A Brazil-Portugal comparative study on the antecedents of Couchsurfing use

Abstract

Tourism is one of the areas most affected by the shared economy through organizations such as CouchSurfing, AirBnb and BeLocal Exchange. The aim of this article was to propose a theoretical model to identify the consumption history of CouchSurfing from the perspective of guest consumers. For this, a study with a quantitative approach was carried out, comparing the reality of this type of accommodation in Brazil and Portugal. A survey was conducted with 421 people from Brazil and 408 from Portugal who have already used CouchSurfing as guests. As a method of data analysis, basic descriptive statistics, exploratory factor analysis and structural equation modeling analysis were used. The results of the multigroup analyzes of the study indicated similarities and differences between the samples from Brazil and Portugal. The similarities were that, in both, the hypotheses that the Performance Expectation, the Co-creation Perception, the Anti-Industry Perspective and the Perceived Risk are related to the Intention to Use were supported.

Keywords
Sharing Economy; Motivations of consumption; CouchSurfing; Brazil; Portugal

Resumo

O turismo é uma das áreas mais afetadas pela economia compartilhada por meio de organizações como CouchSurfing, AirBnb e BeLocal Exchange. O objetivo deste artigo foi propor um modelo teórico para identificar os antecedentes de consumo do CouchSurfing na perspectiva dos consumidores-hóspedes. Para isso, foi realizado um estudo de abordagem quantitativa, comparando a realidade deste tipo de hospedagem no Brasil e em Portugal. Foi realizado um survey com 421 pessoas do Brasil e 408 de Portugal que já utilizaram CouchSurfing como hóspedes. Como método de análise de dados, foram utilizados estatística descritiva básica, análise fatorial exploratória e análise de modelagem de equações estruturais. Os resultados das análises multigrupos do estudo indicaram semelhanças e diferenças entre as amostras do Brasil e de Portugal. As semelhanças foram que, em ambas, as hipóteses de que a Expectativa de Desempenho, a Percepção de Co-criação, a Perspectiva Anti-Indústria e o Risco Percebido têm relação com a Intenção de Uso foram suportadas.

Palavras-chave
Economia compartilhada; Antecedentes de consumo; CouchSurfing; Brasil; Portugal

Resumen

El turismo es una de las áreas más afectadas por la economía compartida a través de organizaciones como CouchSurfing, AirBnb y BeLocal Exchange. El objetivo de este artículo era proponer un modelo teórico para identificar el historial de consumo de CouchSurfing desde la perspectiva de los consumidores invitados. Para ello, se realizó un estudio con enfoque cuantitativo, comparando la realidad de este tipo de alojamiento en Brasil y Portugal. Se realizó una encuesta con 421 personas de Brasil y 408 de Portugal que ya han utilizado CouchSurfing como invitados. Como método de análisis de datos, se utilizaron estadísticas descriptivas básicas, análisis factorial exploratorio y análisis de modelos de ecuaciones estructurales. Los resultados de los análisis multigrupo del estudio indicaron similitudes y diferencias entre las muestras de Brasil y Portugal. Las similitudes fueron que, en ambos casos, se respaldaron las hipótesis de que la Expectativa de rendimiento, la Percepción de la co-creación, la Perspectiva anti-industria y el Riesgo percibido están relacionadas con la Intención de uso.

Palabras clave
Economía compartida; Motivaciones del consumo; CouchSurfing; Brasil; Portugal

1 INTRODUCTION

Sharing economy can be considered a peer-to-peer activity based on the use, concession or shared access to goods and services, coordinated by online hosting services (Hamari, Sjöklint & Ukkonen, 2016). Countless organizations like Airbnb, Couchsurfing, Uber and BlaBlaCar are included in this context. One of the most affected areas by the sharing economy is Tourism, given that residents share houses, cars, tours, and food with tourists (Heo, 2016).

A sharing economy platform that has stood out in the tourism field is Couchsurfing. At Couchsurfing, members offer free accommodation to each other, company for sightseeing, friendly gatherings, among other benefits.

The objective of the present study is to offer a theoretical model to identify the antecedents of sharing economy tourism platforms consumption in Brazil and Portugal, Couchsurfing being the locus of this investigation. The theoretical model perceives sharing economy platforms as a new technology, based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) by Venkatesh, Thong and Xu (2012). In addition, sharing economy concepts/constructs are present in this study, by investigating which factors influence Couchsurfing use intention.

In order to achieve this objective, a quantitative study was carried out, surveying consumers in Brazil and Portugal. The samples are composed only of guests, not hosts, and the data was analyzed using structural equation modeling.

The proposition of a theoretical model to identify the antecedents of consumption and the consumer co-creation perception in the sharing economy regarding tourism are important contributions to the development of consistent concepts on this subject in the field of marketing and tourism. One contribution of this study is to propose a theoretical model that merges concepts from a theory focused on the consumption of new technologies (UTAUT 2) and concepts from the literature on sharing economy focused on factors that contribute to the intention to use such platforms.

2 THEORETICAL FRAMEWORK

2.1 Sharing Economy in Tourism

There has been a proliferation of consumption models in which access is enabled through sharing or communion of resources/products/services, now redefined by technology and peer communities (Rifkin, 2016; Bardhi & Eckhardt, 2012). According to Rifkin (2016), sharing economy has created new economic models, which reduce compulsive consumption, optimizing and encouraging a more sustainable way of life. Sharing economy platforms go from systems aimed at “renting” access to private objects or facilities – such as cars (Zipcar) and accommodation (Airbnb) – to collaborative lifestyle systems like Couchsurfing. For Kennedy (2015), the sharing economy can intensify social activities (or sociability) and social exchanges.

Theoretical discussions regarding practices described as sharing and collaborative consumption have grown due to the increasing number of both profit and nonprofit organizations (Belk, 2010; Botsman & Rogers, 2011; Bardhi & Eckhardt, 2012; Belk, 2014a; Rifkin, 2016). Belk (2010) defines sharing as the act and process of distributing what is ours to others for their use and/or the act and process of receiving or taking something from others for our use. Collaborative consumption is defined by the author as consumers coordinating the acquisition and distribution of a resource, for a fee or other remuneration, such as bartering, trading, involving an exchange of non-monetary compensation (Belk, 2014a). Therefore, the difference between the two concepts is that collaborative consumption involves a fee or other form of remuneration, and sharing does not. These two consumer behaviors are inserted within the context of the sharing economy (Rifkin, 2016)

In the field of tourism and hospitality, the relevance of the sharing economy is visible due to its high growth rate and the impact on the tourism industry. Tourism is one of the most affected fields, due to the fact that residents share houses, cars, tours and food with tourists (Heo, 2016). However, researchers have paid little attention to the impact of the relatively new sharing economy platforms on the tourism landscape (Molz, 2013; Heo, 2016). Heo (2016) believes that the sharing economy has blurred the boundaries between consumers and service providers, as well as residents and business entities in tourist destinations.

Couchsurfing, for example, is a free worldwide website that connects travelers with locals in more than 235 countries and territories and is currently one of the most visited “hospitality services” on the Internet (Botsman & Rogers, 2011). This platform allows its members to create an online profile, similar to Facebook, and to offer free housing to travelers from all over the world, registered on the site, or who can be hosted by another user (Belk, 2014b).

Belk (2014b) claims that Couchsurfing can connect travelers using a network of people interested in sharing their lives in a profound and meaningful way, without any financial or material exchange, only the exchange of experiences. According to Belk (2014b) a visit can result in friendships that last beyond the visit, creating a feeling of generosity and warmth among the members of the community, which can be verified by the many testimonies posted on the website. Couchsurfing can be considered, therefore, an example of sharing because there is no currency exchange.

2.2 Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2)

Considering sharing platforms as disruptive technologies, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) by Venkatesh, Thong and Xu (2012), unfolding the UTAUT model, can offer relevant constructs for an analysis of the antecedents of Couchsurfing consumption.

The UTAUT model was developed in 2003 to study the technology use and acceptance in an organizational environment, and considers four constructs to be directly related to the acceptance and use of technology: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions (Venkatesh et al., 2003). The authors also propose four moderators on the correlation between the intention to use and use itself. They are: gender, age, experience, and voluntary use.

In order to improve UTAUT and to apply it to the consumer context, Venkatesh, Thong and Xu (2012) presented UTAUT 2. The new model includes three new constructs and claims that the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivations (new), Price (new), and Habit (new) aim to explain the Intention Behavior variable, as well as the Habit and Intention Behavior variables aim to explain the variable Intention to Use. In addition, the UTAUT 2 moderators are: gender, age, and experience.

The present study on Couchsurfing considers that the constructs Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions of UTAUT 2 can be useful to evaluate the antecedents of Couchsurfing consumption. Therefore, items from UTAUT 2 questionnaire are used in this research.

