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Article

Airbnb Hospitality: Exploring Users and Non-Users’ Perceptions and Intentions

by
Asad Mohsin
1,* and
Jorge Lengler
2
1
School of Management and Marketing, University of Waikato, Hamilton 3240, New Zealand
2
Department of Marketing and Management, Business School, Durham University, Durham DH1 3LB, UK
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(19), 10884; https://doi.org/10.3390/su131910884
Submission received: 22 June 2021 / Revised: 4 September 2021 / Accepted: 10 September 2021 / Published: 30 September 2021
(This article belongs to the Collection Sustainable Hospitality Management and Marketing: From Theory to Survival Reality)
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Abstract

:
Although the use of Airbnb services is growing, research relating to its value, risk, satisfaction, and repurchase intentions involving the millennial generation is scarce. This study investigates actual experience of social, utilitarian and hedonic values, risks, satisfaction, and repurchase intentions of Airbnb millennial consumers. It further assesses perceptions of similar values, risks, and what it would take to generate satisfaction and repurchase intention amid Airbnb non-consumers. PLS Path Modelling is used to test the hypothesised relationships and compare Airbnb consumers and non-consumers. A conceptual model proposing five hypotheses is tested using a dataset of 206 responses representing consumers and non-consumers from New Zealand. The results of the two groups are compared to assess differences in the relationships specified in the proposed conceptual model. The findings have theoretical, managerial, and social implications as it expands the literature by comparing consumers/non-consumers relating to sharing economy and identifies factors that lead to satisfaction and repurchase intentions linked to the millennials, hence generating managerial implications. The findings also suggest social, utilitarian, and hedonic values that have implications for the millennial generation.

1. Introduction

The Airbnb initiated in 2008 in San Francisco (USA) as a peer-to-peer platform has taken commercial accommodation to new heights of innovative experiences, posing new challenges for the traditional hotel/motel industry. Though usage of Airbnb was limited around 2011, it has since grown at an astonishing pace [1,2,3]. In the sharing economy, peers work as service providers (e.g., hosts of Airbnb and Uber drivers) for consumers (e.g., guests using Airbnb and passengers using Uber). While peers make the transaction directly, the company (e.g., Airbnb and Uber) functions as a platform provider [4]. PWC [5] predicted that by 2025 the sharing economy would reach $335 billion, among which peer-to-peer accommodation becoming one of the most significant growth sectors reaching 5.6 million listings as of 30 September 2020. Different from the traditional market, the sharing economy not only brings economic profits, but also plays a significant role in providing environmental and social benefits [6,7,8]. Airbnb is described as a business platform where customers can begin by searching for accommodation based on destinations, travelling time, price, and size. In addition, Airbnb helps with listings based on the neighbourhoods, amenities, additional descriptions, photos, and reviews by previous guests [1].
Airbnb [1] indicates that the growth of Airbnb is linked to several factors, but most noticeable is the huge popularity among the millennial generation consumers, aged between 26 and 40 years old. Millennials are expected to account for two-thirds of all customers and travellers by 2025, and over 80% of millennials demand unique travelling experience that satisfies the hedonic values as their key motive. Mittendorf, Berente, and Holten [9] focus squarely on understanding the behaviours of millennials—the generation important to the sharing economy. Despite the growth and attention in recent years involving the sharing economy globally [10,11,12], research originating from different geographical regions on consumers valuation and attributes towards Airbnb is scarce. Recently, authors So et al. [13], in their study about motivations and constraints of Airbnb consumers, suggest that as Airbnb is still a relatively new form of accommodation with no clear operational standards, consumers opinions will vary by groups. This study adds to the literature millennial groups’ values, satisfaction, and repurchase intentions and compares it with perceptions of those who have never used the Airbnb service.
Several studies have investigated online consumer behaviour, satisfaction, and trust and its impact on repurchase intention [14,15,16]. Airbnb has emerged as a market-leading commercial accommodation platform in the space of sharing economy [17]. As the popularity of Airbnb grew in recent times, so too has the research linked to Airbnb service quality, satisfaction, trust, repurchase intention, perceived value, behavioural intention, loyalty, impact on tourism, and impact on hotel accommodation [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. These studies have also emphasised motivation of the hosts and consumers and their hedonic and utilitarian values, satisfaction levels, and repurchase intention. Airbnb is seen to be different from the traditional hospitality/hotel industry, and, hence, perceived risks linked to uncertainty, being able to get in the house, negative consequences of booking using Airbnb, security and safety have generated interest [6,8,33].
Nonetheless, research on the impact of personal values on millennial consumers of Airbnb remains limited and a comparison between experienced and inexperienced consumers of the Airbnb is almost non-existent [34,35,36]. Airbnb listings vary in many ways, hence, the practical and experimental benefits they offer may not always go hand in hand [36]. So, what are the opinions of the millennial generation about Airbnb in both cases, that is, whether they have used or not used the platform? To answer the question, the main objectives of the study are to:
  • Gauge the impact of social, utilitarian, and hedonic values on satisfaction with the use of Airbnb services across two millennial groups-consumers and non-consumers.
  • Examine the differences on the risk perception and satisfaction with Airbnb service across the two groups of consumers.
  • Gauge the relationship between satisfaction and repurchase intention of Airbnb services across the two groups of consumers.
  • Assess whether the Airbnb consumers and non-consumers have similarities in their behaviour towards the relationships stated above.
Besides, risk perceptions could be different between the Airbnb consumers and the inexperienced non-consumers. Thus, this study responds to the calls that more research in the sharing economy area is needed to clarify the effects of millennials’ values and perceptions linked to Airbnb services [13].The study discusses the outcomes of a newly developed conceptual model and six hypotheses, linked with the two Airbnb groups (see Figure 1). To test hypotheses PLS (partial least squares), path modelling is used because of its flexibility in handling small sample sizes [37].

2. Literature Review

2.1. Sharing Economy-Airbnb

A quick Google Scholar search reveals that the majority of studies linked to Airbnb were published in the last decade. Most of these studies originated from USA, Canada and Europe, and Asia has in recent years has attracted some attention [38]. The sharing economy has been keenly researched. Bostsman and Roger [4] discussed the pros and cons of this phenomenon, Cusumano [39] and Denning [40] studied the growing threats and challenges, Botsman and Rogers [4] classified how the sharing economy can be analysed, such as the environment that encourages the sharing economy functioning and its implications. Aviatal et al. [41] and Bucher et al. [6] emphasised sharing platform functions in terms of human interaction. Hamari, Sjöklint, and Ukkonen [42] focused on the social perspective. They studied the motivation of sharing and its influence on society and regulations. The emergence and expansion of Airbnb within a decade of its inception has generated enormous attention as one of the most innovative business ventures of recent decades with the results noted above [43]. Though the 2020 pandemic impacted demand as with other commercial accommodation, the expansion of Airbnb is evident from the fact that from a very slow start in 2008, it grew to over five million active worldwide listings in 2018 [44]. Guttentag [12,25] provides a literature review on the progress of Airbnb and identifies six themes, e.g., Airbnb guests, Airbnb hosts, Airbnb supply and its impact on destinations, Airbnb regulations, Airbnb’s impact on the tourism sector, and the Airbnb Company.
Although Tussyadiah [45] studied consumer satisfaction within Airbnb, limited attention has been given to the post-purchase behaviour of Airbnb consumers. Some researchers have studied motivation and satisfaction of the consumers of Airbnb, with a limited focus on repurchase intentions linked to the millennial generation. Others have focussed on perceived values as a homogenous group [19]. Considering that Airbnb listings vary in many ways, such variations may have implications for consumer repurchase intentions. Barnes and Mattson [46] highlighted the economic factor as the leading driver, followed by technology familiarity, socio-cultural benefits, and environmental concerns (i.e., sustainability). Similarly, Hamari et al. [42] confirm that sustainability, enjoyment, and economic gains are the main factors that encourage people to participate in a sharing economy.

2.2. Airbnb’s Consumers and Non-Consumers

Research exploring the differences between Airbnb consumers and non-consumers’ is of value to help uncover factors thought important for marketing and the growth of Airbnb. A study by Poon and Huang [47] compared Airbnb consumers and non-consumers’ perception. The study found that both consumers and non-consumers expressed few differences in their perceived importance of accommodation attributes, but the two groups vary in their perceptions and evaluation of Airbnb when compared to hotels. Specifically, they preferred hotels when travelling with family for a shorter trip, whereas Airbnb was preferred when travelling with friends and for longer trips. The current study is very different as it assesses the impact of social, utilitarian, and hedonic values of the millennial generation consumers and non-consumers. It also assesses risk perceptions and repurchase intentions.
Recent studies have explored regulatory issues about Airbnb [48,49,50], discrimination [51,52], Airbnb branding strategies [53,54] and its influence on the hotel industry [35,55]. Airbnb also provides a platform for individuals to participate in hospitality entrepreneurial activities [34] as it has grown as an alternative global accommodation choice [35]. When it comes to the perceived risks, new Airbnb hosts (those with less than six months of hosting experience) are more worried about the security and being taken advantage of by consumers. Ikkala and Lampinen [56] found that to select the right guests, some hosts are lowering their listing prices. This may indicate that the hosts are not only seeking economic benefits but also care for emotional and social values. Guttentag et al. [12,25] have noted that people are more strongly attracted to Airbnb by its practical attributes than by its experiential attributes. These studies inspire to investigate the motives and values of Airbnb consumers.

