Next Article in Journal
Where to Go with Corporate Sustainability? Opening Paths for Sustainable Businesses through the Collaboration between Universities, Governments, and Organizations
Previous Article in Journal
Faux Fur Trade Networks Using Macroscopic Data: A Social Network Approach
Previous Article in Special Issue
Activators of Airline Customers’ Sense of Moral Obligation to Engage in Pro-Social Behaviors: Impact of CSR in the Korean Marketplace
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 13
Issue 3
10.3390/su13031425
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Influence of Site Personalization and First Impression on Young Consumers’ Loyalty to Tourism Websites

by
José Alberto Martínez-González
1,* and
Carmen D. Álvarez-Albelo
2
1
Departamento de Dirección de Empresas e Historia Económica, Facultad de Economía, Empresa y Turismo, Universidad de La Laguna, 38.200-La Laguna, Santa Cruz de Tenerife, Canary Island, Spain
2
Departamento de Economía, Contabilidad y Finanzas, Facultad de Economía, Empresa y Turismo, Universidad de La Laguna, 38.200-La Laguna, Santa Cruz de Tenerife, Canary Island, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(3), 1425; https://doi.org/10.3390/su13031425
Submission received: 28 December 2020 / Revised: 25 January 2021 / Accepted: 25 January 2021 / Published: 29 January 2021
(This article belongs to the Special Issue Consumer Behavior and Corporate Marketing in the Tourism Sector)
Browse Figures
Versions Notes

Abstract

:
This study analyzes the influence of site personalization, first impression, and design on young consumers’ loyalty to tourism websites. It is a new and necessary study, taking into account the multimedia profile and purchasing potential of the studied segment, the need for increasing online consumer loyalty, and the tourism websites’ relevance. Based on previous findings and using a sample of 609 young consumers, a causal model (PLS) is designed that is practical, novel, and significantly predicts online loyalty. The descriptive analysis results show young consumers’ positive attitudes toward e-commerce and their high online use and potential for making online purchases. The significant influence of site personalization on consumers’ first impression is also confirmed. In addition, first impression influence perceived website quality, and, in turn, this quality affects consumers’ online purchase intention and loyalty to the website. Finally, it is shown that online purchase intention has a direct and positive influence on website loyalty. Thus, this study provides tourism managers with the knowledge to encourage young consumers’ loyalty to their websites in a market orientation context. It can be achieved by acting on the site’s personalisation, the first impression, and the design of the site. The generational approach (Generation Z) also allows the conclusions and implications to be transferred to other regions and sectors.

1. Introduction

Internet and information as well as communication technologies (ICTs) have transformed commerce and marketing in tourism. Tourism has evolved toward a digital mode faster than other sectors [1,2,3,4]. Today, websites have become the link between consumers, products, and tourism companies. More and more tourists compare different sites’ contents and share opinions, experiences, and evaluations to improve their decision-making process [5,6,7]. Due to this process, the tourism industry is making increasing efforts, so that, in a context of market orientation, sites are adapted to consumers [8,9,10].
The literature also confirms the growing interest in understanding, in-depth, the factors influencing tourists’ loyalty to websites [11,12]. This interest is because online loyalty leads to increased sales and profitability for tourism companies [4,13]. Other reasons are related to the insecure and intangible nature of tourism, e-commerce, the high competition in the online tourism market, and consumers’ growing demands [4,14].
Despite its relevance, online loyalty is difficult to achieve, particularly as consumers can change destinations at a mere “click” due to the low switching costs [15,16,17,18]. Among the factors that influence online loyalty, the influence of site personalization, first impression, and website quality have been highlighted [4,6].
Additionally, the literature on e-commerce in tourism has identified the need to study specific consumer segments [19,20]. It is the case of Generation Z, a population segment that includes people born between 1995 and the late 2000s and shares similar perceptions and experiences [21]. Despite their reduced loyalty, more than 95% of young people use the Internet, mobile telephones, and social networks when they decide to purchase a tourist product [22,23]. Particularly relevant is university students’ segment because they have high educational levels, a great potential for online influence and purchasing, and represent the young digital consumers well [23,24,25].
This paper studies young consumers’ online loyalty to tourism websites, whether carried out through the Web, mobile telephones, or social networks, to address the demands and concerns mentioned above. This study is relevant due to young people’s technological nature and their potential for online purchasing and influence in tourism. To this must be added the need to increase this segment’s online loyalty and the importance that personalization and site design have in forming the first impression. This study enriches the theoretical literature in this field for several reasons. First, loyalty to tourism websites is studied, and not just to a particular site. Second, the independent variables are site personalization, first impression, website quality, and online purchase intention. These variables are considered very important in e-commerce in tourism and have not been studied jointly before. Since no real data on online shopping behaviour is available, online loyalty is analyzed through intention. It has been demonstrated that intention is the best predictor of real and future tourism behaviour, as in other sectors [5]. On a practical level and using the PLS method of structural equations, a causal model of online loyalty formation is provided. The model is novel, fair, realistic, and a practical way to predict online loyalty to tourism companies. In addition, a generational approach can facilitate the adoption of online marketing actions at a global level. However, the generational approach must be taken with some caution. There is still an open debate about the global validity of the results of studies with younger generations. Finally, an Importance-Performance Analysis (IPMA) is included in this study to facilitate the analysis, as other tourism researchers have done [26].

2. Theoretical Review

Online loyalty has some similarities to loyalty in traditional commerce, but it also has apparent differences. The Internet breaks down geographical and temporal limitations, broaden product choice, and reduces switching costs [14]. Moreover, the formation of online loyalty involves processes including internal and external variables to consumers that are different from the formation of loyalty in traditional commerce. These variables are related to the websites’ characteristics, the commercial proposals and messages on the sites, and users’ relationship with the sites [4]. For all these reasons, there is interest in the literature to better understand the role that websites play in online consumer loyalty in a market-oriented context [4,9,10].
Today, it is accepted that online loyalty is a multi-dimensional construct. It has cognitive, behavioural, and even emotional content [4,27]. From the behavioural point of view, online loyalty refers to the repetitive behaviour of consumers. As an attitude, which is the most widespread meaning in the literature, online loyalty is related to consumers’ predisposition or intention to repeat using the site or recommend it to others [6,7,28]. The predominance of an attitudinal approach to loyalty is based on the difficulty of measuring past and present online loyalty behaviour. Additionally, it has been found that intention is the best predictor of real behaviour in tourism [29,30].
In a more concrete way and within the attitudinal approach, online loyalty in tourism can be understood in two ways. On the one hand, online loyalty refers to consumers’ intention to repeat the online purchase of the same product, service, or brand, or recommend it through the Web, mobile phone, or social networks. All this occurs even if the repetition and recommendation are carried out through a different site. In this case, it would be about online loyalty to the product, destination, or a company (e.g., a hotel). Online loyalty also refers, as in this study, to website loyalty even if it is not about purchasing the same product, service, or brand, and even though this recommendation is made offline [4,27,28]. A combination of both alternatives is also possible. In the context of e-loyalty, the recommendation component of loyalty is less frequent than the repetition component, unlike the offline context in which both aspects are equally relevant [4,5].
Several theoretical and empirical approaches have been used to study online loyalty to websites [31,32]. First, the classical theories developed in the offline context (e.g., Oliver’s Theory) were used to study online loyalty. These theories focus mainly on the behavioural approach to loyalty but have been little used to study website loyalty [33,34,35]. The classical theories have been followed by predictive studies based on consumers’ repetition intention and developed fundamentally for the study of loyalty in traditional commerce. The Theory of Reasoned Action (TRA) [36,37] is the reference theory. TRA assumes that online loyalty depends on the consumer’s attitude and, to a lesser extent, on social norm [38,39]. The Theory of Planned Behavior (TPB) [40,41] constituted an expanded model of the TRA and was developed to improve the predictability of intention in the absence of unforeseen events [42]. TPB dictates that perceived control, together with attitude and social norm, are the variables that best explain the formation of online consumer loyalty [43,44,45]. Both models have been empirically tested and provide reasonable predictions of intention in various fields.
Despite their relevance and use, models based on these theories are not without criticism [5,46,47]. For example, several studies have found that the prediction of loyalty through TPB is usually less than 40% [48]. In addition, in the e-commerce context, consumers do not have such a volitional control as in the traditional trade [49]. Both TRA and TPB models have also received criticism related to their excessive linearity, the instability of variables, and weakness of measurement instruments they use [50]. Finally, it has been found that the weight of the social influence of communication on online loyalty is higher than that exerted by the subjective norm [51,52]. The limitations of both models led to the development in e-commerce of the Technology Acceptance Model (TAM) [53,54]. TAM, which is also based on TRA, is the most used of the three models in e-commerce [55,56]. The model is based on website utility and ease of use, but it has also been criticized because these variables have not been demonstrated that these variables provide a significant prediction of loyalty to tourism websites [5,29,46].
More recent studies on website loyalty have considered the limitations mentioned above and have focused on developing models with greater predictive power [4,7,57]. In many cases, these models have maintained the theoretical basis of the reference models and have incorporated new variables and relationships [48,58]. These variables include, regarding the tourism company, its reputation and image [59], perceived value and price [60], and eWOM communication [61]. This communication is defined as the evaluation, comments, recommendations, and opinions developed online by users and consumers. From the consumer’s perspective, commitment and trust [62] and satisfaction [59,63] stand out. Of particular importance in the literature are also aspects related to site personalization, first impression, and site quality [64,65,66,67]. This study is based in these variables.