The Performance Expectancy is a variable that measures the extent to which the subjects believe that the use of a certain technology will provide benefits for them as consumers (Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012). In the original model, Venkatesh et al. (2003) claims that Performance Expectancy is the strongest predictor of intention to use a technology. In Indrawati and Haryoto (2015) and Morosan and DeFranco (2016) studies, this relationship is also confirmed. Thus, the initial hypothesis is that the Performance Expectancy evaluates how the sharing economy tourism platform can contribute to people's lives in terms of utility and perceived benefits.

H1: Performance Expectancy positively influences the intention to use Couchsurfing.

Effort Expectancy refers to the degree of ease associated with the use of a certain technology by consumers. It is the degree of ease associated with the use of the system (Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012).

Venkatesh, Thong and Xu (2012) consider Effort Expectancy to have a positive relationship with Intention to Use mobile marketing. Later studies applied in different contexts, such as the ones carried by Giglio et al. (2017), also confirm this relationship. Therefore, regarding the sharing economy tourism platforms, an initial hypothesis is proposed to verify whether they provide an interface that is easy for consumers to learn and use.

H2: Effort Expectancy positively influences the intention to use Couchsurfing.

Social Influence aims to assess how important others’ perception of a certain technology is to the consumers (for example, their family and friends). It is defined by the intensity in which the people who are a part of the subject’s social circle influence his or her use of a technology (Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012).

A fundamental assumption is that users tend to consult their social network about the use of new technologies and can be influenced by the perceived social pressure from their social groups. Regarding consumption, non-users have greater control over their choices and its consequences on their social image, indicating that social influence plays a significant role in consumer behavior (Slade et al., 2015). In previous studies, such as Indrawati and Haryoto’s (2015), a relationship between Social Influence and Intention to Use is confirmed. In the present study, Social Influence evaluates three types of people, who can influence and encourage the use of sharing economy platforms in tourism: (i) people who are important to the individual (ii) people who influence their behavior (iii) people whose opinions they value. In some cases, the same person can fit two or even three of those categories. Therefore, one more hypothesis is proposed:

H3: Social Influence positively influences the intention to use Couchsurfing.

The Facilitating Conditions construct aims to measure consumers’ perceptions regarding the support and resources available to perform a behavior (Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012). They are considered to be environmental factors that facilitate or impede the intention to use and the acceptation of a technology. They include aspects that can directly influence real behavior, such as the individual’s training or knowledge. The concept of Facilitating Conditions consists of items regarding not only perceived behavioral control, but also the relationship between the organization's attempts to overcome barriers to use and the intention of potential users to use (Chang, 2012).

H4: Facilitating Conditions positively influence the intention to use Couchsurfing.

2.3 Background from previous studies on sharing economy

In addition to Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, the present study also considers other antecedents of sharing economy consumption from previous studies. They are: Cost Saving, Expectation to Share Cultural Experiences, Anti-Industry Perspective, Trust, Peer Group Identity, Perception of Value Co-creation, and Perceived Risk.

2.3.1 Cost Saving

The Cost Saving construct is thought to be one of the main motivations for people who choose the sharing economy model (Bardhi & Eckhardt, 2012; Bellotti et al., 2015; Botsman & Rogers, 2011; Lamberton & Rose, 2012). Belk and Sobh (2007), for example, point out that sharing economy makes it possible for people to consume a larger and more varied set of things than what they could pay for, therefore making cost saving an important motivation. The authors draw attention to the fact that there is a risk of loss or damage, depending on the sharing procedures, but if all parties act according to the rules, all of them will benefit. Following this logic, Cost Saving can be a motivation that would overcome, for example, the fear of loss or damage.

Lamberton and Rose (2012) suggest that collaborative consumption systems, such as car sharing platforms (Zipcar, for example), may be preferred because they allow access to the desired product/service at a low cost. According to the authors, cost saving would be a factor consistent with rational models, in which consumers seek products that offer the greatest benefit at the lowest cost possible. This perspective is similar to Sacks’ (2011), who stated that consumers participate in sharing economy because it allows access to products and services at low prices, suggesting an extrinsic cost-saving reward of P2P systems.

Since it is less expensive to share an accommodation than it is, for example, to pay for a hotel room, participants would have an easier access to something that may not be available for financial reasons, if there was no possibility of sharing. In his study, Tussyadiah (2016) conceptualizes Cost Saving as economic benefits, considering it to be one of the antecedents of future intention to use sharing economy services. This indicates that Cost Saving can contribute to the intention to use.

H5: Cost Saving positively influence the intention to use Couchsurfing

2.3.2 Expectation to Share Cultural Experiences

Heo (2016) points out that the desire to connect with the local community can be a factor that contributes to the popularity of the sharing economy in tourism. Tussyadiah and Pesonen (2015) also claim that engaging in such platforms allows people to create and maintain social connections, interacting directly with the hosts and therefore connecting with local communities in a unique experience. Liang, Choi and Joppe (2017) state, based on previous studies, that the search for local life experiences would be an attraction for Airbnb users. In this sense, the Expectation to Share Cultural Experiences can be a factor that has a positive correlation with the intention to use sharing economy tourism platforms.

Based on the discussion above, it is possible to formulate a definition of the Expectation to Share Cultural Experiences construct as the desire to connect with the local community and to have unique and authentic experiences. In accordance with Swarbrooke and Horner studies (2002), this represents a cultural factor that motivates tourists, since the authors argue that the experience of other cultures and visits to points of interest are cultural motivating factors. Guttentag (2015) adds that tourists expect the social experiences of staying with locals to lead to more authentic traveling experiences.

In their study on Airbnb, Liang, Choi and Joppe (2017) focus on existential authenticity, which emphasizes human nature. The authors adopt Grayson and Martinec’s definition (2004), referring to the authenticity perceived as the perception of the cognitive recognition of Airbnb consumers about their real experiences when staying in Airbnb spaces. For them, the perceived authenticity seems to play an important role in the buyback process for Airbnb’s housing consumers. According to that, the following hypothesis is formulated:

H6: The Expectation to Share Cultural Experiences positively influences the intention to use Couchsurfing.

2.3.3 Anti-Industry Perspective

According to Botsman and Rogers (2011), the choice to consume sharing economy services may also represent a rejection to a materialistic acquisition in a competitive market. Therefore, this type of consumption can be associated with "non-consumption" (Albinsson & Perera, 2012) and "anti-consumption" (Ozanne & Ballantine, 2010). Ozanne and Ballantine (2010) state, for example, that the sharing of collectively owned assets can be considered a possible form of anti-consumption behavior, which has not been adequately addressed in the literature. For Ozanne and Ballantine (2010), sharing is not an extreme form of active revolt against the market, but it is similar to minimizing behaviors.

Therefore, it is possible to consider the anti-industry perspective as an antecedent of tourism sharing economy platforms consumption. Lamberton and Rose (2012) describe the Anti-Industry Perspective as the psychological gains derived from a decision that denies support for the traditional ownership market. According to them, this aspect corresponds to a position of resistance to the market.

H7: The Anti-Industry Perspective positively influences the intention to use Couchsurfing.

2.3.4 Trust

Sharing economy platforms operate based on ​​relationships between peers who do not know each other. Ert, Fleischer and Magen (2016) argue that trust is a subjective feeling that causes the consumer to behave in a certain way according to an implicit or explicit promise. It is a key ingredient for peer-to-peer online sales, since two strangers are not likely to engage in a monetary transaction without trusting each other (Bonsón Ponte, Carvajal-Trujillo and Escobar-Rodríguez, 2015; Kim, Chung & Lee, 2011). Therefore, one of the main ways to facilitate trust in a peer-to-peer context is the creation of reputation mechanisms through online rating.

H8: Trust positively influences the intention to use Couchsurfing.

2.3.5 Peer Group Identity

Peer Group Identity refers to the conception of self, in terms of “The defining characteristics of a self-inclusive social category that becomes stereotypically interchangeable within its members” (Hogg, 1992, p. 90). The group identity is an important determinant for participating in a virtual community and, when it is established, members start to think in terms of “We”. They try to maintain a positive relationship with the group members (Bagozzi & Dholakia, 2002), and are likely to engage in community activities (Algesheimer, Dholakia & Hermann, 2005). The link between an individual and peers precedes and contributes to his or her identification with the peer group (Algesheimer, Dholakia & Hermann, 2005). Therefore, a harmonious relationship with peers leads consumers to seek and interact with other members of the group, similar to them (Algesheimer, Dholakia & Hermann, 2005).

H9: Peer Group Identity positively influences the intention to use Couchsurfing.

According to Möhlmann (2015), regarding collaborative consumption, Trust simultaneously refers to trusting the collaborative consumption service and the other consumers with whom a service is being shared. Therefore, it is noted that impressions regarding the attributes of hosts and services described on the platform and available on social media have an important role in establishing trust. This leads to the conclusion that the more the consumer identifies with the platform's proposal and with the profile presented on the hosts’ website, the more he or she trusts the service.