2.3. Theoretical Background and Hypotheses Development

To investigate the perceptions of Airbnb’s consumers and non-consumers, and their relationship to the social, utilitarian, and hedonic values, this study reviewed two main theoretical backgrounds: (1) the MEC (means-end chain) by Gutman, [57] and (2) the prospect theory proposed by Kahneman and Tversky [58].
The MEC model by Gutman [57] connects customer value and customer behaviour. It presents customers’ cognitive abstractions in three hierarchical levels (i.e., attributes, consequences, and values). Thus, Gutman’s model considers values as the main drivers of customer behaviour, and customers’ choice patterns. Therefore, the MEC model is commonly used as a theoretical framework for investigating the differences between motivations and perceived values [59]. An empirical study of customer value perception is considered essential in the tourism marketing area. Particularly, the means-end model has been widely adopted in investigating the perceived values [60]. Perceived value is defined as the consumer’s assessment of the benefits of the services that lead to satisfaction [61,62].
Lawson et al. [63] suggest that consumers who frequent Airbnb accommodations consider they are pushing a prosocial movement and that social values are important determinants of their satisfaction. Certainly, Guttentag et al. [12] found that social value is an important driver based on the consumers’ increasing awareness of a perceived sustainability in sharing consumption that is related to social or altruistic values. Hence, Airbnb is perceived to be a social and sustainable value-oriented service, which helps in reducing environmental harms, recycling resources, and purchasing products not associated with mass commercial activity. For example, first, it can help to reduce building more motels and hotels. Second, it can also support local residents and the local economy [4,63]. It raises a question whether social value has any impact on satisfaction within the context of Airbnb use amongst millennials and whether it differs between consumers and non-consumers. Thus, our H1 states:
Hypotheses 1 (H1).
Social values will positively affect the satisfaction with the Airbnb experience.
The second construct assesses utilitarian values’ relationship and impact on satisfaction in using the Airbnb services. Motivations that drive an action can be categorised as extrinsic and intrinsic [64]. Extrinsic motivation relates to the performance of an activity, which leads to a utilitarian benefit and thereby utilitarian value. Several authors have identified convenience, broad product offerings, rich product information, and monetary savings as benefits with utilitarian values in online shopping [31,65,66,67,68,69]. The utilitarian values are identified as one of the main drivers of a sharing economy related to accommodation [4,35,70,71,72,73]. Our hypothesis H2 explores the effects of utilitarian values on the satisfaction with Airbnb services:
Hypotheses 2 (H2).
Utilitarian values will positively affect the satisfaction with the Airbnb experience.
The third value being assessed in this study relates to the hedonic benefits. These benefits include adventure, social, enjoyment, and sense of belonging. That Airbnb can create and maintain social bonds can be interpreted as the enjoyment of collaborative consumption [4,35,74,75,76]. Many Airbnb listed accommodations offer hedonic values by being different, unique, and with an authentic home experience [30]. Customers can also fulfil their hedonic values from their prosocial behaviours [77,78,79]. Thus, our hypothesis H3 explores the effects of hedonic values on the satisfaction among Airbnb consumers and non-consumers:
Hypotheses 3 (H3).
Hedonic values will positively affect the satisfaction with the Airbnb experience.
Kahneman and Tversky [58] developed prospect theory to describe the process of an individual’s decision-making under uncertainty and risk. The study by Meng and Weng [80] further pointed out that an individual’s behaviour is based on two aspects, namely the outcome of the action (i.e., loss or gains) and risk attitudes. In general, people are loss-averse, and they perceive loss as a more significant and fundamental impact than getting an equal amount of gains psychologically speaking [58]. In this case, the perceived risks stand for the potential loss while the perceived value means the gains after comparing the benefits and sacrifices received when pursuing a desired outcome [81]. Prospect Theory has been widely used in modelling and predicting human behaviour [82]. Ponte, Carvajal-Trujillo, and Escobar-Rodríguez [83] suggest that perceived value has a positive influence on customer behaviour while perceived risks have a negative impact on customer behaviour.
The research in relation to risk perception linked to Airbnb is scarce [50]. A study by Liang [19] points out that perceived risk in Airbnb is present amongst consumers expecting several negative consequences after booking the Airbnb accommodation. Similarly, the Airbnb hosts also have several negative expectations related to the guests being unknown. In assessing perceived risk specific to Airbnb, Liang et al. [19] found that perceived authenticity was negatively correlated with perceived risk. Do Airbnb consumers often fear whether they will be able to get into the house? Will they be secure? What might be the quality of the accommodation? Forsythe et al. [84] have noted such factors as perceived risks. Those factors could influence the satisfaction specifically in the case of inexperienced potential consumers. On the other hand, most of these factors are entirely secure when one books a quality commercial hotel accommodation and perceived risks are almost non-existent. Consumers who have the intention to use Airbnb are willing to concentrate on seeking the ‘right’ accommodation despite the risk perceptions. Hence, how does perceived risk impact satisfaction in the case of Airbnb consumers? Thus, our H4 tests whether the perceived risk has a negative effect on satisfaction with Airbnb services:
Hypotheses 4 (H4).
Perceived risk will negatively affect the satisfaction with the Airbnb experience.
Park and Kim [85] claimed that when continuous satisfaction is generated amongst consumers, brand loyalty kicks in. Hence, satisfaction with the use of Airbnb will happen when customers perceive Airbnb services as providing value and meeting the needs and criteria of all consumers and hosts. Satisfaction is defined as the confidence in the reliability of a company to significantly and positively influence customer behaviour [86]. Studies by Chou, Chen, and Lin [87] and Ku [88] highlighted that satisfaction can generate trust through the initial as well as the customer retention stage. Further, satisfaction can also have a positive impact on customer loyalty [89]. Chitty, Ward, and Chua [90] defined customer satisfaction as the comparison between the sacrifices (i.e., costs) and rewards (i.e., benefits) during the consumption process. In the hospitality industry, satisfaction is defined as the extent to which the hotel stay meets the customer’s desires, expectations, and requirements [91,92]. Yee et al. [93] find that offering more relational benefits positively impact satisfaction and repurchase intentions in internet banking. The impact of satisfaction has also been noted by Jeong, Oh, and Gregoire, [94] highlighting that, the higher the level of satisfaction, the greater the chance for customers to visit the destination again. As a result, it could also be expected that the positive effect of satisfaction on repurchase intention can be mirrored in the Airbnb service. Thus, hypothesis H5 below:
Hypotheses 5 (H5).
Satisfaction with the Airbnb experience will positively affect the repurchase intention of the service.
Prospect theory [58] suggests that people’s evaluation of alternatives available to them impacts their behaviour, their evaluations are linked to outcomes or expected outcomes and their risk attitudes. Usually based on outcomes and its associated probabilities, people choose the alternative with the highest value. Prospect theory has been applied in modelling and predicting consumer behaviour [81]. How does it affect commercial accommodation alternatives? To expand the literature by studying the perceptions of consumers who have not used the Airbnb services with those who have, H6 is proposed. The hypothesis tests the relationships between perceived social, utilitarian, and hedonic values, perceived risk, and satisfactions of non-consumers with actual experience of consumers.
Hypotheses 6 (H6).
Consumers and non-consumers will have similar behaviour regarding the relationships of the model.
Hence, based on the above six hypotheses, a new conceptual model is proposed to test the relationships between social, utilitarian, and hedonic values, perceived risk, and satisfaction within the context of Airbnb and its millennial consumers. The model also tests the impact of satisfaction on repurchase intention and if there are differences between consumers and non-consumers of the Airbnb (see Figure 1). The model and hypotheses draw upon a literature review [4,18,19,25,35,63,65,66,67,68,73,75,76,77,80,82,89,95] and adapts to the objectives of the current study. The study is expected to contribute some new insights through the test results of the conceptual model proposed and how these relationships compare with non-consumers of Airbnb. An overview of the conceptual framework is presented in Figure 1 below.

3. Methodology

3.1. PLS Path Modelling and Multi-Group Analysis

Analysis is done using PLS (partial least squares) path modelling in this study because of its flexibility in handling small sample sizes [37,96]. There are 103 cases in each subsample. Authors initially estimated the proposed model (Figure 1) using an aggregated dataset combining Airbnb consumers and non-consumers. In the second stage, we tested the effects of the different subsample groups (consumers and non-consumers) in the hypothesised relationships. To do so, we included a discrete moderator variable, which is interpreted as dividing our aggregated dataset into two subsamples [96,97]. The moderator used in our model is the user category (consumers and non-consumers). Thus, as it is not possible to compare groups is PLS using a global criterion, we compared the path coefficients of the subsamples [96]. By doing this, it enabled us to assess path coefficient differences between the two groups.
According to Chin [97], the path coefficient significance differences across subsamples can be tested with pairwise t-tests. To do so, we assessed all subsample models’ goodness-of-fit. Multigroup analysis was carried out using measurement invariance. The permutation results indicate that there are no differences between the users and non-users measurement models.

