Perceived value of the online environment of tourism agencies and its impacts on the purchase decision: the mediating role of attitude

Abstract

This article aims to understand how the perception of value in an online tourism setting influences the purchase intention indirectly, given that consumer confidence and attitude impact on their online behavior. The theoretical model used was inspired by the background of trust by Beldad, De Jong and Steehouder (2010a), in the trust model in online travel agencies (Agag & El-masry, 2017), in the technological acceptance model (TAM) and in the theory of rational action (TAR), applied to the retail context of tourism services. The results confirm the mediation of the attitude in the relationship between trust and purchase intention, consistent with the numbers of markets in countries with low education for Internet use and security problems for online transactions. The results also made it possible to propose the measurement of the perception of value that considers dimensions of technological acceptance, which is convenient when dealing with consumers' online behavior. Thus, the impact of the trust construct on the purchase intention is best explained by the indirect path via attitude. Therefore, individuals who trust the website of online travel agencies have greater intentions to hire services in this virtual store environment when they have positive attitudes towards the aforementioned retail contexto.

Keywords
Perception of online value; Confidence; Role of Attitude; Tourism Services

Resumo

O presente artigo visa compreender como que a percepção de valor no contexto de e-commerce turístico influencia na intenção de compra de forma indireta, dado que a confiança e atitude do consumidor impactam no seu comportamento online. O modelo teórico utilizado foi inspirado nos antecedentes da confiança por Beldad, De Jong e Steehouder (2010a), no modelo de confiança em agências de turismo online (Agag & El-masry, 2017), no modelo de aceitação tecnológica (TAM) e na teoria da ação racional (TAR), aplicado ao contexto de varejo de serviços de turismo. Os resultados, por sua vez, confirmam a mediação da atitude na relação entre confiança e intenção de compra, consistente com os números de mercados de países com baixa educação para o uso da internet e problemas de segurança para transações online. Os resultados também possibilitaram propor a mensuração da percepção de valor que considera dimensões da aceitação tecnológica. Assim, o impacto do construto confiança na intenção de compra é melhor explicado pelo caminho indireto via atitude. Logo, indivíduos que confiam no website de agências de turismo online possuem maiores intenções de contratar serviços nesse ambiente de loja virtual quando possuem atitudes positivas perante ao referido contexto de varejo.

Palavras-chave
Percepção de valor online; Confiança; Papel da Atitude; Serviços de Turismo

Resumen

Este artículo tiene como objetivo comprender cómo la percepción de valor en el contexto del comercio electrónico turístico influye indirectamente en la intención de compra, dado que la confianza y la actitud del consumidor impactan su comportamiento online. El modelo teórico utilizado se inspiró en los antecedentes de confianza de Beldad, De Jong y Steehouder (2010a), el modelo de confianza en las agencias de viajes online (Agag & El-masry, 2017), el modelo de aceptación tecnológica (TAM) y la teoría de la acción racional (TAR), aplicada al contexto de servicios turísticos. Los resultados confirman la mediación de la actitud en la relación entre la confianza y la intención de compra, consistente con el número de mercados en países con baja educación para el uso de Internet y problemas de seguridad online. Los resultados también permitieron proponer medición de la percepción del valor, considerando las dimensiones de la aceptación tecnológica. De esta manera, las personas que confían en el sitio web de las agencias de viajes tienen mayores intenciones de contratar servicios en este entorno de tienda virtual cuando tienen actitudes positivas a la luz del contexto mencionado anteriormente.

Palabras clave
Percepción del valor en línea; Confianza; Rol de la actitud; Servicios de turismo

1 INTRODUCTION

Planning and undertaking a trip, whether for leisure or business, domestic or international, is a prevalent habit across the world. According to the World Travel & Tourism Council (WTTC), the global Tourism and Travel industry is one of the sectors that generates the most growth for the economy (World Travel & Tourism Council, 2020). In 2018, the sector contributed $8.8 billion to the world economy, generated 10.4% of all global activities on the planet and created 319 million jobs, representing one in ten jobs generated worldwide.

Traditionally, this sector relied on intermediaries for consumer sales, but the rapid development of information and communication technology (ICT) has drastically changed the tourism market (Ho & Lee, 2007; Ip, Law, & Lee, 2011). The advent of the Internet enabled the use of a new distribution channel that shortened the distance between suppliers and final consumers, in addition to providing cost savings (Law & Wong, 2003), and significantly impacting hospitality operations (Amaro & Duarte, 2013; Gregori, Daniele, & Altinay, 2014).

