The effects of brand and online reviews on consumer trust and purchase intentions in developing countries: the case of the online travel agencies in Brazil

Abstract

Intensive use of social media has helped online travel agencies (OTAs) to identify what things consumers find more relevant when planning their vacations online. Several online companies have emerged and started competing with well-established players in this market. However, unlike in developed countries, where online tourism seems to have already reached maturity, in emerging countries - such as Brazil - the e-market is still tiny, but with great potential. In this study, we analyze the effects of brand and online reviews on consumer trust and purchase intention for OTAs in Brazil. We conducted a 2 x 2 factorial design experiment comparing well-known and lesser-known brands with the presence (or not) of online reviews. Our results indicate that brand equity is the primary driver of trust, although online reviews are very important to consumers of lesser-known brands. We also confirmed qualified antecedents of trust that drive consumer’s purchase intention. The study provides useful information for start-ups, small businesses, and larger and well-known companies to set up their e-commerce strategies.

Keywords:
Social Commerce; Online Tourism; Trust; Brand; Travel Agencies

Resumo

O uso intensivo das mídias sociais tem ajudado as agências de viagens online (OTAs) a identificar o que os consumidores consideram mais relevante ao planejar suas férias no ambiente online. Nesse setor, várias empresas online têm surgido e concorrido com empresas maiores e já estabelecidas no mercado. Porém, ao contrário do verificado nos países desenvolvidos, onde o turismo online parece já ter atingido sua maturidade, nos países emergentes - como o Brasil - o turismo ainda é pequeno, mas com grande potencial. Este estudo analisa a influência da marca e da presença de componentes do comércio social (comentários, avaliações e recomendações) na confiança e na intenção de compra dos consumidores em OTAs no Brasil. Para tal, realizou-se um experimento de design fatorial 2 x 2, comparando uma marca bem conhecida e uma menos conhecida com a presença (ou não) de avaliações online. Os resultados apontaram que o valor da marca é o principal direcionador da confiança, embora as avaliações online sejam muito importantes para os consumidores de marcas menos conhecidas. Identificaram-se, ainda, importantes antecedentes da confiança que impulsionam a intenção de compra do consumidor. O estudo fornece informações úteis para startups, pequenos negócios, e empresas maiores e bem conhecidas para definir suas estratégias de e-commerce.

Palavras-chave:
Comércio Social; Turismo Online; Confiança; Marca; Agências de Viagens

1. Introduction

With the popularization of social networking sites (SNS), several changes have occurred in the process of exchanges between people and companies, giving rise to a new type of e-commerce - called s-commerce - that changed the way online shopping is made (Lin et al., 2017). S-commerce makes use of social media to promote transactions with a collaborative environment (made up of friends, relatives, acquaintances, or strangers) who share their online shopping experiences and product and service related information. Specifically, in tourism and hospitality, the intensive use of social media has helped organizations to identify what online consumers consider most relevant when planning their vacation.

However, the presence of comments, reviews, and recommendations, defined by Hajli (2015) as s-commerce components (SCC), is not yet fully understood by academia and the business community. The online environment has reduced barriers to entry, and made possible the emergence of a large number of companies dedicated to s-commerce (Kim & Park, 2013), that has allowed small businesses and lesser-known brands to compete with well-established brands in the market (Alhulail et al., 2018). Complementarily, several studies have shown that the brand image of both traditional and online businesses affects consumer behavior in services (Aghekyan-Simonian et al., 2012; Lien et al., 2015). According to them, consumers are more likely to buy online goods and services from well-established brands than from unconsolidated companies including hospitality.

The search for hotels and tourist destinations has changed sorely since the rise of the Internet, which has led online travel agencies (OTA) and hotel websites to grow considerably in recent years. In this sense, consumer’s trust in virtual business is mentioned constantly in the literature as a decisive element for the formation of positive attitudes towards online shopping (Kim & Park, 2013). According to Ponte et al. (2015), trust is one of the most relevant factors for conducting tourism-related business online as it reduces the uncertainty associated with online consumption and favors the consumer's predisposition to buy. In this new business environment, the presence of s-commerce tools and the brand of website can be identified as two relevant factors that influence consumer’s trust and purchase intention as trust is considered a key factor in the structuring of tourism products and services websites (Agag & El-Masry, 2017; Filieri et al., 2015). A strong brand can facilitate the attraction of new customers who feel more comfortable in making a purchase decision (Ling et al., 2010) a as the presence of social media tools can influence the consumer’s decisions (Yan et al., 2016).

In developed countries, online tourism is already consolidated. On the other hand, in emerging countries such as Brazil the e-market is still small but with great potential. With digital insertion, online bookings continue to increase in Latin America, making it easier for travelers to connect to products and experiences (Deloitte, 2018). More specifically to Brazil, digital media constitute the main source of information for foreign and Brazilian tourists who, due to easy access to the Internet in most destinations, can now organize their own trips. The Brazilian online tourism market reached US$10 billion in 2016, a 73% increase since 2013, growing about 15% per year (Ebit, 2017). Among tourists who visited the country for leisure reasons or business in 2017, around 80% did not use traditional travel agencies for organizing their trips using only the Internet (Brazil, 2018).

The tourism industry has proven to be a very important sector that has created employment and income in several nations. In developing countries, even though it represents an important economic force, tourism and hospitality still require some attention. This is mainly due to the problems involving infrastructure, scarce resources, political unstable environments, consumers with low education and income, and the presence of a variety of non-branded products and services (Pels & Sheth, 2017). In this scenario, travel and tourism agencies and operators are especially relevant, forming one of the most important links in the chain for the development of the sector. This segment in Brazil comprises around 36,000 companies, being 99.5% small businesses (Sebrae, 2017).

This context justifies this research, since the results of studies conducted in mature economies do not necessarily apply to developing ones (Lima et al., 2020). In fact, there is a vast literature defending that consumers from emerging countries tend to behave differently from those in Europe and the US, and that it is not possible to generalize previous findings, especially regarding the tourists' expectations (Burgess & Steenkamp, 2006; Pels & Sheth, 2017). Currently, 80% of world consumers live in emerging markets (Burgess & Steenkamp, 2006), with Latin America being one of the most neglected areas in this type of research (Fastoso & Whitelock, 2011).

The earlier studies on online tourism focused on comparing the travel agencies and hotel brands’ websites (Lien et al., 2015). However, as far as we know, there are no studies involving the combination of different brands (in this case, lesser-known and well-known) with the presence (or not) of s-commerce components on user trust in emerging markets. Given the importance of brand and s-commerce components to consumers on the online tourism industry, and the scarcity of prior studies in this context, we aimed to investigate how brands and online reviews influence the Brazilian consumer’s trust and purchase intention on OTAs. So, we conducted an experiment involving different virtual shopping scenarios analyzing the conjoint effects of brand and online reviews of Brazilian OTAs on consumers’ trust and purchase intention.