3.2. Sample and Sampling Measures and Procedures

In order to understand the personal values, perceived risks, satisfaction, and repurchase intentions within the context of Airbnb among millennials, this study uses a survey questionnaire for primary data collection. Typically, research related to consumer behaviour largely uses two sampling methods, namely snowball sampling and purposive sampling [98]. The data was collected in two groups, i.e., consumers who have used Airbnb and non-consumers who have not used Airbnb. A couple of filter questions were asked to assess the suitability of responses. The first filter question was to determine whether they represent the millennial age group (born in the period 1980–1994), the second to determine if they have experienced using Airbnb service to qualify as ‘users’. If respondents had not used Airbnb, they were segmented as ‘non-users’. The respondents who had used Airbnb shared their actual experiences relating to values, risks, satisfaction, and repurchase intention. The non-users with an intention to use Airbnb services in the future were encouraged to share their perceptions relating to the values and risks, assign a value to each measure, and indicate if these would impact their perceived satisfaction. Moreover, they were asked if their perceived satisfaction would generate repurchase intention of the service. All respondents represented the millennial generation born between 1980 and 1994 (26–40 years) in New Zealand. Appendix A presents sample characteristics. A snowball sampling method was used in this case. Social media and personal networking were used to accumulate responses. The total sample size consisting of 206 observations was used to test relationships as stated in Figure 1. A comparison test was also done to assess differences/similarities of the two subsamples: Airbnb consumers (n = 103, 50%) and non-consumers (n = 103, 50%). All survey items are evaluated using a 7 Point Likert Scale, ranging from “1 = strongly disagree” to “7 = strongly agree”. The measures used in the constructs and their sources are identified in Table 1. The impact of values and perceived risk on satisfaction and impact of satisfaction on repurchase intention is tested using hypotheses 1 to 5. Similarities/differences in the behaviour between the Airbnb consumers and non-consumers are tested through hypothesis 6.

4. Results

4.1. Model Estimation and Validity

Table 2 shows the path coefficient results for the relationships estimated in the overall model (aggregated dataset) and each of the subsamples of this study (Figure 1). To estimate the model, we followed the procedures recommended by Sarsted et al. [103] and calculated the model using the bootstrapping procedure with 5000 subsamples. Hence, we used a large number of subsamples on the bootstrapping calculation following PLS’ multi-group analysis procedure as this ensures stability of the results [104].
According to Chin [97], the model and data to compare multiple groups with pairwise t-test have to satisfy three assumptions: (1) the data should not be too non-normal; (2) each sub model has to present acceptable goodness-of-fit, and (3) the measurement across the different sub models must be invariant. The first condition has been verified through an analysis of Skewness and Kurtosis results. The Skewness results for all variables in the measure model fall within the range of −1 to +1 defined by the literature [105]. Regarding the Kurtosis, only one variable (Sense of belonging) displayed result larger than 2, allowing us to consider that thirteen variables have not deviated from the distributional assumption [96,97].
The second condition has been assessed by different criteria. Authors assessed content validity based on the literature review and by consulting experts in the area of tourism management. Based on these initial procedures, it was concluded that the measures used in this study had content validity. Our analyses also reveal that all measures displayed loadings above the cut-off point of 0.70 suggested by the literature [106,107] (Table 1). Regarding the reliability of the constructs, our results indicate that all average variance extracted (AVE) values were above 0.50 (Table 2). Table 2 also shows that all constructs’ composite reliability values were higher than 0.80. Cronbach’s Alpha (α) has been calculated for all constructs on the overall sample and the two subsamples. The results of Cronbach’s alpha indicate that all constructs on the three samples were internally consistent (α ≥ 0.60; [105]). However, Eisinga, Grotenhuis, and Pelzer [108] advocate that Cronbach’s alpha is not an adequate reliability measure for two-item factors. As two of our constructs (Social Values and Perceived Risk) were based on two observed variables each, we followed the procedure proposed by Eisinga et al. [108] calculated the Spearman–Brown coefficient for those two factors. According to the authors, the Spearman–Brown coefficient is the most appropriate reliability statistic for a two-item scale. The results are shown in Table 2. Authors also assessed the discriminant validity of all constructs. This was assessed based on two measures. The first was the analysis of Fornell and Larcker’s [109] approach to assess the construct inter-correlations. Table 3 indicates that construct correlations were significantly different from 1 and the shared variance between any two constructs in the model was less than the average variance explained in the items by the construct. Due to the low sensitivity of traditional methods (i.e., [109] method) in assessing discriminant validity in variance-based structural equational modelling, we have applied Henseler, Ringle, and Sarsted [110]’s Heterotrait–Monotrait Ratio (HTMT) of the correlations. Henseler et al. [110] advocate that HTMT provides more accurate results for establishing discriminant validity in PLS. The HTMT is the average of the heterotrait-heteromethod correlations relative to the average of monotrait–heterotrait correlations of the constructs [110]. The results of our calculation (Table 4) indicate that the HTMT values for any pairs of constructs in the model are lower than the HTMT threshold of 0.85 established by Kline [111]. These results indicate that the constructs have discriminant validity. Further, we checked the whole model and each submodel’s R2 in regards to the two endogenous in each subsample. Table 2 indicates that the R2 values for the endogenous constructs satisfaction with Airbnb service and repurchase intention have been deemed acceptable for the aggregate sample model and each of the subsample models. The R2 values obtained are within the range of results obtained by other studies in the area e.g., [112] and above the cut-off point of 10% defined by Falk and Miller [113]. In addition to the R2 results, we also assessed the overall model’s goodness-of-fit based on Hu and Bentler’s [114] standardised root mean square residuals (SRMR). Henseler et al. [103] propose that the SRMR as a goodness-of-fit measure can avoid model misspecification. The SRMR is the difference between the observed correlation and the model implied correlation matrix. As a result, SRMR allows assessing the average magnitude of the discrepancies between observed and expected correlations as an absolute measure of fit criteria. According to Hu and Bentler [114], SRMR values lower than 0.08 indicate good fit. The overall model, consumers sample model, and non-consumers sample model displayed SRMR values of 0.035, 0.054, and 0.050, respectively (Table 2). Thus, the three models are deemed acceptable.
Finally, we assessed the invariance of the six constructs across our two subsamples. The measurement invariance is assessed by checking the loadings and weights of our indicators to each construct across the different subsamples. The results of the variance of the measurement model checks indicate that all six constructs are invariant (p > 0.10) across the two subsamples.
The following section discusses the results of the hypotheses testing on the overall model (aggregated dataset) and the moderating effects of each of the subsamples.

4.2. Structural Results

The analysis tested the proposed model and the relationships in the overall sample and two subsamples. The results of the model estimation and the goodness-of-fit have been displayed in Table 2. The R2 results of the dependent constructs repurchase intention of Airbnb services and satisfaction with Airbnb services have been deemed acceptable in the three models (aggregate and two subsamples) estimated in this study.
The results of the overall sample (aggregate) model path coefficients indicate that social values (0.288; p < 0.001) have a strong positive effect on satisfaction with Airbnb service. This result provides support for H1.
Our hypothesis H2 proposed that utilitarian values have a positive effect on the satisfaction with the Airbnb experience across the overall sample. The results obtained for the path coefficient fail to provide support for H2 regarding the overall sample (0.066; p > 0.10).
However, our results support the notion that hedonic values positively affect the satisfaction with Airbnb services (0.362; p < 0.001) in the overall sample. This result provides support for H3.
Perceived risk of using Airbnb, in its turn, has negatively affected satisfaction with the service in the overall sample (−0.118; p < 0.001), providing support for H4. The path coefficient results also suggest that the satisfaction with the Airbnb service has a strong positive effect on repurchase intention among the aggregate sample (0.654; p < 0.001). Thus, H5 has been accepted.
To test if the effects of personal values and perceived risk on satisfaction with Airbnb service and satisfaction on repurchase intention would statistically differ between the two subsamples of the study (consumers and non-consumers), we tested the path coefficient differences of the estimated relationships of the proposed model (Figure 1). This this analysis is described in the next section.