This transformation culminated in the emergence of Online Travel Agencies (OTA), and OTAs have outperformed traditional offline agencies (World Travel & Tourism Council, 2020). Thus, the Internet is now an essential distribution channel for travel companies (Lee & Morrison, 2010; Oneto, Ferreira, Giovannini, & Silva, 2015), being an effective marketing tool and facilitating communication between travel companies and its customers (Buhalis & Law, 2008; Llach, Marimon, & Alonso-Almeida, 2013).

The exponential growth in the use of the world wide web to search for information and electronic commerce has both benefits and costs. As for the benefits, there are the speed, convenience, quantity, and ease of obtaining information regarding the desired product or service, while the costs are related to the lack of face-to-face contact with the goods offered, concern for the seller’s reputation, the shipping process, and the payment method (Levin, Levin, & Heath, 2003).

OTA’s perceived value is directly related to its store environment, which influences consumer buying behavior (Lee & Kim, 2018; Poncin & Ben Mimoun, 2014). Additionally, a virtual travel agency that has high perceived value is recognized as reliable which is a crucial factor in attracting customers in the e-commerce industry (Beldad, De Jong, & Steehouder, 2010a).

To reduce the possibility of interacting with ineligible suppliers, online buyers need to rely on their experience and other evidence to determine which sites are reliable (Gefen & Straub, 2003; Moyano, Fernandez-Gago, & Lopez, 2012). Trust, therefore, serves as the basis for the initial relationship and is most important for maintaining a long-term relationship in the success of electronic commerce (Kim, Xu, & Gupta, 2012; Kim, Chung, & Lee, 2011).

Studies in the field of online retail investigated the direct relationship between trust and its impact on consumer purchase (Hsu, Chuang, & Hsu, 2014; Jarvenpaa, Tractinsky, & Vitale, 2000; Pavlou & Fygenson, 2006), with some works focused on the tourism industry (Ayeh, Au, & Law, 2013; Filieri, Alguezaui, & McLeay, 2015). However, there is still a considerable gap in the understanding of consumer motivations related to electronic tourism commerce (Oneto et al., 2015). This article seeks to broaden the scope of understanding of this phenomenon, by adding the variable attitude as an explanatory mechanism for improving the relationship between perceived value of tourism agencies and purchase intention.

A theoretical model was formulated based on: i) the technological acceptance model (TAM) (Davis, 1989), ii) the antecedents of trust (Beldad, de Jong, & Steehouder, 2010a; Beldad, de Jong, & Steehouder, 2010b), iii) the trust model in online travel agencies (Agag & El-Masry, 2017), and iv) the rational action theory (RAT) (Fishbein & Ajzen, 1975), to understand the impact generated by the constructs of perceived value and trust in the intention to purchase travel services in OTAs. Additionally, to understand the effect of the attitude on the relationship between trust and intention to buy online for tourism services.

The justification for the present work resides in the importance of managing tourism agencies from a marketing perspective, mainly regarding the integration of offline and online channels, in which few publications have addressed this topic in Brazilian journals in the last decade (Flores, Cavalcante, & Raye, 2012; Krause & Bahls, 2016; Scherer, Hahn, Stein, & Bolzan, 2015). The current economic crisis in the country and in the world, due to the COVID-19 pandemic, has forced companies to think of alternatives to readjust their business models and market performance (Hudecheck, Sirén, Grichnik, & Wincen, 2020).

2 DEVELOPMENT OF HYPOTHESES AND RESEARCH MODEL

Perceived value (PV) is an element of relationship in the marketing field (Oh, 2003) and conceptualized as a comprehensive assessment of the consumer on the usefulness of the product or service offered. This is based on the relationship between the attributes offered and delivered (Zeithaml, 1988), which is highly related to the shopping experience and, consequently, to the consumer’s behavior and maintenance of competitive advantage (Poncin & Ben Mimoun, 2014).

This construct can be interpreted in terms of the purchase orientation of a specific customer, traditionally subdivided into two parts: hedonic and utilitarian. The first is related to an experiential individual who seeks pleasure in the buying process; while the second refers to a more objective approach, focused on the best cost-benefit ratio (Blut, Teller, & Floh, 2018).

In the present study, the PV construct is understood as the antecedent of the purchase act in OTAs, mainly due to its predictive nature of the buying behavior (Poncin & Ben Mimoun, 2014). Still, it is considered as a second order construct, composed by: Consumer Experience (CE); Website Reputation (SR); Perceived Site Size (PSS); Perceived Ease of Use (PEU); Perceived Usefulness (PU); Site Quality (SQ); Problem Solving (PS) and; Electronic word-of-mouth (eWOM).

CE is related to the consumer’s degree of familiarity with the store environment and is considered an important antecedent of the store’s degree of trust and, consequently, of the purchase intention (Beldad, de Jong, & Steehouder, 2010a).

SR in the context is electronic commerce and is conceptualized as a collective measure of reliability arising from assessments by members of a given community (Josang, Ismail, & Boyd, 2007), in addition to being a credibility metric regarding the relationship between promises and achievements of the company (Casaló, Flavián, & Guinalíu, 2007). Therefore, SR is seen as a major contributor to consumer trust (Beldad, de Jong, & Steehouder, 2010a).

In the e-commerce environment, the size of virtual suppliers has a positive influence on the level of consumer trust (Jarvenpaa et al., 2000). In this way, consumers can deduce trust through the size of the website, as there is a direct proportional relationship between PSS and reliability (Beldad, de Jong, & Steehouder, 2010a).

The PEU and PU constructs come from the TAM model (Davis, 1989), which is considered the most effective model in the investigation of user acceptance (Ayeh et al., 2013). The first refers to the degree of perceived effort saved with the adoption of a certain technology. The second is the degree of perceived performance improvement (Davis, 1989).

In the context of tourism and hospitality, numerous studies have applied TAM as a way of understanding and explaining consumer acceptance of new technology, including frontline hotel systems (Kim, Ferrin, & Rao, 2008) and consumer intention to buy online travel services (Amaro & Duarte, 2013; Casaló et al., 2007). The findings of these studies showed that PEU and PU are crucial determinants in consumer acceptance of new technology. Additionally, Agag and El-Masry (2017) found a significant path between PU and consumer trust in the online travel community. Therefore, it is understood that the determinants of the TAM model act as drivers of the degree of consumer trust of OTAs.

In electronic commerce, previous studies confirmed the positive relationship between SQ and consumer trust (McKnight, Choudhury, & Kacmar, 2002). In the area of ​​tourism and hospitality a significant and positive relationship between quality of the website and consumer trust is supported by the works of Filieri, Alguezaui, and McLeay (2015) and Kim, Chung, and Lee (2011). If an OTA’s website is perceived as safe, responsive, empathic, and stable, the consumer’s impression of this virtual environment is positive, as well as their perception of quality and trust.

As for the PS construct, it is seen as a primary dimension of the quality of retail services, whose focus is directed on addressing the situations of return, exchange, and complaints (Dabholkar, Thorpe, & Rentz, 1996). It is a way to understand the store team’s ability to deal with possible problems and setbacks (Caro & García, 2008). In this way, an OTA that has optimized tools to help the customer in complicated situations, due to the process of choosing and purchasing a tourism service, is seen as more reliable.

According to Ardnt (1967), word-of-mouth is based on informal, personal, and non-commercial communication between sender and receiver, in which the subject of the message can refer to a brand, product, or service, being its online version known as eWOM. In a similar way, this construct is conceptualized as positive or negative communication, formed by potential or current customers, in relation to a brand, product and/ or service, transmitted to a group of people and organizations inserted in the virtual environment (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004).

The construct eWOM has a direct proportional relationship with the degree of consumer trust, since a variation in the content and/or the amount of personal recommendation interferes with perceived quality and reliability of the brand, product, or service (Román & Cuestas, 2008).

Trust (TRU), in turn, is understood as a relationship between two parties, whether they are people and/or organizations, based on vulnerability and expectations (Ponte, Carvajal-Trujillo, & Escobar-Rodríguez, 2015), and is considered an important precondition for adopting electronic services and building lasting customer relationships (Beldad, de Jong, & Steehouder, 2010a)

Considering that the components of perceived value are related to the trust construct, coupled with the fact that PV is studied as a predictor of the individual’s behavioral intentions and that TRU is seen as a crucial antecedent of online shopping behavior, it is plausible to postulate that there is a direct and positive relationship between PV and TRU in the travel services retail context, according to hypothesis H1.

H1: Perceived value (PV) of the website of virtual tourism agencies positively impacts consumer trust (TRU) in the context of online travel services.

The most prominent source of trust in a physical retail environment is the seller, in which trust depends on the seller’s expertise and friendliness (Doney & Canon, 1997). However, in online shopping this physical seller is replaced by help buttons and search features, thereby removing the basis of consumer trust in the shopping experience. In addition, online shopping contains a level of risk, with consumers unable to physically check the quality of a product or monitor the security of sending personal and financial information while shopping on the Internet.

Given that TRU has an impact on consumer purchase intention (PI) – understood as a behavior resulting from the function between store environment and perceived value of a product or service (Poncin & Ben Mimoun, 2014) – this study aims to improve the understanding of the phenomenon by adding the attitude variable (ATT) as a construct that better explains the individual’s predisposition for online shopping and; the impacts of perceived security and privacy in a retail environment predominantly occupied by OTAs.

Attitude towards behavior is defined as a positive or negative feeling for each individual, also called evaluative affection, towards the achievement of a target behavior (Fishbein & Ajzen, 1975). According to Eagly and Chaiken (1993), the attitude is defined as a psychological tendency manifested through the judgment of an individual, who evaluates a situation according to his or her degree of like or dislike. Thus, ATT appears based on the rational action theory as one of the precedents of behavior.

Therefore, in the scope of electronic commerce, consumer attitude towards online shopping can be influenced by the perceived consequences and experiences lived in other similar shopping channels, which are reflected in the purchase intention (Van der Heijden, Verhagen, & Creemers, 2003). Previous studies showed that the ATT construct has a positive influence on behavioral intent (Hartmann & Apaolaza-Ibáñez, 2012; Spears & Singh, 2004). Likewise, the trust construct is related to attitude, and this significantly impacts consumer purchase intentions (Agag & El-Masry, 2017; Chow & Holden, 1997; Macintosh & Lockshin, 1997). Therefore, it is plausible to postulate that there is a direct and positive relationship between trust and attitude, as well as attitude and purchase intention.

H2: Trust (TRU) positively impacts consumer attitude (ATT).

H3: Attitude (ATT) positively impacts the intention to buy online (PI).

Considering the indirect path between trust and purchase intention through consumer attitude, it is understood that ATT is the psychological mechanism that makes the online consumer, based on their trust, exercise their intention to purchase travel services. The understanding of the intention to buy online is greater when the relationship between the TRU and PI constructs is mediated by ATT, since the individual’s inherent inclination to buy over the Internet, developed from previous evaluations, better explains the notion that the level of trust in the OTA impacts the customer’s purchase intention, as postulated in hypothesis H4.

H4: Attitude (ATT) mediates the relationship between trust (TRU) and consumer purchase intention (PI) in the context of online travel services retail, with the impact of TRU being positive on PI via mediation of attitude.

The theoretical model tested in the present work was based on the construction of the hypotheses, as shown in Figure 1. The attitude construct was investigated twice, since there was an empirical check of its function as a consequence of the TRU and antecedent of the PI (H2 and H3). This is in addition to the performance of this construct as a mediating intervening variable (H4), with each step evaluated by different methodological processes, the justification for this division is explained later.

Figure 1
Theoretical model and research hypotheses

3 RESEARCH METHOD

To achieve the proposed objectives, a self-administered online questionnaire was applied, using the Google Forms tool, with customers who had a recent experience in purchasing some type of travel service (for example, airline tickets, hotel, car rental, cruise booking, travel package, travel insurance, etc.) in a physical agency or online in the last 18 months. These individuals were accessed via corporate e-mail and a panel created from social networks, totaling 229 respondents. There was no missing data, nor the need to eliminate outliers, when processing and cleaning the database.

Data analysis utilized the regression technique, divided into two stages: the first consisted of validating the measurements of the measurement model and the structural model, using structural equation modeling (PLS-SEM). That is, an exploratory statistical modeling tool that allows multivariate data analysis, empirically testing complex models with a large number of constructs and relationships between them (Hair, Hult, Ringle, & Sarstedt, 2017). For this, we used the SmartPLS 3.0 software (Ringle, Da Silva, & Bido, 2014) based on the algorithm of partial least squares.

The second stage involved the mediation analysis via model 4 in the analytical tool called Macro PROCESS, an extension of the SPSS software, according to the methodological parameters of Hayes (2018). The choice of this analysis division is justified, since the Macro PROCESS tool has greater statistical robustness and specific characteristics for mediation analysis, enabling the punctual location in a scalar measurement of when the mediating effect starts, potentiating inferences for theory construction (Prado, Korelo, & Silva, 2014).

As for the operationalization of the constructs, scales previously validated and tested in retail research contexts were chosen. All items are reflective in nature, and the scales are in a 7-point Likert format. All items were appropriately translated into Portuguese (linguistic validation), with the details of the scales shown in Table 1.

Table 1
Operationalization of constructs

Construct	Item	Item translated	Reference
CE	CE1	I rate my online shopping experience level as high	(Agag & El-Masry, 2017)
	CE2	I classify my level of knowledge about the Internet as advanced
	CE3	My experience with the Internet, in years, is high
SR	SR1	This sales and relationship channel is well known	(Agag & El-Masry, 2017)
	SR2	This sales and relationship channel has a good reputation in the market
	SR3	This sales and relationship channel has a reputation for being honest
PSS	PSS1	This sales and relationship channel is a very large company	(Agag & El-Masry, 2017)
	PSS2	This sales and relationship channel is one of the largest travel suppliers in the market
	PSS3	This sales and relationship channel has a global presence
PEU	PEU1	I think learning to use the online channel would be easy	(Agag & El-Masry, 2017)
	PEU2	I think that interacting with online travel sites/apps does not require a lot of mental effort
	PEU3	I think it is easy to use the online channel to complete my travel tasks
PU	PU1	Buying a travel service through this channel and relationship helps me to clarify my doubts when planning a trip	(Agag & El-Masry, 2017)
	PU2	Buying a travel service through this channel and relationship helps me organize my trip in a more efficient way
	PU3	In general, buying travel service through this channel and relationship is very useful for planning trips
SQ	SQ1	This sales and relationship channel provides enough information	(Agag & El-Masry, 2017)
	SQ2	This sales and relationship channel provides reliable information
	SQ3	This sales and relationship channel provides reliable services
	SQ4	This sales and relationship channel guarantees the privacy and security of its customers
PS	PS1	When a customer has a problem, this company shows interest in solving it	(Caro & García, 2008)
	PS2	Employees are able to handle customer complaints efficiently
eWOM	eWOM1	I would recommend this sales and relationship channel to anyone looking for my advice	(Román & Cuestas, 2008)
	eWOM2	I encourage friends and relatives to buy through this sales and relationship channel
	eWOM3	I say positive things about this sales and relationship channel to people
TRU	TRU1	I believe this type of sales channel is trustworthy	(Agag & El-Masry, 2017)
	TRU2	This sales channel is reliable
	TRU3	This sales channel has integrity
	TRU4	I believe that most companies in this type of sales channel will act for the benefit of the customer
ATT	ATT1	I like the idea of buying travel services through this sales and relationship channel	(Agag & El-Masry, 2017)
	ATT2	Buying travel services through this channel is a good idea
	ATT3	Buying travel services through this channel would be nice
PI	PI1	My desire to purchase a travel service through this sales channel is high	(Agag & El-Masry, 2017)
	PI2	If I buy a travel service, I would consider buying through this sales channel
	PI3	I intend to continue to quote through this sales and relationship channel in the future
	PI4	My intention is to continue using this sales channel instead of an alternative channel

• Source: the authors (2020)

4 PRESENTATION OF DATA AND DISCUSSION OF RESULTS

For a closer understanding of the entirety of the phenomenon, the data are presented in three modalities. The first is related to the characteristics of the respondents; while the second is based on the analysis of the measurement model and the structural model, centered on the parameters recommended by Hair et al. (2017). The third analysis is modality and deepens the findings of the previous steps, expanding the theoretical understanding by reducing the scope of the model, focusing specifically on the relationship of mediation of the attitude in the direct relationship between consumer trust and purchase intention, tested empirically from the parameters of Hayes (2018). The last step that covers part of the hypothesis tests, specifically H4, has the function of making the evidence of attitude mediation more robust to explain the relationship between the variables trust and purchase intention.

Among the 229 respondents, 63.8% are female and 36.2% male. As for the level of education in the sample, it was identified that 0.9% have completed elementary school, 33.2% have a secondary education, 38.9% have a higher education and 27% have a postgraduate degree. The respondents’ ages ranged between 14 and 74 years, with an average age of 33.3 and standard deviation of 12.5 years. In addition, 5.2% reported having a family income of up to the minimum wage (R$ 954); 17% make minimum wage and up 3 times the minimum wage (R$ 954 to R $ 2,862); 25.3% make three to five times the minimum wage (R$ 2,862 to R $ 4,770); 42% from five to fifteen times the minimum wage (R$ 4,770 to R$ 14,310) and; 10% reported having a monthly family income of more than fifteen times the minimum wage (over R$ 14,310). As for the sales channel, it was found that 79% of respondents bought travel services in online stores. Only 21% said they had purchased in physical stores.

According to Hair et al. (2017), it is necessary to check the reliability of the constructs, the discriminant validity and the convergent validity, to prove the quality of the measurement model. The verification of the reliability of the reflective constructs sought to prove the unidimensionality of the indicators, that is, to empirically ratify the high correlation between the indicators, an intrinsic characteristic of the reflexive manifest variables. Internal consistency, in turn, is achieved with values ​​of composite reliability in the range of .60 to .90, in addition to the reliability of the indicator by means of a Cronbach alpha greater than .60 (Hair et al., 2017). The survey data indicate that all relationships between indicators and their respective constructs are within the expected parameters (Table 2).

Table 2
Reliability and convergent validity

Construct	Cronbach’s Alpha	Composite reliability
PI	.832	.888
ATT	.871	.921
TRU	.787	.864
CE	.783	.873
SR	.767	.866
PSS	.800	.882
PEU	.601	.779
PU	.852	.911
SQ	.885	.920
PS	.841	.926
eWOM	.833	.900

• Source: the authors

The next step consisted of analyzing the discriminant validity, performed two ways: by checking the cross loads and using the Fornell-Larcker criterion (Ringle et al., 2014). The values of the comparison of the external loads of all indicators signify that they have greater correlations with their own measures than with indicators belonging to other constructs (Table 3), showing a good measure of quality for model validation.

Table 3
Values for cross-loadings

	ATT	CONF	EC	IC	PFU	PTS	PU	QS	RS	SP	eBAB
ATT1	0.912	0.627	0.208	0.750	0.258	0.328	0.427	0.457	0.416	0.361	0.500
ATT2	0.885	0.637	0.304	0.713	0.349	0.384	0.478	0.554	0.529	0.327	0.544
ATT3	0.877	0.623	0.168	0.730	0.257	0.345	0.449	0.498	0.482	0.422	0.539
CON1	0.599	0.834	0.129	0.591	0.206	0.344	0.371	0.476	0.400	0.319	0.487
CON2	0.602	0.860	0.199	0.598	0.322	0.318	0.501	0.630	0.522	0.418	0.543
CON3	0.564	0.814	0.128	0.544	0.172	0.276	0.331	0.506	0.394	0.395	0.486
CON4	0.434	0.609	-0.001	0.400	0.108	0.176	0.452	0.436	0.324	0.424	0.402
EC1	0.264	0.197	0.810	0.227	0.497	0.364	0.183	0.259	0.324	0.034	0.207
EC2	0.236	0.158	0.853	0.233	0.592	0.247	0.197	0.301	0.338	0.144	0.282
EC3	0.135	0.034	0.840	0.210	0.538	0.375	0.166	0.244	0.377	0.075	0.195
IC1	0.687	0.561	0.189	0.829	0.279	0.437	0.428	0.558	0.537	0.452	0.618
IC3	0.710	0.535	0.282	0.855	0.351	0.445	0.437	0.528	0.546	0.286	0.529
IC4	0.553	0.522	0.153	0.736	0.265	0.389	0.418	0.480	0.411	0.279	0.507
PFU1	0.233	0.197	0.427	0.297	0.749	0.216	0.249	0.324	0.351	0.169	0.267
PFU2	0.078	0.065	0.224	0.155	0.608	0.102	0.221	0.226	0.185	0.014	0.165
PFU3	0.330	0.270	0.668	0.357	0.837	0.314	0.371	0.497	0.489	0.129	0.389
PTS1	0.292	0.237	0.478	0.387	0.363	0.831	0.307	0.407	0.609	0.115	0.349
PTS2	0.370	0.401	0.279	0.464	0.207	0.893	0.429	0.464	0.634	0.289	0.434
PTS3	0.339	0.266	0.234	0.400	0.227	0.810	0.332	0.379	0.498	0.201	0.352
PU1	0.421	0.496	0.089	0.429	0.264	0.298	0.834	0.637	0.539	0.524	0.597
PU2	0.431	0.464	0.247	0.484	0.382	0.413	0.911	0.657	0.641	0.442	0.611
PU3	0.481	0.425	0.229	0.472	0.383	0.402	0.890	0.673	0.661	0.499	0.639
QS1	0.482	0.528	0.219	0.565	0.403	0.414	0.650	0.830	0.626	0.546	0.634
QS2	0.447	0.509	0.351	0.537	0.506	0.423	0.663	0.893	0.744	0.500	0.716
QS3	0.547	0.635	0.266	0.577	0.429	0.436	0.613	0.874	0.731	0.529	0.713
QS4	0.471	0.591	0.268	0.511	0.397	0.434	0.648	0.850	0.716	0.591	0.701
RS1	0.296	0.254	0.385	0.400	0.424	0.678	0.394	0.492	0.711	0.143	0.381
RS2	0.490	0.513	0.380	0.576	0.416	0.587	0.699	0.771	0.909	0.524	0.726
RS3	0.509	0.504	0.282	0.524	0.415	0.489	0.605	0.731	0.850	0.592	0.627
SP1	0.424	0.466	0.112	0.414	0.196	0.258	0.576	0.622	0.536	0.939	0.559
SP2	0.342	0.441	0.079	0.359	0.085	0.186	0.444	0.539	0.454	0.918	0.553
eBAB1	0.605	0.527	0.293	0.660	0.412	0.399	0.630	0.719	0.716	0.469	0.878
eBAB2	0.497	0.554	0.180	0.593	0.270	0.364	0.560	0.609	0.539	0.526	0.860
eBAB3	0.431	0.515	0.234	0.472	0.341	0.403	0.625	0.747	0.602	0.562	0.859

• Source: the authors (2020)

Using the Fornell-Larcker criterion as an alternative way to ratify, at the construct level, the discriminant validity, there is a need for the AVE² of a certain construct to maintain a greater correlation with itself, than with other latent variables (Hair et al., 2017). That said, it is possible to note the discriminant validity achieved by the constructed theoretical model - demonstrated in Table 4.

Table 4
Fornell-Larcker criteria

	ATT	CONF	EC	IC	PFU	PTS	PU	QS	RS	SP	eBAB
ATT	0.891
CONF	0.706	0.786
EC	0.254	0.156	0.835
IC	0.816	0.686	0.267	0.820
PFU	0.323	0.267	0.651	0.388	0.737
PTS	0.395	0.361	0.390	0.495	0.312	0.846
PU	0.506	0.523	0.218	0.526	0.393	0.424	0.879
QS	0.564	0.656	0.322	0.634	0.504	0.495	0.746	0.862
RS	0.533	0.528	0.415	0.612	0.500	0.689	0.701	0.818	0.827
SP	0.415	0.489	0.104	0.418	0.155	0.241	0.554	0.627	0.536	0.928
eBAB	0.592	0.614	0.275	0.665	0.397	0.450	0.701	0.802	0.719	0.598	0.866

• Source: the authors (2020)

Regarding checking the explanatory power of the theoretical model and the statistical significance of the paths, the significance and relevance test of the path coefficients was performed using the procedure called bootstrapping with 10,000 subsamples. The result is shown in Figure 2. All paths were significant at the 95% confidence level.

Figure 2
Structural model and path coefficient

The analysis of the theoretical model by PLS-SEM allows us to conclude that there is a relationship between the perception of value and the degree of consumer trust (β1 = .640; p <.001), confirming hypothesis H1. In addition, the high explanatory power of approximately 70% (R² =.693) indicates that TRU contributes to a good portion of the explanation of the behavior of directly purchasing travel services (β2 = .214; p = .005) and also when it passes through attitude (β3 = .7206, p <0.001; β4 = .699, p <.001), corroborating hypotheses H2 and H3. Therefore, it is possible to confirm that trust is an important antecedent of the intention to buy, since the direct relationship not hypothesized was significant. In addition, the TRU construct also has high explanatory power as an antecedent of the attitude, and this positively impacts the purchase intention. Thus, the empirical results of the present study replicate the findings of Agag and El-Masry (2017).

Regarding the empirical confirmation that TRU indirectly impacts PI via ATT was not performed via PLS (partial least squares technique), but it is possible to identify that the betas of the relationship between TRU and ATT (.706) and ATT and PI suggest an indirect path with greater effect than the direct path between TRU and PI. Another observation is the final R² of the model (.693), is more influenced by the indirect path. For greater robustness of the mediation test, even with these indications of a more explained relationship through the indirect path, the H4 hypothesis test depended on the application of the ordinary least squares (OLS) technique contained in the simple mediation model (Model 4). This is understood as a causal system in which the predictor variable influences the outcome of the phenomenon through an intervening variable (Hayes, 2018; Prado, Korelo, & Silva, 2014), as shown in Figure 3.

Figure 3
Simple mediation model

From the data from the Macro PROCESS regression analysis with 10,000 subsamples, it is clear that all individual paths (a, b, and c) are statistically significant at the 95% confidence level, as well as the indirect effect (ab), which is approximately twice the direct effect (c). In addition to the tests of direct and indirect relationships and the size of the effects of each relationship, the Sobel test (Sobel, 1982) corroborates the mediating effect of the attitude when using the size of the non-standard effects of the indirect paths (a and b) and their respective errors (Sa = .0496; Sb = 0.0534) to prove the existence of a mediating relationship (Sobel Statistics = 9.5660; standard error = .0512; p-value <.01). Therefore, it proves the existence of a complementary mediation (Zhao, Lynch, & Chen, 2010), indicating that although the explanation of the phenomenon is mainly due to the variance of the mediating mechanism (ATT), one must consider the direct positive impact of predictor variable (TRU) in the dependent variable (PI).

This result is in line with what was predicted in the theory, since consumer trust is understood as an important precondition for the adoption of online services (Beldad, de Jong, et al., 2010a), culminating in the strong impact of this construct on electronic retail (Agag & El-Masry, 2017). Furthermore, the attitude is also declared as a significant factor in the consumer’s buying behavior, since the intention to consume, under the perspective of the rational action theory, is emphasized by the attitude towards the buying behavior, which is influenced by the customer’s beliefs (Agag & El-Masry, 2017; Ajzen & Fishbein, 1977).

Therefore, the data generated via Macro PROCESS model 4 contributes empirically to the proof that ATT is a variable that consistently improves the explanation of the consumer trust relationship as an antecedent of the purchase intention construct, corroborating hypothesis H4.

5 CONCLUSIONS

The objective of the article was to understand the antecedents of the intention to purchase tourism services, based on the theoretical construction of Fishbein and Ajzen (1975), Davis (1989) and Beldad et al. (2010a). In addition to the interest in expanding the findings of Agag and El-Masry (2017), focusing specifically on the mediating effect of the attitude construct in the context of online travel purchases.