3. Proposed Model and Hypotheses

Tourism companies manage websites to promote online purchasing and consumer loyalty [4,67]. In this context and within the framework of market orientation, tourism marketing and IT professionals join forces to make the sites as personalized as possible [68,69]. Websites’ personalization is defined as adapting content, structure, and messages to consumers’ wishes, preferences, and needs [70]. Therefore, to encourage personalization, a company must obtain information on those preferences and needs, either before or after the transaction [65,69,70]. Website personalization entails an automatic response of the site, either for a specific consumer or for a broad segment. This process can occur before, in the moment, or after the transaction [71]. The consumer must perceive site personalization, which could be initiated by the consumer, by the website, or by the transaction [69,72].
Regarding the consequences of website personalization, its direct influence on the formation of the immediate first impression has been verified [73]. The first impression is defined as a consumer’s cognitive judgment or an emotional state derived from exposure to a website [74,75]. This process is unconscious and fast (around 50 ms), and, through it, the consumer selectively organizes the central and peripheral information available on the website. Finally, the process depends on the consumer’s goals and expectations and the attributes of the site, and it involves an immediate, consistent, and stable representation of it [73]. It has been shown that the first impression of the site is first organized around aspects related to aesthetics. Usability and content come second. Therefore, these are aspects that a company must manage when considering website personalization [76,77]. Based on the above, we hypothesize:
Hypothesis 1 (H1).
Website personalization has a direct and positive influence on the consumer’s first impression of the site.
In the literature, it has been found that the first impression of the site exerts a halo effect that facilitates the development of a positive consumer attitude to continue browsing [73,77,78]. This deliberate impression influences the consumer’s perceived quality of the site, which is defined as the consumer’s evaluation about how the site conforms to their wishes, needs, and expectations [79,80,81]. There is no consensus in the literature about a quality site [73,77]. However, in the literature review carried out by Król and Zdonek [82], it was found that website quality is mainly related to functional aspects (usability and content) and non-functional aspects (e.g., aesthetics, ergonomics, and security). It should be noted that, at the stage of the deliberate impression that follows the first impression, usability and content of the site are more relevant to the consumer than non-functional aspects [73,76,77]. Consequently, the next hypotheses dictate:
Hypothesis 2 (H2a).
The first impression of the site has a direct influence on the perception of the site’s usability.
Hypothesis 2 (H2b).
The first impression of the site directly influences the perception of site content.
Numerous authors have also studied website design quality and its influence both in tourism and in other sectors [4,83]. The model of website quality by DeLone and McLean [84,85,86] included system quality, information quality, and service quality. Wang, Law, Guillet, Hung, and Fong [79] proposed usability, functionality, and security. Hartono and Holsapple [87] identified aesthetic quality, functional quality (contribution of the site to the performance of desired activities), and symbolic quality (meanings or associations that the site provokes). Regarding the consequences, it has been found that the quality of site design influences variables such as trust and satisfaction [88,89]. In particular, the literature emphasizes that functional aspects of the website (mainly site usability and content) influence consumers’ purchase intention and online loyalty [64,82,87,90]. This effect is similar to that produced by service and price in a traditional establishment [91]. Thus, we hypothesize:
Hypothesis 3 (H3a).
The usability of the site has a direct and positive influence on the intention to purchase online.
Hypothesis 3 (H3b).
The usability of the site has a direct and positive influence on online loyalty.
Hypothesis 4 (H4a).
The content of the site has a direct and positive influence on the intention to purchase online.
Hypothesis 4 (H4b).
The content of the site has a direct and positive influence on online loyalty.
Consumers’ online purchasing intention is defined as the desire to purchase tourism products through websites. Therefore, the greater the desire, the greater the probability that a customer will buy the tourism product in a particular place [92,93,94]. Likewise, it has been found that lack of intention is the main obstacle for the development of e-commerce [95].
Online purchase intention and online loyalty to the site are different constructs that are related [4,6,96]. Online loyalty is conceived as the consumer’s intention to make a new online purchase at the same site or to recommend the site to others [97]. Therefore, it can be affirmed that loyalty may have a direct antecedent intended on the part of the consumer to use e-commerce to make purchases [4,57]. Due to the above, the following hypothesis establishes that:
Hypothesis 5 (H5).
Online purchase intention has a direct and positive influence on online loyalty to the website.
The model proposed in this paper is presented below (Figure 1). The figure shows how site personalization starts the chain of effects that begins with the first impression and continues with its content and usability.

4. Research Methodology

4.1. Questionnaire Development

We examine in this study young consumers’ online loyalty to tourism websites. The study refers to tourism websites in general, not to any specific websites. For data collection, a questionnaire was designed, as is usual in the literature, on the variables included in this work [4,98]. The questionnaire was applied online in the third week of September 2020. First, a literature analysis was conducted to generate content validity with two experts’ collaboration [99]. The studies of Salonen and Karjaluoto [69], Kalaignanam, Kushwaha and Rajavi [65], and Annamalai, Mathew, and Iyer [70] were consulted to design the items related to site personalization. To design the three items related to the first impression, the contributions of Lindgaard, Fernandes, Dudek, and Brown [100], Thielsch and Hirschfeld [78], and Thielsch, Blotenberg, and Jaron [73] were consulted. DeLone and McLean [84,85,86] and Hartono and Holsapple [87] have been considered for the design of items related to site usability and content. Finally, the study of Buhalis, Parra-López, and Martínez-González [4] has been considered to design purchase intention and online loyalty to the site.
Second, to design the items’ content and consider the studies mentioned above, the Delphi technique was used online through two rounds, involving two groups, and via Google Meet [101]. The two groups were made up of three professors who are specialists in developing predictive models of online loyalty in tourism. The authors of this study led the groups. After a pretest and to reduce methodological problems and costs, the principle of simplicity was considered [102,103]. The initial Likert scale included 22 items. Each item included five response alternatives (1: no agreement to 5: total agreement) (Table 1). It should be noted that young consumers answered the items without considering any specific website, but tourism websites are considered globally.
Next, to identify the latent variables, exploratory factor analyses were carried out. After various analyses, a structure of six latent variables was obtained. These variables are:
Website personalization (WPE)
First impression (FIM)
Website usability (WUS)
Website content (WCO)
Purchase intention (PIN)
Online loyalty (LOY) (dependent variable)
Through this analysis, five items were eliminated corresponding to the latent variables WPE (1) (The site is adapted to my wishes), FIM (1) (The first impression according to the aesthetics of the site), WUS (2) (The site is adapted to my needs), and WCO (1) (The information on the site is useful). Therefore, the final scale included 17 items. The latent variables’ nature was not significantly altered because the items are reflective, and all indicators belonging to the same construct are interchangeable. In addition, by eliminating these items, the questionnaire’s overall reliability (Cronbach’s alpha) changed from 82% to 84%. Two items about loyalty (LOY) were accepted because the items have a reduced correlation with other items and the correlation between them was higher than 70% [104]. In previous studies, it should be noted that the measurement of online loyalty to websites has been carried out through two items (4). The items refer to online purchases of tourist products on e-commerce sites using the web, mobile telephones, or social networks indistinctly. An additional item was included in the scale, which is the extent to which consumers have made online purchases of tourism products and services on any site through the web, mobile telephones, or social networks. It verifies the experiences and the online purchasing potential of young people. They marked yes or no to answer this item.