Assessing the Differences between Airbnb Consumers and Non-Consumers

The results of the bootstrapping procedure indicate that social values positively influenced satisfaction with Airbnb services in the two subsamples of our study (consumers: 0.259; p < 0.001, and non-consumers: 0.372; p < 0.001). In regard to the effect of utilitarian values on satisfaction, it can be concluded that the result was positive and significant in the consumers’ sample (0.139; p < 0.05). However, in the case of non-consumers, the effect of utilitarian values on perceived satisfaction with use of Airbnb activities was not significant (0.011; p > 0.10).
Our multi-group model estimation indicates that hedonic values positively influenced satisfaction in the two samples (consumers: 0.454, p < 0.001; non-consumers: 0.203; p < 0.01).
Perceived risk negatively affected satisfaction with Airbnb services in one of our subsamples (consumers: −0.159, p < 0.05). The relationship between perceived risk and perceived satisfaction with Airbnb services was not significant for the non-consumers subsample (−0.065, p > 0.10).
The last relationship tested in our model across the two subsamples was the influence of satisfaction with Airbnb service on the repurchase intention. The path coefficients of those relationships were positive and significant in both subsamples, indicating that for consumers (0.728, p < 0.001) and non-consumers (0.567, p < 0.001) the satisfaction with Airbnb service did positively affect their repurchase intention.
Despite the numeric differences verified between the two subsamples regarding the path coefficients of each hypothesised relationship, we assessed if those differences were statistically significant. This procedure allowed us to test research hypotheses H6. Authors assessed those differences by comparing path coefficients of the two subsamples [110,115]. Table 5 displays the numeric difference within pairs of subsamples (|diff|), the t-values (parametric), and the t-values after permutation. Although the numeric differences are large between some of the path coefficients of the two subsamples, only in one case has the difference been considered statistically significant (hedonic values–satisfaction relationship). Ahead is the analysis of those path coefficient differences and H6 testing.
As the results of Table 5 indicate, the comparison been consumers and non-consumers have not been statistically significant for the social values–satisfaction relationship. This means that consumers and non-consumers have similar behaviour regarding the influence of social values on satisfaction with Airbnb services. Taking into consideration the results presented in Table 2 and Table 5, it can be argued that the two samples indicated that social values strongly positively influence satisfaction with Airbnb service.
The results indicate that the difference between consumers and non-consumers’ path coefficients to the utilitarian values–satisfaction with Airbnb service relationship was not statistically significant (|diff| = 0.127, p > 0.10). Although the path coefficient of that relationship was positive and significant in the consumers’ models and non-significant in the non-consumers’ model (Table 2), the above results show that that difference was not statistically significant.
The results indicate that the difference between consumers and non-consumers’ path coefficients to the utilitarian values–satisfaction with Airbnb service relationship was not statistically significant (|diff| = 0.127, p > 0.10). Although the path coefficient of that relationship was positive and significant in the consumers’ models and non-significant in the non-consumers’ model (Table 2), the above results show that that difference was not statistically significant.
Consumers and non-consumers presented statistically significantly different path coefficient results regarding the effects of hedonic values on satisfaction. The difference between the two subsamples was 0.250 (p < 0.05) (Table 5). These results suggest that the impact of hedonic values on satisfaction is positive and significant for consumers and non-consumers. However, according to Table 5, the impact of hedonic values on satisfaction is significantly stronger in the case of consumers (0.454; p < 0.001).
In regard to the relationship between perceived risk of using Airbnb and satisfaction with the sharing economy service, pairwise test results in Table 5 indicate that the path coefficients of the consumers and non-consumers’ models were not significantly different (|diff| = 0.094; p > 0.10). Although significant and negative for consumers, the path coefficient is very weak (−0.159, p < 0.10) while the results show that for non-consumers that relationship is not significant.
Finally, we assessed the differences between satisfaction and repurchase intention relationships between the two subsamples. According to the results in Table 5, the pairwise comparisons did not indicate statistically significant differences between consumers and non-consumers regarding the influence of satisfaction with the Airbnb and repurchase intention (|diff| = 0.161; p > 0.10). Results from Table 2 and Table 5 suggest that the two subsamples displayed similar behaviour regarding the effects of satisfaction with Airbnb service and repurchase intention. Besides, the fact that the path coefficients of that relationship are positive, strong, and significant in the two subsample models (Table 2) indicates that satisfaction with the Airbnb service leads to a repurchase intention regardless the group of stakeholders (consumers and non-consumers).
The multi-group comparison results allow us to partially accept H6. Thus, it can be argued that consumers and non-consumers display similar behaviour in four of the relationships tested in the model. The two groups presented different results regarding the hedonic values and satisfaction with Airbnb services relationship.

5. Conclusions and Implications

Airbnb has grown significantly with more than three million listings worldwide [116]. Airbnb is a digital platform enabling transactions between consumers and owners of accommodation [4,42,78,117,118,119]. Research has grown in the last decade involving the sharing economy and its implications [4,35,70,71,72]. Recent studies have conceptualized the drivers of the sharing economy, highlighting role of technology [78,120,121], sustainable consumption and production [92], and population growth and urbanization [122]. However, investigations relating to generational impact on values, risks, satisfaction, and repurchase intention of Airbnb services are scarce. Secondly, there is a research gap in knowing and understanding the perceptions about Airbnb of those people who have not used the service. This has been the impetus for the current study. The findings are based on the PLS Path Modelling Multi-Group Analysis of 206 responses, which tested the overall sample and the two subgroups, i.e., consumers and non-consumers involving Airbnb services.
The study sought to test the impact of social, utilitarian, and hedonic values on satisfaction using hypotheses 1 to 3 with the use of Airbnb services by the millennial generation. The overall results of aggregate sample suggest that social values have a strong positive effect on satisfaction, thus, supporting H1. According to Lawson, Gleim, Perren, and Hwang [77], the behaviour of consumers who frequent Airbnb reflects a prosocial movement related to environmental harms, recycling resources, and purchasing products not associated with mass commercial activity. The social values relating to environment and sustainable consumption have relatively matured in the New Zealand society. Customers are widely immersed in environmentally friendly tourism activities. Therefore, it is possible that, comparatively, New Zealanders are more likely to use Airbnb out of the reason of helping local economies and reducing the waste of natural resources. This notion is supported by the two groups (i.e., consumers and non-consumers) and is in line with the findings of Guttentag et al. [12,25] and Böcker and Meelen [7]. The H2 tested the relationship between utilitarian values and satisfaction, proposing that utilitarian values have a positive effect on satisfaction. The results of the aggregate sample fail to support the notion. This contradicts the findings of Lee and Kim [20,123], which suggest that utilitarian values significantly influence customer satisfaction. Several other studies have also identified utilitarian, economic benefits as key drivers of the sharing economy related to commercial accommodation [4,35,70,71,72]. Results of the H2 provide an interesting insight as the overall aggregate sample and non-consumers show no statistical significance, but the consumers do show significance. It could be that the non-consumers of Airbnb are not able to appreciate utilitarian values and/or economic benefit of the Airbnb accommodation because they have not experienced it. However, the results in Table 5 show that consumers and non-consumers do not display significantly different results regarding the effects of utilitarian values on satisfaction.
Perhaps the case with the millennial generation is somewhat different. The test results of H3 provide support for the notion that hedonic values positively affect satisfaction in case of the aggregate sample and the two groups of consumers and non-consumers. According to Arnold and Reynold [76], hedonic value includes adventure, social, enjoyment, and sense of belonging. Several studies confirm that Airbnb can create a sense of community, which can be interpreted as the enjoyment of collaborative consumption [4,35,75,95,124].
Perceived risk in using the Airbnb was tested using H4, which proposed that perceived risk will negatively affect satisfaction with the Airbnb services. The results suggest that the notion negatively affected satisfaction in case of aggregate sample and consumers of Airbnb. However, it did not show any significance in case of non-consumers. Ganapati and Reddick [125] stated that “despite its growth in the last decade, the emerging sharing economy is in a flux and fraught with risks” (p. 80). Perceived risk is an important issue intertwined with online transaction platforms that has been considered by many researchers. Malazizi et al. [126] have identified that perception of risk influences satisfaction and repurchase intention. This is supported by the current findings too. Being an online platform, Airbnb does influence behavioural intention linked to risks perceptions [18,19]. The research related to risk perceptions with Airbnb services is scarce. Liang [19] highlights that perceived risk in Airbnb services are present amongst consumers and hosts. However, consumers planning to use Airbnb pay more attention to their own accommodation needs, utilitarian values such as property information, location, and price, despite the perceived risk in making a purchase decision. Such customers usually have low-risk perceptions compared to customers seeking hedonic values [127].
Hypothesis H5 tested the effects of satisfaction on repurchase intention. The relationships were positive and strongly significant across the aggregate sample, consumers and non-consumers. Results confirm the significant positive impact of satisfaction on repurchase intention in previous research [27,90,91,94]. The findings of the current study are in line with these studies that in Airbnb context, the higher the level of satisfaction, the greater the chances of repurchase. The results in the case of non-consumers reaffirm their intention that if they were satisfied with the service they would repurchase.
Lastly, the H6 compared groups of consumer and non-consumer regarding the relationships tested in the Conceptual Model (Figure 1). The results are largely similar but showed difference in the relationship between hedonic values and satisfaction. The non-consumers perhaps have largely experienced staying in a hotel/motel type of accommodation. They may not value adventure, social enjoyment, and collaborative consumption. Perhaps staying in other people’s houses impact their experience negatively. These reasons might have led to partial acceptance of the H6.
Overall, the hypotheses test results show how social values, utilitarian values, and hedonic values, risk perceptions, satisfaction, and repurchase intentions are related and how it implicates the aggregate sample and the consumers and non-consumers groups using Airbnb services. Pairwise comparison has revealed some statistically significant differences among the relationships tested in the model.

5.1. Theoretical Implications

The sharing economy and Airbnb are active research topics with its peer-to-peer business model and rapid growth since 2012 [18,19,128]. Some recent studies have investigated main drivers of the sharing economy such as technology [78,120,121,129,130], sustainable consumption and production [92], and population growth and urbanization [122]. The findings of the current study suggest some meaningful and sustainable theoretical implications. First, it expands the literature showing direct effects of social, utilitarian, and hedonic values and perceived risks as antecedents to the satisfaction of the millennial generation consumers and non-consumers of Airbnb services. Secondly, the study reaffirms what previous studies suggest, which is that guests’ satisfaction has significant positive impact on repurchase intention of Airbnb accommodation [18,19,90,91,94,131]. However, this contribution expands the literature by linking it to the millennial generation’s use of Airbnb. Thirdly, the study also confirms the MEC theory and its effectiveness in showing that perceived values have a positive and significant impact on the satisfaction and repurchase intention. However, in relation to Kahneman and Tversky’s [58] prospect theory, which deals with an individual’s decision-making under uncertainty and risk, the current study somewhat challenges the theory as it shows that, largely, consumers consider Airbnb as a low risk and focus on getting the right accommodation. Fourthly, in testing the multi-group comparison, the findings suggest that in the overall aggregate model and in case of non-consumers of Airbnb, utilitarian values show no statistical significance in relation to satisfaction, whereas Airbnb consumers show significance. Thus, the study advances the theory by shedding light on the differences between Airbnb consumers and non-consumers. Investigating perceptions of non-consumers of Airbnb adds a new perspective to the current literature.