2. Theoretical Background

The popularity of social media, and the growth of SNS, have stimulated the development of many s-commerce platforms. As a result, e-commerce has changed as a form of social-mediated commerce that involves convergence between online and offline environments (Shanmugam et al., 2016). Currently, websites generally provide a variety of social media tools, including referrals and recommendations, ratings and reviews, and forums and communities, which are the main social commerce components (SCC) (Hajli, 2015). Alhulail et al. (2018) indicate that there are two distinct models of s-commerce. The first one is made up of SNSs like Facebook and Twitter, which have enhanced their business capabilities to help people shop whilst interacting with one another; and the second model brings together traditional e-commerce websites such as Amazon, Walmart, and Booking.com that have been enhanced with social features (including spaces for entering recommendations, comments, and the presence of rating systems) that help people socialize while shopping. This research specifically studies the second type of s-commerce, since most business transactions around the world follow this type of model (Alhulail et al., 2018).

Nowadays, the Internet has proven to be an important platform for the distribution of tourism-related products and services, providing a great opportunity for travel companies to succeed. Overall, the tourism industry has witnessed a progressive shift from traditional booking channels to online distributions, with online travel agencies, also known as third-party booking sites, being the main driving force of this new business environment. Online tourism can be considered as the integration of tourism and Internet technology, referring to all tourism-oriented web-based business systems (Zhang et al., 2011), such as hotel reservations, online purchase of airline tickets, and choice of travel destinations. In this environment, social media is increasingly used by consumers to search for information, compare alternative products and services and make decisions about activities such as hotel selection and travel planning (Sparks et al., 2016).

When planning a travel, consumers search for rich information about destination and accommodation alternatives, and with the growth of OTAs, users are getting virtually all the information they need very easily and quickly. In a study performed by Hernández-Méndez et al. (2015), more than half of respondents said they were influenced by travel comments posted by other Internet users when choosing a destination. Thus, new markets constantly emerge, with more participative, interactive, and active consumers having the power to choose what to buy, even if they are geographically distant, which is enhanced by the Internet.

According to Hajli (2014), practically all transactions require some component involving trust, especially the purchase decision process that occurs in virtual environments. To Kim and Park (2013), s-commerce intends to build trust among its users to induce buying and recommendation intentions, but not only on selling products and services. Therefore, trust should be considered a priority, as it plays a key role in online interactions. Some studies have indicated that quality perceptions about the website, the company reputation and even the social aspects that favor user interaction in an online environment can influence consumer trust and consequently the purchase intention (Agag & El-Masry, 2017; Chang & Chen, 2008).

3. Hypotheses and Research Model

The literature has pointed out trust as one of the most important determinants of s-commerce success (Hajli, 2014; Kim & Park, 2013). On the other hand, Chang and Chen (2008) pointed out that a lack of trust can affect the consumer’s purchase intention until they gain the knowledge necessary to develop enough trust to recommend or shop from the website. Morgan and Hunt (1994, p. 23) conceptualize trust when one party has confidence in an exchange partner’s integrity and reliability. Kim & Park (2013) complement this concept defining trust as the willingness of consumers to trust in the seller’s ability, generosity, integrity, and predictability, in which the seller would take favorable actions for its customers regardless of their capability to monitor or control it. As suggested by the literature, trust plays an important role in e-commerce, being influenced by several characteristics such as firm reputation, word-of-mouth, and website information quality, as explained hereafter.

3.1. Reputation

Firm reputation is defined as customers’ perceptions of to what extent a company is honest and concerned about their welfare (Doney & Cannon, 1997). In s-commerce, users tend to consider the company's reputation as an important determinant in assessing trust in the company, and when purchasing products or services (Kim & Park, 2013). As well, previous studies with e-commerce and s-commerce firms have already demonstrated a close relationship between reputation and trust l (Kim & Park, 2013; Maia et al., 2018; Maia et al., 2019). As in other industries, travel agencies have clients with a strong sense of risk and uncertainty, especially because they cannot try the travel product they want in advance. Thus, establishing a good reputation becomes important for any company interested in encouraging their customers’ purchases (Chang, 2015). In this sense, a good reputation increases the customer’s trust in the company, serving as an important indicator of the quality of products and services offered by online tourism companies. Based on these, we propose the following hypothesis:

• H1: A company's reputation will positively influence consumers’ trust in online travel agencies.

3.2. Information Quality

Information quality refers to the latest, accurate, and complete information provided by a website to its users (Kim & Park, 2013), being related to the usefulness, relevance, and accuracy of the information available in the website as suggested by Maia et al. (2019). Kim and Park (2013) also suggest that, in s-commerce, the information quality present on the website is another important factor of consumer trust. Thus, a website that provides its customers with high-quality information has the chance of being considered a reliable company by these customers. To Liao and Shi (2017), an online tourism service must include friendly websites, presenting clear and relevant travel information, logically classified into different categories, such as flights, hotels, and tours. In the Filieri et al. (2015)’s study, information quality was considered the main antecedent of trust in tourism websites, being that information accuracy is a powerful determinant in the traveler’s decision-making process. In this sense, consumers will tend to trust websites providing timely and accurate information. Therefore, we propose the following hypothesis:

• H2: The website’s information quality will positively influence consumers’ trust in online travel agencies.

3.3. Positive e-WOM

Hajli et al. (2014) conceptualized e-WOM (electronic word-of-mouth) as a construct of social commerce that provides word-of-mouth information to users through recommendations and references, ratings, reviews and comments in forums and online communities. Additionally, Wang and Yu (2015) defined e-WOM communication as the user-generated content that transmits positive or negative information related to sellers and products/services that are disseminated and shared online. E-WOM can be found in different formats and channels, being expressed through text, verbal, visual messages, or a combination of them (consumer photo and Instagram comment, for example).

The striking feature of s-commerce websites is the presence of different mechanisms that allow consumers to evaluate sellers and share information (analytically or synthetically) with other users who surf the website (Wang et al., 2016). Thus, online recommendations can also be considered a factor that influence consumer trust, since they share experiences and comments besides buying products and services (Kim & Park, 2013). Studies have shown that the positive electronic word-of-mouth available through digital social media, also called e-WOM, increases the level of trust in new products among Internet consumers (Hajli, 2014). Based on these arguments, the following research hypothesis is proposed:

H3: Positive e-WOM will positively influence consumers’ trust in online travel agencies.

3.4. Purchase Intention

Kim and Park (2013) define purchase intention as the likelihood of the future purchase of a product or service. The relationship between trust and purchase intention has been investigated for a while. Yoon (2002), for example, analyzed a diversity of trust antecedents (such as transaction security, website features, and search functions), consequences (c.f. purchase intention), and mediating variables (c.f. website awareness) concluding that trust in the website has a significant effect on online shopping intentions. More recently, Maia et al. (2019) analyzed the effects of trust and price on the purchase intention of Brazilian consumers in s-commerce. In this study, trust was identified as the main antecedent of consumer’s purchase intention, followed by competitive prices.

As claimed by Doney and Cannon (1997), buyers’ trust in a seller is perceived as a key factor that significantly influences their purchase decisions and intentions; this is complemented by Kuan and Bock (2007), which suggest that high levels of trust lead to great intentions to buy products and services, especially in the online marketplace. In online hotel reservations, purchase intention reflects the desire of a consumer to book a room through the website. In general, customers expect the hotel to provide the services promised on the website, and the expectation depends on the trust they place on the website (Lien et al., 2015). In this regard, we present the following hypothesis:

• H4: Trust will positively influence consumers’ purchase intention in online travel agencies.

In addition to these direct effect hypotheses, we add indirect relationships (which were manipulated and tested in this study), such as the brand of the online tourism company (well-known or lesser-known) and the presence (or not) of s-commerce components on tourism websites.

3.5. Brand

Previous research has investigated the impact of brand equity on consumer’s behavior in the service sector (Aghekyan-Simonian et al., 2012; Lien et al., 2015), being consumers more likely to buy products and services from brands already established in the market. Due to the intangible elements of the hospitality industry, the hotel name or brand and on-site amenities are important aspects that can influence customer behavior. To develop a strong brand, a hotel must build its image to differentiate itself from other players and communicate key benefits to its customers as well (Lien et al., 2015). In this sense, the website brand can affect the subjective perceptions of consumers and their consequent behaviors, being the more favorable the brand image, the more positive the attitude towards the company and its attributes (Aghekyan-Simonian et al., 2012). Therefore, the following hypotheses are proposed:

• H5a: A well-known brand will positively affect the company’s reputation.

• H5b: A well-known brand will positively affect the website’s information quality.

• H5c: A well-known brand will positively affect the positive e-WOM.

3.6. S-commerce Components

In addition, consumers search in the online environment for what other people say and value about their experiences with a website. Thus, the s-commerce components can also be used as a tool to build a company’s reputation and mitigate the consumer’s perception of uncertainty about products and services (Grund & Gürtler, 2008). More specifically, online tourism companies allow their customers to post reviews as well as search for other customers' opinions, which have made these comments increasingly decisive at the time of purchase (Sparks & Browning, 2011). In this context, consumers may find online reviews available on social media useful when making their decisions (Shan, 2016). To investigate this relationship, we address the following hypotheses:

• H6a: The presence of s-commerce components will positively affect the company’s reputation.

• H6b: The presence of s-commerce components will positively affect the website’s information quality.

• H6c: The presence of s-commerce components will positively affect the positive e-WOM.

Next, we present our research model, containing the six proposed hypotheses which will be tested empirically in the study, as shown in Figure 1.

Figure 1.
Research Model

4. Methodology

This study is a quantitative research, defined as a laboratory experiment, due to the manipulation of some variables to create different scenarios, such as company brand (well-known x lesser-known) and the presence or absence of s-commerce components.

4.1. Experiment Design

The experiment was conducted in a controlled environment, using a 2 x 2 factorial design, containing the following manipulated variables (between subjects) in four fictional scenarios: company brand - 2 levels (well-known company website and lesser-known company website) and presence of s-commerce components - 2 levels (with no comments, ratings or recommendations; and with comments, ratings or recommendations). The website structure was the same for all scenarios, with the company’s logo on the top of the website being the only difference among them. The characterization of each controlled variable used in the study was as follows: (a) Well-known site: characterized by a picture of the Booking.com website; (b) Lesser-known site: characterized by the photo of ViajarBarato reservation website; (c) with components of social commerce: characterized by the presence of positive, neutral, and negative opinions, evaluations and recommendations from other customers regarding the company and its products offered on the website; and (d) without components of social commerce: characterized by the absence of opinions, evaluations, and recommendations of other customers regarding the company and products offered on the website. ^{Appendix A} provides two different scenarios as examples.

As a form of recruiting participants to the experiment, we sent messages to different social media groups inviting their members to participate in a study about e-tourism. The possible acquisition of a tour package was offered to participants through an OTA. Those who accepted the invitation were directed to the link corresponding to one of the four proposed scenarios, which, after viewing, was evaluated through a questionnaire containing demographics and some questions regarding their online shopping behavior, as it related to travel or tourism. As a way of confirming that the scenarios presented in the survey were adequate for the study, a manipulation check was added with a simple choice question (yes/no) by asking if the respondent “knew the website” on which he/she was browsing. The objective was to verify that the manipulation performed was correct. The scenarios were randomized from a robot, which counted the number of answers obtained, directing the next participants to the scenarios with the fewest answers. The data was collected in 2019.

4.2. Development of the instrument

The research questionnaire was developed from constructs previously identified in the literature. Except for “positive e-WOM”, which was proposed by Maia (2019), all other constructs were adapted from Kim and Park (2013)’s work. The questionnaire was first analyzed by three specialists in Tourism and IT, aiming to minimize possible inconsistencies regarding the meaning of the items and other questions inserted in the instrument. The questions were operationalized using a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). Following, a pre-test of the instrument was performed with ten Masters in Administration students, with previous experience in searching or purchasing tour packages through OTAs. We defined as the inclusion criteria that participants should be of legal age and have searched or made online purchases on travel and tourism websites in the last year. Data was collected by convenience, being the sample classified as non-probabilistic. We obtained 208 valid cases after the data collection. The questionnaire presenting the items, references, and descriptive statistics is available in ^{Appendix B}.

4.3. Participants

The sample consists of 140 (67.3%) women and 68 (32.7%) men, with a mean age of 37.4 (± 11.8) years. The predominant income ranges from 1 to 3 Brazilian minimum wages (22.1%), 3 to 5 Brazilian minimum wages (26.4%), and more than 7 Brazilian minimum wages (30.3%). Participants were predominantly graduates (61.1%), followed by respondents with completed higher education (22.6%), mostly married (49%) or single (42%). In addition to socio-demographic characteristics, some aspects of buying and searching habits of tourism-related products are highlighted. Regarding the frequency with which they make tourism trips, the groups of respondents that travel from 1 to 3 times per year (62%) and less than once a year (29.8%) stood out. Regarding the number of times they have already purchased products or services through OTAs, the largest group said they bought more than 9 times (32.7%), followed by respondents who have bought up to 3 times or 3 to 6 times (26%). The top search websites that are frequently used by respondents are Booking.com (73.1%), followed by Decolar.com (59.6%) and TripAdvisor (30.3%). The groups corresponding to the four scenarios were compared for profile and consumption habits using the Chi-square test and no significant differences (p > 0.05) were found among them.

4.4. Instrument Validation

Following, we performed the validation procedures. Confirmatory Factor Analysis (CFA) was conducted using the SmartPLS 3.0 software (Partial Least Squares) structural equation modeling. The validity and reliability of the items and constructs were assessed by examining the loadings of items on their respective latent constructs. All of them loaded heavily and significantly (p < 0.05) on their respective constructs, indicating individual item reliability (Table 1). The reliability of the scales was evaluated using Cronbach’s alpha and the Composite Reliability Index - CR (Table 2). The scores exceeded the minimum threshold level of .70, indicating good internal consistency.

Table 1.
Factor loadings

	Item	Trust	Purchase Intention	Information Quality	e-WOM	Reputation
Trust	TRUST1	.940	.720	.742	.801	.757
	TRUST2	.935	.753	.679	.760	.820
	TRUST3	.951	.756	.698	.785	.764
	TRUST4	.858	.624	.680	.701	.588
Purchase intention	BUYINT1	.749	.943	.635	.684	.702
	BUYINT2	.746	.964	.612	.664	.711
	BUYINT3	.731	.959	.588	.649	.673
Information quality	QUAL1	.660	.513	.884	.723	.527
	QUAL2	.656	.573	.891	.724	.559
	QUAL4	.686	.602	.865	.689	.595
e-WOM	EWOM1	.760	.627	.741	.887	.701
	EWOM2	.659	.561	.667	.855	.638
	EWOM3	.729	.625	.683	.885	.583
	EWOM4	.747	.626	.741	.880	.650
Reputation	REPUT1	.730	.640	.542	.629	.940
	REPUT2	.788	.693	.654	.783	.910
	REPUT4	.686	.679	.561	.604	.916

Brand and s-commerce Components (SCC) are dichotomous variables, which have been transformed into dummies to simulate the four scenarios in the structural model. Thus, there are no validity and reliability assessments, as is done in the case of reflective indicators, since for formative indicators the correlation is not expected (Hair Jr. et al., 2017). Next, the convergent validity was verified using the Average Variance Extracted (AVE) criterion, which exceeded the minimum threshold level of .50 (Table 2), converging to a satisfactory result. This result is ratified by the incidence of higher factor loads in their respective constructs (Table 1) than in the others. We further evaluated discriminant validity by the criterion of Fornell and Larcker, which assumes that the square root of the AVE (diagonal of Table 2) exceeded its inter-construct correlations.

Table 2.
Correlation Matrix and Reliability Assessment of Constructs

	Cronbach’s Alpha	CR	AVE	Trust	Purchase Intention	Information Quality	e-WOM	Reputation
Trust	.94	.96	.849	.922
Purchase Intent	.95	.97	.913	.777	.956
Information Quality	.85	.91	.774	.759	.640	.880
e-WOM	.90	.93	.768	.828	.697	.809	.877
Reputation	.91	.95	.851	.799	.728	.638	.734	.922

We took some steps to verify the existence of a negative effect attributed by the common method bias - CMB (Harman's test, analysis of correlations between the model constructs, and the inclusion of a method factor in the model), as suggested in the literature (Podsakoff et al., 2003), indicating that CMB is not a concern in this study. Finally, multicollinearity was assessed among the independent variables using the variance inflation factor (VIF) scores, which ranged from 1.0 to 3.72, suggesting that this is probably not an issue in this study. Following, we present the main results and discussion of the study.

5. Results and Discussion

The structural model was also analyzed by SmartPLS. Bootstrapping with 5,000 samples was used to estimate the consistency of the model and the significant levels of the path coefficients. Figure 2 presents the structural model, the R² values of the dependent variables, as well as the relationships of the model. According to the results (Figure 2), we found that a well-known brand influences the three independent variables present in the model, strongly impacting Reputation (β = .73; p < .000), e-WOM (β = .40; p < .000), and Information Quality (β = .39; p < .000), showing a greater impact on company's reputation. These results are consistent with previous studies, such as Lien et al. (2015) who also identified the brand image as an important determinant of trust, reflecting that an attractive and valuable brand affects the Brazilian consumer’s trust in the product or service to be purchased in an OTA. In this sense, the website quality (measured by the perception of its information quality) as well as the company's reputation, and the social aspects involved in e-WOM are significantly influenced by the online travel company's brand.

Figure 2.
Research Model Results

When analyzing the simple presence of comments, evaluations, and recommendations, regardless of whether the company is well-known or lesser-known, we found that online reviews do not significantly influence any of the trust's antecedents to the Brazilian consumers. Maia et al. (2018) have already suggested that the mere presence of reviews and comments does not significantly correlate with consumer participation in s-commerce. As explained by the authors, the important thing is the content published by the peers - if they are positive or negative - that will affect the consumer’s trust on the company. In the study conducted here, no significant association was found between the presence of these components in OTAs’ website and reputation, e-WOM, and quality of the website information. Ho-Dac et al. (2013) point out that when it comes to well-known companies, evaluations and recommendations (whether positive or negative) have no significant effect on sales of these companies, suggesting that such tools do not influence the purchase decision of the consumers on these websites. In the hospitality industry, Vermeulen and Seegers (2009) have identified that, for well-known hotels (such as Holiday Inn or Hilton), exposure to ratings will hardly affect the company's brand, as well as reviews of well-known hotels, will have no strong persuasive effects. In other words, it is not enough to have room for publishing comments or ratings on online tourism websites once what consumers look for through these components is the quality of comments and ratings from true and reliable sources that disseminate relevant content, built on with good arguments and recent and updated data (Law et al., 2014).

Still analyzing the conceptual model, we realize that reputation (β = .39; p < .000), e-WOM (β = .38; p < .000), and website information quality (β = .21; p < .000) significantly influence the Brazilian consumers’ trust in online travel agencies, enhancing reputation and electronic word-of-mouth as the main predictors of trust. We can say that company’s reputation, electronic word-of-mouth, and website information quality account for 77.9% of the total variance of trust on OTAs websites, representing a high degree of explanation, while trust explains 60.3% of the variance of the consumers’ intention to buy. The significant impact of trust on purchase intention corroborates other previously conducted online shopping studies (Ling et al., 2010), demonstrating that trust is an important factor influencing online consumer decisions.

Analyzing the significance of the path coefficients present in Figure 2, we can see that the hypotheses H1, H2, H3, H4, and H5 (a-c) were all confirmed. Therefore, the better the issues associated with the company's brand image are addressed, as well as its reputation, good comments, reviews, and the website’s information quality about products/services offered in OTA websites, the greater Brazilian consumers’ trust will be in the website and consequently the higher will be their purchase intention. We did not confirm Hypothesis H6 (a-c) and its developments, which implies recognizing that the presence of s-commerce tools, by itself, does not influence consumer's perception about the company’s reputation, the quality of third-party online reviews, and the information quality of the website in online travel agencies. Therefore, we identified that in Brazil the company brand is an important influencer of the consumer's perception about the quality of the service provided by OTAs. This result is consistent with the study of Mohseni et al. (2018), who found that when online travel agency tourists are familiar with and familiar with their travel brand, they report better shopping experiences and less risk than unfamiliar brands. That is why the website's brand influences purchase intention (Aghekyan-Simonian et al., 2012; Mohseni et al., 2018).

Complementarily, a multi-group analysis (MGA) was conducted to test the influence of the interaction between the brand and s-commerce components (Hair Jr et al., 2017). We used the well-known brand and presence of s-commerce components as moderating variables in the model. Firstly, we analyzed the brand as an independent variable. At this time, the presence of s-commerce components was included as a moderating variable, separating the model into two groups: (a) one containing the scenarios with the presence of s-commerce components (n = 100, representing 48% of the sample) and (b) the other containing the scenarios without the presence of s-commerce components (n = 108, representing 52% of the sample). The multi-group analysis showed no significant difference in any of the relationships of the model in which the s-commerce components act as a moderating variable, with the results not being significant (p > .05). In other words, we can say that the presence or absence of s-commerce components on well-known websites has no significant influence on purchase intention, trust and its antecedents (reputation, e-WOM, and information quality). Some studies have pointed out that when a hotel brand is familiar to customers, it leaves roots and more intense memories in their minds, diminishing the effect of online reviews as a source of credibility and trust in the website (Floh et al., 2013). Still, Ho-Dac et al. (2013) enhance that online comments and ratings matter less to strong and well-known brands in the market when compared to newer brands, which corroborates the results of this research.

Second, we analyzed the s-commerce components as an independent variable, while the website brand was used as a moderating variable (Table 3). The model was estimated by separating two groups: (a) one containing the scenarios with the well-known website (n = 127, representing 61% of the sample) and (b) the other containing the scenarios with the lesser-known website (n = 81, representing 39 % of the sample). In this case, using the multi-group analysis we identified a significant difference with respect to the structural model (Figure 2), in two relationships: the presence of s-commerce components on lesser-known websites influencing e-WOM and company’s reputation.

We identified that, on the lesser-known website, the presence of comments, ratings, and recommendations appears to Brazilian consumers as a substitute for the brand, helping them to assess the credibility and image of the OTAs website and its products. These components become a way for the customer to realize the potential of the company when there is good and positive feedback about the company and its products. In this sense, Ho-Dac et al. (2013) state that positive reviews allow lesser-known brands to become more competitive and recognized, which corroborates our results. In addition, Ye et al. (2009) report that when a consumer is exposed to positive reviews and comments from other consumers, the likelihood of booking a hotel increases, being even more significant in lesser-known hotels (Floh et al., 2013), where the effect of online reviews replaces the status of known hotels or chains.

Table 3.
Result of Structural Equation Analyses and Multi-group analysis (MGA)

Relationships	Lesser-know website		Well-know website		MGA
	PC	p	PC	p	PC	p
SCC ➔ Information Quality	.133	.230NS	-.060	.507 NS	.192	.077NS
SCC ➔ e-WOM	.306	.002**	-.002	.978 NS	.309	.009**
SCC ➔ Reputation	.231	.028*	-.042	.639 NS	.272	.022*
Trust ➔ Purchase Intention	.674	.000***	.778	.000***	.104	.911NS
Information Quality ➔ Trust	.222	.022*	.206	.007**	.016	.462 NS
e-WOM ➔ Trust	.380	.001***	.375	.001***	.004	.480 NS
Reputation ➔ Trust	.335	.000***	.393	.000***	.057	.670 NS
Variance explained (R2)
Trust	.676		.762
Purchase Intention	.447		.600

• Note 1: Significance was estimated by bootstrapping with 5,000 repetitions in SmartPLS.
• Note 2: PC: Path coefficient; p > .05 = NS (no significant coefficients); p < .05 =^*; p < .01 = ^**; p < .001 = ^***.

We also found that, unlike previous analyses, in the model involving the presence of s-commerce components on lesser-known websites, e-WOM (β = .38; p < .000) became the main predictor of trust, followed by reputation (β = .34; p < .000) and the information quality of the website (β = .22; p < .05). The informative effect of good reviews and recommendations is stronger for lesser-known OTAs than for the well-known. Besides, we observed that the relationship between trust and purchase intention regarding lesser-known online travel agency websites was lower than in the other situations analyzed, reinforcing the idea that a famous or well-known brand in the market brings greater credibility to the consumer and, consequently, a greater intention to buy-we found the same in the Brazilian context. Reputation, e-WOM, and information quality account for 67.6% of all variance of trust in lesser-known online travel agencies. Trust explains 44.7% of the variance of purchase intention of consumers in lesser-known online tourism agencies, still representing a high degree of explanation, but less intense than in the other situations analyzed.

6. Final Remarks

In the present study, we analyzed the influence of brand and online reviews on Brazilian consumers’ trust and purchase intention in online travel agency websites. We carried out an experiment through the elaboration of four scenarios, in which the brand of the online tourism company (well-known or lesser-known) and the presence (or not) of s-commerce components were controlled. We found the main factor affecting OTA consumers' trust and purchase intention in Brazil is the company brand - which is essential for customers to trust and buy from online travel agency websites. Therefore, the better the online tourism company brand is perceived or recognized by consumers, the greater will its influence be on the reputation, customer recommendations, and information quality perceived about the products/services offered on the website, which directly influence consumers’ trust and hence their intention to buy.

Another important finding from our study was that the mere presence of comments, ratings, and recommendations on online tourism websites did not contribute significantly to trust-building and its antecedents. However, when analyzing the behavior of online consumers who only assessed the website they did not know, the presence of s-commerce components started to influence the company’s reputation, as well as e-WOM - although not affecting the sense of trust of consumers at the same intensity as on well-known websites. In companies not yet recognized or established in the market, the presence of s-commerce components emerges as an important substitute for the brand, helping the online consumer to gain credibility and trust in the website. So, we found that the informative effect of online reviews is stronger for lesser-known OTAs than for well-known brands. However, brand remains a critical factor for companies that compete in the online environment in Brazil.

Our findings reinforce that online reviews and other third-party sources about tourism services exert a great deal of influence on Brazilian tourists, as happens with different types of services in other countries (Jalilvand et al., 2012). According to Lima et al. (2020), in Brazil, tourists tend to be more suspicious and seek more information from acquaintances and people in their friendship circles aiming to diminish their sense of the risk and ensure a good purchase. We believe this result can extend to other emerging countries. Besides that, the results show similarities between emerging markets, such as Brazil and Egypt, and developed markets, such as Spain, in which online purchase intention depends on perceived trust, being stronger in Brazil (Agag & El-Masry, 2017; Ponte et al., 2015). This suggests the possibility of replicating marketing strategies well developed in mature markets to emerging ones. On the other hand, we identified some different relationships between developed countries and Brazil - specifically the influence of information quality as the main predictor of consumers’ trust in tourism websites in countries such as the UK, Ireland (Filieri et al., 2015), and Spain (Ponte et al. 2015); while in Brazil, the website’s reputation was the main antecedent of trust.

Our results bring theoretical and managerial contributions. For academics, we offer a model capable of understanding important determinants of OTA consumers' intentions, as well as analyzing, through moderating variables, the conjoint effect of brand and the presence of s-commerce components. The causal model developed contributes to previous studies, such as Filieri et al. (2015) and Agag and El-Maskry (2017), who evaluated online tourism companies, considering trust antecedents based on website and company characteristics, among others. The present study confirms as trust antecedents, the characteristics of the website (measured by information quality) and the company (measured by reputation), proposing as a novelty the e-WOM, which can be considered as an antecedent based on third-party information. Therefore, we expected the results of the study to contribute to the development of this field of research.

Regarding managerial practice, the results obtained here may help professionals in the field of Tourism, IT and Marketing, in terms of strengthening brand image and reputation, improvements in online environments and feedback to website developers, and, of course, to managers of different tourism and travel services interested in s-commerce strategies. For start-ups, young entrepreneurs, and small businesses competing with well-known, larger, and well-established companies, the findings can also provide important contributions by including or expanding the use of such tools on their websites.

As limitations, we highlight the sample selection, identified in different groups from the Facebook social network. Although this study is not the only one using this approach, the online consumer selection may not faithfully represent the profile of the population analyzed. Therefore, we suggest cautions about the generalization of the results. Also, it should be noted that the experiment was developed with photos from a single website, based on Booking.com design. These simulations may be less realistic for unknown travel website scenarios. In addition, only three travel proposals and a few comments have been provided on the websites, while in real-life situations consumers often access more touristic products, websites, and reviews. Besides, in the experiment, the website images served only as a stimulus for respondents, not allowing the actual purchase action and search for more information and photos, which may have reduced the realism of the website, affecting their subsequent behavior.

References

• Agag, G., & El-Masry, A. (2017). Why do consumers trust online travel websites? Drivers and outcomes of consumer trust toward online travel websites. Journal of Travel Research, 56(3), 347-369. https://doi.org/10.1177/0047287516643185
  » https://doi.org/10.1177/0047287516643185
• Aghekyan-Simonian, M., Forsythe, S., Kwon, W., & Chattaraman, V. (2012). The role of product brand image and online store image on perceived risks and online purchase intentions for apparel. Journal of Retailing and Consumer Services, 19(3), 325-331. https://doi.org/10.1016/j.jretconser.2012.03.006
  » https://doi.org/10.1016/j.jretconser.2012.03.006
• Alhulail, H., Dick, M., & Abareshi, A. (2018). Factors that impact customers’ loyalty to social commerce websites [International Conference on Information Resources Management] AIS eLibrary 2018 Proceeding. https://aisel.aisnet.org/confirm2018/6/?utm_source=aisel.aisnet.org%2Fconfirm2018%2F6&utm_medium=PDF&utm_campaign=PDFCoverPages
  » https://aisel.aisnet.org/confirm2018/6/?utm_source=aisel.aisnet.org%2Fconfirm2018%2F6&utm_medium=PDF&utm_campaign=PDFCoverPages
• Brazil. Ministério do Turismo. (2018, March 26). Anuário Estatístico de Turismo - 2018 - ano base 2017 Retrieved May 23, 2019. http://www.dadosefatos.turismo.gov.br/2016-02-04-11-53-05/item/366-anu%C3%A1rio-estat%C3%ADstico-de-turismo-2018-ano-base-2017/366-anu%C3%A1rio-estat%C3%ADstico-de-turismo-2018-ano-base-2017.html
  » http://www.dadosefatos.turismo.gov.br/2016-02-04-11-53-05/item/366-anu%C3%A1rio-estat%C3%ADstico-de-turismo-2018-ano-base-2017/366-anu%C3%A1rio-estat%C3%ADstico-de-turismo-2018-ano-base-2017.html
• Burgess, S., & Steenkamp, J. (2006). Marketing renaissance: How research in emerging markets advances marketing science and practice. International Journal of Research in Marketing, 23(4), 337-356. https://doi.org/10.1016/j.ijresmar.2006.08.001
  » https://doi.org/10.1016/j.ijresmar.2006.08.001
• Chang, K. (2015). How travel agency reputation creates recommendation behavior. Industrial Management & Data Systems, 115(2), 332-352. https://doi.org/10.1108/IMDS-09-2014-0265
  » https://doi.org/10.1108/IMDS-09-2014-0265
• Chang, H., & Chen, S. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review, 32(6), 818-841. https://doi.org/10.1108/14684520810923953
  » https://doi.org/10.1108/14684520810923953
• Deloitte. (2018). Moving the global travel industry forward Retrieved April 10, 2020. https://www2.deloitte.com/content/dam/Deloitte/sg/Documents/consumer-business/sea-cb-moving-global-travel-industry-forward.pdf
  » https://www2.deloitte.com/content/dam/Deloitte/sg/Documents/consumer-business/sea-cb-moving-global-travel-industry-forward.pdf
• Doney, P., & Cannon, J. (1997). An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61(2), 35-51. https://doi.org/10.2307/1251829
  » https://doi.org/10.2307/1251829
• Ebit. Webshoppers 36. (2017). Online tourism projects greater growth in 2018 [in Portuguese]. Retrieved April 10, 2020. https://www.mercadoeeventos.com.br/noticias/servicos/turismo-online-projeta-maior-crescimento-em-2018/
  » https://www.mercadoeeventos.com.br/noticias/servicos/turismo-online-projeta-maior-crescimento-em-2018/
• Fastoso, F., & Whitelock, J. (2011). Why is so little marketing research on Latin America published in high quality journals and what can we do about it? International Marketing Review, 28(4), 435-449. http://doi.org/10.1108/02651331111149967
  » http://doi.org/10.1108/02651331111149967
• Filieri, R., Alguezaui, S., & McLeay, F. (2015). Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51, 174-185. https://doi.org/10.1016/j.tourman.2015.05.007
  » https://doi.org/10.1016/j.tourman.2015.05.007
• Floh, A., Koller, M., & Zauner, A. (2013). Taking a deeper look at online reviews: the asymmetric effect of valence intensity on shopping behavior. Journal of Marketing Management, 29(5-6), 646-667. https://doi.org/10.1080/0267257X.2013.776620
  » https://doi.org/10.1080/0267257X.2013.776620
• Grund, C., & Gürtler, O. (2008). The effect of reputation on selling prices in auctions. Jahrbücher für Nationalökonomie und Statistik, 228(4), 345-356.
• Hair Jr, J., Sarstedt, M., Ringle, C., & Gudergan, S. (2017). Advanced issues in partial least squares structural equation modeling Sage Publications.
• Hajli, M. (2014). The role of social support on relationship quality and social commerce. Technological Forecasting and Social Change, 87, 17-27. https://doi.org/10.1016/j.techfore.2014.05.012
  » https://doi.org/10.1016/j.techfore.2014.05.012
• Hajli, N. (2015). Social commerce constructs and consumer's intention to buy. International Journal of Information Management, 35(2), 183-191. https://doi.org/10.1016/j.ijinfomgt.2014.12.005
  » https://doi.org/10.1016/j.ijinfomgt.2014.12.005
• Hernández-Méndez, J., Muñoz-Leiva, F., & Sánchez-Fernández, J. (2015). The influence of e-word-of-mouth on travel decision-making: Consumer profiles. Current Issues in Tourism, 18(11), 1001-1021. https://doi.org/10.1080/13683500.2013.802764
  » https://doi.org/10.1080/13683500.2013.802764
• Ho-Dac, N., Carson, S., & Moore, W. (2013). The effects of positive and negative online customer reviews: Do brand strength and category maturity matter? Journal of Marketing, 77(6), 37-53. https://doi.org/10.1509/jm.11.0011
  » https://doi.org/10.1509/jm.11.0011
• Jalilvand, M., Samiei, N., Dini, B., & Manzari, P. (2012). Examining the structural relationships of electronic word of mouth, destination image, tourist attitude toward destination and travel intention: An integrated approach. Journal of Destination Marketing & Management, 1(1-2), 134-143. https://doi.org/10.1016/j.jdmm.2012.10.001
  » https://doi.org/10.1016/j.jdmm.2012.10.001
• Kim, S., & Park, H. (2013). Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. International Journal of Information Management, 33(2), 318-332. https://doi.org/10.1016/j.ijinfomgt.2012.11.006
  » https://doi.org/10.1016/j.ijinfomgt.2012.11.006
• Kuan, H., & Bock, G. (2007). Trust transference in brick and click retailers: an investigation of the before-online-visit phase. Information & Management, 44(2), 175-187. https://doi.org/10.1016/j.im.2006.12.002
  » https://doi.org/10.1016/j.im.2006.12.002
• Law, R., Buhalis, D., & Cobanoglu, C. (2014). Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management, 26(5), 727-75. https://doi.org/10.1108/IJCHM-08-2013-0367
  » https://doi.org/10.1108/IJCHM-08-2013-0367
• Liao, Z., & Shi, X. (2017). Web functionality, web content, information security, and online tourism service continuance. Journal of Retailing and Consumer Services, 39, 258-263. https://doi.org/10.1016/j.jretconser.2017.06.003
  » https://doi.org/10.1016/j.jretconser.2017.06.003
• Lien, C.-H., Wen, M.-J., Huang, L.-C., & Wu, K.-L. (2015). Online hotel booking: The effects of brand image, price, trust and value on purchase intentions. Asia Pacific Management Review, 20(4), 210-218. https://doi.org/10.1016/j.apmrv.2015.03.005
  » https://doi.org/10.1016/j.apmrv.2015.03.005
• Lima, M., Mainardes, E., & Rodrigues, R. (2020). Tourist expectations and perception of service providers: a Brazilian perspective. Service Business, 14(1), 131-166. https://doi.org/10.1007/s11628-019-00406-4
  » https://doi.org/10.1007/s11628-019-00406-4
• Lin, X., Li, Y., & Wang, X. (2017). Social commerce research: Definition, research themes and the trends. International Journal of Information Management, 37(3), 190-201. https://doi.org/10.1016/j.ijinfomgt.2016.06.006
  » https://doi.org/10.1016/j.ijinfomgt.2016.06.006
• Ling, K., Chai, L., & Piew, T. (2010). The effects of shopping orientations, online trust and prior online purchase experience toward customers' online purchase intention. International Business Research, 3(3), 63-76.
• Maia, C.R. (2019). Os efeitos da marca e dos componentes do comércio social na confiança e intenção de compra de consumidores de agências de viagens online [Dissertação de Mestrado em Administração, Universidade Federal do Rio Grande, Rio Grande/RS, Brasil]. Disponível em: https://ppga.furg.br/images/Dissertacoes/Dissertao_Claudia_Maia.pdf
  » https://ppga.furg.br/images/Dissertacoes/Dissertao_Claudia_Maia.pdf
• Maia, C. R., Lunardi, G. L., Dolci, D., & D’Avila, L. C. (2019). Competitive price and trust as determinants of purchase intention in social commerce. BAR-Brazilian Administration Review, 16(4), e190074. https://doi.org/10.1590/1807-7692bar2019190074
  » https://doi.org/10.1590/1807-7692bar2019190074
• Maia, C., Lunardi, G., Longaray, A., & Munhoz, P. (2018). Factors and characteristics that influence consumers’ participation in social commerce. Revista de Gestão, 25(2), 194-211. https://doi.org/10.1108/REGE-03-2018-031
  » https://doi.org/10.1108/REGE-03-2018-031
• Mohseni, S., Jayashree, S., Rezaei, S., Kasim, A., & Okumus, F. (2018). Attracting tourists to travel companies’ websites: The structural relationship between website brand, personal value, shopping experience, perceived risk and purchase intention. Current Issues in Tourism, 21(6), 616-645. https://doi.org/10.1080/13683500.2016.1200539
  » https://doi.org/10.1080/13683500.2016.1200539
• Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. https://doi.org/10.1177/002224299405800302
  » https://doi.org/10.1177/002224299405800302
• Pels, J. & Sheth, J. (2017) Business models to serve low-income consumers in emerging markets. Marketing Theory, 17(3), 73-391. https://doi.org/10.1177/1470593117704262
  » https://doi.org/10.1177/1470593117704262
• Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
  » https://doi.org/10.1037/0021-9010.88.5.879
• Ponte, B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism Management, 47, 286-302. https://doi.org/10.1016/j.tourman.2014.10.009
  » https://doi.org/10.1016/j.tourman.2014.10.009
• Sebrae. (2017). The panorama of travel agencies and tour operators in Brazil [in Portuguese]. Retrieved April 15, 2020. https://bibliotecas.sebrae.com.br/chronus/ARQUIVOS_CHRONUS/bds/bds.nsf/da973863da48d5c238fca5ac1e928c93/$File/7550.pdf
  » https://bibliotecas.sebrae.com.br/chronus/ARQUIVOS_CHRONUS/bds/bds.nsf/da973863da48d5c238fca5ac1e928c93/$File/7550.pdf
• Shan, Y. (2016). How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation. Computers in Human Behavior, 55, 633-641. https://doi.org/10.1016/j.chb.2015.10.013
  » https://doi.org/10.1016/j.chb.2015.10.013
• Shanmugam, M., Sun, S., Amidi, A., Khani, F., & Khani, F. (2016). The applications of social commerce constructs. International Journal of Information Management, 36(3), 425-432. https://doi.org/10.1016/j.ijinfomgt.2016.01.007
  » https://doi.org/10.1016/j.ijinfomgt.2016.01.007
• Sparks, B., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310-1323. https://doi.org/10.1016/j.tourman.2010.12.011
  » https://doi.org/10.1016/j.tourman.2010.12.011
• Sparks, B., So, K., & Bradley, G. (2016). Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, 53, 74-85. https://doi.org/10.1016/j.tourman.2015.09.011
  » https://doi.org/10.1016/j.tourman.2015.09.011
• Vermeulen, I., & Seegers, D. (2009). Tried and tested: the impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123-127. https://doi.org/10.1016/j.tourman.2008.04.008
  » https://doi.org/10.1016/j.tourman.2008.04.008
• Wang, W.-T., Wang, Y.-S., & Liu, E.-R. (2016). The stickiness intention of group-buying websites: the integration of the commitment-trust theory and e-commerce success model. Information & Management, 53(5), 625-642. https://doi.org/10.1016/j.im.2016.01.006
  » https://doi.org/10.1016/j.im.2016.01.006
• Wang, Y., & Yu, C. (2015). Does social interaction affect consumer decisions on social commerce sites. Social Science Research Network http://doi.org/10.2139/ssrn.2695661
  » http://doi.org/10.2139/ssrn.2695661
• Yan, Q., Wu, S., Wang, L., Wu, P., Chen, H., & Wei, G. (2016). E-WOM from e-commerce websites and social media: which will consumers adopt? Electronic Commerce Research and Applications, 17, 62-73. https://doi.org/10.1016/j.elerap.2016.03.004
  » https://doi.org/10.1016/j.elerap.2016.03.004
• Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180-182. https://doi.org/10.1016/j.ijhm.2008.06.011
  » https://doi.org/10.1016/j.ijhm.2008.06.011
• Yoon, S-J. (2002). The antecedents and consequences of trust in online purchase decisions. Journal of Interactive Marketing, 16(2), 47-63. https://doi.org/10.1002/dir.10008
  » https://doi.org/10.1002/dir.10008
• Zhang, Z., Ye, Q., & Law, R. (2011). Determinants of hotel room price: an exploration of travelers' hierarchy of accommodation needs. International Journal of Contemporary Hospitality Management, 23(7), 972-981. https://doi.org/10.1108/09596111111167551
  » https://doi.org/10.1108/09596111111167551

