Factors that influence consumers’ participation in electronic tourism

Abstract

Con los avanzos de las Tecnologías de Información y Comunicación (TIC), el turismo se ha convertido en un sector clave de comercio electrónico. En este contexto, muchas empresas han tratado de comprender las razones que llevan los consumidores a participar en el turismo electrónico (e-turismo). Por lo tanto, el objetivo de esta investigación fue analizar diferentes factores que influyen la participación de los consumidores en el turismo electrónico, ya sea al elegir un sitio, recomendarlo o comprar productos turísticos. El estudio es una encuesta aplicada a una muestra de 251 miembros de grupos de turismo en Internet, cuyo modelo propuesto fue testado a través de la técnica de modelado de ecuaciones estructurales. Los resultados apuntan como predictores de la participación de los usuarios en el turismo electrónico, la satisfacción con las experiencias anteriores y la calidad de los productos turísticos, que son los aspectos que más influyen en la intención de comprar y recomendar el sitio. La satisfacción con experiencias previas se destaca como el principal predictor en ambas situaciones. Los resultados obtenidos aquí profundizan la comprensión de las actitudes de los consumidores de servicios electrónicos de turismo, y sirven de referencia a los gerentes e investigadores interesados en esta temática.

Keywords
Electronic tourism; E-commerce; Buying behavior

Resumo

Os recentes avanços nas Tecnologias da Informação e Comunicação (TIC) transformaram a indústria do turismo num setor essencial do comércio eletrônico. Nesse contexto, muitas empresas têm procurado entender os principais motivos que levam os consumidores a participar do turismo eletrônico (e-turismo). Assim, objetivou-se nesta pesquisa analisar diferentes fatores que influenciam a participação dos consumidores no e-turismo, seja escolhendo um site, recomendando-o ou comprando produtos turísticos. O estudo se caracteriza como uma pesquisa survey, aplicada a uma amostra de 251 membros de grupos de turismo da Internet, cujo modelo proposto foi testado através da técnica de modelagem de equações estruturais. Os resultados apontaram como preditores da participação dos usuários no e-turismo, a satisfação com experiências prévias e a qualidade dos produtos turísticos, sendo estes os aspectos que mais influenciam a intenção de comprar e recomendar o site. A satisfação com experiências prévias destaca-se como o principal preditor em ambas as situações. As descobertas aqui obtidas aprofundam a compreensão acerca das atitudes do consumidor de serviços de turismo online, servindo de referência para gestores e pesquisadores interessados nesta temática.

Palavras-chave
Turismo eletrônico; Comércio eletrônico; Comportamento de compra

Resumen

Recent advances in Information and Communication Technologies (ICT) transformed the tourism industry in a key sector for e-commerce. In such a context, many companies have sought to understand the main reasons that lead consumers to participate in electronic tourism (e-tourism). Thus, we aim to analyze different factors that influence the participation of consumers in e-tourism, by choosing a website, recommending it, or buying tourism products. We surveyed 251 participants enrolled in tourism discussion groups on the web, analyzing data through Structural Equation Modeling – SEM. Our results pointed out satisfaction with previous experiences and the quality of tourism products as the main drivers of customers’ participation in e-tourism regarding the intention to buy and recommending the tourism website. Satisfaction with previous experiences stands out as the main predictor in both situations. We believe our findings will extend the understanding of consumer’s attitudes to online tourism services aiding managers and researchers interested in this topic.

Palabras clave
Turismo electrónico; Comercio electrónico; Comportamiento de compra

1 INTRODUCTION

Information Technology (IT) has impacted social, business, and organizational relations, changing the performance and the culture of several industries, including tourism. According to Marco, Gómez and Sevilla (2018), the tourism industry has undergone a significant transformation since IT started to be used in the sector in the 1980s and especially after the advent of the Internet in the late 1990s. Such advances have transformed tourism into one of the key sectors for electronic commerce (Ponte, Carvajal-Trujillo & Escobar-Rodríguez, 2015), playing a central role in the competitiveness of organizations operating in this business and tourist destinations as well.

The integration of technology and tourism gave rise to the so-called electronic tourism or e-tourism, defined as the digitization of all processes and value chains in the tourism, travel, hospitality, and catering industries (Buhalis & Deimezi, 2004). According to Buhalis and Law (2008), the term e-tourism has been used for over 25 years, since the conference held in Innsbruck, Austria, in 1994. A few years later, in 1998, an interest group that regularly published on tourism and technology established the Journal of Information Technology & Tourism (JITT). However, according to Biz and Corrêa (2016), scientific research on e-tourism in Brazil is recent and has gained breadth since the foundation of ANPTUR in 2002.

Today, the tourism sector is highly dependent on information, especially because its products and services only can be evaluated after consumption. Due to the intangibility of these services, there is a greater need for information for tourists. Several authors have pointed out that tourism products and services are perfectly suitable for online sales, as they have a higher level of intangibility, involvement, and differentiation than other tangible consumer goods, being, therefore, more easily sold on the web (Ponte, Carvajal-Trujillo & Escobar-Rodrígues, 2015; Oneto, Ferreira, Giovannini & Silva, 2015; Silva, Mendes-Filho & Corrêa, 2017).

According to the Ministry of Tourism, digital media have established themselves as the primary source of information for Brazilian and foreign tourists, who, due to easy access to the Internet in almost all Brazilian destinations, can organize their trips using this vehicle. Every hour, Internet users from around the world perform around 625 thousand travel searches, only on the Google website (Brasil, 2014). Among tourists who visited the country for leisure reasons, in 2017, 79.5% did not use traditional travel agencies to organize their trips. Of those who traveled for business reasons, events, or conventions, 80.3% used the Internet to arrange their needs (Brasil, 2018). Therefore, assuming e-tourism as a recent and relevant topic for future studies in the field of Tourism, we propose the following research question: What factors influence consumers to participate in e-tourism? The research aims to analyze – from the consumers’ perspective – the factors that have influenced consumer participation in e-tourism, whether choosing an online tourism website, purchasing one or more tourism products, or even recommending the website to others. The proposed model was evaluated by using the structural equation modeling technique, based on partial least squares (PLS).

2 LITERATURE REVIEW

This section provides a brief contextualization of the influence of IT on tourism, called electronic tourism (or e-tourism), as well as the different factors that have been highlighted in the literature as potential influencers of consumer participation in this type of electronic commerce. At the end of the section, we present the hypotheses and the research model of the study.