5.2. Managerial Implications

Sharing economy is having a significant impact on employment in the tourism industry [23]. There are several implications that emerge from the current study from which Airbnb consumers and hosts can benefit. The findings suggest that social values generate satisfaction across the two user groups of Airbnb, i.e., experienced consumers and inexperienced non-consumers. Hence, hosts need to consider improving social values by identifying how they can enhance sustainable value, which helps in reducing environmental harms, recycling resources, and purchasing products not associated with mass commercial activity and promote it amongst their consumers, which has immediate implication on sustainability of environment. Woodruff [132] pointed out that it is essential for a company to deliver superior value to the customer in order to stay competitive and succeed in the long term. Malazizi et al. [126] have identified that perception of risk influences eventual satisfaction and repurchase intention and that perceived risks remain an important factor that influences satisfaction rate and in turn repurchase intention. Some possible actions, such as effective security features and the emergency lines and services, can help to reduce the perceived risks in using Airbnb. The growth of the sharing economy, specifically Airbnb, is generating competition and challenges for the hotel/motel industry. The Airbnb hosts need to keep up with the professionalism in generating unique experiences and values for their consumers, which impacts customers’ repurchase intention [133]. As such, our results also contribute to future promotional strategies across Airbnb activity service providers as they can be used as advertising content to reduce potential consumers’ scepticism and enforce the benefits of the service.

5.3. Limitations and Future Research

The findings of the study should be used with caution considering that a convenience sample was used, which means that results cannot be generalized. The sample size is another limitation. Future research with a larger sample size, done as a case study, is proposed to develop comparative studies. Future studies should involve probabilistic samples to provide representative results. Longitudinal studies should also be considered as they provide an overview of sequence of events overtime.

Author Contributions

Conceptualization, A.M.; methodology, J.L.; discussion, A.M.; results description, J.L., conclusion and implications, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

There was no funding involved for this work.

Data Availability Statement

Data is only available to authors; it was not reported publicly.

Conflicts of Interest

There is no conflict of interest involved.

Appendix A. Sample Characteristics

TypeTotal
Non-ConsumersConsumers
GenderFemaleCount6863131
% within Gender51.9%48.1%100.0%
MaleCount354075
% within Gender46.7%53.3%100.0%
Marital StatusSingleCount5751108
% within Marital status52.8%47.2%100.0%
MarriedCount465298
% within Marital status46.9%53.1%100.0%
Children0Count7388161
% within Children45.3%54.7%100.0%
1Count181028
% within Children64.3%35.7%100.0%
2Count10313
% within Children76.9%23.1%100.0%
3Count202
% within Children100.0%0.0%100.0%
4Count022
% within Children0.0%100.0%100.0%
IncomeBelow AverageCount362258
% within Income62.1%37.9%100.0%
Around AverageCount252550
% within Income50.0%50.0%100.0%
Above AverageCount262753
% within Income49.1%50.9%100.0%
Not sureCount162945
% within Income35.6%64.4%100.0%
TotalCount103103206
% within Income50.0%50.0%100.0%

