perceived value; however, perceived authenticity does not have a direct effect on repurchase intention. Furthermore, the role of home benefits in the model did not appear significant in both data sets. The magnitude of the parameters is similar between the two studies, but the second study sample resulted in somewhat stronger predictive power for both perceived enjoyment and repurchase intention as evidenced in the higher amount of variance explained.

Practical Implications

This study also has several managerial implications. The study’s findings can help practitioners in better understanding how each type of environmental stimulus can contribute to evoking visitors’ enjoyment and eventually affect repurchase intentions. The dynamic characteristics of Airbnb accommodations offer increasing potential for atmospherics to generate positive emotions and ensure positive behavioral intention. Owing to the authentic and homelike style of Airbnb accommodations, guests perceive their stay at Airbnb as unique experiences. The findings suggest that, to heighten visitors’ positive emotions, hosts should pay attention to improving atmospherics—not only physical but also contextual attributes that are related to the destination that they are visiting. Offering an accommodation experience that integrates the local culture is important. Airbnb hosts may do so by providing local information that allows their guests to experience the destination like a local. Other selected personal artifacts such as pictures, books, or decorative items as well as personal contact with the host—either in person or via written message—may also increase the perceived overall authenticity of the experience. The significance of authenticity is consistent with other peer-to-peer settings. For example, a peer-to-peer transaction such as swapping homes offers an opportunity for home swappers to experience local authenticity by staying in a local home and engaging with local society (Forno and Garibaldi 2015).

Besides the relatively low importance of social interaction in the Airbnb accommodations, our study findings could also reflect an increasing trend in the Airbnb context in which more and more consumers are renting the entire house or unit rather than sharing with others. Recent statistics show that more than 70% of the Airbnb listings in many cities were for the entire home or apartment (Inside Airbnb 2019), which has reduced the importance of interacting with the host or other customers during the stay. Although the Internet facilitates direct host–guest communication, research has implied that a host’s physical presence and face-to-face interactions with guests tend to generate more positive emotions and enjoyable experiences (Jani and Han 2015; H. Liu et al. 2016). Similarly, Couchsurfing connects members to a global community of visitors, promoting social exchanges between guests and local residents (hosts) and providing acculturation opportunities that may elicit more positive tourist values and attitudes (Decrop et al. 2018; H. Liu et al. 2016). For instance, community belonging has been found to exert a positive impact on the potential of using car2go, a carsharing service (Möhlmann 2015).

This study also highlights the role of enjoyment in the Airbnb settings. Specifically, enjoyment mediates the effects of Airbnb attributes on repurchase intention. Therefore, adding playful aspects to the environment or service is important. Similarly, providing homelike amenities could make visitors feel at home during the stay, contributing to unique stimuli that are important to consumer evaluations. Those factors can highlight each accommodation’s competitive edge. Overall, the findings of this research reveal that perceived enjoyment is significantly evoked by environmental factors, particularly including authenticity and home benefits.

Limitations and Future Research

This study has several limitations that need to be acknowledged to provide insights for future investigations. First, as the data were gathered from two separate online survey panels and the samples only included participants who had stayed at an Airbnb property in the past 12 months, the results cannot be generalized to all travelers. This study also takes the United States as a geographical context. As such, future research could apply our conceptual model to explore how people from different demographic or cultural backgrounds evaluate Airbnb experiences. Similarly, future studies could examine differences in the latent mean scores of focal constructs and the magnitude of paths between leisure and business segments. In addition, this article investigated repurchase intention but did not specify prior frequency of using Airbnb. Future studies need to look at the dynamic and extent of repeat visitations that could potentially impact customers’ decision-making and evaluative processes.

Second, while this research was based on two separate empirical studies, both used cross-sectional data at a single moment in time. The cross-sectional design may not warrant causal inferences. Therefore, future investigations may need to use experimental designs as a way to better establish the causal relationships and strengthen the validity of the relational results. In addition, longitudinal studies could help future research in assessing visitors’ responses across multiple time points, thereby offsetting recall bias and obtaining further reliability of the findings.

Third, this study adopted the Mehrabian-Russell model (Mehrabian and Russell 1974) to guide our investigation specifically on how physical, contextual, and social attributes contribute to consumers’ evaluation of Airbnb experiences. Among various Airbnb attributes, constructs including authenticity, home benefits, and social interaction are regarded as environmental stimuli. Future studies may need to examine other Airbnb attributes such as price (D. Wang and Nicolau 2017), value (Mao and Lyu 2017), and quality cues (Jang, Farajallah, and So 2020; Xie and Mao 2017). Scholars could consider these characteristics as different stimuli or situational factors and assess how such features may affect consumers’ perceived enjoyment and repurchase intentions. In addition, because of the scope of this study, we did not collect information on whether respondents booked lodging only or also ventured into Airbnb’s new experience-based activities and tours. Thus, the examination of such experiences presents an interesting avenue for future research. Finally, this study explored the emotional state of perceived enjoyment as a mediator between experiential attributes of Airbnb and repurchase intention. While perceived enjoyment was consistently supported to be critical in determining repurchase decisions with respect to Airbnb, it should be noted that emotional states are not confined only to perceived enjoyment. The two other emotional states, dominance and arousal, need to be considered in future studies in order to identify the role of emotions in a broader sense.

Footnotes

Acknowledgements

This research was conducted when the first author was on the faculty at the College of Hospitality, Retail and Sport Management, University of South Carolina.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the College of Hospitality, Retail and Sport Management, University of South Carolina, Columbia.

Author Biographies

Kevin Kam Fung So, PhD, is William E. Davis Professor and Associate Professor at the School of Hospitality and Tourism Management, Spears School of Business, Oklahoma State University, USA. His research interests focus on services marketing, service management, and applications of advanced quantitative methods and analytical techniques. The majority of his research articles have appeared in top-tier hospitality, tourism, and business journals. He serves on the editorial board for nine journals including Journal of Travel Research, Tourism Management, Journal of Hospitality & Tourism Research, Cornell Hospitality Quarterly, and International Journal of Contemporary Hospitality Management.

Hyunsu Kim is a PhD student in the School of Hotel, Restaurant and Tourism Management, University of South Carolina. His research focuses on service management with a particular emphasis on customer dynamics and value creation.

Haemoon Oh, PhD, is Educational Foundation Distinguished Professor and Dean of the College of Hospitality, Retail and Sport Management at the University of South Carolina. He has published extensively in hospitality, tourism, and business journals on topics related to customer satisfaction, service quality, branding, and marketing scales. His current funded research focuses on employee engagement and its organizational impact.

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