H10: Peer Group Identity positively influences Trust

2.3.6 Perception of Value Co-creation

The concept of Value Co-creation has been much discussed in recent studies regarding the Dominant Service Logic (DSL), introduced by Vargo and Lusch (2004). Lusch and Vargo (2014) argue that the consumer is always a value co-creator and define the Value Co-creation as the process in which actors follow through a growing development of applied and specialized knowledge and skills, in exchange of services and resource integration.

In this line, Baron et al. (2010) state that consumers are not seen simply as objects (operated resources) to be reached, segmented, and researched, but as owners of operating resources (such as skills, abilities, knowledge, initiative, and imagination), integrated to create experiences and values. Consumers are part of a family, commercial and social network, and communities of practice, brought together by the Internet (Baron et al., 2010). Therefore, they are active participants in creating experiences.

Matos (2017) aims to understand how the relationships between members of Couchsurfing are built, considering the existence of utilitarian and hedonic aspects in these relationships and that there could be a preponderance of aspects of Value Co-creation in such process. Thus, it is formulated that:

H11: The Perception of Value Co-creation positively influences the intention to use Couchsurfing.

H12: The Expectation to Share Cultural Experiences positively influences the Perception of Value Co-creation.

2.3.7 Perceived Risk

Perceived Risk is defined in terms of uncertainty and consequence, as it increases the highest levels of uncertainty and/or has a greater chance to associate negative consequences (Liang, Choi & Joppe, 2017; Oglethorpe & Monroe, 1987). Likewise, Kim, Ferrin and Rao (2008) state that the perceived risk is one of the beliefs in possible negative outcomes from the transaction. Forsythe et al. (2006), however, clarify that the risks of financial and product performance are two types of risks that are highly associated with virtual purchases. In a study on Airbnb, Liang, Choi and Joppe (2017) observe that Airbnb consumers have no choice but to estimate the risk of this transaction by analyzing the information available and by communicating with the host, since they cannot experience the current service before they arrive at the property. Therefore, the perceived risk of Airbnb consumers plays a crucial role in their decision to buy back.

In the present study, the conceptualization of Perceived Risk adopted refers to the sharing economy hosting platforms consumers’ opinion about all the possible negative outcomes of booking rooms through these sites (Liang, Choi & Joppe, 2017). Perceived Risk occurs when there is uncertainty, information asymmetry, and fear of opportunism (Liang, Choi & Joppe, 2017). In this sense, it was formulated that:

H13: Perceived Risk negatively influences the intention to use Couchsurfing.

H14: Perceived Risk negatively influences the Perception of Value Co-creation in Couchsurfing consumption.

H15: The Expectation to Share Cultural Experiences has a negative relationship with the Perceived Risk in Couchsurfing consumption.

H16: Trust negatively influences the Perceived Risk in Couchsurfing consumption.

3 METHODOLOGICAL PROCEDURES

This research uses a quantitative approach, and is conducted through a survey, which is described by Hair Jr et al. (2005) as a procedure for obtaining primary data on behavior, intentions, attitudes, perceptions, motivations, and demographic and lifestyle characteristics of a certain group of people, indicated as representative of a target population. A virtual survey via Google Forms platform was carried out, aimed at Couchsurfing consumers in Brazil and Portugal. Non-probability convenience sampling technique was used, obtaining a total of 421 respondents from Brazil and 408 from Portugal.

The instrument used to gather data is adapted and derived from a previous qualitative study, carried out with 20 Couchsurfing consumers in Brazil and 18 Couchsurfing consumers in Portugal. The questionnaire is based on the constructs: "Performance Expectancy", "Effort Expectancy", "Social Influence" and "Facilitating Conditions", whose items are adapted from the UTAUT 2 scale by Venkatesh, Thong and Xu (2012). The questionnaire also considers the constructs: “Cost Saving” (whose items are adapted from Tussyadiah (2016) and from the qualitative study previously carried out); “Expectation to Share Cultural Experiences” (whose items are adapted from Tussyadiah (2016), Liang, Choi and Joppe (2017) and the qualitative study); “Anti-industry perspective” (whose items are adapted from Lamberton and Rose (2012) and from the qualitative study); “Peer Group Identity” (Wang, Yu and Wei (2012) and the qualitative study), “Trust” and “Perceived Risk” (whose items are adapted from Liang, Choi and Joppe (2017) and from the qualitative study); and “Perception of Value Co-creation” (whose items come from the work of Yi and Gong (2012) and the qualitative study carried out previously).

In order to measure the variables in which the questionnaire was based, a 7-point Likert-type interval scale was chosen, ranging from “Totally disagree” to “Totally agree”. SPSS and SmartPLS 3 software were used on the data analysis. Initially, a descriptive analysis of the samples was made in order to describe the respondents' profile.

After this step, the data organizing process (missing data, outlier verification, linearity, and normality) was carried out. Finally, the data was analyzed using exploratory factor analysis and structural equation modeling. The factor analysis corresponds, according to Hair et al. (2009), to a technique suitable for analyzing the patterns of complex multidimensional relationships.

4 DATA ANALYSIS

4.1 Profile of Couchsurfing users from Brazil and Portugal

The majority of the Couchsurfing users in Brazil’s sample are female – from 421 consumers who answered the questionnaire, 227 are women, which corresponds to 54% of the total, and 194 are men, which represents 46%. The ages of Couchsurfing respondents in Brazil range between 18 and 66 years. Regarding the marital status of consumers, most of them are single, representing 74% of the sample (313 people). That may indicate that single people are more willing and open to participate in sites such as Couchsurfing, where the person stays at the home of a stranger while traveling. Regarding the level of education of the respondents, most people have completed higher education (34%, 143 people), 103 people (24%) had incomplete higher education and 11 (3%) respondents had completed high school.

A total of 408 people from Portugal answered the questionnaire. Regarding sex, 258 are men – 63% of the total – and 150 are women – which represents 37%. The age of respondents range between 18 and 63 years. Most people declared themselves single, which corresponded to 79% of the total (323 people). Regarding the level of education of respondents in Portugal, 194 people (48%) said they have completed higher education, 28% of respondents (114 people) have post-graduate degrees, 19% (76) have secondary education, 5% (22) have a PhD and only two people declared to have only high school education.

4.2 Normality and Linearity Analysis

Kolmogorov-Smirnov test was used to assess the normality of the data, calculating the significance for differences in relation to a normal distribution. The results of the tests carried out to analyze the sample from Brazil and the sample from Portugal indicated the value p = 0.000 for all variables. Thus, all p-values are less than 0.005, which means that the hypothesis that the distribution is normal is rejected and attests to non-normality in all variables.

The linearity test is used to assess whether the model has additivity and homogeneity (Hair et al., 2009). To assess the linearity of the data, the Spearman test was conducted, used in cases of samples with non-normal distributions. All tests from both the Brazilian sample and the Portuguese sample regarding the same construct show significant correlations at 0.01 (2 ends). The correlation analysis of the total matrix revealed that the matrix relationships are mostly linear (1% two-tailed).

4.3 Exploratory Factor Analysis (EFA)

Since the present study develops and adapts specific scales for the context of the sharing economy in tourism, it is considered important to understand how the existing factors are grouped with the research items. In this sense, exploratory factor analysis (EFA) was performed. In this study, the exploratory factor analysis of the Couchsurfing sample (Brazil) was initially conducted considering all constructs in order to find the factor structure present in the model as a whole. Subsequently, EFA was conducted, construct by construct, in order to assess the unidimensionality of all constructs. At first, EFA was performed by principal components extraction and varimax orthogonal rotation.

Malhotra (2004) points out that, among the main statistics associated with factor analysis, are: the Barlett sphericity test, the Kaiser-Meyer-Olkin (KMO) sample adequacy measure, the communalities, the factor loading, and the percentage of variance. The results indicate the adequacy of the factor analysis using KMO (0.931) and Barlett’s (sig 0.000).

Communality corresponds to the variance that a variable shares with the other variables considered in a study. The factor loading corresponds to simple correlations between the variables and the factors (Malhotra, 2004). Ideally, communalities and factor loading are above 0.4 (Hair et al., 2014a).

The EFA result regarding the Couchsurfing sample (Brazil) indicates that all communalities are above 0.4. The percentage of variance refers to the total variance attributed to each factor (Malhotra, 2004). The total variance extracted must be greater than 60%. In the first round of EFA, fifteen factors were extracted with an extracted variance of 75.381%.