References

  1. Airbnb. Airbnb’s 2016 Highlights and 2017 Trends We’re Watching. 2017. Available online: https://www.airbnbcitizen.com/airbnbs-2016-highlights-and-2017-trends-were-watching/ (accessed on 16 January 2020).
  2. Griswold, A. “This New Year’s, Airbnb Got the Hockey-Stick Growth that Every Start Up Envies”, Quartz. 3 January 2018. Available online: https://qz.com/877080/airbnbs-growth-in-guests-on-new-yearsis-the-hockey-stick-curve-that-every-startup-wants/ (accessed on 9 September 2021).
  3. LearnAirbnb. The State of Airbnb Hosts. 2016. Available online: https://learnairbnb.com/wp-content/uploads/2017/08/LearnAirbnb.com-Airbnb-Home-Sharing-Report-v1.4.pdf (accessed on 9 September 2021).
  4. Botsman, R.; Rogers, R. What’s Mine Is Yours: How Collaborative Consumption Is Changing the Way We Live; HarperCollins: New York, NY, USA, 2011. [Google Scholar]
  5. PWC. Spotlight on the Sharing Economy. 2015. Available online: https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/sharing-economy.html (accessed on 9 September 2021).
  6. Bucher, E.; Fieseler, C.; Lutz, C. What’s mine is yours (for a nominal fee)–Exploring the spectrum of utilitarian to altruistic motives for Internet-mediated sharing. Comput. Hum. Behav. 2016, 62, 316–326. [Google Scholar] [CrossRef]
  7. Böcker, L.; Meelen, A.A. Sharing for people, planet or profit? Analysing motivations for intended sharing economy participation. Innov. Stud. Utrecht (ISU) Work. Pap. Ser. 2016, 16, 1–22. [Google Scholar] [CrossRef] [Green Version]
  8. Schor, J. Debating the sharing economy. J. Self-Gov. Manag. Econ. 2016, 4, 7–22. [Google Scholar]
  9. Mittendorf, C.; Berente, N.; Holten, R. Trust in sharing encounters among millennials. Inf. Syst. J. 2019, 29, 1083–1119. [Google Scholar] [CrossRef]
  10. Horton, J.J.; Zeckhauser, R.J. Owning, Using and Renting: Some Simple Economics of the Sharing Economy; No. w22029; National Bureau of Economic Research: Cambridge, MA, USA, 2016. [Google Scholar]
  11. Quattrone, G.; Proserpio, D.; Quercia, D.; Capra, L.; Musolesi, M. Who benefits from the sharing economy of Airbnb? In Proceedings of the 25th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Montréal, QC, Canada, 11–15 April 2016; pp. 1385–1394. [Google Scholar]
  12. Guttentag, D.; Smith, S.; Potwarka, L. and Havitz Why Tourists Choose Airbnb: A Motivation-Based Segmentation Study. J. Travel Res. 2017, 57, 342–359. [Google Scholar] [CrossRef]
  13. So, K.K.F.; Oh, H.; Min, S. Motivations and constraints of Airbnb consumers: Findings form a mixed-method approach. Tour. Manag. 2018, 67, 224–236. [Google Scholar] [CrossRef]
  14. Chiu, C.M.; Fang, Y.H.; Cheng, H.L.; Yen, C. On online repurchase intentions: Antecedents and the moderating role of switching cost. Hum. Syst. Manag. 2013, 32, 283–296. [Google Scholar] [CrossRef]
  15. Kim, D.J. An investigation of the effect of online consumer trust on expectation, satisfaction, and post-expectation. Inf. Syst. e-Bus. Manag. 2012, 10, 219–240. [Google Scholar] [CrossRef]
  16. Liu, S.Q.; Mattila, A.S. Airbnb: Online targeted advertising, sense of power, and consumer decisions. Int. J. Hosp. Manag. 2017, 60, 33–41. [Google Scholar] [CrossRef]
  17. An, S.; Suh, J.; Eck, T. Examining structural relationships among service quality, perceived value, satisfaction and revisit intention for Airbnb guests. Int. J. Tour. Sci. 2019, 19, 145–165. [Google Scholar] [CrossRef]
  18. Liang, L.J.; Choi, H.C.; Joppe, M. Exploring the relationship between satisfaction, trust and switching intention, repurchase intention in the context of Airbnb. Int. J. Hosp. Manag. 2018, 69, 41–48. [Google Scholar] [CrossRef]
  19. Liang, L.J.; Choi, H.C.; Joppe, M. Understanding repurchase intention of Airbnb consumers: Perceived authenticity, electronic word-of-mouth, and price sensitivity. J. Travel Tour. Mark. 2018, 35, 73–89. [Google Scholar] [CrossRef]
  20. Lee, S.; Kim, D.-Y. The effect of hedonic and utilitarian values on satisfaction and loyalty of Airbnb users. Int. J. Hosp. Manag. 2018, 30, 1332–1351. [Google Scholar] [CrossRef]
  21. Dogru, T.; Mody, M.; Suess, C. Adding evidence to the debate: Quantifying Airbnb’s disruptive impact on ten key hotel markets. Tour. Manag. 2019, 72, 27–38. [Google Scholar] [CrossRef]
  22. Choi, K.H.; Jung, J.H.; Ryu, S.Y.; Do Kim, S.; Yoon, S.M. The relationship between Airbnb and the hotel revenue: In the case of Korea. Indian J. Sci. Technol. 2015, 8, 1–8. [Google Scholar] [CrossRef] [Green Version]
  23. Fang, B.; Ye, Q.; Law, R. Effect of sharing economy on tourism industry employment. Ann. Tour. Res. 2016, 57, 264–267. [Google Scholar] [CrossRef]
  24. Dogru, T.; Mody, M.; Suess, C.; McGinley, S.; Line, N.D. The Airbnb paradox: Positive employment effects in the hospitality industry. Tour. Manag. 2020, 77, 104001. [Google Scholar] [CrossRef]
  25. Guttentag, D.A.; Smith, S.L.J. Assessing Airbnb as a disruptive innovation relative to hotels: Substitution and comparative performance expectations. Int. J. Hosp. Manag. 2017, 64, 1–10. [Google Scholar] [CrossRef] [Green Version]
  26. Mody, M.; Hanks, L.; Dogru, T. Parallel pathways to brand loyalty: Mapping the consequences of authentic consumption experiences for hotels and Airbnb. Tour. Manag. 2019, 74, 65–80. [Google Scholar] [CrossRef]
  27. Wang, Y.; Asaad, Y.; Filleri, R. What makes host trust Airbnb? Antecedents of hosts’ trust toward Airbnb and its impact on continuance intention. J. Travel Res. 2020, 59, 686–703. [Google Scholar] [CrossRef] [Green Version]
  28. Ju, Y.; Back, K.J.; Choi, Y.; Lee, J.S. Explroing Airbnb service quality attributes and their asymmetric effects on customer satisfaction. Int. J. Hosp. Manag. 2019, 77, 342–352. [Google Scholar] [CrossRef]
  29. Albinsson, P.A.; Yasanthi Perera, B. Alternative marketplaces in the 21st century: Building community through sharing events. J. Consum. Behav. 2012, 11, 303–315. [Google Scholar] [CrossRef]
  30. Möhlmann, M. Collaborative consumption: Determinants of satisfaction and the likelihood of using a sharing economy option again. J. Consum. Behav. 2015, 14, 193–207. [Google Scholar] [CrossRef]
  31. Xie, K.; Mao, Z. The impacts of quality and quantity attributes of Airbnb hosts on listing performance. Int. J. Contemp. Hosp. Manag. 2017, 29, 2240–2260. [Google Scholar] [CrossRef]
  32. Morrison, A.M.; Jing, S.; O’Leary, J.; Cai, L. Predicting usage of the Internet for travel bookings: An exploratory study. Inf. Technol. Tour. 2001, 4, 15–30. [Google Scholar] [CrossRef]
  33. Grönroos, C. Conceptualising value co-creation: A journey to the 1970s and back to the future. J. Mark. Manag. 2012, 28, 1520–1534. [Google Scholar] [CrossRef]
  34. Ert, E.; Fleischer, A.; Magen, N. Trust and reputation in the sharing economy: The role of personal photos in Airbnb. Tour. Manag. 2016, 55, 62–73. [Google Scholar] [CrossRef]
  35. Guttentag, D. Airbnb: Disruptive innovation and the rise of an informal tourism accommodation sector. Curr. Issues Tour. 2015, 18, 1192–1217. [Google Scholar] [CrossRef]
  36. Tussyadiah, I.P.; Pesonen, J. Impacts of peer-to-peer accommodation use on travel patterns. J. Travel Res. 2016, 55, 1022–1040. [Google Scholar] [CrossRef]
  37. Lohmöller, J.-B. Latent Variable Path Modeling with Partial Least Square; Physica-Verlag: Heidelberg, Germany, 1989. [Google Scholar]
  38. Huang, D.; Liu, X.; Lai, D.; Li, Z. Users and non-users of P2P accommodation: Differences in perceived risks and behavioral intentions. J. Hosp. Tour. Technol. 2019, 10, 369–382. [Google Scholar] [CrossRef]
  39. Cusumano, M.A. How traditional firms must compete in the sharing economy. Commun. ACM 2015, 58, 32–34. [Google Scholar] [CrossRef]
  40. Denning, S. An economy of access is opening for business: Five strategies for success. Strategy Leadersh. 2014, 42, 14–21. [Google Scholar] [CrossRef]
  41. Avital, M.; Andersson, M.; Nickerson, J.; Sundararajan, A.; Van Alstyne, M.; Verhoeven, D. The collaborative economy: A disruptive innovation or much ado about nothing? In Proceedings of the 35th International Conference on Information Systems, Auckland, New Zealand, 14–17 December 2014; ICIS 2014. Association for Information Systems, AIS Electronic Library (AISeL): Atlanta, GA, USA, 2014; pp. 1–7. [Google Scholar]
  42. Hamari, J.; Sjöklint, M.; Ukkonen, A. The sharing economy: Why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 2016, 67, 2047–2059. [Google Scholar] [CrossRef]
  43. Zervas, G.; Proserpio, D.; Byers, J. A first look at online reputation on Airbnb, where every stay is above average. Mark. Lett. 2021, 32, 1–6. [Google Scholar] [CrossRef]
  44. Airbnb. “Fast Facts”, Airbnb Press Room. 2018. Available online: https://press.atairbnb.com/fast-facts/ (accessed on 16 January 2020).
  45. Tussyadiah, I.P. An Exploratory Study on Drivers and Deterrents of Collaborative Consumption in Travel. In Information and Communication Technologies in Tourism; Springer: Cham, Switzerland, 2015; pp. 817–830. [Google Scholar]
  46. Barnes, S.J.; Mattsson, J. Understanding current and future issues in collaborative consumption: A four-stage Delphi study. Technol. Forecast. Soc. Chang. 2016, 104, 200–211. [Google Scholar] [CrossRef] [Green Version]
  47. Poon, K.Y.; Huang, W.J. Past experience, traveller personality and tripographics on intention to use Airbnb. Int. J. Contemp. Hosp. Manag. 2017, 29, 2425–2443. [Google Scholar] [CrossRef]
  48. Kaplan, R.A.; Nadler, M.L. Airbnb: A case study in occupancy regulation and taxation. U. Chi. L. Rev. Dialogue 2015, 82, 103. [Google Scholar]
  49. Jiao, J.; Wegmann, J. 13 Urban vacation rentals and the housing market. In The Millennial City: Trends, Implications, and Prospects for Urban Planning and Policy; Routledge: London, UK, 2017. [Google Scholar]
  50. Weber, K.; Roehl, W.S. Profiling people searching for and purchasing travel products on the World Wide Web. J. Travel Res. 1999, 37, 291–298. [Google Scholar] [CrossRef]
  51. Edelman, B.G.; Luca, M. Digital Discrimination: The Case of Airbnb.com; Harvard Business School NOM Unit Working Paper, 14-054; Harvard Business School: Boston, MA, USA, 2014. [Google Scholar]