4.2. Data Collection and Sample Profile

Regarding the sample, it was selected randomly in the first week of September 2020. The sample was selected representatively after completing a stratified cluster study by degree, academic year, and gender. One university was selected for this study, belonging to Spain’s most important tourism destinations: the Canary Islands (La Laguna University). Moreover, this study adopts the approach that considers university students represent young consumers well [25,105], and the contributions of other authors regarding the verification of personal user data of the web community have been taken into account [106]. In recent generational studies, samples of students belonging to only one university center have been used, including Spanish academic centers [107,108]. The sample initially included 637 subjects (43.45% men and 56.55% women), which is an appropriate size when structural equations are used [109]. The mean age of the respondents was 20.77 years old. The online mode was selected because the respondents had online access and young people accept the online context. Interviewers explained the questionnaire’s content and instructions, and they answered questions from respondents through Google Meet.

4.3. Data Analysis

First, data were examined using an SPSS-22 program to obtain descriptive indicators: percentages, means, standard deviations, skewness, and kurtosis. Regarding the causal study, the Partial Least Squares Structural Equation Modelling approach (PLS-SEM) was applied through SmartPLS-3 software. PLS was selected to test the hypotheses due to its potential to explain the theory [110], its great predictive potential of human behaviour, and because it allows the use of a wide range of sample sizes [111]. Moreover, it is unnecessary to assume a normal distribution of data when using PLS [112]. The ultimate guidelines in applying PLS-SEM in tourism research have been followed, and an Importance-Performance Analysis (IPMA) has been included [113,114].

5. Results

5.1. Sampling Data Results

Initially, the sample was made of 637 subjects. After responses with missing data (n = 16, 7 men and 9 women) and outliers (n = 12, 4 men and 8 women) were excluded, a total of 609 (N = 609) respondents were included in the final sample. Thus, the overall response was 95.60%. This elimination did not significantly affect the sample’s representativeness in terms of degree, academic year, or gender. Additionally, the percentage of men and women in the sample was similar to the selected university and population. Finally, the mean age of the respondents was 20.88 years old.

5.2. Descriptive Data

First, each item’s response percentages were analyzed, as they are a relative indicator compared to the total raw score of the item [4]. As shown in Table 2, STUDENTS scored high on all the items with 70.59% of items obtaining a valuation above 70%. The items with the lowest valuation are those related to the first impression (FIM2 and FIM3) (63.84% and 64.66%, respectively), and the item related to website usability (WUS3) (63.56%). Items with the highest scores were those of purchase intention and loyalty, as well as website content (WCO3) (80.00%). In addition, although PLS does not require normality in data distribution, the results showed the existence of relative normality. Most skewness and kurtosis values were in absolute terms around 2 and 7, respectively, which are limits considered suitable for samples higher than 300 subjects [110]. Additionally, since all items’ standard deviation was less than half of the mean, it can be affirmed that there were no extreme balances or values.

5.3. Assessment of the Overall Model

First, the Standardized Root Mean Square Residual indicator (SRMR) was calculated. This indicator is an approximate model fit in the PLS-SEM context and assesses the average magnitude of the discrepancies between expected and observed correlations. As results show, a good fit has been confirmed because the SRMR result was 0.072 (SRMR = 0.72) in the most conservative context, and less than 0.08 [115]. Second, the Common Method Bias (CMB) was applied. CMB represents the spurious variance that is attributable to the measurement method. Since all variance inflation factors (VIF) resulting from a full collinearity test were lower than 3.3, both the indicators and constructs and, thus, the model was considered free of CMB [116].

5.4. Test of the Measurement Model

The study of the measurement model through reflective indicators implies the analysis of individual and composite reliability and the convergent and divergent validity of the relationships between the observed variables and the constructs to which they belong [113]. The study of individual reliability was carried out by observing the factorial loading (λ), which are the simple correlations of the indicators with the construct they intend to measure. Regarding composite reliability (CR), it is an indicator similar to Conbrach’s alpha that is more appropriate in structural equations. Results showed that all reliability values reached the required levels (λ ≥ 0.70, CR ≥ 0.70) [114] (Table 3). Therefore, it was accepted that the measurement model was internally consistent and that all the observed variables were measuring their corresponding construct [117].
Regarding convergent validity, the average variance extracted (AVE) was calculated to analyze the amount of variance that a construct obtains from its indicators as an indication of variance due to a measurement error. AVE was greater than 0.50 (AVE > 0.50) (Table 3), thus, confirming that more than 50% of the variance of the construct was due to its indicators [113].
As for discriminant validity and following the criteria of Fornell and Larcker, it was verified that the AVE indicator of each variable (data in bold in Table 4) was higher than the variance shared with the other variables [118]. The discriminant validity was also confirmed through the Heterotrait-Monotrait Ratio (HTMT) (values above the diagonal in Table 4). The results showed that all HTMT values were lower than 0.85 in all relationships [113].

5.5. Test of the Structural Model

Regarding the structural model, which shows the relationship between the constructs, it was found that, except for H3b, all relationships had positive signs, as do their corresponding hypotheses (Table 5). Next, concerning the magnitude of the causal relationships, it was verified that, except for H3b and H4a, path coefficients (β) (standardized regression weights) reached levels above 0.300, which is an acceptable level (β ≥ 0.300) [111]. As Table 5 shows, causal relationships with greater weights were obtained between First impression (FIM) and Website usability (WUS) (βH2a = 0.660, t-value = 29.990, p < 0.001), and between First impression (FIM) and Website content (WCO) (βH2b = 0.500, t-value = 18.930, p < 0.001). The weakest relationship was between Website usability (WUS) and Website loyalty (LOY) (βH3b = −0.210, t-value = 4.010, p < 0.001), and between Website content (WCO) and Purchase intention (PIN) (βH4a = 0.020, t-value = 0.780, p > 0.001). The significance was obtained through a bootstrapping analysis carried out with 5000 sub-samples [113]. Therefore, all hypotheses were confirmed except for H3b and H4a.

5.6. Analysis of the Predictive Validity of the Model

First, the R2 indicator (coefficient of determination) was calculated within the sample to analyse the model’s predictive validity. Results showed that the value of R2 related to the dependent construct (LOY) was 0.520 (R2 = 0.520), with 0.500 being the minimum acceptable value (Table 2) [117]. Next, the Q2 indicator was calculated through the SmartPLS blindfolding process in a redundancy-based prediction mode. The results showed that all values were above zero (Q2 ≥ 0), both the items and the constructs [113]. Third, the PLSPredict analysis showed that all the dependent construct indicators produced lower prediction errors using RMSR (root mean squared error) than the LM (linear regression model) benchmark [117]. Therefore, the proposed model has sufficient predictive potential.

5.7. Importance-Performance Analysis

Importance-Performance Analysis (IPMA) was carried out to determine the constructs to prioritise and promote [119]. IPMA is a method developed to compare the constructs’ performance related to a target construct (through their average scores) with their antecedent importance (through total effects) [120]. The results showed (Figure 2) that, in the model, there were no variables with reduced importance or reduced performance. Purchase intention (PIN) and content (WCO) are the most relevant model constructs due to their importance and high performance about website loyalty. As shown in Figure 2, both variables are near the maximum productivity line and require priority attention in resources and time [121].

6. Discussion and Implications

This study aims to better comprehend the formation of online loyalty to tourism websites by young consumers. Other authors’ suggestions regarding studying new population segments and complementing existing theories with different variables and relationships have been adopted. The site’s personalization, consumers’ first impression, website quality, and online intention to purchase have been included as independent variables. These variables have not been studied jointly before. In the sections below, the theoretical and practical implications are discussed.