The exploratory factor analysis of the Couchsurfing sample (Portugal) was conducted first by principal components and varimax orthogonal rotation, regarding all constructs previously proposed for Couchsurfing, in order to find which factor structure is present as a whole. The results of the Barlett sphericity test (sig 0.000) and the Kaiser-Meyer-Olkin (KMO) (0.925) proved to be adequate, since the ideal is the Barlett sphericity test value to be less than 0.05 and the KMO value to be between 0.5 and 1.0 (Hair et al., 2014a).

The first round of EFA's result of the Couchsurfing sample (Portugal) indicates that all communalities are above 0.4, respecting the parameters indicated by Hair et al. (2014a). The total explained variance is 73% and it is considered to be according to the parameter, since it should ideally be above 60%. In the first round of EFA, fourteen factors were extracted.

4.4 Measurement Model Analysis

The criteria used to evaluate the measurement model of reflective constructs include simple and composite reliability, convergent validity (AVE and outer loadings), discriminant validity (cross loadings, Fornell-Larcker criterion, and HTMT). The data were run by Consistent PLS Algorithm. During the analyses of Brazil and Portugal samples, some indicators presented outer loadings below the value indicated in previous literature (below 0.7). Therefore, after repeating the Consistent PLS Algorithm procedure a few times, it was necessary to remove some indicators.

Figure 1
Theoretical model regarding the Couchsurfing sharing platform after removing items with outer loadings below 0.708 (No 2)

Figure 1 presents the final model obtained regarding the Couchsurfing samples from Brazil and Portugal. The items CONF4, CONF5, CONF6, CONF7, CONF8, CONF9, RP8, RP9, and PCC8 were removed, after repeated procedures due to outer loadings below 0.708. With the removal of items with low loadings, the values of reliability and AVE increased.

4.5 Couchsurfing sample measurement model evaluation (Brazil)

Simple reliability, composite reliability, and convergent validity

Convergent validity is the extent to which an indicator correlates positively with alternative indicators of the same construct. Therefore, the indicators items (measures) of a specific construct must converge or share a high proportion of variance (Hair et al., 2014b). To establish convergent validity, researchers must consider the indicators’ outer loadings and average extracted variance (AVE). The outer loadings of all indicators must be statistically significant and greater than or close to 0.7. Table 1 presents the evaluation of outer loadings of the first order constructs.

Table 1
Outer Loadings of first order constructs in the Couchsurfing sample evaluation (Brazil)

Construct	Indicators	Outer Loadings
Facilitating Conditions	CF1	0.861
	CF2	0.876
	CF3	0.784
Trust based on feedback	CONF1	0.818
	CONF2	0.915
	CONF3	0.8
Cost Saving	EC1	0.717
	EC2	0.837
	EC3	0.907
	EC4	0.631
	EC6	0.649
	EC7	0.84
Performance Expectancy	ED1	0.88
	ED2	0.897
	ED3	0.697
	ED4	0.762
Effort Expectancy	EE1	0.788
	EE2	0.902
	EE3	0.838
	EE4	0.961
Expectation to Share Cultural Experiences	EEC1	0.889
	EEC2	0.919
	EEC3	0.874
	EEC4	0.887
	EEC5	0.901
	EEC6	0.898
	EEC7	0.877
	EEC8	0.915
	EVS1	0.875
	EVS2	0.88
	EVS3	0.823
	EVS4	0.808
	EVS5	0.845
Peer Group Identity	GIP1	0.781
	GIP2	0.877
	GIP3	0.744
	GIP4	0.809
Social Influence	IS1	0.781
	IS2	0.877
	IS3	0.744
	IS4	0.809
Anti-industry perspective	PAI1	0.821
	PAI3	0.809
	PAI4	0.859
	INTUSO1	0.827
Intention to use	INTUSO2	0.75
	INTUSO3	0.808
	INTUSO4	0.871
	RP2	0.694
Perceived Risk	RP3	0.659
	RP4	0.795
	RP5	0.833
	RP6	0.581
	RP7	0.613

• Source: Study data (2019)

Table 2 presents the second order construct outer loadings (Perception of Co-creation) and its dimensions regarding Feedback to Others, Host Support, and Co-creation Experiences.

Table 2
Outer Loadings Evaluation of the second order construct Perception of Co-creation of the Couchsurfing sample (Brazil)

Second order construct	Indicators	Outer Loading	Second order construct dimensions	Indicators		Outer Loadings
Perception of Co-creation	PCC1	0.767355484	Feedback to Others		PCC1	0.8405695
	PCC2	0.830930001			PCC2	0.918422
	PCC3	0.762332872			PCC3	0.8387902
	PCC10	0.717177592			PCC10	0.7976625
	PCC4	0.791210076	Host Support		PCC4	0.8757128
	PCC5	0.798616334			PCC5	0.8708533
	PCC6	0.785046411			PCC6	0.8532744
	PCC7	0.789552864			PCC7	0.8589821
	PCC9	0.638963974			PCC9	0.7124709
	PCC11	0.800148585	Co-creation experiences		PCC11	0.8749291
	PCC12	0.827750387			PCC12	0.9123292
	PCC13	0.81355054			PCC13	0.892069

• Source: Study data (2019)

The presented outer loadings are considered adequate, according to the parameters.

AVE is the measure used to establish convergent validity at a construct level. An AVE equal to or greater than 0.50 indicates that the construct explains more than half the variance of its indicators. The AVE of each reflective construct must be evaluated (Hair et al., 2014b). Table 3 presents the values of AVE, simple reliability and composite reliability.

Table 3
Measurement Model of the Couchsurfing sample regarding Simple Reliability, Composite Reliability, and AVE (Brazil)

	Couchsurfing Brazil
Construct	Simple Reliability	Composite Reliability	AVE
Facilitating Conditions	0.877	0.879	0.708
Trust based on Feedback	0.881	0.883	0.715
Cost Saving	0.892	0.896	0.594
Performance Expectancy	0.887	0.885	0.661
Effort Expectancy	0.930	0.928	0.765
Expectation to Share Cultural Experiences	0.977	0.977	0.769
Peer Group Identity	0.880	0.880	0.647
Social Influence	0.879	0.879	0.647
Co-creation Perception	0.943	0.944	0.571
Anti-Industry Perception	0.849	0.847	0.588
Intention to use	0.890	0.888	0.664
Perceived Risk	0.904	0.902	0.579

• Source: Study data (2019)

According to Hair et al. (2014b), the traditional criterion for internal consistency is Cronbach's alpha, which offers an estimate of reliability based on the observed variables’ intercorrelations. Cronbach's alpha assumes that all indicators are equally reliable, but the PLS-SEM prioritizes the indicators according to their individual reliability. In addition, Cronbach's alpha is sensitive to the number of items present on a scale and usually tends to underestimate the internal consistency reliability. Due to Cronbach's alpha’s limitations, Hair et al. (2014b) recommend the use of composite reliability to evaluate internal consistency.

The composite reliability varies between 0 and 1, with high values indicating higher levels of reliability. It is generally interpreted in the same way as Cronbach's alpha. Specifically, values between 0.6 and 0.7 are considered acceptable for exploratory research and values between 0.7 and 0.9 are satisfactory for more advanced research (Hair et al., 2014b).

Table 3 indicates that all Couchsurfing sample (Brazil) values for simple reliability and for composite reliability are above the minimum limit of 0.70, indicating internal consistency.

Discriminant validity

Discriminant validity is the extent to which a construct is truly distinct from others by empirical standards (Hair et al., 2014b). Therefore, establishing discriminant validity implies that a construct is unique and captures a phenomenon that is not represented by other constructs in the same model. To assess the discriminant validity, Henseler, Ringle and Sarstedt (2015) refer to the Heterotrait-Monotrait Ratio (HTMT) criterion. The HTMT criterion of the Couchsurfing sample (Brazil) is according to the required parameter.

Table 4
HTMT criterion (Couchsurfing Brazil)

	Host Supp.	Facil.Cond.	Cos Sav.	Perf. Exp.	Eff.Exp.	Exp.Exp.Cult.	Exp.Co-creation	Trust Feed backs	Feedbacks to Others	Peer Group Iden.	Soc. Inf.	Cocreation Perc.	Anti Industry Pers.	Intention to ue	Perceived Risk
Host Support
Facil. Cond.	0.424
Cost Saving	0.377	0.417
Perform. Expect.	0.346	0.501	0.582
Effort Expect.	0.392	0.668	0.421	0.507
Expect. to Share Cult. Exp.	0.527	0.417	0.395	0.252	0.371
Cocreat. Expect.	0.801	0.441	0.324	0.304	0.360	0.653
Trust Feed back	0.470	0.486	0.415	0.329	0.366	0.376	0.467
Feed Back to others	0.782	0.521	0.353	0.451	0.442	0.388	0.698	0.549
Peer Group Identity	0.437	0.416	0.149	0.261	0.341	0.621	0.486	0.267	0.431
Social Influence	0.153	0.213	0.195	0.386	0.217	0.127	0.118	0.108	0.227	0.293
Co-creation Percept.	1.000	0.502	0.402	0.414	0.438	0.555	0.914	0.538	0.950	0.485	0.198
Anti-Industry Perspect.	0.308	0.232	0.436	0.364	0.260	0.241	0.240	0.129	0.271	0.217	0.351	0.324
Intention to use	0.514	0.462	0.438	0.535	0.413	0.492	0.556	0.310	0.500	0.442	0.211	0.568	0.548
Perceived Risk	0.213	0.183	0.062	0.286	0.241	0.187	0.283	0.086	0.272	0.240	0.044	0.268	0.064	0.410

• Source: Study data (2019)

4.6 Evaluation of the sample measurement model for Couchsurfing (Portugal)

Simple reliability, composite reliability, and convergent validity

The convergent validity analysis was carried out based on the values ​​of outer loadings and AVE. Outer loadings were close to or above 0.7, which indicates they are within the parameters. Table 5 shows the first order constructs’ outer loadings.