  52. Todisco, M. Share and Share Alike: Considering Racial Discrimination in the Nascent Room-Sharing Economy. Stan. L. Rev. Online 2014, 67, 121. [Google Scholar]
  53. Sundararajan, A. What Airbnb Gets about Culture That Uber Doesn’t. Harvard Business Review. 2014. Available online: https://hbr.org/2014/11/what-airbnb-gets-about-culture-that-uber-doesnt (accessed on 9 September 2021).
  54. Tam, J.L. Customer satisfaction, service quality and perceived value: An integrative model. J. Mark. Manag. 2004, 20, 897–917. [Google Scholar] [CrossRef]
  55. Zervas, G.; Proserpio, D.; Byers, J.W. The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. J. Mark. Res. 2017, 54, 687–705. [Google Scholar] [CrossRef] [Green Version]
  56. Ikkala, T.; Lampinen, A. Defining the price of hospitality: Networked hospitality exchange via Airbnb. In Proceedings of the Companion Publication of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, Baltimore, MD, USA, 15–19 February 2014; pp. 173–176. [Google Scholar]
  57. Gutman, J. Means–end chains as goal hierarchies. Psychol. Mark. 1997, 14, 545–560. [Google Scholar] [CrossRef]
  58. Kahneman, D.; Tversky, A. On the interpretation of intuitive probability: A reply to Jonathan Cohen. Cognition 1979, 7, 409–411. [Google Scholar] [CrossRef]
  59. Botschen, G.; Thelen, E.M.; Pieters, R. Using means-end structures for benefit segmentation: An application to services. Eur. J. Mark. 1999, 33, 38–58. [Google Scholar] [CrossRef]
  60. Gallarza, M.G.; Saura, I.G. Value dimensions, perceived value, satisfaction and loyalty: An investigation of university students’ travel behaviour. Tour. Manag. 2006, 27, 437–452. [Google Scholar] [CrossRef]
  61. Sweeney, J.C.; Soutar, G.N. Consumer perceived value: The development of a multiple item scale. J. Retail. 2001, 77, 203–220. [Google Scholar] [CrossRef]
  62. Zhuang, W.; Cumiskey, K.J.; Xiao, Q.; Alford, B. The impact of perceived value on behaviour intention: An empirical study. J. Glob. Bus. Manag. 2010, 6, 1. [Google Scholar]
  63. Prothero, A.; Dobscha, S.; Freund, J.; Kilbourne, W.E.; Luchs, M.G.; Ozanne, L.K.; Thøgersen, J. Sustainable consumption: Opportunities for consumer research and public policy. J. Public Policy Mark. 2011, 30, 31–38. [Google Scholar] [CrossRef]
  64. Deci, E.L.; Ryan, R.M. Intrinsic Motivation and Self-Determination in Human Behaviour; Plenum Press: New York, NY, USA, 1985. [Google Scholar]
  65. Szymanski, D.M.; Hise, R.T. E-satisfaction: An initial examination. J. Retail. 2000, 76, 309–322. [Google Scholar] [CrossRef]
  66. Rintamaki, T.; Kanto, A.; Kuusela, H.; Spence, M.T. Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions. Int. J. Retail Distrib. Manag. 2006, 34, 6–24. [Google Scholar] [CrossRef]
  67. Ballantine, P. Effects of interactivity and product information on consumer satisfaction in an online retail setting. Int. J. Retail Distrib. Manag. 2005, 33, 461–471. [Google Scholar] [CrossRef]
  68. Childers, T.L.; Carr, C.L.; Peck, J.; Carson, S. Hedonic and utilitarian motivations for online shopping behaviour. J. Retail. 2001, 77, 511–535. [Google Scholar] [CrossRef]
  69. Skogland, I.; Siguaw, J.A. Are your satisfied customers loyal? Cornell Hotel. Restaur. Adm. Q. 2004, 45, 221–234. [Google Scholar] [CrossRef]
  70. Gansky, L. The Mesh: Why the Future of Business is Sharing; Penguin: London, UK, 2010. [Google Scholar]
  71. Lamberton, C.P.; Rose, R.L. When is ours better than mine? A framework for understanding and altering participation in commercial sharing systems. J. Mark. 2012, 76, 109–125. [Google Scholar] [CrossRef]
  72. Sacks, D. The Sharing Economy. 2011. Available online: https://www.fastcompany.com/1747551/sharing-economy (accessed on 9 September 2021).
  73. Lane, J.; Woodworth, R.M. The Sharing Economy Checks in: An Analysis of Airbnb in the United States; CBRE Hotel’s Americas Research: Los Angeles, CA, USA, 2016. [Google Scholar]
  74. Hirschman, E.C.; Holbrook, M.B. Hedonic consumption: Emerging concepts, methods and propositions. J. Mark. 1982, 46, 92–101. [Google Scholar] [CrossRef] [Green Version]
  75. Owyang, J.; Tran, C.; Silva, C. The Collaborative Economy; Altimeter: Westchase, FL, USA, 2013. [Google Scholar]
  76. Arnold, M.J.; Reynolds, K.E. Hedonic shopping motivations. J. Retail. 2003, 79, 77–95. [Google Scholar] [CrossRef]
  77. Lawson, S.J.; Gleim, M.R.; Perren, R.; Hwang, J. Freedom from ownership: An exploration of access-based consumption. J. Bus. Res. 2016, 69, 2615–2623. [Google Scholar] [CrossRef] [Green Version]
  78. Belk, R. You are what you can access: Sharing and collaborative consumption online. J. Bus. Res. 2014, 67, 1595–1600. [Google Scholar] [CrossRef]
  79. Babin, B.J.; Attaway, J.S. Atmospheric affect as a tool for creating value and gaining share of customer. J. Bus. Res. 2000, 49, 91–99. [Google Scholar] [CrossRef]
  80. Meng, J.; Weng, X. Can prospect theory explain the disposition effect? A new perspective on reference points. Manag. Sci. 2018, 64, 3331–3351. [Google Scholar] [CrossRef]
  81. Camerer, C. Three cheers—Psychological, theoretical, empirical—For loss aversion. J. Mark. Res. 2005, 42, 129–133. [Google Scholar] [CrossRef]
  82. Nguyen, Q.; Villeval, M.C.; Xu, H. Trust under the prospect theory and quasi-hyperbolic preferences: A field experiment in Vietnam. Econ. Dev. Cult. Chang. 2016, 64, 545–572. [Google Scholar] [CrossRef]
  83. Ponte, E.B.; Carvajal-Trujillo, E.; Escobar-Rodríguez, T. Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tour. Manag. 2015, 47, 286–302. [Google Scholar] [CrossRef]
  84. Forsythe, S.; Liu, C.; Shannon, D.; Gardner, L.C. Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Mark. 2006, 20, 55–75. [Google Scholar] [CrossRef]
  85. Park, C.H.; Kim, Y.G. Identifying key factors affecting consumer purchase behaviour in an online shopping context. Int. J. Retail Distrib. Manag. 2003, 31, 16–29. [Google Scholar] [CrossRef]
  86. Papadopoulos, P.; Andreou, A.; Kanellis, P.; Martakos, D. Trust and relationship building in electronic commerce. Internet Res. 2001, 11, 322–332. [Google Scholar] [CrossRef]
  87. Chou, S.; Chen, C.W.; Lin, J.Y. Female online shoppers: Examining the mediating roles of e-satisfaction and e-trust on e-loyalty development. Internet Res. 2015, 25, 542–561. [Google Scholar] [CrossRef]
  88. Ku, E.C. Beyond price: How does trust encourage online group’s buying intention? Internet Res. 2012, 22, 569–590. [Google Scholar] [CrossRef]
  89. Liu, C.T.; Guo, Y.M.; Lee, C.H. The effects of relationship quality and switching barriers on customer loyalty. Int. J. Inf. Manag. 2011, 31, 71–79. [Google Scholar] [CrossRef]
  90. Chitty, B.; Ward, S.; Chua, C. An application of the ECSI model as a predictor of satisfaction and loyalty for backpacker hostels. Mark. Intell. Plan. 2007, 25, 563–580. [Google Scholar] [CrossRef]
  91. Ren, L.; Qiu, H.; Wang, P.; Lin, P.M. Exploring customer experience with budget hotels: Dimensionality and satisfaction. Int. J. Hosp. Manag. 2016, 52, 13–23. [Google Scholar] [CrossRef]
  92. Cohen, B.; Muñoz, P. Sharing cities and sustainable consumption and production: Towards an integrated framework. J. Clean. Prod. 2016, 134 Pt A, 87–97. [Google Scholar] [CrossRef]
  93. Yee, B.Y.; Faziharudean, T.M. Factors Affecting Customer Loyalty of Using Internet Banking in Malaysia. J. Electron. Bank. Syst. 2010, 2010, 592297. [Google Scholar] [CrossRef]
  94. Jeong, M.; Oh, H.; Gregoire, M. Conceptualizing web site quality and its consequences in the lodging industry. Int. J. Hosp. Manag. 2003, 22, 161–175. [Google Scholar] [CrossRef]
  95. Petrick, J.F. Development of a multi-dimensional scale for measuring the perceived value of a service. J. Leis. Res. 2002, 34, 119. [Google Scholar] [CrossRef]
  96. Eberl, M. An application of PLS in multi-group analysis: The need for differentiated corporate-level marketing in the mobile communications industry. In Handbook of Partial Least Squares; Springer: Berlin, Heidelberg, 2010; pp. 487–514. [Google Scholar]
  97. Chin, W. Frequently Asked Questions—Partial Least Squares & PLS—Graph. 2004. Available online: https://www.researchgate.net/publication/258847316_Frequently_Asked_Questions_-_Partial_Least_Squares_PLS_-_Graph (accessed on 9 September 2021).
  98. Saunders, M.L.; Lewis, P.; Thornhill, A. Research Methods for Business Students; Pearson Education: London, UK, 2019. [Google Scholar]
  99. Homer, P.M.; Kahle, L.R. A structural equation test of the value-attitude-behaviour hierarchy. J. Personal. Soc. Psychol. 1988, 54, 638. [Google Scholar] [CrossRef]
  100. Chai, S.; Das, S.; Rao, H.R. Factors affecting bloggers’ knowledge sharing: An investigation across gender. J. Manag. Inf. Syst. 2011, 28, 309–342. [Google Scholar] [CrossRef]
  101. Bhattacherjee, A. Individual trust in online firms: Scale development and initial test. J. Manag. Inf. Syst. 2002, 19, 211–241. [Google Scholar]
  102. Fornell, C.; Johnson, M.D.; Anderson, E.W.; Cha, J.; Bryant, B.E. The American customer satisfaction index: Nature, purpose, and findings. J. Mark. 1996, 60, 7–18. [Google Scholar] [CrossRef] [Green Version]
  103. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common Beliefs and Reality about Partial Least Squares: Comments on Rönkkö & Evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar]
  104. Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 3. Bönningstedt: SmartPLS. 2015. Available online: http://www.smartpls.com (accessed on 18 February 2020).
  105. Hair, J.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis; Prentice Hall: Hoboken, NJ, USA, 2005. [Google Scholar]
  106. Chin, W. Issues and opinion on structural equation modelling. MIS Q. 1998, 22, 7–15. [Google Scholar]
  107. Henseler, J.; Ringle, C.M.; Sinkowics, R.R. The use of partial least squares path modelling in international marketing. Adv. Int. Mark. 2009, 20, 277–319. [Google Scholar]
  108. Eisinga, R.; Grotenhuis, M.; Pelzer, B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int. J. Public Health 2013, 58, 637–642. [Google Scholar] [CrossRef]