2.1 Electronic Tourism

The tourism economy currently is driven by information technology and telecommunications (Jaremen, 2016). Different companies related to this sector, such as tour operators, travel agencies, rental agencies, cruises, and hotels, admit the growing impact of IT in their activities. The integration of tourism and technology provides different types of competitive advantage to companies since it allows the creation of support tools for managers so that they quickly adapt their offerings to changes caused by the globalization of markets, the emergence of new competitors, and the motivation of tourists (Ramos, 2008).

Zhang, Fang, Wei, Ramsey, McCole and Chen (2011) argue the complexity of the business models present in tourism requires the constant development of new systems that provide greater flexibility, automation, integration, storage, and collaboration between tourists and suppliers of tourism products and services. According to Herrero, San Martín and Hernández (2015), in the last few decades, there has been an explosion of web platforms and social media available to all people and organizations, which is used for publishing content of any kind, including tourist content. In addition, the emergence of Web 2.0 has redefined the adoption of online tourism by consumers, including a wide variety of electronic applications (such as social networks, analytics, blogs, interactive websites, and photo and video sharing platforms). Those applications facilitate interactions among individuals and between companies and users (Ukpabi & Karjaluoto, 2017), directly impacting the sector's results.

Electronic tourism involves activities that result in the exchange/search for information, reservations, and purchases of tourist products online. Buhalis and Deimezi (2004) argue that e-tourism reflects the digitalization of all processes in the value chain in the tourism, travel, hospitality, and food industries. In particular, it can be considered as the integration of tourism and Internet technologies, which involves activities such as hotel reservations, Internet purchases of airline tickets, and choosing travel destinations, either through a remote computer or smartphone. The use of mobile devices, for example, gains relevance because tourists that are traveling can search for restaurants or other mobile services through available applications or local consultation (Navio-Marco, Ruiz-Gómez & Sevilla-Sevilla, 2018).

Most innovations in the information technology sector have changed the way tourism companies conduct their business, as the tourist service buying process is based mainly on information gathering through many channels, such as travel agencies, brochures, word-of-mouth, and websites of tourist service suppliers (Jaremen, 2016). Due to the greater availability of tourist information present in electronic media, the Internet has been, according to Navio-Marco, Ruiz-Gómez & Sevilla-Sevilla (2018), propitious for tourists to find their destination, accommodation, as well as some private renting services, enabling them to organize their tourism packages. Especially for tourism companies, the Internet has offered the possibility of making information and reservations available to a large number of consumers at relatively low costs, providing an essential communication tool between tourism providers, intermediaries, and final consumers (Di Pietro, Di Virgilio & Pantano, 2011).

Leung, Law, Van Hoof and Buhalis (2013) claim that tourists have become more independent and sophisticated and have a wide variety of tools to organize their trips. These include booking systems and online travel agencies (such as Expedia), search engines (such as Google), Web 2.0 portals (such as TripAdvisor), price comparison sites (such as Decolar.com), as well as collective buying sites, individual suppliers, and intermediary sites. Buhalis and Law (2008) identified consumer knowledge as one of the leading research topics in e-tourism. As a result of these new technologies, the new tourist is defined as a demanding consumer, who depends more on the information provided by peers, with previous experience and sophisticated information, which allows him/her to search for customized products. Thus, companies and tourism agencies should prioritize quickly identifying and anticipating Internet users’ needs and desires (Fernández-Poyatos & Papí-Gálvez, 2017).

2.2 Antecedents of consumers’ participation in e-Tourism

Beldad, De Jong and Steehouder (2010) carried out a systematic literature review, covering different empirical studies on people’s trust and the adoption of computer-mediated services. Their results suggested three groups of antecedents of electronic services: (i) customer/client-based antecedents, such as the user experience with the technology used to carry out the transaction or the tendency of the user to trust it; (ii) website-based antecedents, involving the quality of the website or the quality of the information available on it; and (iii) company-based antecedents, such as reputation, familiarity with and size of the company.

Filieri, Alguezaui and McLeay (2015) and Agag and El-Masry (2017) adapted the model developed by Beldad et al. (2010) to analyze the antecedents and consequences of consumer’s trust in travel and tourist websites. Filieri et al. (2015) tested five antecedents of trust about consumer-generated content (CGC), namely: perceived source credibility, information quality, website quality, customer satisfaction, and user’s previous experiences with CGC. As the antecedent of customer-based trust, they included user experience (knowledge and skills) in using CGC; website-based antecedents included website quality, information quality, and perceived source credibility; and as a company-based antecedent, they included users’ previous experience with CGC. The authors concluded that all factors mentioned, except for perceived source credibility and the user’s previous experience, influence consumer’s trust concerning CGC.

Agag and El-Maskry (2017) tested a model with seven antecedents of consumer trust toward online travel websites, namely: consumer experience, propensity to trust, reputation, perceived website sizes, ease of use, perceived usefulness, and website quality. The results confirmed that all the factors mentioned, except consumer experience, influence user trust in online travel websites. The survey also revealed that consumers’ attitude towards the website is the main determinant of the intention to purchase travel online.

Buhalis and Law (2008) also emphasized the search for information and the way technology changes the behavior of tourists. In this context, comments, ratings, and recommendations of third-party standout, as well as the exchanges of experience and information, both positive and negative. This online communication between individual users, through the sharing of knowledge and experience of a new product, also influences trust (Hajli, Hajli & Khani, 2013). According to Bassani, Milan, Lazzari, and De Toni (2018), information that is received and interpreted by the consumer serves as a basis for him/her to make evaluations about a specific product. These factors, based on third-party sources – also called electronic word-of-mouth (e-WOM) – would also represent an essential antecedent for consumer participation in e-tourism.

Xiang, Magnini and Fesenmaier (2015) suggest that traditional travel products such as flight tickets, lodging, and rental car continue to dominate the online travel market. In this sense, the quality of the tourism product, service, or package that the consumer wants to buy is very relevant in the field of e-tourism. According to Dedeke (2016), in the extant literature on this topic, both theoretical and empirical evidence shows that perceived product-quality influences consumers’ purchase intention. In this regard, the presence and availability of pictures on the website, for example, would affect customers’ intention to purchase a product (Dedeke, 2016). Thus, websites should incorporate pictures, photos, and other audiovisual elements that help users to reduce the intangibility of the service and, therefore, minimize the perceived risk (Herrero et al., 2015). In this sense, the perception of product quality can also represent an important antecedent of consumers’ participation in e-tourism.

Hence, based on the previous literature, we have identified different antecedents that can influence consumers’ participation in e-tourism, which can be grouped into five main categories: website-based antecedents (measured by Website Quality), company-based antecedents (measured by Reputation), customer-based antecedents (measured by Satisfaction with previous experiences), third-party information­-based antecedents (measured by e-WOM), and product-based antecedents (measured by Product Quality). Next, we present our hypotheses and propose a research model that will be tested empirically in this study.