Table 5
Evaluation of Outer Loadings of the first order constructs in the Couchsurfing sample (Portugal)

Construct	Indicators	Outer Loadings
Facilitating Conditions	CF1	0.855
	CF2	0.933
	CF3	0.769
Trust based on Feedback	CONF1	0.875
	CONF2	0.892
	CONF3	0.800
Cost Saving	EC1	0.857
	EC2	0.858
	EC3	0.917
	EC4	0.716
	EC6	0.511
	EC7	0.660
Performance Expectancy	ED1	0.836
	ED2	0.789
	ED3	0.704
	ED4	0.786
Effort Expectancy	EE1	0.832
	EE2	0.831
	EE3	0.863
	EE4	0.921
Expectation to Share Cultural Experiences	EEC1	0.858
	EEC2	0.916
	EEC3	0.804
	EEC4	0.833
	EEC5	0.815
	EEC6	0.853
	EEC7	0.869
	EEC8	0.881
	EVS1	0.850
	EVS2	0.859
	EVS3	0.850
	EVS4	0.855
	EVS5	0.734
Peer Group Identity	GIP1	0.753
	GIP2	0.971
	GIP3	0.754
	GIP4	0.851
Social Influence	IS1	0.847
	IS2	0.869
	IS3	0.802
	IS4	0.506
Anti-Industry Perspective	PAI1	0.841
	PAI3	0.704
	PAI4	0.912
Intention to Use	PROUSO1	0.879
	PROUSO2	0.737
	PROUSO3	0.796
	PROUSO4	0.913
Perceived Risk	RP1	0.946
	RP2	0.705
	RP3	0.690
	RP4	0.916
	RP5	0.674
	RP6	0.702
	RP7	0.650

• Source: Study Data (2019)

Table 6 shows the second order construct Perception of Co-creation’s outer loadings and its dimensions Feedback to others, Host Support, and Co-creation Experiences. All outer loadings are ​​above 0.4.

Table 6
Outer Loadings evaluation regarding second order construct Perception of Co-creation and its dimensions in the Couchsurfing sample (Portugal)

Second Order Construct	Indicators	Outer Loadings	Second Order Construct Dimensions	Indicators	Outer Loadings
Perception of Co-creation	PCC1	0.706286	Feedback to others	PCC1	0.812211
	PCC2	0.722033		PCC2	0.825862
	PCC3	0.684341		PCC3	0.788327
	PCC10	0.781153		PCC10	0.931273
	PCC4	0.651828	Host Support	PCC4	0.741927
	PCC5	0.834126		PCC5	0.899944
	PCC6	0.814168		PCC6	0.89603
	PCC7	0.80482		PCC7	0.897013
	PCC9	0.722216		PCC9	0.760073
	PCC11	0.803753	Co-creationExperiences	PCC11	0.875431
	PCC12	0.839696		PCC12	0.903682
	PCC13	0.820168		PCC13	0.889795

• Source: Study data (2019)

All AVE values are above 0.5, indicating adequacy to the parameters. The reliability analysis was performed using Cronbach's alpha values and composite reliability. All values are above 0.7 and were, therefore, satisfactory, according to Table 7.

Table 7
Simple Reliability, Composite Reliability and AVE regarding the Measurement Model of the Couchsurfing sample (Portugal)

	Couchsurfing Portugal
Construct	Simple Reliability	Composite Reliability	AVE
Facilitating Conditions	0.885	0.890	0.731
Trust based on Feedback	0.891	0.892	0.734
Cost Saving	0.886	0.892	0.587
Performance Expectancy	0.861	0.861	0.608
Effort Expectancy	0.921	0.921	0.744
Expectation to Share Cultural Experiences	0.970	0.970	0.715
Peer Group Identity	0.904	0.903	0.701
Social Influence	0.848	0.849	0.593
Co-creation Perception	0.943	0.943	0.564
Anti-industry perspective	0.838	0.829	0.561
Intention to use	0.903	0.901	0.696
Perceived Risk	0.907	0.905	0.582

• Source: Study data (2019)

Discriminant validity

The verification of the discriminant validity was based on the HTMT evaluation. The results are adequate according to the parameters indicated by the literature.

Table 8
HTMT (Couchsurfing Portugal)

	Host Sup.	Facil.Cond.	Cost Sav.	Perf.Exp.	Eff. Exp.	Exp.Exp.Culturais	Co-creation Exp.	Conf.Feedbacks	Feedback to others	Peer Group Iden.	Soc. Inf.	Perc. Cocreation	Anti-Industry Per.	Int. to use	Perceived Risk
Host Support
Facilit. Coditions	0.385
Cost Saving.	0.347	0.326
Performance Expect.	0.346	0.327	0.423
Effort Expect.	0.329	0.557	0.351	0.737
Expect. to share Cult. Exper.	0.528	0.549	0.451	0.388	0.441
Expect. of Co-creat.	0.786	0.460	0.322	0.270	0.365	0.635
Trust based on Feedback	0.423	0.379	0.288	0.174	0.257	0.393	0.426
Feedback to Others	0.748	0.285	0.301	0.385	0.303	0.390	0.624	0.414
Peer Group Identity	0.282	0.290	0.180	0.226	0.313	0.440	0.363	0.207	0.367
Social Influence	0.190	0.164	0.126	0.266	0.226	0.138	0.145	0.112	0.243	0.305
Co-creation Perception	0.999	0.409	0.365	0.372	0.364	0.565	0.894	0.467	0.927	0.376	0.228
Anti-Industry Perception	0.263	0.160	0.358	0.318	0.244	0.285	0.201	0.104	0.298	0.197	0.230	0.297
Intention to Use	0.430	0.369	0.340	0.461	0.447	0.468	0.557	0.230	0.476	0.362	0.206	0.519	0.387
Perceived Risk	0.188	0.243	0.068	0.259	0.208	0.216	0.297	0.056	0.244	0.172	0.073	0.253	0.144	0.442

• Source: Study Data(2019)

4.7 PLS Multigroup analysis (PLS-MGA)

Hair et al. (2017) explain that PLS-SEM applications generally analyze the complete set of data, implicitly assuming that the data result from a homogeneous population, which, for them, is generally unrealistic. For this reason, the authors state that it is important to consider the question of heterogeneity of data between groups, which can be observable or non-observable.

Observed heterogeneity refers to differences between two or more data sets regarding observable characteristics, such as gender, age, or country. Researchers often use these observable characteristics to divide data into separate groups of observations and perform PLS-SEM specific to group analyses – for example, dividing customer samples by gender (Hair et al., 2017). Unobservable heterogeneity, on the contrary, implies that the differences between two or more groups do not emerge before specific observable characteristics or combinations of some characteristics, but become apparent when it comes to differences in structural path coefficients. In the present study, observable heterogeneity was investigated, since the two samples were collected from people of different countries (Brazil and Portugal).

The authors point out that, before comparing the parameter estimates of specific groups for significant differences using multigroup analysis, it is necessary to measure the invariance, also referred to as equivalence measurement. By establishing the measurement of invariance, researchers can be sure that the differences in a model’s estimates do not result from different content or the meaning of latent variables across groups. Variations in structural relationships between constructs could result from different meanings that groups of respondents attribute to the phenomenon being measured, rather than differences in structural relationships. Reasons for such differences may come, for example, from (a) the fact that respondents have different cultural values that interpret some measures in a different conceptual way, (b) gender, ethnicity, or another individual difference that involves different responses to the instrument, (c) respondents who use options available on the scale differently (example, tendency to choose or not choose extremes) (Hair et al., 2017). Therefore, the authors recommend to establish the measurement of the invariance during multigroup comparisons to ensure the results and conclusion validity.

