# A model of tourists’ loyalty: the case of Airbnb

Lidija Lalicic (Department of Tourism and Service Management, MODUL University Vienna, Vienna, Austria)
Christian Weismayer (Department of Applied Statistics and Economics, MODUL University Vienna, Vienna, Austria)

ISSN: 1757-9880

Publication date: 12 March 2018

## Abstract

### Purpose

Airbnb, a popular peer-to-peer accommodation platform, exceeds the yearly revenue of hotel chains, such as Marriot and Hilton. However, the reason why consumers engage with peer-to-peer accommodations and become loyal is not completely clear yet. This study aims to investigate Airbnb as a service setting. In doing so, more insights into the relevance of concepts, such as service quality (SQ) and hospitality factors, to explain consumers’ behavioral intentions with peer-to-peer accommodations and its effect on loyalty can be gained.

### Design/methodology/approach

Through an online survey among Airbnb users and structural equation modeling, the model connecting the measurement constructs is analyzed.

### Findings

This study shows that SQ and importance of having social and authentic experiences are significant antecedents of tourists’ loyalty toward Airbnb hosting services. Interestingly, perceived economic benefits do not impact the level of loyalty, neither does feelings of perceived reduce risk.

### Originality/value

The theoretical contributions reveal tourists’ behavioral patterns in the peer-to-peer accommodation context influenced by standard service factors used for other types of accommodations. This study has particular implications for the accommodation sector when segmenting customers according to their needs and designing appropriate marketing strategies.

### 研究目的

Airbnb是目前流行的大众提供住宿民宿的在线平台，其年收益已经超过很多连锁酒店，包括万豪和希尔顿在内。然而， 关于消费者如何消费和热衷于这种Airbnb民宿的原因还知之尚浅。本研究主要以Airbnb为题进行研究，其研究结果深刻讨论Airbnb相关概念，比如服务质量和酒店管理因素等， 以解释分析消费者行为动机和游客忠诚的联系纽带。

## Keywords

#### Citation

Lalicic, L. and Weismayer, C. (2018), "A model of tourists’ loyalty: the case of Airbnb", Journal of Hospitality and Tourism Technology, Vol. 9 No. 1, pp. 80-93. https://doi.org/10.1108/JHTT-02-2017-0020

### Publisher

:

Emerald Publishing Limited

## 1. Introduction

The role of loyal consumers in the changing landscape of the hospitality industry is a crucial strategic asset. Given an expected revenue growth of Airbnb by $3.5bn in 2020, exceeding hotel chains such as Marriot and Hilton, this matters more than ever before (Gallager, 2017; Guttentag and Smith, 2017). Recent studies demonstrate how the rise of this alternative accommodation type impacts hotel revenues, tourism employment, destination management and tourist behavior (Choi et al., 2015; Guttentag, 2015; Tussyadiah, 2015; Fang et al., 2016; Guttentag and Smith, 2017). Predominantly, tourists book an Airbnb accommodation because of the social interaction with the host and to explore the destination in an authentic way. Furthermore, guests like to book Airbnb accommodations because of the favorable prices and the chance to contribute to the sharing economy (Guttentag, 2015; Tussyadiah, 2015, 2016). Guttentag et al. (2017) called for more research that explains why tourists choose peer-to-peer accommodations over hotels, but more importantly, turn into loyal consumers. A clear understanding of consumers’ engagement with innovative business models, such as Airbnb, will help marketers design informed strategies, figure out how to best address consumers’ needs and adjust their service and product offerings accordingly (Tussyadiah and Pesonen, 2015; Oskam and Boswijk, 2016; Varma et al., 2016; Liu and Mattila, 2017; Wang and Nicolau, 2017). To gain insights into consumers’ behavioral intentions, the relevance of concepts, such as service quality (SQ) and hospitality factors, needs to be tested. In hospitality research, there is comprehensive literature on service factors enhancing tourists’ loyal behaviors. However, little is known whether consumers choosing peer-to-peer accommodations over hotels also evaluate them in the same manner (Tussyadiah and Zach, 2015; Tussyadiah, 2016; Heo, 2016).Thus, which service-related elements influence tourists’ behavioral intentions for peer-to-peer accommodation, such as Airbnb, remains unclear. Therefore, the present study aims to investigate which factors contribute to tourists’ loyalty toward the Airbnb accommodation usage. Theoretical contributions reveal tourists’ behavioral patterns in the peer-to-peer accommodation context influenced by factors of the standard service framework used for other types of accommodations. For practitioners, recommendations will be given on how to adjust their marketing and hosting strategies to facilitate tourists. ## 2. Theory building ### 2.1 Service quality Tourists who engage in peer-to-peer sharing are highly educated with a higher income, travel frequently and are open to different types of accommodations (Tussyadiah, 2015). Furthermore, these tourists show a higher level of innovativeness, aim to be different and try out new ways of traveling (Guttentag et al., 2017), they are called “novelty seeking travelers.” However, these tourists might, thus, be accustomed to different standards of quality as they are not used to less conventional types of accommodation and expect a similar quality standard in alternative accommodation settings (Tussyadiah and Zach, 2015; Guttentag et al., 2017). Tussyadiah and Zach (2015) performed an in-depth analysis of reviews of peer-to-peer accommodations compared to hotels and stated that for tourists, peer-to-peer accommodations with basic services are still comparable to a hotel. This implies that the basic service framework is applicable. Service literature demonstrates consumers’ past experience as a significant reference point for analyzing SQ (Cronin et al., 2000). At first, SQ is perceived as the primary determinant of consumer satisfaction and behavioral intentions (Bodet, 2008; Cronin et al., 2000; de Ruyter et al., 1997). Second, in a hospitality context, there is an obvious link between quality, value and loyalty (Cronin et al., 2000; Tarn, 1999), as the theory of disconfirmation demonstrates that customer satisfaction depends on the extent to which the received service matches expectations (Shankar et al., 2003). The most prominent model to assess consumers’ confirmation is the SERVQUAL instrument. De Ruyter et al., 1997 referred to the instrument as a function of the different levels between consumers’ expectations and perceptions. Five key determinants of SQ are identified reliability, responses, assurance, empathy and tangibles. Various studies demonstrated how SQ directly affects various behavioral intentions (Parasuraman et al., 1988; Oliver, 1999; Liu et al., 2000; Bodet, 2008). Studies in an Airbnb setting highlight the role of amenities to be subjective to guests’ intentions to return (Tussyadiah and Zach, 2015; Liang et al., 2017). Given the recent entry of Airbnb, the framework needs to be further developed, and preferably tested in the light of existing service theories. Thus, in the case of understanding consumers’ behavioral intentions toward peer-to-peer accommodation, the concept of perceived SQ cannot be ignored but rather has to be tested for its appropriateness. Following the disconfirmation paradigm, it is expected that consumers’ SQ evaluations positively influence tourists’ loyalty toward Airbnb. This leads to following hypothesis: H1. Service quality has a direct positive effect on loyalty. ### 2.2 Hospitality hosting behavior The interaction and home benefits are one of the main motivations for consumers to book an Airbnb accommodation (Guttentag et al., 2017). In a traditional accommodation setting (i.e. hotel), these elements would be referred to as “peripheral service elements,” that enhance our understanding of consumer repurchase behavior (De Ruyter et al., 1997). However, as demonstrated by Guttentag et al. (2017), for peer-to-peer accommodations, they belong to the core of the experience, leading tourists to enjoy Airbnb much more. Research performed on guests’ homes demonstrates that the role of the host determines the guests’ overall experience and intentions to return (Wang et al., 2007). The host’s competence is called the hospitality hosting behavior (HHB), which is explained as a service provided by the host to guests. This means that the hosts have the responsibility to provide an environment where guests feel secure and comfortable (Ariffin et al., 2013; Lashley et al., 2004). Furthermore, the quality of the hosts’ behavior not only develops a strong bond between the tourist and the accommodation but also enhances the emotional value of the experience (Arrifin and Maghzi, 2012; Su and Wall, 2010; Ariffin et al., 2013). Recent studies have demonstrated the significant role of hosts in choosing an Airbnb accommodation over other types of accommodation (Guttentag and Smith, 2017). Tussyadiah (2016) showed how tourists value the distinct role of the host and accompanying intimacy as a part of their travel experience. Guttentag et al. (2017) indicated that tourists value the hosts’ efforts and hereby want to return to a similar type of accommodation setting. In other words, it is anticipated that the quality of the hosts affects tourists’ loyalty toward the use of Airbnb, which leads to the second hypothesis: H2. Hospitality hosting behavior has a direct positive effect on loyalty. ### 2.3 Perceived risk reduction Consumers often calculate the tolerance of the risk, which is positively related with the perceived benefits. Thus, the greater the perceived benefit, the more willing the consumer is to take the risk (Mitchell and Vassos, 1998). Also, consumers tend to perceive risk they are familiar with as less harmful (Mitchell and Vassos, 1998). The extent of the risk, which implies the number of other consumers exposed to the risk, shapes the consumers’ risk aversion behavior (Slovic, 1993; Mitchell and Vassos, 1998). Hotels and restaurants, for example, imply various risk reduction methods associated with food safety, such as quality assurance, brand image and loyalty, and also word-of-mouth and price reduction. Yeung and Morris (2001) demonstrated that the likelihood that consumers will purchase again is much higher if the perceived risk is lower. In the case of Airbnb, consumers risk perceptions are related to their concerns about receiving poor-quality service. Studies related to the Airbnb review-system demonstrated that this helps consumers to feel more secure and safe when booking and staying at someone’s home (Liu and Mattila, 2017). For example, a trustworthy host photo has a positive effect on consumers’ intentions to book (Ert et al., 2016). Furthermore, the level of transparency about the host behavior while hosting guests facilitates risk reduction for the booking process (Lee et al., 2015; Ert et al., 2016). Also while hosting, the host has a significant role to deal with risk reduction of their guests (Tussyadiah and Zach, 2015; Guttentag et al., 2017; Liang et al., 2017). Hence, it is suggested that: H3. Perceived risk reduction has a direct positive effect on loyalty. ### 2.4 Economic and social authentic appeals Tourists’ expenditures are related to motivations and experiences (Alegre and Cladera, 2010). Consumers spending money wisely on accommodation, food or trips relevant to their holiday tend to perceive their trip as more satisfying, resulting in a likelihood of repurchasing these elements in the future again (Díaz-Pérez et al., 2005). In addition, the motivation to travel influences the number of days spent at a destination (Alegre and Cladera, 2010). In an Airbnb setting, consumers tend to stay longer and seek for authentic experiences by staying in more residential neighborhoods (Tussyadiah, 2016). On top of that, consumers who believe that engagement in sharing practices provides them with economic benefits, which then answers the consumers’ call for a less costly but similar or higher value and, thus, positive experiences (Hamari et al., 2015). In line with the tourist expenditure literature, it is anticipated that: H4. Economic appeal has a direct positive effect on loyalty. Various authors show that it is the desire for community and social interaction that drives many customers to choose peer-to-peer accommodations over any other type of accommodation (Ert et al., 2016; Guttentag, 2015; McArthur, 2015; Tussyadiah, 2015). Another reason for tourists to engage in peer-to-peer accommodation, according to Tussyadiah (2015), is the novelty-seeking aspect. Tourists tend to strive to fulfill their desire for new experiences, and to satisfy their curiosity and novelty seeking (Sweeney and Soutar, 2001; Tussyadiah, 2015, Liang et al., 2017). According to Cohen (1988), it is the value of authenticity that drives tourists to travel to different places in new ways. Various authors refer to authenticity as a motivational force that influences tourists to make novel choices (Wang, 1999; Steiner and Reisinger, 2006). Furthermore, authenticity is perceived as an input of tourist behavior that subsequently leads to loyalty (Grayson and Martinec, 2004). In the framework of this study, authenticity is sought through enjoyment, escape and novelty seeking by booking peer-to-peer accommodations as well as interacting with the local host community (Tussyadiah, 2015; Kolar and Zabkar, 2007). Liang et al. (2017) demonstrated how perceived authenticity of the experience influences perceived value and behavioral intentions, while choosing an Airbnb accommodation. Therefore, it is suggested that: H5. Social authentic appeal has a direct positive effect on loyalty. ## 3. Methodology ### 3.1 Data collection and sampling Data were gathered in May 2016 by means of an online survey in English facilitated by SSI Sawtooth software. The online questionnaire was stored on a Web server running in Austria and was distributed with the help of international bachelor students via social media channels. The survey was designed in a way that anonymity and confidentiality of the respondents were ensured. There were no participation incentives given to the respondents. Given that users can only book an Airbnb accommodation through its website, the online survey mode is the best option to reach the potential target group. Because of the online data collection approach, convenience sampling is the only option and commonly used in studies analyzing peer-to-peer settings, such as Airbnb (Leiner, 2014; Varma et al., 2016; Pezenka et al., 2017). Furthermore, the Airbnb platform is interlinked with social media platforms, such as Facebook and Google+. This implies that users can be reached through those platforms too (Varma et al., 2016; Pezenka et al., 2017). Consequently, respondents were invited via a link to the online questionnaire through the before-mentioned platforms. As a probabilistic attempt was not possible because of an undefined entire population of Airbnb users, profile comparisons with Airbnb user characteristics found in the literature were conducted and later on in the descriptive result section further elaborated. With regard to the sampling appropriateness, at least one of the detected studies comes close to a valid online random selection approach (Zekanovic-Korona and Grzunov, 2014). Within the social media spaces, an inclusion criterion was implemented so that only those respondents who had used Airbnb in the past were presented with Airbnb-related questions. The following question being the first one was used to separate users from non-users: “Have you used Airbnb?” (scale: yes vs no). A total of 557 respondents classified themselves as Airbnb users in response to this question with an average booking frequency of 2.7 times. ### 3.2 Questionnaire development All constructs were measured in an Airbnb setting. Respondents were asked to evaluate their Airbnb experiences in general rather than focusing on a specific overnight stay or destination at an Airbnb accommodation. They rated the importance of five dimensions: service quality, hospitality hosting behavior, perceived risk reduction (PRR), social authentic appeal (SAA) and economic appeal (EA) on a five-point rating scale (1 = extremely unimportant, 2 = unimportant, 3 = neutral, 4 = important and 5 = extremely important). See Table II for an overview of items per construct. The majority of participants of the current study were born in Europe (324), 188 in Asia, 21 in North America, 4 in Africa and Oceania and 3 in South America. A total of 13 respondents did not indicate the country they were born in. In comparison with typical Airbnb users, the average age of 24.14 years matches with the age range of millennial travelers situated between 18 and 35 years (Airbnb, 2016; Pentescu, 2016), or the average age of 25 years determined from another random sample (Zekanovic-Korona and Grzunov, 2014). The majority indicated that they were single (336), 170 lived in a partnership, 47 were married and 4 divorced. Regarding the highest level of education completed, the majority had finished high school (222). The main profession was classified as student (367), and 132 were employed. The higher number of students in the sample can be explained by a few recent trends. A recent study based on a sample of nearly 4,800 US adults showed that sharing economy service users, such as rideshare or home-sharing users, are likely to be college educated (Independent, 2016). The average income was €1,021.67. Compared with a different peer-to-peer setting, only 24 per cent of people having a household income of +75,000$ use ride-hailing or home-sharing services (Independent, 2016). A lately published study (Airbnb, 2016) identified that millennials tend to spend money on experiences, travel more often and seek for authentic experiences, even though they have a low income.

Respondents annually traveled five times on average, with an average accommodation price of €95.17/night. They primarily traveled with friends (247).

### 4.2 Validity and reliability

Given that some of the detected measurement constructs were modified according to the study context, Cronbach’s α was determined. The values of all six constructs were close to or above the threshold level of 0.7 (Hair et al., 2006) and therefore deemed acceptable (Table II). Apart from the mean values contained in Table II, composite reliability (CR) values are listed to highlight the measurement construct reliability. Furthermore, Table III lists the average variance extracted (AVE) in its main diagonal, tackling the convergent validity of the measurement constructs. All constructs exceeded the recommended CR threshold of 0.5 (Fornell and Larcker, 1981), whereby two out of the six constructs fell somewhat below the AVE threshold, namely, HHB with a value of 0.398 and EA with a value of 0.446. Furthermore, Table III lists discriminant validity statistics in the lower triangle matrix. Fornell and Larcker (1981) supposed that the AVE of each construct should exceed the shared variance between the constructs. As the used items were collected from different sources to account for as much variance of the dependent variable as possible, overlaps are very likely to occur. However, just three constructs shared more variance (SV) with other constructs compared to the variance explained by its own indicators: HHB with SQ: AVE = 0.398 vs SV = 0.491, SQ with PRR: AVE = 0.578 vs SV = 0.712 and SAA with EA: AVE = 0.446 vs SV = 0.469. This means that discrimination validity is given for 12 out of 15 pairs and is not markedly violated for the remaining three.

Service quality was measured on the basis of So et al.’s study (2016) and adapted to the study setting. (Note: One of the SQ items was deleted because of its low factor loading.) The hospitality hosting behavior was based on a scale proposed by Ariffin et al.(2013) capturing the importance of the hosts’ hospitality skills. Perceived risk reduction was developed on the basis of the main idea of capturing a preferably broad part of dimensions used in the tourism context (Tussyadiah, 2015; Tussyadiah and Zach, 2015). Social authentic appeal as well as economic appeal were measured on a scale developed by Tussyadiah and Pesonen (2015) and Hamari et al. (2015). The loyalty (L) construct was based on Cronin et al.’s (2000) and So et al.’s (2016) scale measured on a five-point Likert scale (1 = disagree strongly, 2 = disagree a little, 3 = neither agree nor disagree, 4 = agree a little and = agree strongly). Responses, a negatively worded loyalty item, were reversed to avoid distortions of e.g. Cronbach’s α values.

### 3.3 Measurement

The structural equation modeling in Figure 1 was estimated using Mplus (Muthén and Muthén, 1998). The underlying measurement models were brought to a latent level by means of confirmatory factor analysis. The weighted least squares means and variance adjusted (WLSMV) estimator was used to derive parameter estimates. It is an appropriate estimator for ordinal scaled variables, like all items used here. Furthermore, WLSMV circumvents non-normality problems and performs better on several characteristics in comparison to other appropriate estimators, such as the robust maximum likelihood estimator or the mean- and variance-adjusted unweighted least square estimator (Li, 2014).

## 4. Results

### 4.1 Sample description

Table I provides a detailed overview of the sample. Of the 557 respondents, 194 were male and 363 female. The overhang of women compared to men matches with a study that placed a link to an online questionnaire on the official Airbnb Facebook wall (Zekanovic-Korona and Grzunov, 2014).

### 4.3 Model testing

Figure 1 portrays the whole model as hypothesized, including the measurement models and the structural model. Standardized parameter estimates for factor loadings, correlation coefficients and regression weights are accompanied by their standard errors. According to the most often used fit indices (Hu and Bentler, 1999), the results come close to a good model fit. Recommended thresholds are >0.9 and >0.95, respectively, for Tucker–Lewis index (TLI) and the comparative fit index (CFI), and <0.06 for the root mean squared error of approximation (RMSEA). For the actual model, CFI (0.909) and TLI (0.896) are satisfactory, and RMSEA is close to the acceptable cutoff value (0.089). The p-value derived from the χ2 distribution is significant indicating a poor fit of the overall model. However, for the WLSMV estimator dealing with ordered categories, it is unreliable.

All five importance dimensions show significant correlations between each other (p < 0.001) and therefore some overlapping or related content is determined. Out of these five important constructs, two significantly influence loyalty (L), namely, the SAA (0.377, p < 0.001) and the SQ (0.332, p = 0.043). Thus, H1 and H5 are accepted, whereas H2, H3 and H4 cannot be accepted. Furthermore, 16 per cent of the variance (R2) of loyalty (L) can be explained by the independent measurement constructs. If importance of the SAA increases, loyalty (L) increases too. The same is true for the SQ. The underlying items of loyalty (L) have high communalities, namely, 93.2 per cent for l1 (“I would recommend Airbnb again”) and 86.8 per cent for l2 (“I will use Airbnb again”). The third loyalty variable l3 (“I will switch from Airbnb to another service provider.”) shows a very low communality value of 10.9 per cent. However, this item tackles switching behavior.

## 5. Discussion

### 5.1 Conclusion

The growing research on Airbnb demonstrates the various motivations of engaged consumers, effects on employment and adoptive pricing strategies for the hospitality industry. However, the important question of which factors led consumers to develop loyal behaviors toward the Airbnb community has not been fully answered. This study integrated service-related factors, such as SQ and hosting behavior. Furthermore, more context-related factors, such as PRR, were integrated to assess their effect on the behavioral intentions. In doing so, the study was able to enhance existing studies related to Airbnb guests’ satisfaction and behavioral intentions.

### 5.2 Theoretical contributions

The first insights derive from the service perspective taken in this study. It turns out that the hosting quality is positively evaluated by the respondents. However, even though tourists positively analyze items representing this concept, this does not seem to influence the intentions to engage with Airbnb experiences or to repurchase an accommodation from the Airbnb community in the future again. Interestingly, the second service-oriented variable, SQ shows positive evaluations and subsequently leads to higher levels of loyalty. This study confirms that in sharing practices, a significant link between quality and loyalty is present. This hints at the first suitability of the study at hand, namely, that generic service concepts can also be used to explain consumer behavior in a peer-to-peer accommodation context.

Second, the context-related factors reveal contradicting outcomes. For example, the perceived economic benefits do not influence Airbnb guests’ loyalty, as supposed to other studies in the field (Hamari et al., 2015). Furthermore, because of an increase in demand, there is a large supply creating a fierce competition, forcing hosts to set the standards high and adjust the prizes accordingly (Ikkala and Lampinen, 2014). Given these developments, where Airbnb homes are commercializing, the EA is of less relevance for consumers. Thus, this can be one explanation of why EA is not useful for explaining loyalty. Furthermore, a recent study of Airbnb demonstrated that most millennials would prioritize travel over buying a home or paying off debt (Airbnb, 2016). Expenditures during the holidays are of less importance compared to the experience as “travelling is a core of their identity” and they care about extra amenities they pay for in addition to get the experience they desire (Airbnb, 2016).

The role of PRR does not determine loyalty. Jiang et al. (2017) found a direct effect of perceived risk on behavioral intentions; however, PRR does not seem to influence loyalty. Guests seem to value the experience of social and authentic items much more, as they lead to higher levels of loyalty in this study. This is partially in line with Tussyadiah and Pesonen’s (2015) study demonstrating tourists’ engagement because of social appeal. Also Guttentag et al. (2017) demonstrated values such as interaction, novelty and authenticity to be important explanatory variables for Airbnb guests. In particular, the concept of authenticity shows to be a dominant factor to provide a better understanding of consumers’ involvement and perceived risk levels (Hamari et al., 2015; Liang et al., 2017). This study furthermore confirms that consumers tend to become more loyal if they were able to experience the local culture and perceive a sense of authentic experience, and thus, are willing to take the risk of trying out a peer-to-peer accommodation rather than a standard hotel. This is in line with Jaing et al.’s study (2017) showing how perceived authenticity mediates the perceived risk and subsequently leads to behavioral intentions. Given the early stage of research related to the Airbnb phenomenon, this study was able to assess a set of factors to explain the concept of loyalty for the Airbnb community in more details (Guttentag et al., 2017; Tussyadiah, 2016; Liang et al., 2017).

### 5.3 Managerial implications

This study has various implications for the Airbnb community. As SQ plays an important role in engaging consumers in Airbnb experiences, hosts should be aware of the fact that guests request a specific standard, besides the interactive part of the hosting experience. As shown by this study, SQ is still an important competitive element for any type of accommodation, also for Airbnb homes. Airbnb can provide hosts with an SQ check-list to ensure standardization and quality assurance across manifold Airbnb listings. Furthermore, hosts might benefit from possible trainings or workshops around the role of hosting and service management to provide what the guests ask for. Airbnb has a great consumer service support; however, the hosts have to be informed and guided as well and respond to these expected standards of SQ.

Furthermore, as this study shows, hosts have to continuously focus on enhancing the social and authentic experience in the destination as this positively influences guests repurchase intentions. Thus, for hosts it is a necessity to incorporate services, such as local guidance, offering information or providing an easy way to explore the city, like city guides designed by local hosts. These attempts lead to loyal behavior. For marketers, the role of authentic and social appeal might lead to new tourism offerings that allow for experiencing these elements more in-depth. Airbnb can develop partnerships with local initiatives to develop a community where tourists are much more integrated. Sharing then goes beyond accommodation but more into the local life experiences too.