• Additional info

  The authors guarantee that the manuscript is not currently under review at any other journal and no portion of the manuscript has been previously published.

Appendix A - Experiment scenarios

(a)
Well-known website scenario with components of s-commerce - hotel offer 1

(b)
Lesser-known website scenario without components of s-commerce - hotel offer 1

Appendix B - Measurement items

Constructs n Mean SD Reputation - adapted from Kim and Park (2013) 206 3.49 1.31 REPUT1 This online tourism firm is well known. 206 3.40 1.48 REPUT2 This online tourism firm has a good reputation. 204 3.66 1.14 REPUT3 This online tourism firm has the reputation for being honest.* 203 3.73 1.05 REPUT4 I am familiar with the name of this online tourism firm. 203 3.47 1.56 Information Quality - adapted from Kim and Park (2013) 205 3.93 .87 QUALI1 The website provides accurate information about the item that I want to purchase. 204 4.51 .96 QUALI2 Overall, I think the website provides useful information. 203 4.35 .94 QUALI3 The website provides reliable information.* 202 3.69 .99 QUALI4 The website provides sufficient information when I try to make a transaction. 202 3.80 1.06 Trust - adapted from Kim and Park (2013) 205 3.71 1.00 TRUST1 This online tourism firm is trustworthy. 205 3.83 1.08 TRUST2 I believe in this online tourism firm. 205 3.62 1.14 TRUST3 This online tourism firm wants to be known as a company that keeps its promises and commitments. 205 3.76 1.08 TRUST4 This online tourism firm, despite having its own interests, takes into consideration what is best for me too 204 3.63 1.04 e-WOM - Maia (2019) 205 3.73 .92 WOM1 The website presents good comments and evaluations about the company and its products offered. 204 3.72 1.06 WOM2 The online reviews available on this website are positive. 204 3.68 1.05 WOM3 The comments and evaluations available on the website are very favorable to the company and its products. 204 3.75 1.04 WOM4 The website presents comments and evaluations beneficial to the company and its products. 203 3.76 1.07 Purchase Intention - adapted from Kim and Park (2013) 208 3.87 1.11 BUYINT1 I am likely to purchase products or tourist packages in this website. 207 3.79 1.16 BUYINT2 It is likely that I will purchase products or tourist packages on this website in the near future. 208 3.96 1.15 BUYINT3 Given the opportunity, I intend to purchase products or tourist packages on this website in the future. 207 3.85 1.18 Note. * item eliminated after validation procedures

Constructs	n	Mean	SD
Reputation - adapted from Kim and Park (2013)	206	3.49	1.31
REPUT1 This online tourism firm is well known.	206	3.40	1.48
REPUT2 This online tourism firm has a good reputation.	204	3.66	1.14
REPUT3 This online tourism firm has the reputation for being honest.*	203	3.73	1.05
REPUT4 I am familiar with the name of this online tourism firm.	203	3.47	1.56
Information Quality - adapted from Kim and Park (2013)	205	3.93	.87
QUALI1 The website provides accurate information about the item that I want to purchase.	204	4.51	.96
QUALI2 Overall, I think the website provides useful information.	203	4.35	.94
QUALI3 The website provides reliable information.*	202	3.69	.99
QUALI4 The website provides sufficient information when I try to make a transaction.	202	3.80	1.06
Trust - adapted from Kim and Park (2013)	205	3.71	1.00
TRUST1 This online tourism firm is trustworthy.	205	3.83	1.08
TRUST2 I believe in this online tourism firm.	205	3.62	1.14
TRUST3 This online tourism firm wants to be known as a company that keeps its promises and commitments.	205	3.76	1.08
TRUST4 This online tourism firm, despite having its own interests, takes into consideration what is best for me too	204	3.63	1.04
e-WOM - Maia (2019)	205	3.73	.92
WOM1 The website presents good comments and evaluations about the company and its products offered.	204	3.72	1.06
WOM2 The online reviews available on this website are positive.	204	3.68	1.05
WOM3 The comments and evaluations available on the website are very favorable to the company and its products.	204	3.75	1.04
WOM4 The website presents comments and evaluations beneficial to the company and its products.	203	3.76	1.07
Purchase Intention - adapted from Kim and Park (2013)	208	3.87	1.11
BUYINT1 I am likely to purchase products or tourist packages in this website.	207	3.79	1.16
BUYINT2 It is likely that I will purchase products or tourist packages on this website in the near future.	208	3.96	1.15
BUYINT3 Given the opportunity, I intend to purchase products or tourist packages on this website in the future.	207	3.85	1.18

• Note. ^* item eliminated after validation procedures

Publication Dates

• Publication in this collection
  10 June 2022
• Date of issue
  May-Jun 2022

History

• Received
  25 Nov 2020
• Reviewed
  08 Apr 2021
• Accepted
  24 May 2021
• Published
  18 Mar 2022