6.1. Theoretical Implications

Regarding responses to items, it can be confirmed that young people give importance to all the variables presented in this study, thus, confirming the “technological” nature of this population segment [23]. The results are in line with other studies in which young people value more than 50% of the items related to the design and the personalization of the website in the market orientation framework [13,83].
Although some previous studies highlight the reduced loyalty of young consumers [23], in this study, the results show that online loyalty to sites is high, as shown in other studies [4]. More specifically, the items LOY1 and LOY2 obtained values above 80% in this study. This is because, in this work, online loyalty refers to the website, and not to the product or the tourism brand. Likewise, the results show that young people have a high potential for online purchasing, confirming other authors’ findings [22,23]. The items related to purchasing intention obtained levels above 78%. In addition, 98.25% of young people affirm that they have purchased tourist products through websites.
This model explains the variance of loyalty by more than 50% (R2 = 0.520) regarding this study’s causal model. First, personalization has a significant influence on the first and immediate impression (βH1 = 0.420, t = 11.460, p = 0.000), as has been verified in the literature for other population segments [73,77]. The first impression can be considered the first evaluation of the quality of the website by the consumer, fundamentally determined by the aesthetic aspects [76]. Of the three items related to the first impression, the item related to the site’s aesthetic aspects is the one that obtained the highest score (FIM1 = 76.71%). The usability and content of the site obtained lower scores, while being above 50%.
The influence of the first impression on the website design’s perceived quality has been confirmed [77,80]. However, this high influence in both cases is somewhat greater for usability (βH2a = 0.660, t = 29.900, p = 0.000) than for content (βH2b = 0.500, t = 18.930, p = 0.000). This may be because, by their generational profile, the site’s technology-related aspects are somewhat more important to the young consumer than the content. Despite the direct and high effect of the first impression on the website’s perceived quality, the three items related to quality did not obtain a very high rating. However, it was higher than 63%. This may be because the formation of the first impression is an immediate and fast process (around 50ms), and, to a large extent, an unconscious one [77]. Young people may not give too much prominence to the role of the first impression.
Likewise, the influence of website quality on intention and online loyalty has been confirmed, as other authors have proposed [82,87]. It may be because, in this study, the perceived quality of the website has been measured by using two different constructs (site usability and site content). The usability of the site has a significant influence on purchase intention (βH3a = 0.320, t = 6.160, p = 0.000), but this influence is negative in the case of online loyalty (βH3b = −0.210, t = 4.010, p = 0.000). On the contrary, the influence of the perceived quality of site content on purchase intention is low (βH4a = 0.020, t = 0.780, p = 0.440), but this relationship is positive and significant regarding loyalty to the website (βH4b = 0.337, t = 5.660, p = 0.000). Therefore, the results show that, in Generation Z, the perception of high usability of the website favours the intention to purchase online in tourism, but counteracts loyalty. On the contrary, a high perception of website content quality favours loyalty, but hardly influences purchase intention online. Despite the above, the Importance-Performance Analysis (IPMA) carried out shows that site content is a priority variable compared to usability on the path to online loyalty.
The direct and positive influence of purchase intention on website loyalty has been confirmed (βH5 = 0.420, t = 3.750, p = 0.000) [4]. As other authors have suggested, online purchase intention and online loyalty to the site are different constructs that are related [6]. This result may be because the purchase intention in this study is considered a general attitude toward buying on websites, while online loyalty refers to the same site’s repetition or recommendation. Therefore, it seems reasonable to say that it is not possible to be loyal to websites without a positive attitude toward electronic commerce. The strategic importance of purchase intention to predict loyalty to the site has been confirmed in this study through the IPMA analysis, as found by some other authors [4]. Finally, the causal analysis results confirm the potential of the PLS model of structural equations to explain the theory [110] and its predictive potential [111].

6.2. Practical Implications

In addition to the theoretical implications in the previous sections, the results obtained in this study provide practical implications for tourism companies to encourage young consumers’ loyalty to tourism websites. These will make it easier to guide professionals in their market orientation and develop an appropriate and effective website design and management.
First, the descriptive results show that young people’s perceptions, attitudes, and predisposition favour using websites and buying through them. This information is useful for business marketing management to encourage intention and loyalty. Moreover, Generation Z values the personalization and quality design of tourism websites. Therefore, tourism companies must adapt websites to the wishes, needs, and expectations of this population segment in a market-oriented framework. It means finding out in advance through market research what those needs, wants, and expectations are.
Second, it is concluded that the proposed model is practical to understand, predict, and encourage young consumers’ loyalty to tourism websites. The model can also predict online loyalty by focusing on two variables: managing website personalization and quality design. The results allow us to affirm that the importance of market orientation in the online context underlies the model, which happens in traditional commerce. Additionally, the personalization associated with market orientation must be perceived by consumers. Moreover, actions related to market orientation and marketing in both online and offline contexts must be coordinated.
Third, website personalization starts the chain of effects that influences online loyalty to the site. Companies must manage this variable due to the significant influence on young consumers’ first impression of website quality. Specifically, marketers and IT professionals should personalize the site by adapting it to consumers’ wishes and needs. They must consider those aesthetics prevail in the first impression, followed by usability and content. Later, when the consumer browses the site, and a deliberate impression is formed, aesthetics take a back seat, while usability and content prevail.
Fourth, professionals’ actions to personalize tourism websites and improve their quality, as perceived by consumers, influence intentions to use e-commerce. Since purchase intention is defined in this study as a general attitude, these actions favour intention throughout the sector and the company itself through loyalty. This collective benefit constitutes a call for collaboration in the sector, producing synergies.
Fifth, the generational approach adopted allows e-commerce companies in tourism to consider taking global actions to encourage young consumers’ online loyalty to websites. After all, e-commerce is global. However, the generational approach must be taken with some caution. There is still an open debate about the global validity of studies with young consumers, with few studies on Generation Z, particularly in tourism.

7. Conclusions

This paper studies the loyalty to e-commerce websites in tourism among young people. Online loyalty to websites is an approach to online loyalty that is less common in the literature. The study has been carried out considering the nature of tourism, the existing gaps in the literature on loyalty to sites, and other authors’ suggestions in this field. Theoretical and practical knowledge about online loyalty to websites has been improved, and the following conclusions can be drawn.
First, this study on consumer loyalty responds to the rapid evolution of commerce in tourism toward a digital model and the need to increase the commercial and financial benefits of tourism companies associated with greater loyalty. However, it is not easy to achieve consumer loyalty due to consumers’ growing demands, the high competition in the sector, and the intangible nature of tourism. All these aspects suggest the need to study consumers’ loyalty to tourism websites in greater depth. It is also essential to develop new predictive models so that tourism companies know how to encourage loyalty to their websites.
Second, websites have become the connecting link between consumers, companies, and products. Various predictive studies about site loyalty have been developed through different theoretical approaches and website quality factors. However, those studies have not been devoid of limitations, and several authors have proposed the study of a wider range of independent variables. Specifically, this study addresses site personalization, the consumer’s first impression, and the website’s perceived quality. Regarding website quality, site usability and content have been particularly considered. These variables must be managed in a market-oriented context and a coordinated manner in both the online and offline commercial environment. Therefore, this study calls for marketers and IT professionals to work together to design websites that are increasingly tailored to consumers’ wishes, expectations, and needs.
Third, the study has investigated further young consumers’ online loyalty to websites. It is particularly relevant due to young people’s digital nature and their high purchasing potential and influence on e-commerce in the tourism sector. Through the generational approach adopted, there is a possibility of generalizing the results globally for the entire segment. However, the generational approach must be approached with caution as there is still some debate about its global validity.
Although our study contributes to improving theoretical and practical knowledge about online loyalty to websites, it is not without limitations. The main limitation of this work is related to access to the sample. It has been complicated. Additionally, the study has been limited to a single geographic context. Future research lines may be of interest to study the validity of the model for the same segment in other universities, geographical contexts, and sectors to verify the adopted generational approach.
Moreover, the global nature of generational studies’ results with Generation Z is not entirely conclusive, particularly in tourism. A future line of research could delve into these aspects. Additionally, it would be interesting to study the model by selecting some specific tourism websites for a more in-depth analysis.