### 5.4 Limitations and future research

This study faces some limitations that are worth mentioning. First of all, replication of this study with a larger sample is needed for the generalizability of the results. Replication studies making use of different data collection techniques could confirm or disprove present findings. Alternatively, future guest profile studies might allow for the weighting of one’s own sample according to the population’s characteristics. Tackling the non-response problem could enhance the quality of the convenience sample. In addition, follow-up studies could incorporate a pre-test to rule out item ambiguity. The operationalization of social and authentic appeal should be validated, which also holds true for the PRR construct. In addition, future studies should include brand theories that can help to explain the users’ engagement with peer-to-peer accommodation practices as well as brand communalities. Furthermore, brand identification can contribute to the explanation of further behavioral patterns among tourists, in particular price sensitivity. This would allow marketers to continuously design more appropriate marketing strategies for various segments.

Apart from touristic related items, it would be fruitful to incorporate demographic variables in the whole model to embed the proposed model in an objectively measurable context. This study focused on the issue of PRR, whereas future studies could also integrate trust, in particular brand trust, as a mediating factor of user engagement and participation.

Another interesting research avenue relates to booking behavior under the light of social network analysis. As Airbnb is often an agglomeration of tight networks of friends, it would be interesting to see the patterns in booking behavior within such specific networks and the loyalty-measure could be examined in this respect. On a destination level, tourists’ expenditures and behavioral patterns within a destination could be measured through the use of innovative techniques.

Structural model

## Table I.

Descriptive statistics

 Gender Male: 194 Female: 363 Origin (continent) Europe: 324 Africa: 4 Asia: 188 Oceania: 4 North America: 21 South America: 3 Age (years) Mean: 24.14 (min.: 16, max.: 62) Marital status Single: 336 Married: 47 Partnership: 170 Divorced: 4 Highest level of education No high school degree: 6 Bachelor degree: 181 High school: 222 PhD degree: 7 College/vocational degree: 94 Master degree: 40 Other: 7 Profession Employed: 132 Student: 367 Self-employed: 29 Retired: 2 Unemployed: 14 Other: 4 Income Mean: €1,021.67

## Table II.

Measurement constructs and items

Construct (abbrev.) Survey question (abbrev.) Means Composite reliability Cronbach’s α
Hospitality hosting behavior (HHB) The host knows my name and/or nationality (hhb1) 3.23 0.722 0.67
Warm welcome (hhb2) 3.90
The host understands my special requirements (hhb3) 3.93
The host should not try to impress but rather take care (hhb4) 3.79
Service quality (SQ) Host’s responsiveness (sq2) 4.20 0.845 0.77
Host’s assurance (sq3) 4.01
Host’s empathy (sq4) 3.76
Host’s reliability (sq5) 4.43
Perceived risk reduction (PRR) Host’s trustworthiness (prr1) 4.51 0.887 0.80
Financial safeness (prr2) 4.30
Physical safeness (prr3) 4.42
Host’s confidentiality (prr4) 4.09
Social authentic appeal (SAA) To know people from the local neighborhoods (saa1) 2.95 0.875 0.81
Get insider tips on local attractions (saa2) 3.93
Have nice interactions (saa3) 3.77
Understand local culture (saa4) 3.97
Experience local life (saa5) 4.09
Authentic experience (saa6) 4.07
Economic appeal (EA) Support local residents (ea1) 3.46 0.756 0.62
Save money (ea3) 3.90
Value-for-money (ea4) 4.18
Loyalty (L) I would recommend AirBnBagain (l1) 4.31 0.692 0.69
I will use AirBnB again (l2) 4.30
I will switch from AirBnB to another service provider (l3) 3.42

## Table III.

Shared variance and average variance extracted

Latent constructs HHB SQ PRR SAA EA L
HHB 0.398
SQ 0.491 0.578
PRR 0.387 0.712 0.664
SAA 0.321 0.258 0.185 0.546
EA 0.221 0.223 0.201 0.469 0.446
L 0.034 0.077 0.036 0.129 0.048 0.637

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## Corresponding author

Lidija Lalicic can be contacted at: lidija.lalicic@modul.ac.at