2.3 Hypotheses

According to Alcántara-Pilar, Blanco-Encomienda, Armenski and Del Barrio-García (2018), websites have become the most important means of promoting tourism today and can lead to a positive perception of a particular travel destination. In online environments, the website becomes the company’s storefront, in which first impressions are formed (Filieri et al., 2015). Therefore, if consumers perceive a website to be of high quality, they are likely to have a more positive attitude about the online retailer, developing a willingness to buy products on this website (Chang & Chen, 2008) or at least return to it in the future. For the same authors, website quality is defined as the assessment that users make about the features of a website and whether they meet their needs. In the context of e-tourism, the quality of a website refers to consumers’ perceptions of its availability, adaptability, and response time (Filieri et al., 2015). Based on the above statements, we propose the following hypothesis:

H1 : Website quality positively affects the participation of Internet users in e-tourism.

Rodríguez-Díaz, Rodríguez-Voltes and Rodríguez-Voltes (2018) claim that online reputation is an important strategic element of competitiveness, that directly influences customers’ purchasing behavior, especially in industries where communication via the Internet is essential in developing the main business. Huang and Benyoucef (2013) suggest that travel agencies need to build their reputation advantages to gain competitiveness in the market. Therefore, these companies must have a good reputation so that customers trust in the travel products that are offered to them (Keh & Xie, 2009). Complementarily, Santos, Souza Neto, Pereira, Gândara, and Silva (2016) point out that company’s reputation can be a major factor in choosing a product or service in a tourist destination. Thus, we propose the following hypothesis:

H2 : Reputation positively affects the participation of Internet users in e-tourism.

According to Bagozzi (1981), past experiences can be an important determinant of behavior change. In this sense, previous experience with a tourism company or destination, for example, can influence the consumer in his/her next choice of trip or tourism provider. A tourism experience evaluated as favorable by travelers has a strong relationship with the satisfaction of travelers and their behavioral intentions (Antón, Camarero, & Garcia, 2014). In the online context, Lehto, Kim and Morrison (2006) showed that previous experiences with a company’s website and knowledge about a specific destination influence both the type of content searched and the time spent during an online search for travel planning information. Carvalho, Ferreira, Kanazawa, Machado, and Giraldi (2016) argue that a positive experience with a tourism website influences the formation of an image of another tourist destination and the intention to visit it. Consumer’s satisfaction represents the overall evaluation of a consumption experience with a company, product, or service, which is based on his or her accumulated experiences, not resulting from a specific transaction (Olsen & Johnson, 2003). Marchiori and Cantoni (2015), on the other hand, identified that when a traveler lacks experience with a company, he or she can disconfirm his or her prior belief following exposure to online content. In this regard, we hypothesize as follows:

H3 : Satisfaction with previous experiences positively affects the participation of Internet users in e-tourism.

According to Rodríguez-Díaz et al. (2018), traditional word-of-mouth has been replaced by online word-of-mouth (e-WOM), which produces higher and almost immediate impact on customer expectations and business results. Herrero et al. (2015) point out that in the tourism and hospitality sector, e-WOM has particular relevance for users during the pre-purchase phase (i.e. information search and final choice). Therefore, e-WOM can be seen as a variable that depends on consumer satisfaction with the tourism company or website to recommend it to friends and acquaintances. Öz (2015) suggests that tourism companies should give particular importance to social media messages, especially because some of these messages can be evaluated as a form of e-WOM and can potentially influence thousands of consumers worldwide. In the same direction, Nilashi, Ibrahim, Yadegaridehkordi, Samad, Akbari, and Alizadeh (2018) claim that the image of a tourist destination is affected by the content generated and posted by travelers and tourists themselves, in addition to the information controlled by the agents. According to Herrero et al. (2015), the online content generated by other people is a source of information as important as the official information available about a destination. Usually, the potential tourist search for information about the destination he or she is interested in, that in most cases is not promotional, once he or she desires to understand the local context, escaping from the "influence" of promotional items (Santos & Perinotto, 2016). By having remarkable experiences, these remain active in memory being diffuse to others through e-WOM (Cechinel & Santos, 2018). Therefore, the following hypothesis is proposed:

H4 : E-WOM positively affects the participation of Internet users in e-tourism.

Finally, Santos et al. (2016) describe that consumer behavior has been changing over time. Currently, consumers do not buy a product just to satisfy his or her needs; consumption is often related to the feelings and emotions that the experience with a product or service is able to offer. Dedeke (2016) states the perceived quality of a tourism product or service is defined as the impression of the potential customer about the level of quality that a product or service can offer if purchased. Tourism products are usually bought without experiencing them first, therefore the online shopping experience is not so different from the regular one (Cosma, Bota & Tutunea, 2012). Thus, understanding how people choose among several travel products can improve sales performance and marketing success. In the context of e-tourism, more specifically, perceived product quality is a determinant factor of competitive advantage, once it drives the purchasing intention (Dedeke, 2016). Thus, we propose the following hypothesis:

H5 : Product quality positively affects the participation of Internet users in e-tourism.

Figure 1 presents the research model, containing five different hypotheses that were tested regarding the intention to buy and recommending the tourism website. We intend to contribute to the studies of Beldad et al. (2010), Filieri et al. (2015), and Agag and El-Maskry (2017), by including new variables based on the tourism product offerings (Product Quality) and the third-party information (e-WOM). In addition, we analyze such antecedents as predictors of consumer participation in e-tourism, unlike previous studies that focused on consumer trust on the website. Next, we present the study methodology.

Figure 1
Research Model

3 METHODOLOGY

The study is characterized as an exploratory-descriptive research, operationalized through a survey of 273 users of the social network Facebook. The respondents are enrolled in 36 different online tourism discussion groups focused on some activity related to tourism, such as tips about places to visit, airline tickets, hotels, backpackers, travel reports, etc. From this total, we excluded 22 respondents of the study for presenting too many questions unanswered or marked in the same alternative, totalizing a sample of 251 valid cases. Respondents were instructed to think about one of their last shopping experiences in using online travel websites for tourism-related products and services before answering the questions. Data were collected during May 2019.

We developed the research questionnaire with structured questions, which were operationalized from measures validated in previous studies being adapted for the present research. The questions were translated from English to Portuguese and then re-translated to English (back translation process). The differences between the original and the translated version were adjusted by the authors, before submitting the instrument to a panel of experts for the final assessment.

We operationalized the items referred to the purchasing process or product searching in e-tourism using a seven-point Likert type scale, ranging from (1) “strongly disagree” to (7) “strongly agree”. The same scale was used to evaluate consumer’s participation in e-tourism regarding his/her intention to buy and to recommend the website – measures also adapted from studies already validated. We added nine questions related to the profile of the respondent (such as gender, age, schooling, marital status, place of living, family income, online tourism websites usually visited to make purchases or research, frequency of trips, number of times he/she has made online purchases or reservations in tourism websites). We also added another nine questions related to the product searched and/or bought on the evaluated tourism website (type of product, website visited, frequency of use of the website for shopping/searching, whether the trip was carried out alone or accompanied, trip motivation, device used for searching/purchasing, price range of the product, whether the product/service was purchased or just searched and the price comparison found on this website in relation to competitors).

The questionnaire was previously pretested with 27 Business Administration graduate students in which profile was similar to the intended sample. We asked them to identify possible formatting problems and/or confusing questions on the questionnaire. After some adjustments on the instrument, we sent messages through the social media platform Facebook, inviting members enlisted in tourism discussion groups to participate in a survey about e-tourism. We asked them to access an online questionnaire through a link and, if possible, share it with their network of friends and acquaintances. We defined as inclusion criteria that all participants should be over 18 years old and have searched or purchased a product on online tourism websites in the last twelve months. The sample is classified as non-probabilistic, being the respondents selected by convenience.

3.1 Instrument validation

After the data collection, we proceeded to the validation of the scales. As a preliminary step, we performed the exploratory factor analysis (EFA) for each construct individually, freeing the number of extracted factors. The analysis confirmed the unidimensionality of the constructs, since the factor loadings converged to a single factor. We used Cronbach’s alpha coefficients to evaluate the reliability of the scales, with scores ranging from 0.78 to 0.95, suggesting a good internal consistence of the scales (Hair et al., 2009).

Then, we applied the technique of structural equation modeling based on variance using the software SmartPLS 3.0 (Partial Least Squares). PLS has been widely used in the areas of Marketing, Strategy, Information Systems and Tourism (Valle & Assaker, 2016; Hair Jr. et al., 2019), being especially suitable for predictive applications and theory building. Based on this methodology, the data were analyzed and interpreted in two stages: (1) the evaluation of the measurement model and (2) the evaluation of the structural model.

To evaluate the measurement model, the discriminant and convergent validities of the model were verified through confirmatory factor analysis (CFA) (Table 1). Discriminant validity is established when the factor loading of an item on its associated construct is greater than all of its cross-loadings on the other constructs. It is expected a minimum of 0.70 in the respective factor, and lower loadings in other factors. This analysis resulted in the elimination of a specific item measuring from Reputation. We re-run the PLS algorithm, and the final solution confirmed all items with high factor loadings (greater than or equal to 0.70, as suggested in the literature), being statistically significant at the level of 5% in their respective constructs (indicating the reliability of the items).

Table 1
Confirmatory Factor Analysis (CFA)

	e-Wom	Satisfaction experiences	Purchase Intention	Product quality	Recommending	Reputation	Website quality
EWOM1	.835	.468	.381	.384	.242	.439	.339
EWOM2	.836	.396	.377	.502	.316	.532	.390
EWOM3	.862	.395	.368	.443	.291	.436	.347
EWOM4	.838	.366	.376	.503	.266	.522	.461
EXP1	.356	.847	.646	.457	.438	.518	.447
EXP2	.424	.875	.606	.542	.425	.596	.615
EXP3	.444	.887	.664	.637	.500	.603	.585
EXP4	.449	.873	.707	.641	.535	.678	.602
INT1	.404	.744	.903	.604	.702	.632	.579
INT2	.370	.600	.891	.536	.641	.491	.471
INT3	.432	.697	.923	.610	.639	.607	.541
PROD1	.384	.491	.480	.754	.341	.468	.540
PROD2	.399	.590	.510	.777	.380	.568	.554
PROD3	.490	.548	.541	.800	.430	.457	.510
PROD4	.433	.441	.501	.813	.419	.473	.521
REC1	.302	.479	.670	.453	.940	.430	.331
REC2	.350	.548	.686	.462	.956	.485	.380
REC3	.295	.536	.728	.515	.960	.441	.386
REPUT2	.517	.594	.538	.543	.382	.884	.544
REPUT3	.446	.598	.569	.552	.444	.864	.507
REPUT4	.476	.532	.495	.460	.357	.747	.473
SITE1	.352	.534	.460	.462	.296	.449	.798
SITE2	.408	.589	.529	.642	.343	.601	.822
SITE3	.271	.416	.371	.443	.268	.451	.721
SITE4	.373	.455	.455	.528	.284	.378	.769

Convergent validity of the constructs was evaluated using the factor loading analysis, and the average variance extracted (AVE) criterion, whose values exceeded the minimum limit of .50 (Table 2). Both factor loadings and construct AVE values serve as a basis to ensure that the constructs of the proposed model demonstrate convergent validity. We still assessed discriminant validity through Fornell and Larcker’s criterion, in which the square root of the AVE (highlighted in bold on the diagonal in Table 2) must be greater than the correlations between constructs in the model. In addition, we used the criterion of the Heterotrait-Monotrait ratio of correlations (HTMT), in which the ratio between the constructs is expected to be less than 0.90, which was also met in this research (Hair Jr. et al., 2017).

Table 2
Shared Variance, Correlations, and Reliability of Constructs

	Mean	α	CR	AVE	Purch Intent	Sat.Prev. Exp	Product Quality	Recomm	Reput.	Website Quality	eWom
Purchase Int.	6.08	.891	.932	.821	.906
Sat.Prev.Exp.	6.32	.894	.926	.758	.756	.871
Product Qual.	5.84	.794	.866	.618	.647	.658	.786
Recommend	5.74	.948	.967	.906	.730	.548	.501	.952
Reputation	6.35	.778	.872	.696	.642	.690	.624	.475	.834
Website Qual	6.08	.783	.860	.605	.589	.647	.674	.385	.610	.778
e-Wom	5.93	.864	.907	.710	.445	.482	.544	.332	.573	.456	.843

We assessed the reliability of scales through composite reliability (CR) and Cronbach’s alpha. All scores exceeded the minimum threshold level of 0.70, indicating good reliability (Table 2). Finally, we assessed the multicollinearity among the independent variables using the variance inflation factor (VIF) scores, whose individual scores ranged between 1.631 and 2.437, indicating that multicollinearity is probably not a serious concern in this study (Hair Jr. et al., 2014). The appendix presents the questionnaire and descriptive statistics.

4 RESULTS AND DISCUSSION

In order to describe the sample, we highlight the main characteristics of the 251 participants of the study. Concerning gender, 170 (67.7%) are women, and 81 (32.3%) are men. The predominant age ranges were concentrated between 21 and 30 (45.8%) and 31 and 40 (33.1%) years old. As to marital status, single (55%) and married (31.9%) represented the majority of the sample. The predominant family income range was concentrated between 1 and 3 Brazilian minimum salaries (37.5%). Regarding schooling, 30.7% have completed superior education and 49.8% post-graduation. Participants are mostly concentrated in the states of Rio Grande do Sul (n = 129; 51.4%), São Paulo (n = 42; 16.7%), and Santa Catarina (n = 19; 7.6%).

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, 59.9% of the respondents travel from 1 to 3 times per year. Regarding the frequency with which they buy tourism products on the Internet, the largest group said they had purchased more than nine times (44.2%). The top search websites used by respondents are Booking.com (74.1%), Decolar.com (57%), and Tripadvisor (40.2%). Besides, 45.8% of the respondents said they use other tourism websites.

Concerning the tourism products searched by the respondents, 50.6% chose accommodation, and 39.8% airline tickets to evaluate. The largest group said they have bought products in the evaluated website from 1 to 3 times (45.8%), followed by respondents who have bought between 4 and 5 times (24.7%) and more than ten times (22.4%). About the travel, 28.3% planned to travel alone while the others (71.7%) considered traveling with friends, family, or as a couple. The main electronic devices used to search or purchase products was the combined use of smartphones and notebooks/desktop (41.4%), followed by the single use of notebooks/desktops (38.6%) – 18.7% said they use only smartphones. The approximate average spending on the tourism product purchased/searched was R$ 1,923.00 (with over 60.1% of the sample claiming to have spent more than R$ 1,000.00 on the purchase). Compared to the products offered on other tourism websites, 54.2% stated that the chosen website presented a slightly lower price, 22.7% much cheaper, and for 15.9%, the price was practically the same; for 84.9% of the respondents, they traveled for leisure/sightseeing. Finally, the vast majority (94%) of the respondents said they purchased the searched tourism product, while 6% only searched it on the website, but did not buy it.

We verified the sample size adequacy using G*Power 3.1.9.4 software. For this, we observed the parameters suggested by Hair et al. (2014): (a) the recommended power of 0.80 and (b) the size of the effect (f² = 0.15). The calculated minimum sample was estimated in 92 cases, indicating the sample size in this study is adequate. To analyze the structural model, we also used the software SmartPLS 3. We used the bootstrap resampling technique with 5,000 samples to estimate the consistency of the model in general and the statistical significance of the established relationships. The path coefficients of the structural model, as well as the R² values of the dependent variables, are shown in Table 3.

Table 3
Result of Structural Equation Analyses

Hypotheses	Relationships	r	t-values
H1a	Website Quality → Purchase Intention	0.05	0.63 ns
H1b	Website Quality → Recommending	-0.09	1.14 ns
H2a	Reputation → Purchase Intention	0.16	1.59 ns
H2b	Reputation → Recommending	0.14	1.59 ns
H3a	Satisfaction with previous experiences → Purchase Intention	0.49	4.61 ***
H3b	Satisfaction with previous experiences → Recommending	0.35	4.51 ***
H4a	e-WOM → Purchase Intention	-0.01	0.15 ns
H4b	e-WOM → Recommending	-0.01	0.15 ns
H5a	Product Quality → Purchase Intention	0.20	2.96 **
H5b	Product Quality → Recommending	0.25	2.84 **
Explained variance (R2)
Purchase Intention		62.4%
Recommending		34.6%

• Note: p > 0.05 = NS (no significant coefficients); p < 0.05 =*; p < 0.01 = **; p < 0.001 = ***.

According to the results, we found that only Satisfaction with previous experiences (β = 0.49, p <0.000; β = 0.35; p <0.000) and Product Quality (β = 0.20; p <0.01; β = 0.25; p <0.01) influence positively and significantly the participation of consumers in e-tourism, regarding the intention to buy and recommending the tourism website as well – being satisfaction with previous experiences the main predictor variable of the model, in both causal relationships. The model explains 62.4% of the variance of the dependent variable Purchase intention, which represents a high degree of explanation and predictive power, and 34.6% of the variation of the dependent variable Recommending, indicating a moderate degree of explanation. In this sense, we observe that only hypotheses H3 and H5 were confirmed, suggesting the participation of consumers in e-tourism is influenced, especially by the antecedents based on the consumer and the tourism product or service offered. The other three hypotheses (H1, H2, and H4) were rejected, showing the evaluated antecedents based on the website, company, and third-party information do not significantly influence the participation of consumers in e-tourism regarding their intention to buy or recommending the tourism website.

According to Forgas-Coll, Palau-Saumell, Matute, and Tárrega (2017), the tourist experience is directly and positively related to his/her behavioral intentions, being considered an important element for his/her participation in e-tourism, where users consider their previous experiences with a company as relevant to their purchase decision and recommending the website to others, whether they are known or not. These results are consistent with previous studies which concluded that past experience retained in an individual's memory is a valuable source of information, being considered highly reliable, suggesting the higher the level of satisfaction with previous experiences with a company or website, the more favorable the future behavior of this tourist will be (Sharma & Nayak, 2019), both about the company and the selected destination.

We confirmed that positive experiences with e-tourism have an effective impact on their behavioral intention, either revisiting or recommending the website to other people – acquaintances, non-acquaintances, friends, and relatives. Likewise, Alves, Stefanini, and Moretti (2018) also confirmed in their research that the level of customer experience with a given company or service/product positively affects his/her intention to repeat this purchase and even recommend it. Xia, Zhang and Zhang (2018) also suggest that the evaluation that consumers make regarding their previous experiences with a tourism company contributes positively to the formation of a global image to the company and the destination.

Similarly, perceived quality of tourism products, in terms of attractiveness, quality, design, and remarkable experiences, will influence consumer’s purchase intention, as well as the company’s or website’s recommendation. In general, consumers still feel a certain degree of anxiety regarding the purchase of products or services online, including tourism products (Chang & Chen, 2008). According to Kim, Kandampully, and Bilgihan (2018), consumers build an emotional attachment relationship to a product conceptually similar to the emotional attachment developed between people. In this sense, it could be desirable that websites offering online tourism services can provide their customers with detailed information about their products and services to facilitate them to plan itineraries and make travel reservations; besides, these companies could arrange a variety of travel packages and value-added services (Liao & Shi, 2017).

The new tourist is defined as a demanding consumer, who depends on information and search for personalized products. In this sense, tourism companies and online travel agencies should quickly identify and anticipate these user’s needs and desires to adapt their products and services offered to them (Fernández-Poyatos & Papí-Gálvez, 2017). Dedeke (2016) claims that, in the online context, perceived product quality is a determining factor of competitive advantage of companies that operate in e-tourism, being positively related to consumers’ purchase intention. Finally, Sullivan and Kim (2018) complement saying that product evaluation factors are also important attributes for determining the intention to repurchase and recommend the company, website, or selected tourism destination.

5 CONCLUDING REMARKS

With the advance of different information and communication technologies, tourism has become one of the key sectors of electronic commerce. In general, tourism companies have been interested in understanding the motivations that have led consumers to participate in electronic tourism. In academia, research of this nature has become a relevant target in international studies. The literature on e-tourism is quite extensive, especially in developed countries, such as the United States, Canada, and the European Union. In developing countries, as is the case of Brazil, published research, although existing, is scarce, which highlights the relevance of this research.

There is a growing expansion of the tourism market in Brazil. So, the need to understand the behavior of Internet users regarding the use of new technologies, including those associated with tourism is increasingly evident. In this sense, we surveyed 251 Internet users, members of tourism discussion groups on the web. Our study provides significant contributions to the field of knowledge of Tourism and Business, more specifically in the field of Management Information Systems and Consumer behavior of online tourism products. Intending to analyze different factors that influence the participation of users in e-tourism, we proposed a causal model that contributes to previous studies identified in the literature that also evaluated online tourism companies, considering, however, different antecedents of trust. More specifically, we sought to analyze the influence of antecedents based on the characteristics of the website (measured through the Website Quality), company (measured through Reputation), third-party information (measured by e-WOM) and consumer (measured by Satisfaction with previous experiences). We also proposed the inclusion of the tourist product as another characteristic that influences the participation of users in e-tourism – measured through the Product Quality.

We identified that satisfaction with previous experiences and the quality of tourism products are the main aspects that influence the consumer’s intention to purchase and recommend the website, highlighting the former as the main predictor of consumer participation in e-tourism. In this sense, when a customer has positive experiences with an online tourism company, the higher will be his/her willingness to recommend it and buy another product on this website in the future. As managerial contributions, companies that offer tourism products could develop strategies to attract potential customers, motivating them to visit their websites, and after the shopping experience, create positive relationships between them, either by providing discounts or purchasing coupons, for example. In addition, because the tourism product is highly intangible, these companies should improve their relationship with consumers, trying to build a more concrete image in users’ minds about their products and services by providing more details such as photos, trip reports, descriptions, and information about the destination or product/service offered.

Although non-probability sampling was used, which suggests cautions about the generalization of the results, we can highlight some data regarding the behavior of the participants of the study as potential consumers of online tourism products or services. Regarding their habits, we could evidence the importance of the company's brand in consumers’ choice of online tourism website. Even though the participants listed a wide variety of online tourism websites, the most used and cited by respondents were Booking.com, Decolar, and TripAdvisor – companies that are well-known, reputable, and already consolidated in the market.

Another interesting remark was that the vast majority of participants indicated they travel accompanied. This information can be relevant for travel agencies and hotels which can organize and develop sales, promotional packages, and specific tourism products to this group of consumers – which marketing initiatives can be implemented to attract new or even retain their customers. Further, the study provides some interesting evidence about the prices offered on tourism websites. Most respondents stated the website chosen to make their assessment presented slightly lower prices than that of competing websites, showing that in tourism a large price difference between one company and another may not be decisive in the choice of purchase a product or package, even because the price is also associated with product quality. This suggests that consumers tend to take other factors into account when they decide to buy or not a tourism product.

Our findings deepen the understanding of the attitudes of consumers of online tourism products and services, serving as a reference for managers and researchers interested in adopting and studying initiatives related to e-tourism. Our results may help entrepreneurs and managers of online tourism companies to develop strategies that improve their relationship with Internet users and, consequently, influence their attitudes or intentions to buy products or services related to travel through this channel, once more and more the new tourist has been heavily influenced by technological advances. As limitations of the study, we highlight the selection of the participants and the small number of respondents who are enrolled in discussion groups and communities related to travel and tourism. With regard to future research, we could identify some gaps and opportunities from the literature review and field research. So, we suggest (i) to study the use of the smartphone by the tourist in searching for information about events, tourist places, accommodation, and food when he/she is already at the place or destination, and (ii) analyze the influence of social media components on the tourist behavior in choosing a destination, purchasing tourist products and services, or selecting one or another tourism company online.

• How to cite: Santos, F.: Lunardi, G. L.; Maia, C. R.; Anãnã, E. S. (2020). Factors that influence Consumers’ Participation in Electronic Tourism. Revista Brasileira de Pesquisa em Turismo, São Paulo, 14 (2), p. 139-155, maio/ago.http://dx.doi.org/10.7784/rbtur.v14i2.1831
• Open data: http://dx.doi.org/10.17632/ddkzkc4d42.4

REFERÊNCIAS

• 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
• Alcántara-Pilar, J., Blanco-Encomienda, F., Armenski, T.., & Del Barrio-García, S. (2018). The antecedent role of online satisfaction, perceived risk online, and perceived website usability on the affect towards travel destinations. Journal of Destination Marketin., & Management, 9, 20-35. https://doi.org/10.1016/j.jdmm.2017.09.005
  » https://doi.org/10.1016/j.jdmm.2017.09.005
• Alves, C. A., Stefanini, C. J., Silva, L. A.., & Moretti, S. L. (2018). O papel da experiência de compra na intenção de recompra. Revista Ciências Administrativas ou Journal of Administrative Sciences, 24(2). https://doi.org/10.5020/2318-0722.2018.6393
  » https://doi.org/10.5020/2318-0722.2018.6393
• Antón, C., Camarero, C.., & Laguna-García, M. (2017). Towards a new approach of destination loyalty drivers: Satisfaction, visit intensity and tourist motivations. Current Issues in Tourism, 20(3), 238-260. https://doi.org/10.1080/13683500.2014.936834
  » https://doi.org/10.1080/13683500.2014.936834
• Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41(4), 607. https://doi.org/10.1037/0022-3514.41.4.607
  » https://doi.org/10.1037/0022-3514.41.4.607
• Bassani, M., Milan, G., Lazzari, F.., & De Toni, D. (2018). O Efeito País de Origem na Avaliação de Cervejas Especiais e na Intenção de Compra dos Consumidores: Um Estudo Experimental. Revista Brasileira de Marketing, 17(2), 278-295. https://doi.org/10.5585/remark.v17i2.3727
  » https://doi.org/10.5585/remark.v17i2.3727
• Beldad, A., De Jong, M.., & Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Computers in Human Behavior, 26(5), 857-869. https://doi.org/10.1016/j.chb.2010.03.013
  » https://doi.org/10.1016/j.chb.2010.03.013
• Biz, A.., & Correa, C. (2016). Abordagem brasileira sobre turismo e tecnologias da informação e comunicação: 10 anos de produção do Seminário da ANPTUR. Revista Turism., & Desenvolvimento (RT&D)/Journal of Touris., & Development, 26.
• Brasil. Ministério do Turismo (2014). A importância da internet para o turismo Viajantes se apoiam nas redes sociais para definir o roteiro, organizar a viagem e compartilhar informações. Disponível em: http://www.turismo.gov.br/ultimas-noticias/2872-a-importancia-da-internet-para-o-turismo.html Acesso em: 1 nov. 2018. .
  » http://www.turismo.gov.br/ultimas-noticias/2872-a-importancia-da-internet-para-o-turismo.html
• Brasil. Ministério do Turismo (2018). Anuário Estatístico de Turismo - 2018 - ano base 2017. Disponível em: 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 Acesso em: 15 fev. 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
• Buhalis, D.., & Deimezi, O. (2004). E-tourism developments in Greece: Information communication technologies adoption for the strategic management of the Greek tourism industry. Tourism and Hospitality Research, 5(2), 103-113. https://doi.org/10.1057/palgrave.thr.6040011
  » https://doi.org/10.1057/palgrave.thr.6040011
• Buhalis, D.., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet - The state of eTourism research. Tourism Management, 29(4), 609-623. https://doi.org/10.1016/j.tourman.2008.01.005
  » https://doi.org/10.1016/j.tourman.2008.01.005
• Carvalho, D. T., Ferreira, L. B., Kanazawa, F. N., Machado, P. M.., & Giraldi, J. D. M. E. (2016). Experiência em website de marca-país e a formação da imagem de destino turístico: um estudo na Islândia. Revista Brasileira de Pesquisa em Turismo, 10(1), 108-128. https://doi.org/10.7784/rbtur.v10i1.1019
  » https://doi.org/10.7784/rbtur.v10i1.1019
• Cechinel, E.., & Santos, A. R. (2018). Comi, Gostei e Postei: Tripadvisor e Experiências Marcantes em Restaurantes. Rosa dos Ventos-Turismo e Hospitalidade, 10(3). https://doi.org/10.18226/21789061.v10i3p538
  » https://doi.org/10.18226/21789061.v10i3p538
• 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
• Cosma, S., Bota, M.., & Tutunea, M. (2012). Study about customer preferences in using online tourism Products. Procedia Economics and Finance, 3, 883-888. https://doi.org/10.1016/S2212-5671(12)00245-6
  » https://doi.org/10.1016/S2212-5671(12)00245-6
• Dedeke, A. (2016). Travel web-site design: Information task-fit, service quality and purchase intention. Tourism Management, 54, 541-554. https://doi.org/10.1016/j.tourman.2016.01.001
  » https://doi.org/10.1016/j.tourman.2016.01.001
• Di Pietro, L., Di Virgilio, F.., & Pantano, E. (2011). Social network for the choice of tourist destination: attitude and behavioural intention. Journal of Hospitality and Tourism Technology, 3(1), 60-76. https://doi.org/10.1108/17579881211206543
  » https://doi.org/10.1108/17579881211206543
• Fernández-Poyatos, M.., & Papí-Gálvez, N. (2017). eTurismo: estudio de criterios de segmentación clásicos del usuario online que compra por internet. Revista ICONO14 Revista Científica de Comunicación y Tecnologías Emergentes, 15(2), 168-189. https://doi.org/10.7195/ri14.v15i2.1066
  » https://doi.org/10.7195/ri14.v15i2.1066
• 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
• Forgas-Coll, S., Palau-Saumell, R., Matute, J.., & Tárrega, S. (2017). How do service quality, experiences and enduring involvement influence tourists' behavior? An empirical study in the Picasso and Miró Museums in Barcelona. International Journal of Tourism Research, 19(2), 246-256. https://doi.org/10.1002/jtr.2107
  » https://doi.org/10.1002/jtr.2107
• Hair, J. F., Jr., Black, W. C.., & Babin, B. J. (2009). Multivariate data analysis7th ed. Englewood Cliffs, NJ: Prentice Hall.
• Hair Jr, J., Risher, J., Sarstedt, M., Ringle, C. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
  » https://doi.org/10.1108/EBR-11-2018-0203
• Hair Jr, J., Sarstedt, M., Hopkins, L.., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
  » https://doi.org/10.1108/EBR-10-2013-0128
• Hair Jr, J. F.; Sarstedt, M.; Ringle, C. M.; Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling SAGE Publications. https://doi.org/10.1007/978-3-319-05542-8_15-1
  » https://doi.org/10.1007/978-3-319-05542-8_15-1
• Hajli, M., Hajli, M.., & Khani, F. (2013, April). Establishing trust in social commerce through social word of mouth. In 7th International Conference on e-Commerce in Developing Countries: with focus on e-Security (pp. 1-22). IEEE https://doi.org/10.1109/ECDC.2013.6556738
  » https://doi.org/10.1109/ECDC.2013.6556738
• Herrero, Á., San Martín, H.., & Hernández, J. (2015). How online search behavior is influenced by user-generated content on review websites and hotel interactive websites. International Journal of Contemporary Hospitality Management, 27(7), 1573-1597. https://doi.org/10.1108/IJCHM-05-2014-0255
  » https://doi.org/10.1108/IJCHM-05-2014-0255
• Huang, Z.., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259. https://doi.org/10.1016/j.elerap.2012.12.003
  » https://doi.org/10.1016/j.elerap.2012.12.003