Author Contributions

Both authors have participated equally in: conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, and project administration. Both authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

The data are not publicly available due to restrictions of privacy.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Živoder, S.; Ateljević, I.; Čorak, S. Conscious travel and critical social theory meets destination marketing and management studies: Lessons learned from Croatia. J. Destin. Mark. Manag. 2015, 4, 68–77. [Google Scholar] [CrossRef]
  2. Tseng, A. Why do online tourists need sellers’ ratings? Exploration of the factors affecting regretful tourist e-satisfaction. Tour. Manag. 2017, 59, 413–424. [Google Scholar] [CrossRef]
  3. Abou-Shouk, M.A. Destination management organizations and destination marketing: Adopting the business model of e-portals in engaging travel agents. J. Travel Tour. Mark. 2018, 35, 178–188. [Google Scholar] [CrossRef]
  4. Buhalis, D.; Parra López, E.; Martinez-Gonzalez, J.A. Influence of young consumers’ external and internal variables on their e-loyalty to tourism sites. J. Destin. Mark. Manag. 2020, 15, 409. [Google Scholar] [CrossRef]
  5. Amaro, S.; Duarte, P. Travellers’ intention to purchase travel online: Integrating trust and risk to the theory of planned behavior. Anatolia 2016, 27, 389–400. [Google Scholar] [CrossRef]
  6. Bi, Y.; Kim, I. Older travelers’ e-loyalty: The roles of service convenience and social presence in travel websites. Sustainability 2020, 12, 410. [Google Scholar] [CrossRef] [Green Version]
  7. Gharibi, N.; Rudsari, S.M.M.; Nasouti, M. Tourists’ E-loyalty to a destination website: A case of an Iranian tourism website. Int. J. Tour. Policy 2020, 10, 88–96. [Google Scholar] [CrossRef]
  8. Herrero, A.; San Martín, H.S.; Collado, J. Market orientation and SNS adoption for marketing purposes in hospitality microenterprises. J. Hosp. Tour. Manag. 2018, 34, 30–40. [Google Scholar] [CrossRef] [Green Version]
  9. Sugandini, D.; Feriyanto, N.; Yuliansyah, Y.; Sukwadi, R.; Uii, M. Web quality, satisfaction, trust and its effects on government website loyalty. Int. J. Qual. Res. 2018, 12, 885–904. [Google Scholar] [CrossRef]
  10. Mpandare, M.; Li, G. Utilising enterprise social media for product innovation: The role of market orientation. Sustainability 2020, 12, 3913. [Google Scholar] [CrossRef]
  11. Yi, X.; Fu, X.; Yu, L.; Jiang, L. Authenticity and loyalty at heritage sites: The moderation effect of postmodern authenticity. Tour. Manag. 2018, 67, 411–424. [Google Scholar] [CrossRef]
  12. Albayrak, T.; Karasakal, S.; Kocabulut, Ö.; Dursun, A. Customer loyalty towards travel agency websites: The role of trust and hedonic value. J. Qual. Assur. Hosp. Tour. 2020, 21, 50–77. [Google Scholar] [CrossRef]
  13. Martínez, J.A.; Parra, E.; Buhalis, D. The loyalty of young residents in an island tourism destination: An integrated model. J. Destin. Mark. Manag. 2017, 6, 444–455. [Google Scholar] [CrossRef]
  14. Luo, Y.; Ye, Q. Understanding consumers’ loyalty to an online outshopping platform: The role of social capital and perceived value. Sustainability 2019, 11, 5371. [Google Scholar] [CrossRef] [Green Version]
  15. Hasan, B. Perceived irritation in online shopping: The impact of website design characteristics. Comput. Hum. Behav. 2016, 54, 224–230. [Google Scholar] [CrossRef]
  16. Ruiz, C.; Tronch, J.; Sanz, S. The role of emotions and social influences on consumer loyalty towards online travel communities. J. Serv. Theory Pract. 2016, 26, 534–558. [Google Scholar] [CrossRef]
  17. Hadi, N.U.; Aslam, N.; Gulzar, A. Sustainable service quality and customer loyalty: The role of customer satisfaction and switching costs in the Pakistan cellphone industry. Sustainability 2019, 11, 2408. [Google Scholar] [CrossRef] [Green Version]
  18. Kaya, B.; Behravesh, E.; Abubakar, A.M.; Kaya, O.S.; Orús, C. The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction and e-loyalty. J. Internet Commer. 2019, 18, 369–394. [Google Scholar] [CrossRef]
  19. Duffett, R. The youtube marketing communication effect on cognitive, affective and behavioural attitudes among generation Z consumers. Sustainability 2020, 12, 5075. [Google Scholar] [CrossRef]
  20. Su, C.H.; Tsai, C.H.; Chen, M.H.; Lv, W.Q.U.S. sustainable food market generation Z consumer segments. Sustainability 2019, 11, 3607. [Google Scholar] [CrossRef] [Green Version]
  21. Tabassum, S.; Khwaja, M.G.; Zaman, U. Can narrative advertisement and eWOM influence generation z purchase intentions? Information 2020, 11, 545. [Google Scholar] [CrossRef]
  22. Priporas, C.V.; Stylos, N.; Kamenidou, I.E. City image, city brand personality and generation Z residents’ life satisfaction under economic crisis: Predictors of city-related social media engagement. J. Bus. Res. 2020, 119, 453–463. [Google Scholar] [CrossRef]
  23. Revinova, S.; Ratner, S.; Lazanyuk, I.; Gomonov, K. Sharing economy in Russia: Current status, barriers, prospects and role of universities. Sustainability 2020, 12, 4855. [Google Scholar] [CrossRef]
  24. Kamenidou, I.C.; Mamalis, S.A.; Pavlidis, S.; Bara, E.Z.G. Segmenting the generation Z cohort university students based on sustainable food consumption behavior: A preliminary study. Sustainability 2019, 11, 837. [Google Scholar] [CrossRef] [Green Version]
  25. Lin, Y.C.; Lai, H.J.; Morrison, A.M. Social servicescape and Asian students: An analysis of spring break island bed and breakfast experiences in Taiwan. Tour. Manag. Perspect. 2019, 31, 165–173. [Google Scholar] [CrossRef]
  26. Albayrak, T.; Caber, M.; González, M.R.; Aksu, A. Analysis of destination competitiveness by IPA and IPCA methods: The case of Costa Brava, Spain against Antalya, Turkey. Tour. Manag. Perspect. 2018, 28, 53–61. [Google Scholar] [CrossRef]
  27. Gonçalves, H.; Salgueiro, M.F.; Rita, P. Online purchase determinants of loyalty: The mediating effect of satisfaction in tourism. J. Retail. Consum. Serv. 2016, 30, 279–291. [Google Scholar] [CrossRef] [Green Version]
  28. Múgica, J.; Berné, C. Direct and indirect tourism online channels. Do they have a different potential for customer loyalty? Sustainability 2020, 12, 4761. [Google Scholar] [CrossRef]
  29. Zhang, H.; Fu, X.; Cai, L.A.; Lu, L. Destination image and tourist loyalty: A meta analysis. Tour. Manag. 2014, 40, 213–223. [Google Scholar] [CrossRef]
  30. Almeida, A.; Moreno, S. Understanding tourism loyalty: Horizontal vs. destination loyalty. Tour. Manag. 2018, 65, 245–255. [Google Scholar] [CrossRef]
  31. Wu, C.; Hsu, C.L. How to improve E-Satisfaction and E-Loyalty and strengthen the links between them: Value from regulatory fit. Hum. Factors Ergon. Manuf. 2015, 25, 353–369. [Google Scholar] [CrossRef]
  32. Chen, C.F.; Wang, J.P. Customer participation, value co-creation and customer loyalty. A case of airline online check-in system. Comput. Hum. Behav. 2016, 62, 346–352. [Google Scholar] [CrossRef]
  33. Toufaily, E.; Ricard, L.; Perrien, J. Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model. J. Bus. Res. 2013, 66, 1436–1447. [Google Scholar] [CrossRef]