As it could be seen throughout the process, the measurement of the invariance has already been established by the analysis of the reflective measurement models. It was chosen the model factor and not the composite model. The path coefficients from different samples are almost always numerically different. Multigroup analysis helps to better understand whether these differences are statistically significant (Hair et al., 2017). Technically, a multigroup analysis tests the null hypothesis that the path coefficient between two groups is not significantly different. The alternative hypothesis is that the path coefficients are different.

The present study carried out a multigroup analysis on Couchsurfing through samples from Brazil and Portugal. The results of the multigroup analysis are presented in Table 9.

Table 9
Results of Couchsurfing multigroup analysis

	Original Structural Coefficients (Brazil)	Original Structural coefficients (Portugal)	Average structural coefficients (Brazil)	Average structural coefficients (Portugal)	t values (Brazil)	t values (Portugal)	p values (Brazil)	p values (Portugal)
Facilitating conditions -> Intention to Use	0.077	0.030	0.080	0.031	1.528	0.649	0.127	0.516
Cost Saving -> Intention to Use	0.044	0.074	0.047	0.077	0.822	1.436	0.411	0.151
Performance Expectancy -> Intention to Use	0.189	0.101	0.187	0.102	3.508	2.064	0.000	0.039
Effort Expectancy -> Intention to Use	-0.024	0.085	-0.026	0.084	0.575	1.454	0.565	0.146
Expectation to Share Cultural Experiences -> Co-creation Perception	0.515	0.522	0.516	0.523	9.158	9.351	0.000	0.000
Expectation to Share Cultural Experiences -> Intention to Use	0.137	0.068	0.139	0.066	2.853	1.204	0.004	0.229
Expectation to Share Cultural Experiences -> Perceived Risk	-0.189	-0.250	-0.194	-0.253	3.137	3.668	0.002	0.000
Trust Based on Feedback -> Intention to Use	-0.007	0.002	-0.011	0.006	0.155	0.031	0.877	0.975
Trust Based on Feedback -> Perceived Risk	0.011	0.116	0.010	0.114	0.211	2.172	0.833	0.030
Peer Group Identity-> Trust Based on Feedback	0.240	0.191	0.242	0.192	4.572	3.379	0.000	0.001
Peer Group Identity -> Intention to Use	0.075	0.097	0.075	0.099	1.452	2.155	0.147	0.031
Social Influence -> Intention to Use	-0.054	0.051	-0.050	0.055	1.471	1.453	0.142	0.146
Co-creation Perception -> Host Support	0.931	0.933	0.931	0.933	84.365	105.054	0.000	0.000
Co-creation Perception -> Co-creation Experiences	0.866	0.849	0.866	0.849	43.637	36.147	0.000	0.000
Co-creation Perception -> Feedback to others	0.873	0.848	0.872	0.848	52.722	37.082	0.000	0.000
Co-creation Perception -> Intention to Use	0.170	0.210	0.167	0.206	2.844	3.587	0.005	0.000
Anti-Industry Perspective-> Intention to Use	0.308	0.135	0.305	0.134	7.672	3.279	0.000	0.001
Perceived Risk -> Co-creation Perception	-0.164	-0.130	-0.168	-0.130	3.216	2.581	0.001	0.010
Perceived Risk -> Intention to Use	-0.220	-0.258	-0.222	-0.260	5.031	5.646	0.000	0.000

• Source: Study data (2019)

The conclusions regarding Couchsurfing multigroup analysis were summarized in Chart 1.

Chart 1
Conclusions regarding Couchsurfing multigroup analysis

Hypothesis	Brazil	Portugal
Facilitating Conditions ->Intention to Use	Unsupported	Unsupported
Cost Saving -> Intention to Use	Unsupported	Unsupported
Performance Expectancy ->Unsupported	Supported	Supported
Effort Expectancy -> Unsupported	Unsupported	Unsupported
Expectation to Share Cultural Experiences -> Co-creation Perception	Supported	Supported
Expectation to Share Cultural Experiences -> Intention to Use	Supported	Unsupported
Expectation to Share Cultural Experiences -> Perceived Risk	Supported	Supported
Trust based on Feedback-> Intention to Use	Unsupported	Unsupported
Trust based on Feedback -> Perceived Risk	Unsupported	Supported
Peer Group Identity ->Trust based on Feedback	Supported	Supported
Peer Group Identity -> Intention to Use	Unsupported	Supported
Social Influence -> Intention to Use	Unsupported	Unsupported
Co-creation Perception -> Intention to Use	Supported	Supported
Anti-Industry Perspective -> Intention to Use	Supported	Supported
Perceived Risk -> Co-creation Perception	Supported	Supported
Perceived Risk -> Intention to Use	Supported	Supported

• Source: The authors

4.8 Results Discussion

The theoretical model regarding Couchsurfing proposes 16 hypotheses. The results of multigroup analyses indicate similarities and differences between the samples from Brazil and Portugal. The similarities are that, in both samples, the hypotheses that Performance Expectancy, Co-creation Perception, Anti-Industry Perspective, and Perceived Risk are related to Intention to Use were supported.

The fact that the Performance Expectancy hypothesis is supported indicates that both Brazilian and Portuguese respondents realize that Couchsurfing offers performance benefits that exceed its costs. The confirmation of this hypothesis during this investigation regarding the tourism sharing platform is consistent with the results found by Indrawati and Haryoto (2015), Morosan and DeFranco (2016), and Giglio et al. (2017). This indicates that part of the consumption of Couchsurfing in Brazil and Portugal can be explained by the benefit perceived by consumers.

The hypothesis that the Perception of Co-creation is related to the Intention to Use Couchsurfing is supported in both samples. Therefore, it is possible to infer that both samples consider the level of Value Co-creation with the host as an important factor to continue to use Couchsurfing. In this sense, the quality of the relationship experienced with the hosts and its intensity contributes to the opinion of respondents in Portugal and Brazil.

The Anti-Industry Perspective is supported for Couchsurfing samples from Brazil and Portugal, in line with Botsman and Rogers (2011) statement that the option for sharing economy also represents a rejection of the traditional and competitive market. The confirmation of this hypothesis may even indicate a characteristic of the Couchsurfing public, linked to a profile that seeks more alternative and less materialistic and commodified forms of consumption. This may be an indication of the anti-consumption behavior pointed out by Ozanne and Ballantine (2010) and Lamberton and Rose (2012).

The fact that Perceived Risk is a supported hypothesis for both samples indicated that both Portuguese and Brazilian consumers perceive possible negative aspects that can happen after using Couchsurfing. It was noted that, for both country profiles, Perceived Risk is a construct that influences the decision to use the platform or not.

Another similarity is that the hypotheses that the Facilitating Conditions, Cost Savings, Effort Expectancy, Trust, and Social Influence are related to Intention to Use are unsupported. Regarding the hypothesis about the Facilitating Conditions, the result indicated that the resources and support available to use the platforms are not considered important in choosing to use Couchsurfing or not. According to Venkatesh, Thong and Xu (2012), the Facilitating Conditions are considered environmental factors that facilitate or impede technology acceptance. However, according to the results of the present study, such factors are not considered as essential for the acceptance of the sharing economy platform Couchsurfing, neither in Brazil nor in Portugal.

Cost saving is also not a supported factor for both Couchsurfing samples. An important aspect of the data collection process is that, when people were asked to answer a questionnaire, many Couchsurfing respondents from both Brazil and Portugal protested when it came to items related to Cost Saving. Respondents argued through messages that Couchsurfing was not about saving money while traveling, but about making friends, soul connections, cultural exchanges, etc. The results of the investigation during the quantitative stage, therefore, reinforce the respondents' protests in the survey process.

Effort Expectancy is also not a supported hypothesis in both samples, which may indicate that easiness or difficulty of use is not an essential factor for using the Couchsurfing platform or application, according to the respondents. Since the hypothesis about Social Influence is not supported either in the sample from Brazil or Portugal, it is believed that Couchsurfing users are less concerned with the opinions of others.

In addition, the hypotheses that Expectation to Share Cultural Experiences is directly related to Co-creation Perception and is related to Perceived Risk are also supported in both samples. Therefore, regarding both samples, the desire to experience a connection with the local culture contributes to a lower Perceived Risk in Couchsurfing experience and a higher level of Value Co-creation.

The hypothesis that Peer Group Identity is directly related to Trust is also supported in both samples, which indicates that the more a consumer identifies with a host's profile and perceives similarities between them, the more the consumer trusts that the Couchsurfing experience will be positive. This result is consistent with what Kunz and Seshadri (2015) state about trust and sympathy between members of the platforms being central mediators for the relationship development.

Another confirmed hypothesis is that Perceived Risk is related to Perception of Value Co-creation, which indicates that the lower the perception that there will be risks in the experience with Couchsurfing, the greater the perception of co-created value.