The research results showed that the tested theoretical model has good levels of adjustment and high explanatory power. This is in addition to highlighting the role of attitude as a strong link between trust in online transactions – developed on the site chosen for the purchase of travel services, or past experiences with online shopping – and purchase intention. This finding reinforces the need for attention to the online consumer, in addition to the evident importance of seeking to increase trust in online transactions in a dynamic and continuous effort.

In relation to increasing trust, travel sites need to be backed by all possible data security and respect the consumer in their rights. In addition to these rights, build an online strategy based on the dimensions that comprise the perception of value (consumer experience; website reputation; perceived website size; perceived ease of use; perceived usefulness; website quality; problem solving and; electronic word-of-mouth). In this work, the perceived value comprised characteristics of the online context with the inclusion of dimensions previously tested for the adoption of technology.

These findings contribute to the theoretical construction in an original way (Colquitt & Zapata-Phelan, 2007), since there is empirical evidence that there is a mediated relationship between the retailer’s perception of trust and customer behavior via his own conduct/attitude towards the online retail context of travel services.

As for the limitations of the study, it is impossible to generalize the results for samples with different characteristics to the one studied. This factor, however, does not disqualify the sample, which, composed of 229 respondents, is sufficient for the development of the statistical tests that were presented. Still, the study presented restrictions regarding a qualitative analysis of the surveyed items. If a complementary qualitative investigation had been possible, more explanatory, and detailed results would possibly be obtained. Despite this being a recognized restriction, the proposal to carry out a quantitative research was met within the statistical criteria, in which it contemplated the validation of the conceptual model and found the veracity of the hypothesized relationships (H1, H2, H3, and H4).

Therefore, the suggestion for future studies is also made. According to Zhao et al. (2010), the existence of complementary mediation is an indication of the omission of another mediating variable, with this suppression being a possibility of theoretical construction. Therefore, it is suggested that future quantitative studies in this area seek to understand the complexity of the model by adding new mediating variables, such as online resistance (Laukkaken, Sinkkonen, & Laukkaken, 2008; Laukkaken, 2016; Mani & Chouk, 2017) and other variables that may condition the mediation of the attitude.

Resistance to online shopping, seen by the dimensions of product and consumer characteristics, can act as an explanatory mechanism for a negative intention of virtual consumption, as a counterpoint to the positive mediating effect of the attitude. New studies can test the dual mediation of attitude and resistance. Thus, it would be possible to explain the non-intention and intention to consume online in the same model. In addition to mediations, new investigations can also test conditions that help explain why consumers tend to have no intention of consuming online.

Finally, it is important to note that a new context in the sale of tourism services arises from the current crisis generated by COVID-19 (Blenkinsop & Abnett, 2020; Georgiopoulos & Triandafyllou, 2020). The tourism market is one of the sectors that suffers most from reduced sales and, it seems that it will be one of the last to return to some normality. Even if the agencies resume part of their activities, the need for social distance will further enhance the exchange of traditional services for online service. This reinforces the relevance of greater knowledge of online purchasing processes applied to services in the tourism sector. In addition to the structural issues that can be changed in the travel agency market, such as most transactions being consolidated through online channels, there are consumer reactions to the risk of being exposed to the new coronavirus, which can generate conditions and contexts that must be controlled in new research on the topic.

Therefore, managerially, it is implied that to thrive in the current digital age marked by a health crisis that generates distance between individuals, companies must undergo technological transformations at different levels. Its main operations (e.g., sale, delivery of goods and services, customer relations, accounting, billing, etc.) will need to be digitized, in addition to the need for incorporating information and communication technologies, which in the sector tourism services, for example, may mean the development of a digital platform containing the company’s service offers and packages, enabling real-time interaction with consumers, speed of service, and innovation in processes. Therefore, allowing organizations to quickly develop and add new digital offers and promote the construction of values ​​in the relationship with the customer in the virtual environment, so that they obtain conditions for revenue growth and, trust and preference, in the purchase intention by the consumer (Brown, 2020).

• How to Cite: Brandão, M.M.; Sincorá, L.A.; Sarcinelli, A.F. (2021). Perceived value of the online environment of tourism agencies and its impacts on the purchase decision: the mediating role of attitude. Revista Brasileira de Pesquisa em Turismo, São Paulo, 15 (2), e-1977, May./Aug. http://dx.doi.org/10.7784/rbtur.v15i2.1977
• Open data: https://data.mendeley.com/datasets/yx9cymwrnf/1

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  » https://doi.org/10.1086/651257

Edited by

Editor: Glauber Eduardo de Oliveira Santos.

Publication Dates

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

History

• Received
  21 Mar 2020
• Accepted
  18 June 2020