  34. Chen, J.V.; Yen, D.C.; Pornpriphet, W.; Widjaja, A. E-commerce web site loyalty: A cross cultural comparison. Inf. Syst. Front. 2015, 17, 1283–1299. [Google Scholar] [CrossRef]
  35. Al-Adwan, A.S.; Kokash, H.; Al Adwan, A.; Alhorani, A.; Yaseen, H. Building customer loyalty in online shopping: The role of online trust, online satisfaction and electronic word of mouth. Int. J. Electron. Mark. Retail. 2020, 11, 278–306. [Google Scholar] [CrossRef]
  36. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research; Addison-Wesley: Reading, MA, USA, 1975; ISBN1 10:0201020890. ISBN2 13: 978-0201020892. [Google Scholar]
  37. Ajzen, I.; Fishbein, M. Understanding Attitudes and Predicting Social Behaviour; Prentice-Hall: Englewood Cliffs, NJ, USA, 1980; ISBN1 0139364439. ISBN2 978-0139364433. [Google Scholar]
  38. Hansen, T.; Risborg, M.S.; Steen, C.D. Understanding consumer purchase of free-of cosmetics: A valuedriven TRA approach. J. Consum. Behav. 2012, 11, 477–486. [Google Scholar] [CrossRef]
  39. Paul, J.; Modi, A.; Patel, J. Predicting green product consumption using theory of planned behavior and reasoned action. J. Retail. Consum. Serv. 2016, 29, 123–134. [Google Scholar] [CrossRef]
  40. Ajzen, I. From intentions to action: A theory of planned behavior. In Action Control: From Cognition to Behavior; Kuhl, J., Beckmann, J., Eds.; Springer: Berlin, Germany, 1985; pp. 11–39. ISBN 978-3-642-69746-3. [Google Scholar]
  41. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  42. Chen, M.F.; Tung, E.J. Developing an extended Theory of Planned Behaviour Model to predict consumers’ intention to visit green hotels. Int. J. Hosp. Manag. 2014, 36, 221–230. [Google Scholar] [CrossRef]
  43. Lam, T.; Hsu, C.H. Theory of planned behavior: Potential travelers from China. J. Hosp. Tour. Res. 2004, 28, 463–482. [Google Scholar] [CrossRef]
  44. Akbari, M.; Fozouni, Z.; Pino, G.; Maleksaeidi, H. An extended model of theory of planned behavior to investigate highly-educated Iranian consumers’ intentions towards consuming genetically modified foods. J. Clean. Prod. 2019, 227, 784–793. [Google Scholar] [CrossRef]
  45. Garay, L.; Font, X.; Corrons, A. Sustainability-oriented innovation in tourism: An analysis based on the decomposed Theory of Planned Behavior. J. Travel Res. 2019, 58, 622–636. [Google Scholar] [CrossRef]
  46. Bigné, E.; Sanz, S.; Ruiz, C.; Aldás, J. Why some internet users don’t buy air tickets online. In Information and Communication Technologies in Tourism; Gretzel, U., Law, R., Fuchs, M., Eds.; Springer: Vienna, Austria, 2010; pp. 209–221. ISBN 978-3-211-99406-1. [Google Scholar]
  47. Hasbullah, N.A.; Osman, A.; Abdullah, S.; Salahuddin, S.N.; Ramlee, N.F.; Soha, H.M. The relationship of attitude, subjective norm and website usability on consumer intention to purchase online: An evidence of Malaysian youth. Procedia Econ. Financ. 2016, 35, 493–502. [Google Scholar] [CrossRef] [Green Version]
  48. Xu, X.; Schrier, T. Hierarchical effects of website aesthetics on customers’ intention to book on hospitality sharing economy platforms. Electron. Commer. Res. Appl. 2019, 35, 856. [Google Scholar] [CrossRef]
  49. Belk, R. You are what you can access: Sharing and collaborative consumption online. J. Bus. Res. 2014, 67, 1595–1600. [Google Scholar] [CrossRef]
  50. Armitage, C.J.; Conner, M. Efficacy of the theory of planned behaviour: A meta-analytic review. Br. J. Soc. Psychol. 2001, 40, 471–499. [Google Scholar] [CrossRef] [Green Version]
  51. Wang, Y.; Yu, C. Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning. Int. J. Inf. Manag. 2017, 37, 179–189. [Google Scholar] [CrossRef]
  52. Kim, L.H.; Qu, H.; Kim, D.J. A study of perceived risk and risk reduction of purchasing air-tickets online. J. Travel Tour. Mark. 2009, 26, 203–224. [Google Scholar] [CrossRef]
  53. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  54. Belgiawan, P.F.; Schmöcker, J.D.; Abou-Zeid, M.; Walker, J.; Fujii, S. Modelling social norms: Case study of students’ car purchase intentions. Travel Behav. Soc. 2017, 7, 12–25. [Google Scholar] [CrossRef]
  55. Cheng, H.H.; Huang, S.W. Exploring antecedents and consequence of online group-buying intention: An extended perspective on theory of planned behavior. Int. J. Inf. Manag. 2013, 33, 185–198. [Google Scholar] [CrossRef]
  56. Pantano, E.; Priporas, C.V. The effect of mobile retailing on consumers’ purchasing experiences: A dynamic perspective. Comput. Hum. Behav. 2016, 61, 548–555. [Google Scholar] [CrossRef] [Green Version]
  57. Morosan, C.; Jeong, M. Users’ perceptions of two types of hotel reservation web sites. Int. J. Hosp. Manag. 2008, 27, 284–292. [Google Scholar] [CrossRef]
  58. Amaro, S.; Andreu, L.; Huang, S. Millennials’ intentions to book on Airbnb. Curr. Issues Tour. 2019, 22, 2284–2298. [Google Scholar] [CrossRef]
  59. Chenini, A.; Mustapha, A. Building destination loyalty using tourist satisfaction and destination image: A holistic conceptual framework. J. Tour. Herit. Serv. Mark. 2018, 4, 37–43. [Google Scholar] [CrossRef]
  60. Zhang, H.; Zhao, L.; Gupta, S. The role of online product recommendations on customer decision making and loyalty in social shopping communities. Int. J. Inf. Manag. 2018, 38, 150–166. [Google Scholar] [CrossRef]
  61. Bulut, Z.A.; Karabulut, A.N. Examining the role of two aspects of eWOM in online repurchase intention: An integrated trust–loyalty perspective. J. Consum. Res. 2018, 17, 407–417. [Google Scholar] [CrossRef]
  62. Sanz-Blas, S.; Ruiz-Mafé, C.; Pérez-Pérez, I. Key drivers of services website loyalty. Serv. Ind. J. 2014, 34, 455–475. [Google Scholar] [CrossRef]
  63. Bai, B.; Law, R.; Wen, I. The impact of website quality on customer satisfaction and purchase intentions: Evidence from Chinese online visitors. Int. J. Hosp. Manag. 2008, 27, 391–402. [Google Scholar] [CrossRef]
  64. Jeon, M.M.; Jeong, M. Customers’ perceived website service quality and its effects on e-loyalty. Int. J. Contemp. Hosp. 2017, 29, 438–457. [Google Scholar] [CrossRef]
  65. Kalaignanam, K.; Kushwaha, T.; Rajavi, K. How does web personalization create value for Online Retailers? Lower cash flow volatility or enhanced cash flows. J. Retail. 2018, 94, 265–279. [Google Scholar] [CrossRef]
  66. McKercher, B.; Denizci-Guillet, B.; Ng, E. Rethinking loyalty. Ann. Tour. Res. 2012, 39, 708–734. [Google Scholar] [CrossRef]
  67. Molinillo, S.; Anaya-Sánchez, R.; Liébana-Cabanillas, F. Analyzing the effect of social support and community factors on customer engagement and its impact on loyalty behaviors toward social commerce websites. Comput. Hum. Behav. 2020, 108, 105980. [Google Scholar] [CrossRef]
  68. Krishnaraju, V.; Mathew, S.K.; Sugumaran, V. Web personalization for user acceptance of technology: An empirical investigation of E-government services. Inf. Syst. Front. 2016, 18, 579–595. [Google Scholar] [CrossRef]
  69. Salonen, V.; Karjaluoto, H. Web personalization: The state of the art and future avenues for research and practice. Telemat. Inform. 2016, 33, 1088–1104. [Google Scholar] [CrossRef] [Green Version]
  70. Annamalai, K.P.; Mathew, S.K.; Iyer, L.S. Embarrassment products, web personalization and online buying behavior: An experimental study. Data Base Adv. Inf. Syst. 2019, 50, 92–108. [Google Scholar] [CrossRef]