  109. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  110. Henseler, J.; Ringle, C.M.; Sarsted, M. A new criterion for assessing discriminant validity in variance-based structural equation modelling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  111. Kline, R. Principles and Practice of Structural Equation Modeling, 3rd ed.; Guilford Press: New York, NY, USA, 2011. [Google Scholar]
  112. Navarro, A.; Acedo, F.J.; Robson, M.J.; Ruzo, E.; Losada, F. Antecedents and consequences of firms’ export commitment: An empirical study. J. Int. Mark. 2010, 18, 41–61. [Google Scholar] [CrossRef]
  113. Falk, R.F.; Miller, N. A Primer for Soft Modelling; University of Akron Press: Akron, OH, USA, 1992. [Google Scholar]
  114. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  115. Stylos, N.; Vassiliadis, C. Differences in sustainable management between four-and five-star hotels regarding the perceptions of three-pillar sustainability. J. Hosp. Mark. Manag. 2015, 24, 791–825. [Google Scholar] [CrossRef]
  116. Li, H.; Srinivasan, K. Competitive Dynamics in the Sharing Economy: An Analysis in the Context of Airbnb and Hotels. Mark. Sci. 2019, 38, 365–391. [Google Scholar] [CrossRef]
  117. Frenken, K.; Schor, J. Putting the sharing economy into perspective. Environ. Innov. Soc. Transit. 2017, 23, 3–10. [Google Scholar] [CrossRef]
  118. Sundararajan, A. The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism; MIT Press: Cambridge, MA, USA, 2016. [Google Scholar]
  119. Tenenhaus, M.; Vinzi, V.E.; Chatelin, Y.-M.; Lauro, C. Pls path modeling. Comput. Stat. Data Anal. 2005, 48, 159–205. [Google Scholar] [CrossRef]
  120. Puschmann, T.; Alt, R. Sharing economy. Bus. Inf. Syst. Eng. 2016, 58, 93–99. [Google Scholar] [CrossRef]
  121. Ringle, C.M. Segmentation for Path Models and Unobserved Heterogeneity: The Finite Mixture Partial Least Squares Approach; Research Papers on Marketing and Retailing; University of Hamburg: Hamburg, Germany, 2006. [Google Scholar]
  122. Cohen, B.; Kietzmann, J. Ride on! Mobility business models for the sharing economy. Organ. Environ. 2014, 27, 279–296. [Google Scholar] [CrossRef]
  123. Li, X.; Benjaafar, S.; Kong, G.; Courcoubetis, C. Peer-to-peer product sharing: Implications for ownership, usage and social welfare in the sharing economy. Manag. Sci. 2019, 65, 477–493. [Google Scholar]
  124. Buczynski, B. Sharing Is Good: How to Save Money, Time and Resources through Collaborative Consumption; New Society Publisher: Gabriola Island, BC, Canada, 2013. [Google Scholar]
  125. Ganapati, S.; Reddick, C.G. Prospects and challenges of sharing economy for the public sector. Gov. Inf. Q. 2018, 35, 77–87. [Google Scholar] [CrossRef]
  126. Malazizi, N.; Alipour, H.; Olya, H. Risk Perceptions of Airbnb Hosts: Evidence from a Mediterranean Island. Sustainability 2018, 10, 1349. [Google Scholar] [CrossRef] [Green Version]
  127. O’curry, S.; Strahilevitz, M. Probability and mode of acquisition effects on choices between hedonic and utilitarian options. Mark. Lett. 2001, 12, 37–49. [Google Scholar] [CrossRef]
  128. Olson, K. National Study Quantifies Reality of the ‘Sharing Economy’ Movement. Retrieved August 2013, 8, 2014. [Google Scholar]
  129. Parasuraman, A.; Grewal, D. The impact of technology on the quality-value-loyalty chain: A research agenda. J. Acad. Mark. Sci. 2000, 28, 168–174. [Google Scholar] [CrossRef]
  130. Keymolen, E. Trust and technology in collaborative consumption. Why it is not just about you and me. Bridg. Distances Technol. Regul. 2013, 135, 135–150. [Google Scholar]
  131. Wu, L.Y.; Chen, K.Y.; Chen, P.Y.; Cheng, S.L. Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. J. Bus. Res. 2014, 67, 2768–2776. [Google Scholar] [CrossRef]
  132. Woodruff, R.B. Customer value: The next source for competitive advantage. J. Acad. Mark. Sci. 1997, 25, 139–153. [Google Scholar] [CrossRef]
  133. Hellier, P.K.; Geursen, G.M.; Carr, R.A.; Rickard, J.A. Customer repurchase intention: A general structural equation model. Eur. J. Mark. 2003, 37, 1762–1800. [Google Scholar] [CrossRef] [Green Version]
Sustainability 13 10884 g001 550
Figure 1. Conceptual Model.
Figure 1. Conceptual Model.
Sustainability 13 10884 g001
Table
Table 1. Latent Variables Composition and Variable Loadings [12,25,30,79,99,100,101,102].
Table 1. Latent Variables Composition and Variable Loadings [12,25,30,79,99,100,101,102].
Latent VariablesDefinitionMeasuresLoadingAdapted from
Utilitarian ValueThe definition of utilitarian value is the value that comes from the task related and rational consumption (Babin et al., 1994) [79].Money saving0.858(Homer and Kahle’s, 1988) [99]
A wide variety of housing0.806
Ease to use0.732
Hedonic Value The definition of hedonic value is the value that a customer received from the fun and enjoyable experience of consumption (Babin et al., 1994) [79].Sense of belonging0.752(Homer and Kahle’s, 1988) [99]
Friends making0.817
Experience the local culture0.834
Social ValueThe definition of the social value here refers to a social and sustainable value, which helps in reducing environmental harms, recycling the resources, and purchasing products not associated with mass commercial activity.Natural resources saving0.923(Mohlman, 2015, Guttentag et al., 2017) [12,25]
Help with local economy0.889
Perceived RiskPerceived risk is defined as the uncertainty that customers feel when they are consuming a goods or services.Safety and privacy concerns0.895(Chai et al., 2011; Bhattacherjee, 2002) [100,101]
Uncertain house/service quality0.920
SatisfactionSatisfaction is defined as the degree to which the customer feels satisfied about the Airbnb.Ideal accommodation0.769(Fornell et al., 1996) [102]
Expectation fulfilment0.892
Overall Satisfaction0.905
Repurchase intention The repurchase intention is defined as the likelihood of customers choosing Airbnb again.I am likely to choose Airbnb next time.1.000(Bhattacherjee, 2001) [101]
Table
Table 2. Overall Sample and Value-Specific Results Analysis.
Table 2. Overall Sample and Value-Specific Results Analysis.
Overall Sample n = 206Airbnb Consumers n = 103Non-Consumers n = 103
Latent Variables
Hedonic ValuesComposite reliability0.8430.8410.817
AVE0.6420.6390.598
Cronbach’s Alpha0.7200.7150.663
Social ValuesComposite reliability0.9020.8640.927
AVE0.8210.7610.864
Cronbach’s Alpha0.780 0.7000.842
Spearman Brown Coefficient 0.784
Utilitarian ValuesComposite reliability0.8420.8280.838
AVE0.6410.6180.638
Cronbach’s Alpha0.7220.6950.723
Perception of RiskComposite reliability0.9030.9270.821
AVE0.8230.8630.704
Cronbach’s Alpha0.7860.8420.682
Spearman Brown Coefficient 0.786
SatisfactionComposite reliability0.8920.8850.895
AVE0.7350.7220.740
Cronbach’s Alpha0.8220.8060.827
Repurchase intentionComposite reliability1.0001.0001.000
AVE1.0001.0001.000
Cronbach’s Alpha1.0001.0001.000
RelationshipsCoefficient and significance level
H1: Social Values → Satisfaction0.288 ***0.259 ***0.372 ***
H2: Utilitarian Values → Satisfaction0.0660.139 **0.011
H3: Hedonic Values → Satisfaction0.362 ***0.454 ***0.203 **
H4: Perception of Risk → Satisfaction−0.118 ***−0.159 *−0.065
H5: Satisfaction → Repurchase intention0.654 ***0.728 ***0.567 ***
R2 Satisfaction0.4190.5830.281
R2 Repurchase Intention0.4280.5300.322
Standardised Root Mean Square Residual (SRMR) goodness-of-fit assessment (GoF)0.0350.0540.050
Notes: AVE = Average Variance Extracted; * Significance at 0.10, ** significance at 0.05, *** significance at 0.01.
Table
Table 3. Correlation Matrix of Constructs.
Table 3. Correlation Matrix of Constructs.
Latent VariablesHedonic ValuesPerceived RiskRepurchase IntentionSatisfactionSocial ValuesUtilitarian Values
Hedonic Values0.801
Perceived Risk−0.2390.907
Repurchase Intention 0.656−0.1571.000
Satisfaction0.574−0.2740.6540.858
Social Values0.520−0.1880.6130.5270.906
Utilitarian Values0.523−0.2340.5080.4070.4280.800
Note: the diagonal is the square root of the Average Variance Extracted (AVE) of the constructs in the whole sample.
Table
Table 4. Heterotrait–Monotrait Ratio of Correlations (HTMT) Results.
Table 4. Heterotrait–Monotrait Ratio of Correlations (HTMT) Results.
Latent VariablesHedonic ValuesPerceived RiskRepurchase IntentionSatisfactionSocial Values
Hedonic Values
Perceived Risk0.315
Repurchase Intention0.7690.176
Satisfaction0.7260.3250.699
Social Values0.6860.2310.6900.640
Utilitarian Values0.7190.3130.5880.4950.563
Table
Table 5. Multi-group Comparison Results–H6 Testing.
Table 5. Multi-group Comparison Results–H6 Testing.
RelationshipPair Test|diff|tParametrictPermutation
Social Values → SatisfactionConsumers vs. non-consumers−0.1131.0681.080
Utilitarian Values → SatisfactionConsumers vs. non-consumers0.1271.0391.037
Hedonic Values → SatisfactionConsumers vs. non-consumers0.2502.066 **2.085 **
Perception of Risk → SatisfactionConsumers vs. non-consumers0.0940.7210.722
Satisfaction → Repurchase intentionConsumers vs. non-consumers0.1611.6811.670
Notes: |diff| = Path Coefficient difference; ** Significance at 0.05.
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Mohsin, A.; Lengler, J. Airbnb Hospitality: Exploring Users and Non-Users’ Perceptions and Intentions. Sustainability 2021, 13, 10884. https://doi.org/10.3390/su131910884

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Mohsin A, Lengler J. Airbnb Hospitality: Exploring Users and Non-Users’ Perceptions and Intentions. Sustainability. 2021; 13(19):10884. https://doi.org/10.3390/su131910884

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Mohsin, Asad, and Jorge Lengler. 2021. "Airbnb Hospitality: Exploring Users and Non-Users’ Perceptions and Intentions" Sustainability 13, no. 19: 10884. https://doi.org/10.3390/su131910884

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Mohsin, A., & Lengler, J. (2021). Airbnb Hospitality: Exploring Users and Non-Users’ Perceptions and Intentions. Sustainability, 13(19), 10884. https://doi.org/10.3390/su131910884

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