One of the differences between the samples is the hypothesis that Expectation of Shared Cultural Experiences is related to Intention to Use, since it was supported only for the Brazilian sample, not for the Portuguese. The hypothesis that Trust based on feedback is related to Perceived Risk is not supported for the Brazilian sample and was supported for the Portuguese. The hypothesis that Peer Group Identity is related to Intention to Use was supported for the Portuguese sample, not for the Brazilian.

5 CONCLUDING REMARKS

The main objective of this study was to propose a theoretical model to identify the antecedents of sharing platforms consumption in the context of Tourism, focusing on Couchsurfing. It is possible to consider that the objective was achieved, since a model based on UTAUT 2 constructs is proposed, also considering other constructs previously addressed by studies on sharing economy.

The results found in the present study can help developing strategies more appropriate to the audience profiles. Companies linked to the sharing economy phenomenon can benefit from these findings to better develop services in a more consistent way, considering the identified consumption motivations. It is important to mention that these companies are not only limited to Couchsurfing and Airbnb, but also concern other companies in the Tourism sector that are linked to the sharing economy such as BeLocal Exchange, Worldpackers, HomeAway, Diaspora Black, among others, which can also benefit from the results.

Sharing economy organizations such as Couchsurfing (with a high degree of sharing) are more able to build co-creation and positive sharing of values ​​with the consumer such as friendships, social ties, and socialization. For this reason, managers must strive to promote attributes related to sharing that distinguish this platform from more traditional organizations. Thus, management recommendations for organizations that fit more into pure sharing practices (with no fees involved) like Couchsurfing may involve the following actions: emphasizing socialization, investing in community growth, and avoiding direct references to issues related to money.

Couchsurfing managers must be careful not to engage the platform in activities that refer to monetary/business matters, in order not to inhibit the growth of the community or weaken the connections formed between members. They should avoid strategies that monetize services or that change the nature of service. Monetizing services can damage the legitimacy of the Couchsurfing relationships, causing a negative image among members, weakening the sense of community.

A limitation of this study is the impossibility of generalizing the results due to the type of sampling used (non-probability). As the present work is aimed at understanding the perspective of the guest/consumer of sharing economy in tourism, it is suggested that future studies should also be carried out in order to understand the host’s perspective.

Another suggestion for future studies is the development of studies regarding ​Consumer Behavior from the perspective of market segmentation, for example, investigations aimed at the female audience or the black audience in the sharing economy, focusing on sharing economy in general as in tourism.

ACKNOWLEDGEMENT

This study was supported by CAPES through the granting of a scholarship from the Institutional Program for Sandwich Doctorate Scholarships (PDSE) to Luciana Alves Rodas Vera.

Appendix A Questionnaire – Please rate your degree of agreement with the following items.

Please rate your degree of agreement with the items in a 7-point scale, ranging from 1 = I strongly disagree to 7 = I strongly agree.

When considering my experience with Couchsurfing, I think the platform... ...is useful for finding accommodation. 1 2 3 4 5 6 7 ...increases my chances of finding accommodation that fit my profile. 1 2 3 4 5 6 7 ...helps me get the right accommodation quickly. 1 2 3 4 5 6 7 ...it is a convenient tool for finding accommodation. 1 2 3 4 5 6 7 ...offers high quality services. 1 2 3 4 5 6 7 ...it is easy to learn. 1 2 3 4 5 6 7 ...offers clear and understandable interaction. 1 2 3 4 5 6 7 ...it is easy to use. 1 2 3 4 5 6 7 ...is a tool of which the necessary skills are easily acquired. 1 2 3 4 5 6 7 I chose to use Couchsurfing, since... ...people who are important to me think that I should use this platform. 1 2 3 4 5 6 7 ...people who influence my behavior use this platform. 1 2 3 4 5 6 7 ...people whose opinions are valuable for me use this platform. 1 2 3 4 5 6 7 ...my image among my peers would improve. 1 2 3 4 5 6 7 When choosing to use Couchsurfing, I feel that... ...I have the resources necessary to use it. 1 2 3 4 5 6 7 ...I have the knowledge necessary to use it. 1 2 3 4 5 6 7 ...the platform is compatible with other technologies that I use. 1 2 3 4 5 6 7 ...I can get help from the platform when I have difficulty using it. 1 2 3 4 5 6 7 ...it fits my lifestyle. 1 2 3 4 5 6 7 ...I am very connected to the platform community. 1 2 3 4 5 6 7 ...the platform users and I share the same goals. 1 2 3 4 5 6 7 ...the friendships I have with other members of the platform mean a lot to me. 1 2 3 4 5 6 7 ...I see myself as part of the platform group. 1 2 3 4 5 6 7 Using Couchsurfing... ...saves me money. 1 2 3 4 5 6 7 ...helps to reduce my travel costs. 1 2 3 4 5 6 7 ... makes travel more accessible. 1 2 3 4 5 6 7 ... benefits me financially. 1 2 3 4 5 6 7 ... makes my trip possible, as I could not afford a hotel. 1 2 3 4 5 6 7 ...saves me time. 1 2 3 4 5 6 7 ...allows me to invest resources in other tourist activities. 1 2 3 4 5 6 7 ...allows me to build relationships. 1 2 3 4 5 6 7 ...allows me to have different experiences. 1 2 3 4 5 6 7 ...allows me to meet new people. 1 2 3 4 5 6 7 ...allows me to get in touch with people with different worldviews. 1 2 3 4 5 6 7 ...allows me to belong to a group of people with similar interests. 1 2 3 4 5 6 7 ...allows me to get insider tips on local attractions. 1 2 3 4 5 6 7 ...allows me to have a more meaningful interaction with places. 1 2 3 4 5 6 7 ...allows me to meet people who live in the region. 1 2 3 4 5 6 7 ...helps me connect with the local community. 1 2 3 4 5 6 7 ...allows me to get to know local ways of life. 1 2 3 4 5 6 7 …allows me to experience other cultures from living with the host. 1 2 3 4 5 6 7 ...helps me to know the region from a local’s perspective. 1 2 3 4 5 6 7 ...helps me to get a more realistic view of the regions I travel to. 1 2 3 4 5 6 7 I chose to use Couchsurfing because... ...sharing rooms while traveling allows me to combat the practice of abusive prices in the hotel industry. 1 2 3 4 5 6 7 ...I refuse to play the role of a consumer in the hotel industry. 1 2 3 4 5 6 7 ... I prefer to contribute to the development of more familiar economies than to large hotel chains. 1 2 3 4 5 6 7 ...it is appealing to me to contribute to the sharing economy. 1 2 3 4 5 6 7 When I chose to use Couchsurfing... ...I often read online reviews by other users of the platform to see if they had a good impression. 1 2 3 4 5 6 7 ... to make sure I choose the right place, I often read reviews from other users of the platform. 1 2 3 4 5 6 7 ...I often check the reviews of other users of the platform to help choose good places. 1 2 3 4 5 6 7 ...I often gather information from online reviews of platform users before choosing to book accommodation. 1 2 3 4 5 6 7 ...I trust that the information presented corresponds to reality. 1 2 3 4 5 6 7 ...I think the other users of the platform are trustworthy when dealing with each other. 1 2 3 4 5 6 7 ...I think other users of the platform will not take advantage of me. 1 2 3 4 5 6 7 ...I think the platform offers a safe environment in which I can use the service. 1 2 3 4 5 6 7 On my next travels... ...I would probably choose Couchsurfing if I needed accommodation. 1 2 3 4 5 6 7 ...I would rather share a home using Couchsurfing than book a hotel. 1 2 3 4 5 6 7 ...I am more likely to use Couchsurfing instead of booking a hotel. 1 2 3 4 5 6 7 ...I can see myself using a sharing economy platform like Couchsurfing in the future. 1 2 3 4 5 6 7 When I think about using Couchsurfing ... ...I do not trust. 1 2 3 4 5 6 7 ...I do not think I will be able to access the house successfully. 1 2 3 4 5 6 7 ...I imagine that I will not be able to accurately assess the quality of the place. 1 2 3 4 5 6 7 …I think I will have trouble staying with strangers. 1 2 3 4 5 6 7 ...I think it is too risky. 1 2 3 4 5 6 7 ...I think I will lose my privacy staying with strangers. 1 2 3 4 5 6 7 ...I feel that I will cause inconvenience to the host. 1 2 3 4 5 6 7 ...I am afraid of suffering some type of violence at the accommodation 1 2 3 4 5 6 7 ...I am afraid of suffering sexual harassment at the accommodation. 1 2 3 4 5 6 7 Having participated in that experience enabled me to... ...provide platform members with requested information about where I stayed. 1 2 3 4 5 6 7 ... provide necessary information so that other members of the platform could make their choices. 1 2 3 4 5 6 7 ...answer the platform members about questions regarding my accommodation experience. 1 2 3 4 5 6 7 ...be more responsible with other people's belongings. 1 2 3 4 5 6 7 ...be friendly with the host. 1 2 3 4 5 6 7 ...be polite to the host. 1 2 3 4 5 6 7 ...be courteous to the host. 1 2 3 4 5 6 7 ...suggest, if necessary, the host a useful idea on how to improve the service. 1 2 3 4 5 6 7 …tell the host when you receive a good service from him/her. 1 2 3 4 5 6 7 ...provide advice to other members of the platform. 1 2 3 4 5 6 7 ... having experienced the destination in a totally different way than it would have been if I had gone to a hotel. 1 2 3 4 5 6 7 ...want to share my experiences with my hosts. 1 2 3 4 5 6 7 ...be more open to novelties. 1 2 3 4 5 6 7

When considering my experience with Couchsurfing, I think the platform...
...is useful for finding accommodation.	1		2		3		4		5		6		7
...increases my chances of finding accommodation that fit my profile.	1		2		3		4		5		6		7
...helps me get the right accommodation quickly.	1		2		3		4		5		6		7
...it is a convenient tool for finding accommodation.	1		2		3		4		5		6		7
...offers high quality services.	1		2		3		4		5		6		7
...it is easy to learn.	1		2		3		4		5		6		7
...offers clear and understandable interaction.	1		2		3		4		5		6		7
...it is easy to use.	1		2		3		4		5		6		7
...is a tool of which the necessary skills are easily acquired.	1		2		3		4		5		6		7
I chose to use Couchsurfing, since...
...people who are important to me think that I should use this platform.	1		2		3		4		5		6		7
...people who influence my behavior use this platform.	1		2		3		4		5		6		7
...people whose opinions are valuable for me use this platform.	1		2		3		4		5		6		7
...my image among my peers would improve.	1		2		3		4		5		6		7
When choosing to use Couchsurfing, I feel that...
...I have the resources necessary to use it.		1		2		3		4		5		6	7
...I have the knowledge necessary to use it.		1		2		3		4		5		6	7
...the platform is compatible with other technologies that I use.		1		2		3		4		5		6	7
...I can get help from the platform when I have difficulty using it.		1		2		3		4		5		6	7
...it fits my lifestyle.		1		2		3		4		5		6	7
...I am very connected to the platform community.		1		2		3		4		5		6	7
...the platform users and I share the same goals.		1		2		3		4		5		6	7
...the friendships I have with other members of the platform mean a lot to me.		1		2		3		4		5		6	7
...I see myself as part of the platform group.		1		2		3		4		5		6	7
Using Couchsurfing...
...saves me money.		1		2		3		4		5		6	7
...helps to reduce my travel costs.		1		2		3		4		5		6	7
... makes travel more accessible.		1		2		3		4		5		6	7
... benefits me financially.		1		2		3		4		5		6	7
... makes my trip possible, as I could not afford a hotel.		1		2		3		4		5		6	7
...saves me time.		1		2		3		4		5		6	7
...allows me to invest resources in other tourist activities.		1		2		3		4		5		6	7
...allows me to build relationships.		1		2		3		4		5		6	7
...allows me to have different experiences.		1		2		3		4		5		6	7
...allows me to meet new people.		1		2		3		4		5		6	7
...allows me to get in touch with people with different worldviews.		1		2		3		4		5		6	7
...allows me to belong to a group of people with similar interests.		1		2		3		4		5		6	7
...allows me to get insider tips on local attractions.		1		2		3		4		5		6	7
...allows me to have a more meaningful interaction with places.		1		2		3		4		5		6	7
...allows me to meet people who live in the region.		1		2		3		4		5		6	7
...helps me connect with the local community.		1		2		3		4		5		6	7
...allows me to get to know local ways of life.		1		2		3		4		5		6	7
…allows me to experience other cultures from living with the host.		1		2		3		4		5		6	7
...helps me to know the region from a local’s perspective.		1		2		3		4		5		6	7
...helps me to get a more realistic view of the regions I travel to.		1		2		3		4		5		6	7
I chose to use Couchsurfing because...
...sharing rooms while traveling allows me to combat the practice of abusive prices in the hotel industry.		1		2		3		4		5		6	7
...I refuse to play the role of a consumer in the hotel industry.		1		2		3		4		5		6	7
... I prefer to contribute to the development of more familiar economies than to large hotel chains.		1		2		3		4		5		6	7
...it is appealing to me to contribute to the sharing economy.		1		2		3		4		5		6	7
When I chose to use Couchsurfing...
...I often read online reviews by other users of the platform to see if they had a good impression.		1		2		3		4		5		6	7
... to make sure I choose the right place, I often read reviews from other users of the platform.		1		2		3		4		5		6	7
...I often check the reviews of other users of the platform to help choose good places.		1		2		3		4		5		6	7
...I often gather information from online reviews of platform users before choosing to book accommodation.		1		2		3		4		5		6	7
...I trust that the information presented corresponds to reality.		1		2		3		4		5		6	7
...I think the other users of the platform are trustworthy when dealing with each other.		1		2		3		4		5		6	7
...I think other users of the platform will not take advantage of me.		1		2		3		4		5		6	7
...I think the platform offers a safe environment in which I can use the service.		1		2		3		4		5		6	7
On my next travels...
...I would probably choose Couchsurfing if I needed accommodation.		1		2		3		4		5		6	7
...I would rather share a home using Couchsurfing than book a hotel.		1		2		3		4		5		6	7
...I am more likely to use Couchsurfing instead of booking a hotel.		1		2		3		4		5		6	7
...I can see myself using a sharing economy platform like Couchsurfing in the future.		1		2		3		4		5		6	7
When I think about using Couchsurfing ...
...I do not trust.		1		2		3		4		5		6	7
...I do not think I will be able to access the house successfully.		1		2		3		4		5		6	7
...I imagine that I will not be able to accurately assess the quality of the place.		1		2		3		4		5		6	7
…I think I will have trouble staying with strangers.		1		2		3		4		5		6	7
...I think it is too risky.		1		2		3		4		5		6	7
...I think I will lose my privacy staying with strangers.		1		2		3		4		5		6	7
...I feel that I will cause inconvenience to the host.		1		2		3		4		5		6	7
...I am afraid of suffering some type of violence at the accommodation		1		2		3		4		5		6	7
...I am afraid of suffering sexual harassment at the accommodation.		1		2		3		4		5		6	7
Having participated in that experience enabled me to...
...provide platform members with requested information about where I stayed.		1		2		3		4		5		6	7
... provide necessary information so that other members of the platform could make their choices.		1		2		3		4		5		6	7
...answer the platform members about questions regarding my accommodation experience.		1		2		3		4		5		6	7
...be more responsible with other people's belongings.		1		2		3		4		5		6	7
...be friendly with the host.		1		2		3		4		5		6	7
...be polite to the host.		1		2		3		4		5		6	7
...be courteous to the host.		1		2		3		4		5		6	7
...suggest, if necessary, the host a useful idea on how to improve the service.		1		2		3		4		5		6	7
…tell the host when you receive a good service from him/her.		1		2		3		4		5		6	7
...provide advice to other members of the platform.		1		2		3		4		5		6	7
... having experienced the destination in a totally different way than it would have been if I had gone to a hotel.		1		2		3		4		5		6	7
...want to share my experiences with my hosts.		1		2		3		4		5		6	7
...be more open to novelties.		1		2		3		4		5		6	7

Perfil socioeconômico

1. Gender

Male ( )

Female ( )

2. Age: _____________________________

3. Marital Status

Single ( )

Married ( )

Divorced ( )

Common-law partners ( )

Widowed ( )

4. Education

Up to high school ( )

Incomplete undergraduate ( )

Undergraduate degree ( )

Graduate degree ( )

Master's Degree ( )

PhD ( )

5. When adding your income to the income of the people who live with you, approximately, how much is the household income? (Consider the income of everyone who lives in your home).

1 Minimum wage (R$ 937) – 3 minimum wages (R$ 2811)

4 minimum wages (R$ 3748) – 6 minimum wages (R$ 5622)

7 minimum wages (R$ 6559) – 10 minimum wages (R$ 9370)

More than 11 minimum wages (R$ 10307)

• Thank you for your participation in the study.

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• How to cite: Vera, L. A. R.; Gosling, M. S.; Silva, J. A. (2021). A Brazil-Portugal comparative study on the antecedents of Couchsurfing use. Revista Brasileira de Pesquisa em Turismo, São Paulo, 15 (2), e-1923, May./Aug. http://dx.doi.org/10.7784/rbtur.v15i2.1923

Edited by

Editor: Glauber Eduardo de Oliveira Santos.

Publication Dates

• Publication in this collection
  30 Apr 2021
• Date of issue
  May-Aug 2021

History

• Received
  20 Dec 2019
• Accepted
  03 Apr 2020