  71. Ho, S.Y.; Bodoff, D. The effects of web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS Q. 2014, 38, 497–520. [Google Scholar] [CrossRef]
  72. Benlian, A. Web personalization cues and their differential effects on user assessments of website value. J. Manag. Inf. Syst. 2015, 32, 225–260. [Google Scholar] [CrossRef]
  73. Thielsch, M.T.; Blotenberg, I.; Jaron, R. User evaluation of websites: From first impression to recommendation. Interact. Comput. 2014, 26, 89–102. [Google Scholar] [CrossRef] [Green Version]
  74. Porat, T.; Tractinsky, N. It’s a pleasure buying here: The effects of web-store design on consumers’ emotions and attitudes. Hum. Comput. Interact. 2012, 27, 235–276. [Google Scholar] [CrossRef]
  75. Tuch, A.N.; Presslaber, E.E.; Stöcklin, M.; Opwis, K.; Bargas-Avila, J.A. The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments. Int. J. Hum. Comput. Stud. 2012, 70, 794–811. [Google Scholar] [CrossRef]
  76. Kim, H.; Fesenmaier, D.R. Persuasive design of destination web sites: An analysis of first impression. J. Travel Res. 2008, 47, 3–13. [Google Scholar] [CrossRef] [Green Version]
  77. Douneva, M.; Jaron, R.; Thielsch, M.T. Effects of different website designs on first impressions, aesthetic judgments, and memory performance after short presentation. Interact. Comput. 2016, 28, 552–567. [Google Scholar] [CrossRef] [Green Version]
  78. Thielsch, M.T.; Hirschfeld, G. Spatial frequencies in aesthetic website evaluations—Explaining how ultra-rapid evaluations are formed. Ergonomics 2012, 55, 731–742. [Google Scholar] [CrossRef] [PubMed]
  79. Wang, L.; Law, R.; Guillet, B.D.; Hung, K.; Fong, D.K.C. Impact of hotel website quality on online booking intentions: eTrust as a mediator. Int. J. Hosp. Manag. 2015, 47, 108–115. [Google Scholar] [CrossRef]
  80. Tandon, A.; Aakash, A.; Aggarwal, A.G. Impact of EWOM, website quality, and product satisfaction on customer satisfaction and repurchase intention: Moderating role of shipping and handling. Int. J. Syst. Assur. Eng. Manag. 2020, 11, 349–356. [Google Scholar] [CrossRef]
  81. Wang, L.; Law, R. Relationship between hotels’ website quality and consumers’ booking intentions with internet experience as moderator. J. China Tour. Res. 2020, 16, 585–605. [Google Scholar] [CrossRef]
  82. Król, K.; Zdonek, D. Aggregated indices in website quality assessment. Future Internet 2020, 12, 72. [Google Scholar] [CrossRef] [Green Version]
  83. Parra-Lopez, E.; Martínez-González, J.A.; Chinea-Martin, A.C. Drivers of the formation of e-loyalty towards tourism destinations. Eur. J. Manag. 2018. [Google Scholar] [CrossRef] [Green Version]
  84. Delone, W.H.; Mclean, E.R. Information system success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–95. [Google Scholar] [CrossRef]
  85. Delone, W.H.; Mclean, E.R. Information system success revisited. In Proceedings of the 35th Hawaii International Conference on System Sciences, Big Island, HI, USA, 7–10 January 2002. [Google Scholar]
  86. Delone, W.H.; Mclean, E.R. The DeLone and McLean model of information systems success: A ten year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar] [CrossRef]
  87. Hartono, E.; Holsapple, C.W. Website visual design qualities: A threefold framework. ACM Trans. Manag. Inf. Syst. 2019, 10, 1. [Google Scholar] [CrossRef]
  88. Kuo, P.J.; Zhang, L.; Cranage, D. What you get is not what you saw: Exploring the impacts of misleading hotel website photos. Int. J. Contemp. Hosp. Manag. 2015, 27, 1301–1319. [Google Scholar] [CrossRef]
  89. Herrero, A.; Martín, H.S.; Hernandez, J.M. How online search behavior is influenced by user-generated content on review websites and hotel interactive websites. Int. J. Contemp. Hosp. Manag. 2015, 27, 1573–1597. [Google Scholar] [CrossRef] [Green Version]
  90. Shaouf, A.; Lü, K.; Li, X. The effect of web advertising visual design on online purchase intention: An examination across gender. Comput. Hum. Behav. 2016, 60, 622–634. [Google Scholar] [CrossRef]
  91. Rodrigues, L.F.; Costa, C.J.; Oliveira, A. How does the web game design influence the behavior of e-banking users? Comput. Hum. Behav. 2017, 74, 163–174. [Google Scholar] [CrossRef]
  92. Moslehpour, M.; Pham, V.K.; Wong, W.K.; Bilgiçli, I. E-Purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability 2018, 10, 234. [Google Scholar] [CrossRef] [Green Version]
  93. Lien, C.H.; Wen, M.J.; Huang, L.C.; Wu, K.L. Online hotel booking: The effects of brand image, price, trust and value on purchase intentions. Asia Pac. Manag. Rev. 2015, 20, 210–218. [Google Scholar] [CrossRef]
  94. Kim, S.S. Purchase intention in the online open market: Do concerns for e-commerce really matter? Sustainability 2020, 12, 773. [Google Scholar] [CrossRef] [Green Version]
  95. Hsu, J.S.C.; Lin, T.C.; Fu, T.W.; Hung, Y.W. The effect of unexpected features on app users’ continuance intention. Electron. Commer. Res. Appl. 2015, 14, 418–430. [Google Scholar] [CrossRef]
  96. Kim, B. Understanding key antecedents of consumer loyalty toward sharing-economy platforms: The case of Airbnb. Sustainability 2019, 11, 5195. [Google Scholar] [CrossRef] [Green Version]
  97. Llach, J.; Marimon, F.; Alonso, M.M.; Bernardo, M. Determinants of online booking loyalties for the purchasing of airline tickets. Tour. Manag. 2013, 35, 23–31. [Google Scholar] [CrossRef]
  98. Yin, C.Y.; Poon, P.; Su, J.L. Yesterday once more? Autobiographical memory evocation effects on tourists’ post-travel purchase intentions toward destination products. Tour. Manag. 2017, 62, 263–274. [Google Scholar] [CrossRef] [Green Version]
  99. Chen, K.C.; Gursoy, D.; Lau, K.L.K. Longitudinal impacts of a recurring sport event on local residents with different level of event involvement. Tour. Manag. Perspect. 2018, 28, 228–238. [Google Scholar] [CrossRef]
  100. Lindgaard, G.; Fernandes, G.; Dudek, C.; Brown, J. Attention web designers: You have 50 milliseconds to make a good first impression. Behav. Inf. Technol. 2006, 25, 115–126. [Google Scholar] [CrossRef]
  101. Šegota, T.; Mihalič, T.; kuščer, K. The impact of residents’ informedness and involvement on perceptions of tourism impacts: The case of the destination. J. Destin. Mark. Manag. 2017, 6, 196–206. [Google Scholar] [CrossRef]
  102. Bergkvist, L.; Rossiter, J. The predictive validity of multiple-item versus single-item measures of the same constructs. J. Mark. Res. 2007, 44, 175–184. [Google Scholar] [CrossRef]
  103. Tran, L.T.T. Online reviews and purchase intention: A cosmopolitanism perspective. Tour. Manag. Perspect. 2020, 35, 100722. [Google Scholar] [CrossRef]
  104. Yong, A.G.; Pearce, S. A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutor. Quant. Methods Psychol. 2013, 9, 79–94. [Google Scholar] [CrossRef]
  105. Gardiner, S.; Grace, D.; King, C. The generation effect: The future of domestic tourism in Australia. J. Travel Res. 2014, 53, 705–720. [Google Scholar] [CrossRef]
  106. Korobiichuk, I.; Fedushko, S.; Juś, A.; Syerov, Y. Methods of Determining Information Support of Web Community User Personal Data Verification System. In Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing; Szewczyk, R., Zieliński, C., Kaliczyńska, M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; Volume 550, pp. 144–150. ISBN 978-3-211-99406-1. [Google Scholar] [CrossRef]