• Jaremen, D. (2016). Advantages from ICT usage in Hotel Industry. Czech Journal of Social Sciences Business and Economics, 5(3), 6-18. https://doi.org/10.24984/cjssbe.2016.5.3.1
  » https://doi.org/10.24984/cjssbe.2016.5.3.1
• Keh, H.., & Xie, Y. (2009). Corporate reputation and customer behavioral intentions: The roles of trust, identification and commitment. Industrial Marketing Management, 38(7), 732-742. https://doi.org/10.1016/j.indmarman.2008.02.005
  » https://doi.org/10.1016/j.indmarman.2008.02.005
• 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
• Kim, S., Kandampully, J.., & Bilgihan, A. (2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, 243-254. https://doi.org/10.1016/j.chb.2017.11.015
  » https://doi.org/10.1016/j.chb.2017.11.015
• Lehto, X., Kim, D.., & Morrison, A. (2006). The effect of prior destination experience on online information search behaviour. Tourism and Hospitality Research, 6(2), 160-178. https://doi.org/10.1057/palgrave.thr.6040053.
  » https://doi.org/10.1057/palgrave.thr.6040053
• Leung, D., Law, R., Van Hoof, H.., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Trave., & Tourism Marketing, 30(1-2), 3-22. https://doi.org/10.1080/10548408.2013.750919
  » https://doi.org/10.1080/10548408.2013.750919
• 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
• Liu, X., He, M., Gao, F.., & Xie, P. (2008). An empirical study of online shopping customer satisfaction in China: a holistic perspective. International Journal of Retai., & Distribution Management, 36(11), 919-940. https://doi.org/10.1108/09590550810911683
  » https://doi.org/10.1108/09590550810911683
• 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 2019. Dissertação (Mestrado em Administração) – Universidade Federal do Rio Grande/RS.
• Marchiori, E.., & Cantoni, L. (2015). The role of prior experience in the perception of a tourism destination in user-generated content. Journal of Destination Marketin., & Management, 4(3), 194-201. https://doi.org/10.1016/j.jdmm.2015.06.001
  » https://doi.org/10.1016/j.jdmm.2015.06.001
• Navío-Marco, J., Ruiz-Gómez, L.., & Sevilla-Sevilla, C. (2018). Progress in information technology and tourism management: 30 years on and 20 years after the internet-Revisiting Buhali., & Law's landmark study about eTourism. Tourism Management, 69, 460-470. https://doi.org/10.1016/j.tourman.2018.06.002
  » https://doi.org/10.1016/j.tourman.2018.06.002
• Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E.., & Alizadeh, A. (2018). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of Computational Science, 28, 168-179. https://doi.org/10.1016/j.jocs.2018.09.006
  » https://doi.org/10.1016/j.jocs.2018.09.006
• Olsen, L.., & Johnson, M. (2003). Service equity, satisfaction, and loyalty: from transaction-specific to cumulative evaluations. Journal of Service Research, 5(3), 184-195. https://doi.org/10.1177/1094670502238914
  » https://doi.org/10.1177/1094670502238914
• Oneto, A., Ferreira, J., Giovannini, C.., & da Silva, J. (2015). Confiança e satisfação na compra de turismo online. Revista Brasileira de Pesquisa em Turismo, 9(2), 221-239. https://doi.org/10.7784/rbtur.v9i2.738
  » https://doi.org/10.7784/rbtur.v9i2.738
• Öz, M. (2015). Social media utilization of tourists for travel-related purposes. International Journal of Contemporary Hospitality Management, 27(5), 1003-1023. https://doi.org/10.1108/IJCHM-01-2014-0034
  » https://doi.org/10.1108/IJCHM-01-2014-0034
• Ponte, E., 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 Ma-nagement, 47, 286-302. https://doi.org/10.1016/j.tourman.2014.10.009
  » https://doi.org/10.1016/j.tourman.2014.10.009
• Ramos, C. (2008). A integração dos sistemas de informação e do turismo, o caso IMPACTUR. In: CONFERÊNCIA DA ASSOCIAÇÃO PORTUGUESA DE SISTEMAS DE INFORMAÇÃO, 8., 2008. Anais…
• Rodríguez-Díaz, M., Rodríguez-Voltes, C.., & Rodríguez-Voltes, A. (2018). Gap analysis of the online reputation. Sustainability, 10(5), 1603. https://doi.org/10.3390/su10061953
  » https://doi.org/10.3390/su10061953
• Santos, B. R.., & Perinotto, A. R. C. (2016). Museu virtual: análise de suas potencialidades como ferramenta de comunicação turística. Turydes Revista Turismo y Desarrollo, 20. https://doi.org/10.5902/2316882X22791
  » https://doi.org/10.5902/2316882X22791
• Santos, S., Souza Neto, V., Pereira, L., Gândara, J.., & Silva, S. (2016). Destino turístico inteligente: acessibilidade no centro histórico de São Luís–Maranhão, um estudo sobre a reputação online no TripAdvisor. Marketin., & Tourism Review, 1(2). https://doi.org/10.29149/mtr.v1i2.3843
  » https://doi.org/10.29149/mtr.v1i2.3843
• Sharma, P.., & Nayak, J. (2019). Understanding memorable tourism experiences as the determinants of tourists' behaviour. International Journal of Tourism Research, 21(4), 504-518. https://doi.org/10.1002/jtr.2278
  » https://doi.org/10.1002/jtr.2278
• Silva, D.; Mendes-Filho, L., & Corrêa, C. (2017). Comentários de Viagem na Internet: Fatores que influenciam a intenção de escolha de um destino de viagem. Revista de Turismo y Patrimonio Cultural, 15(1), 229-244. https://doi.org/10.25145/j.pasos.2017.15.014
  » https://doi.org/10.25145/j.pasos.2017.15.014
• Sullivan, Y.., & Kim, D. (2018). Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments. International Journal of Information Management, 39, 199-219. https://doi.org/10.1016/j.ijinfomgt.2017.12.008
  » https://doi.org/10.1016/j.ijinfomgt.2017.12.008
• Ukpabi, D.., & Karjaluoto, H. (2017). Consumers’ acceptance of information and communications technology in tourism: A review. Telematics and Informatics, 34(5), 618-644. https://doi.org/10.1016/j.tele.2016.12.002
  » https://doi.org/10.1016/j.tele.2016.12.002
• Valle, P.., & Assaker, G. (2016). Using Partial Least Squares Structural Equation Modeling in Tourism Research: A Review of Past Research and Recommendations for Future Applications. Journal of Travel Research, 55, 695-708. https://doi.org/10.1177/0047287515569779
  » https://doi.org/10.1177/0047287515569779
• Xia, M., Zhang, Y.., & Zhang, C. (2018). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user's perspective. Journal of Destination Marketin., & Management, 8, 259-27. https://doi.org/10.1016/j.jdmm.2017.05.002
  » https://doi.org/10.1016/j.jdmm.2017.05.002
• Xiang, Z., Magnini, V.., & Fesenmaier, D. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer Services, 22, 244-249. https://doi.org/10.1016/j.jretconser.2014.08.005
  » https://doi.org/10.1016/j.jretconser.2014.08.005
• Zhang, Y., Fang, Y., Wei, K., Ramsey, E., McCole, P.., & Chen, H. (2011). Repurchase intention in B2C e-commerce - a relationship quality perspective. Informatio., & Management, 48(6), 192-200. https://doi.org/10.1016/j.im.2011.05.003
  » https://doi.org/10.1016/j.im.2011.05.003

Publication Dates

• Publication in this collection
  07 Aug 2020
• Date of issue
  May-Aug 2020

History

• Received
  30 Aug 2019
• Accepted
  16 Feb 2020