  107. Ha, S. Millennials’ sleep and unethical behavior: Testing the relationship between sleep and academic dishonesty of millennials in a Korean university. Sustainability 2020, 12, 3902. [Google Scholar] [CrossRef]
  108. Jorge, E.; Lopez-Valeiras, E.; Gonzalez-Sanchez, M.B. The importance given to food naturalness attributes by millennial university students. Sustainability 2020, 12, 728. [Google Scholar] [CrossRef] [Green Version]
  109. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–151. [Google Scholar] [CrossRef]
  110. Hair, J.F.; Black, W.C.; Babin, B.; Anderson, R.E. Multivariate Data Analysi; Pearson New International Edition: Halow, UK, 2014; ISBN 978-0-13-813263-7. [Google Scholar]
  111. Hair, J.; Hult, G.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equations Modeling (PLS-SEM), 2nd ed.; SAGE: Los Angeles, CA, USA, 2017; ISBN 978-1-4833-7743-8. [Google Scholar]
  112. Hair, J.F.; Sarstedt, M.; Pieper, T.M.; Ringle, C.M. The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Plan. 2012, 45, 320–340. [Google Scholar] [CrossRef]
  113. Ali, F.; Rasoolimanesh, S.M.; Sarstedt, M.; Ringle, C.M.; Ryu, K. An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int. J. Contemp. Hosp. Manag. 2018, 30, 514–538. [Google Scholar] [CrossRef] [Green Version]
  114. Chin, C.H.; Lo, M.C.; Razak, Z.; Pasbakhsh, P.; Mohamad, A.A. Resources Confirmation for Tourism Destinations Marketing Efforts Using PLS-MGA: The moderating impact of semirural and rural tourism destination. Sustainability 2020, 12, 6787. [Google Scholar] [CrossRef]
  115. Henseler, J.; Ringle, C.M.; Sarstedt, M. Testing measurement invariance of composites using Partial Least Squares. Int. Mark. Rev. 2016, 33, 405–431. [Google Scholar] [CrossRef]
  116. Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. e-Collab. 2015, 11, 1–10. [Google Scholar] [CrossRef] [Green Version]
  117. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  118. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  119. Martilla, J.A.; James, J.C. Importance–Performance Analysis. J. Mark. 1977, 41, 77–79. [Google Scholar] [CrossRef]
  120. Su, C.H.; Cheng, T.W. A sustainability innovation experiential learning model for virtual reality chemistry laboratory: An empirical study with PLS-SEM and IPMA. Sustainability 2019, 11, 1027. [Google Scholar] [CrossRef] [Green Version]
  121. Bacon, D.R. A comparison of approaches to Importance-Performance Analysis. Int. J. Mark. Res. 2003, 45, 55–71. [Google Scholar] [CrossRef]
Sustainability 13 01425 g001 550
Figure 1. Research proposed model. Source: Authors.
Figure 1. Research proposed model. Source: Authors.
Sustainability 13 01425 g001
Sustainability 13 01425 g002 550
Figure 2. Importance-performance analysis (IPMA). Source: Authors.
Figure 2. Importance-performance analysis (IPMA). Source: Authors.
Sustainability 13 01425 g002
Table
Table 1. Sample description (N = 609).
Table 1. Sample description (N = 609).
LevelMenWomenTotal
1º103110213
2º8683169
3º6276138
4º414889
Total292317609
(%)(47.95%)(52.05%)(100%)
Table
Table 2. Descriptive data.
Table 2. Descriptive data.
Constructs and ItemsMin/Max%
≥50%		MD
≥3		SD
≤1.5		Skew.
≤|2|		Kurt.
≤|7|
Web site personalization (WPE)
The site is adapted to my wishes1/568.77%3.440.94−0.37−0.13
The site is adapted to my needs1/576.16%3.810.96−0.45−0.34
The site meets my expectations1/574.52%3.731.00−0.60−0.36
First impression (FIM)
The first impression according to the aesthetics of the site2/576.71%3.840.83−0.27−0.53
The first impression according to the usability of the site1/563.84%3.190.990.12−0.53
The first impression according to the content of the site1/564.66%3.231.00−0.32−0.33
Website usability (WUS)
Easy navigation through the site1/572.60%3.630.97−0.550.37
Speed of the site1/569.86%3.490.92−0.400.06
The site allows interaction1/563.56%3.181.05−0.36−0.38
Website content (WCO)
The information on the site is useful1/575.34%3.771.19−0.67−0.46
The information on the site is up to date1/572.33%3.621.12−0.44−0.58
The information on the site is sufficient1/580.00%4.001.05−0.860.11
Purchase intention (PIN)
I intend to purchase tourism products on the website(s)2/578.90%3.950.81−0.21−0.78
There is a possibility that I will purchase through the website(s)1/583.01%4.150.95−1.080.75
I will continue to buy through tourism websites2/580.55%4.030.90−0.64−0.36
Online loyalty (LOY)
I would recommend the site(s) where I bought tourism products3/583.09%4.110.73−0.17−1.12
I would repeat the purchase of tourist’s products on the same site(s)2/584.27%4.270.73−0.69−0.08
Table
Table 3. Measurement and predictive data.
Table 3. Measurement and predictive data.
ConstructItemLoading λ
>0.70		CR
>0.70		AVE
>0.50		R2
>0.50		Q2
>0
Web site personalization (WPE)WPE10.7400.7900.560------
WPE20.800
WPE30.770
First impression (FIM)FIM10.7200.7200.5400.2400.15
FIM20.710
FIM30.740
Website usability (WUS)WSU10.7400.8400.6300.4500.27
WSU20.790
WSU30.850
Website content (WCO)WCO10.8000.8600.6700.3300.16
WCO20.810
WCO30.850
Website content (WCO)PIN10.7200.8100.5900.2200.14
PIN20.820
PIN30.850
Online website loyalty (LOY)LOY10.9100.8800.7900.5200.18
LOY20.880
Table
Table 4. Discriminant validity: Criteria of Fornell and Larcker and HTMT rate.
Table 4. Discriminant validity: Criteria of Fornell and Larcker and HTMT rate.
ConstructWPEFIMWUSWCOPINLOY
WPE0.7500.7700.6300.4900.2600.150
FIM0.4200.6800.7900.8300.4900.290
WUS0.4200.6600.7900.3800.5000.150
WCO0.3400.5000.2900.8200.1700.250
PIN0.1400.2300.3300.1300.7700.530
LOY0.0600.150−0.0200.1600.3700.890
Table
Table 5. Direct effects, significance, and confirmation of hypotheses.
Table 5. Direct effects, significance, and confirmation of hypotheses.
HypothesisPath Coefficient (β)t-ValuepSupp.
H1: WEP → FIM0.42011.4600.000YES
H2a: FIM → WUS0.66029.9900.000YES
H2b: FIM → WCO0.50018.9300.000YES
H3a: WUS → PIN0.3206.1600.000YES
H3b: WUS → LOY−0.2104.0100.000NO
H4a: WCO → PIN0.0200.7800.440NO
H4b: WCO → LOY0.3375.6600.000YES
H5: PIN → LOY0.4203.7500.000YES
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Martínez-González, J.A.; Álvarez-Albelo, C.D. Influence of Site Personalization and First Impression on Young Consumers’ Loyalty to Tourism Websites. Sustainability 2021, 13, 1425. https://doi.org/10.3390/su13031425

AMA Style

Martínez-González JA, Álvarez-Albelo CD. Influence of Site Personalization and First Impression on Young Consumers’ Loyalty to Tourism Websites. Sustainability. 2021; 13(3):1425. https://doi.org/10.3390/su13031425

Chicago/Turabian Style

Martínez-González, José Alberto, and Carmen D. Álvarez-Albelo. 2021. "Influence of Site Personalization and First Impression on Young Consumers’ Loyalty to Tourism Websites" Sustainability 13, no. 3: 1425. https://doi.org/10.3390/su13031425

APA Style

Martínez-González, J. A., & Álvarez-Albelo, C. D. (2021). Influence of Site Personalization and First Impression on Young Consumers’ Loyalty to Tourism Websites. Sustainability, 13(3), 1425. https://doi.org/10.3390/su13031425

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop