Abstract
Purpose
The purpose of this paper is to determine the drivers of the formation of e-loyalty in a tourist destination, providing a model composed of variables that are under the control of the firm along with others that are not fully controllable by professionals.
Design/methodology/approach
The study was carried out with a sample of 497 subjects, university students and online consumers, and with the use of structural equations (partial least squares).
Findings
Results show that young people give a high valuation to all the variables used in the research. These results contribute to the literature on e-loyalty in tourism destinations and improve tourism loyalty in this population segment.
Research limitations/implications
The main limitation of this research has been related to the number of variables and measurement indicators that, according to the literature review, influence e-loyalty. Finally, a balanced and statistically significant model has been developed that has practical utility and analyzes online purchase of tourism products from a process perspective that includes variables that are internal and external to the firm.
Practical implications
The study suggests that young people have a favourable attitude and predisposition towards e-commerce, which, in turn, favours firms’ efforts to promote consumption and loyalty within the framework of the model’s variables.
Originality/value
This research paper has important value by analysing the initiating variables to determine how e-loyalty can be managed in tourist destinations, in addition to analysing an important segment for future tourism development.
Keywords
Citation
Parra-Lopez, E., Martínez-González, J.A. and Chinea-Martin, A. (2018), "Drivers of the formation of e-loyalty towards tourism destinations", European Journal of Management and Business Economics, Vol. 27 No. 1, pp. 66-82. https://doi.org/10.1108/EJMBE-11-2017-0050
Publisher
:Emerald Publishing Limited
Copyright © 2018, Eduardo Parra-Lopez, José Alberto Martínez-González and Angel Chinea-Martin
License
Published in the European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Tourism professionals and academics are increasingly interested in studying the process of e-loyalty formation[1]. This is especially relevant in the current context of tougher competition, greater consumer demands, higher costs of capturing new customers and, above all, the ease with which customers can change destinations at a mere “click” (Winnie, 2014; Wu and Hsu, 2015).
In addition, achieving customer e-loyalty is a strategic need and objective for tourism firms because it allows them to increase profitability and obtain competitive advantages over the long term (Sobihah et al., 2015). E-loyalty also favours the creation and maintenance of mutually beneficial relationships (Toufaily et al., 2013) and is considered an indicator of success in tourism marketing (Chen, Yen, Pornpriphet and Widjaja, 2015). However, in spite of efforts to achieve e-loyalty in tourism and the benefits it generates, its study in the tourism industry is recent and limited (Llach et al., 2013; Wu and Hsu, 2015). The few studies that have been carried out have centred almost exclusively on identifying the benefits loyalty offers to tourism firms (Elkhani et al., 2014).
In particular, within the literature on e-loyalty formation in destination tourism, there is a need to study certain segments of potential consumers, as is the case of young people who make up generation “Y”, a segment that has already been studied in some detail in the offline context (Nusair et al., 2013; Bilgihan, 2016). The segment is immersed in online activities (Bansal and Chen, 2011), use e-commerce (Jing et al., 2015) and have great potential to influence and consume tourism, leisure and sports products (Bilgihan, 2016). To delve deeper into this issue, this paper aims to study e-loyalty among young people in the context of destination tourism.
The paper presents a series of novelties. First, it contributes to the literature on the formation of e-loyalty in tourism within the youth segment and how this segment uses e-commerce. Second, we analyse the role of certain drivers over which tourist firms can exercise control (website design, quality of service), along with others whose control is relative (firm or website’s image and reputation). Thus, the proposed model includes initiating drivers or variables (scarcely controllable), intermediate ones (more controllable) as well as outcome or consequential variables.
Finally, the model stands out for its equanimity, which facilitates the operation by the tourist firms, and it is formed by latent variables that include items that, although they have homogeneity with respect to the content, in other works, have been studied in an isolated way. Both aspects have statistical significance according to the results obtained.
2. E-loyalty in tourism: conceptualization, drivers and hypotheses
E-loyalty definition
In the online context, the two predominant approaches in the literature on the conceptualization of offline loyalty are also accepted: the behavioural approach and the attitudinal approach (Belanche et al., 2012). In the behavioural approach, e-loyalty refers to the actual replication of a purchase on the web, or to a recommendation that is actually made (Yi and Jeon, 2003). According to the attitudinal approach, which is common in the literature, e-loyalty is conceived as a positive and future disposition by the consumer to make a new online purchase on the same website, or to recommend it to other consumers (Llach et al., 2013; Gonçalves et al., 2016). However, in previous studies (Winnie, 2014; Li et al., 2015), the recommendation component is less frequent in the online context than the repeat one.
Initiating drivers of e-loyalty in tourism
It is reasonable to assume that certain drivers or variables related to consumers and their socio-cultural context will influence how a website is perceived and thus affect their buying behaviour, satisfaction and loyalty (Kim et al., 2009). Likewise, the perception of a website used by a consumer is relevant, since it is the link between the subject and the product that a tourism firm sells (Afsar et al., 2013; Li et al., 2015).
Among the variables influencing the perception of a website are knowledge (Belanche et al., 2012), experience of the environment (Chen, Chang and Lee, 2015) and technological literacy, understood as proficiency at using technology and the internet (Chang and Chen, 2008a). Additionally, in the study of e-loyalty socio-cultural variables have displaced the variables related to the individual (Lee et al., 2009). Very important in this context is lifestyle, as well as attitudes, subjective norms or social pressure that lead consumers to develop certain habits regarding online shopping (Afsar et al., 2013). Thus:
Lifestyle, online shopping habits and technological literacy have a direct and positive influence on consumers’ perception of a website’s design.
The online reputation of a tourism firm is a variable that bears some relation to its image (De Maeyer, 2012). This variable is related to credibility, reliability and coherence and influences the value and service perceived by a tourist user (Ye et al., 2009). Reputation depends on the user’s own perceptions and experiences on the website, initially or at any point during the purchase process (Dijkmans et al., 2015). It is a critical variable in tourism due to the rapid generation and transmission of ratings and comments about the quality of a site, a firm or a service, among other aspects (Martínez et al., 2016). Therefore, the second hypothesis is stated as follows:
The reputation of a tourism firm as perceived by consumers and its image or its website’s image directly and positively influences the quality of the service and the value perceived by consumers.
In an online context, the user has more confidence in the information coming from the internet, especially transmitted through social networks, than that received through traditional communication channels (Li and Zhan, 2011). For this reason, opinions, assessments and general information shared on the internet can be considered to influence perceptions about the value and service offered, as well as influencing buying behaviour, satisfaction and loyalty (Gutiérrez et al., 2013). However, the information received through traditional means can still have some influence on this perception (Winnie, 2014). Taking into account the above, the following hypothesis states that:
The information and comments received by consumers through the website and other means directly and positively influence the service and the value perceived by consumers.
Intermediate drivers of e-loyalty in tourism
The website is the link between the consumer and the tourism products or services that firms sell or offer (Afsar et al., 2013). Consequently, certain aspects of website design may influence the perception of value and trust regarding some attributes of the products offered, such as the tangibility, variety or availability, satisfaction (Chen, Chang and Lee, 2015) and e-loyalty (Cyr et al., 2008), thus facilitating or hindering purchases (Afsar et al., 2013; Chen and Wang, 2016).
Therefore, the following hypothesis states that:
Website design influences in a direct and positive way the perception and evaluation that the consumer have of certain attributes of the tourism product.
Certain variables of tourism firms, such as the quality of service and the value perceived by consumers, also exert a significant influence on e-loyalty (Fuentes et al., 2010) and online satisfaction (Li et al., 2015). Regarding perceived value, which means the difference between what the consumer receives and what he brings, this is one of the variables of tourism firms that most influences e-loyalty (Chen and Wang, 2016; Jiang et al., 2016).
Regarding the quality of the online tourism service, consumers take this into account when making purchasing decisions (Llach et al., 2013). Although there is no agreement regarding the definition of service quality in an online tourist context (Barrera Barrera et al., 2014), the construct can be defined as the extent to which the site confirms the expectations of consumers by evaluating the difference between the service expected and service actually received in the online purchase of a product or service, in this case a tourist destination (Sobihah et al., 2015). In the online tourism context, the most widely accepted dimensions of service quality are those included in the e-SERVQUAL model, an adaptation of the SERVQUAL model to the online context (Elkhani et al., 2014; Li et al., 2015).
Bearing in mind the above on firms’ variables, the following hypothesisis stated as follows:
Consumers’ perceptions of the value and quality of the service directly and positively influence their perception of certain attributes of the product (realism, variety, availability).
The products constitute the nexus of the union between consumers and the results of the shopping experience, that is, their satisfaction and loyalty (Ziaullah et al., 2014). This is emphasised in tourism e-commerce, first, because of the insecurity and absence of real contact in e-commerce, and also due to the intangibility and lack of realism in the case of tourism products (Winnie, 2014).
As discussed, the products synthesise the direct and indirect effects of the above latent variables to generate consumer confidence (Kim et al., 2009), an important aspect for consumers in online shopping because transactions are more impersonal, anonymous and automated (Winnie, 2014). Therefore, the website must provide a certain tangible and realistic character to the products, as well as information on the quantity, variety and availability of products, and on the purchase process (i.e. price, promotions, offers and payment) (Gonçalves et al., 2016). It is for this reason that information about the product must generate in consumers the perception that their expectations will be fulfilled (Kim and Benbasat, 2003). Therefore, the following hypothesis states:
The perception about prices and certain attributes of the product has a direct and positive influence on consumers’ satisfaction.
Satisfaction as a mediator driver
In both offline and online contexts, customer satisfaction is the most researched factor in the literature because it is the variable that most influences the formation of e-loyalty (Anderson and Srinivasan, 2003; Wu and Hsu, 2015). Satisfied customers are more committed and willing to repeat the purchase on the same website, and to recommend it (Li et al., 2015; Chen and Wang, 2016). However, some authors claim that the relationship between satisfaction and loyalty is complex, as shown in some studies, because greater satisfaction is not necessarily accompanied by an increase in loyalty due to the reduced costs of destination change (Sobihah et al., 2015). Similarly, some authors have found that dissatisfied customers can remain loyal (Chang and Chen, 2008b).
Satisfaction can be defined in two ways; first, the satisfaction that refers to the psychological state derived from a customer’s pleasure or dislike after an online shopping experience, compared to the shopping experience in traditional stores (Cyr et al., 2008). Second, the satisfaction that refers to the perception of the degree to which a customer’s previous expectations are confirmed after an online shopping experience (Anderson and Srinivasan, 2003). Some studies consider satisfaction from a cumulative and global perspective (Yang et al., 2009) vs approaches that consider satisfaction in relation to the customer experience during the different phases of the purchase (Afsar et al., 2013). Finally, satisfaction is a multi-dimensional variable, since it has affective (Oliver, 1993a, b), hedonic (Jones et al., 2006) and relational components (Flavián et al., 2006).
Consequently, the seventh hypothesis states that:
Satisfaction in e-commerce has a direct and positive influence on e-loyalty.
Three hypotheses are added to analyse in greater depth the relationships between variables of e-loyalty. First, because the generational cohorts have different values, preferences and buying behaviours, it is an important objective for tourism firms to understand these preferences and generational differences in the young segment. It allows to promote and to offer more adapted products and services regarding e-loyalty and the variables that determine them (Bilgihan, 2016). However, the online behaviour of this “online generation” is still poorly studied (Nusair et al., 2011, 2013; Martínez, 2014).
Taking into account the above about and the results of previous studies (Martínez, 2014; Martínez et al., 2016), the following hypothesis states that:
Young people attach high importance and value to all the variables included in this study.
Finally, it is important for tourist firms to determine, within the same generational segment, if there are gender differences in the formation of e-loyalty, as this will affect whether integrated management can be carried out or if such management has to be differential (Martínez, 2014). It is also based on the premise that generations as a whole are influenced by similar socio-cultural factors and have homogeneous cognitive, affective and behavioural patterns, both in the offline (Charters et al., 2011) and online contexts (Gurtner and Soyez, 2016). Thus, the following two hypotheses are established:
There are no significant differences due to gender in the responses that young people give to the items, that is, to the observed variables and latent variables.
There are no significant gender differences in the causal relationships of the proposed structural model.
The e-loyalty formation model proposed in this paper is shown in Figure 1.
3. Methodology
Method, sample and information sources
Together with the analysis of structural equations (partial least squares (PLS)), descriptive analysis and discriminant in the context of e-loyalty have been used in this work. The PLS method has been chosen because it is rigorous and reflects the theoretical and empirical conditions of the social sciences in which the theories are not sufficiently established and the available information is scarce (Cepeda and Roldán, 2004).
The study was carried out using a sample of 497 subjects (45 per cent men, 55 per cent women). The size of the sample is in accordance with the “ten-fold rule” (Chin, 1998b; Hair et al., 2014) when using the PLS method, and with studies on the perceptions of young people in tourism (Jaafar et al., 2015) and online contexts (Bilgihan, 2016). Regarding age, 98 per cent of the sample was between 18 and 22 years old.
The entire sample consisted of students from different degrees and years at the University of the La Laguna[2]. This segment is an adequate representation of virtual consumers by age, which brings generational character to the study, and because they have a higher level of education than the general public (Gurtner and Soyez, 2016) (Table I).
An ad-hoc designed questionnaire was used as an instrument for collecting data (Hsu et al., 2006). It should be noted that the scales designed to measure loyalty in e-commerce tourism are based on existing measures in the offline context.
To design the scale, we first worked with two experts to identify the most appropriate variables, relationships and measures for the proposed model, thus guaranteeing the validity of content (Roy et al., 2001). It was taken into account that in the recent literature, the variables included in this study can be measured by a small number of items, thus avoiding the methodological problems and costs derived from the use of multiple indicators (Bergkvist and Rossiter, 2007). Next, the Delphi technique (Chan et al., 2001) was used to construct the definitive relations between items.
A Likert scale of 20 items was obtained with 5 response alternatives. Also included in the scale were two additional items: one related to subjects’ gender and the other related to the degree of tourism consumption (the tourist destination) using the web, social networks or mobile telephony.
Dependent and independent drivers or variables
The dependent variable, e-loyalty (LO), is measured by two items: repetition of the online purchase on the same website or the recommendation of the site to other people (Allagui and Temessek, 2004; Toufaily et al., 2013). The literature shows that the recommendation component is less frequent in the online context than the repetition one (Winnie, 2014; Li et al., 2015).
The independent latent variable relative to the subject (SU) has been measured with 3 items based on the literature (Yoo et al., 2012). The website design dimension (DE) includes three items following Yi et al. (2006). Image and reputation (RI) constitute the dimension of the firm and have been measured with two items as explained by Toufaily et al. (2013). Regarding information (IN), two items have been used from the work of Goyette et al. (2010). In relation to the dimension of the firm (FI), the perceived value and the quality of the service were measured with three items, following the works of Parasuraman et al. (2005) and Zehir et al. (2014). Satisfaction (SA) has been measured with two items following Elkhani et al. (2014) and Tseng (2017). Finally, for the design of the items related to the product dimension (PR), attributes considered in this paper and contributions of Ziaullah et al. (2014) have been taken into account regarding the influence of product quality on online satisfaction and loyalty.
4. Results
Results of descriptive analysis
As can be seen in Table II and in relation to H8, young people state they purchase a moderate number of trips to tourist destinations in the online mode, firstly highlighting purchases through websites, followed by the ones made with mobile phones and, finally, social networks. This reduced or moderate consumption may be due to the young age of the sample, as well as the importance they give to security (Table III, item DE2: security 72.55 per cent), suggesting in any case the potential of future purchases. Additionally, the preference for the purchases using mobile telephony rather than social networks, although reduced, denotes the importance of the former for this population segment.
The data in Table III state that the levels of all variables are average/high, since none of the 20 items scored below 50 per cent of the maximum possible value (497×5=2,485). More than 50 per cent of the items obtained more than 70 per cent of the maximum possible score (2,485), with items related to the subject having the lowest score, but above 60 per cent. Consequently, H8 is confirmed.
Results of causal analysis
Firstly, an exploratory factorial analysis with varimax rotation was carried out using the principal components method (Anderson and Gerbing, 1988). An eight-factor structure was obtained (see Table IV). This factor structure was accepted because the variables that make up the factors have a high correlation with each other (higher than 0.70) and a reduced correlation with other variables (Worthington and Whittaker, 2006, p. 821).
The PLS analysis studied the reliability and validity of the relationships between the observed variables (items) and the latent variables with which they are associated. It was shown that the observed variables reached the required minimum level (λ⩾0.70) (Table IV), thus confirming that the indicators were part of their corresponding constructs. In addition, the composite reliability (CR) study showed that because all values were above 0.70, the measurement model was internally consistent and all the indicators or variables observed were measuring their corresponding latent variable (Hair et al., 2014).
Convergent validity and discriminant validity were also analysed (Fornell and Larcker, 1981). To test the former, average variance extracted (AVE) was used and in all cases the result was higher than 0.50, so it was found that more than 50 per cent of the variance of the construct was due to its indicators (Chin, 2010) (Table IV). In the latter, the square root of AVE (on the diagonal of Table V) was found to be greater than the shared variance between the construct and the other constructs of the model.
Additionally, the matrix of cross-factor loadings (Chin, 1998a) was obtained (Table VI). The indicators were more correlated with their own construct than with the others, showing that the measurement model has an acceptable convergent and discriminant reliability and validity.
Regarding the evaluation of the causal model, it was verified that the exogenous latent variables contributed to explaining the variance of the e-loyalty variable significantly, since the path coefficients (β) reached levels above the acceptable minimum level (β⩾0.2), and even at the optimal level (β⩾0.3) (Table VII).
The highest paths associated the subject (SU) with the perception of the site design (DE) (β=0.440), the firm (FI) with the product (PR) (β=0.434) and, especially, the satisfaction (SA) with loyalty (LO) (β=0.882). In contrast, the lowest causal relationships were those that linked the latent design variable (DE) with the product (PR) (β=0.281) and the product (PR) with satisfaction (SA) (β=0.286).
In addition, in all direct causal relationships, the t-statistic obtained levels that verified their high significance (P<0.01), as evidenced in the bootstrapping analysis carried out with 500 sub-samples and 200 cases. Consequently, all hypotheses relating to the proposed causal model are confirmed.
In the study of the structural model, three additional indicators were calculated (Table VIII): R2, ranging from 0 to 1; Q2, developed by Stone (1974) and Geisser (1975) to measure the predictive relevance of the dependent constructs; and the goodness-of-fit (GoF) test, which also ranges from 0 to 1.
It was found that the previous latent variables explained sufficient variance of the consequent variables, since the basic indicator R2 reached in all cases values above the acceptable minimum level (R2⩾0.19). On the other hand, values above 0 of the indicator Q2 (Q2⩾0) verified the predictive relevance of the model. Finally, a GoF value of 0.507 was obtained, which is higher than the minimum acceptable value (GoF⩾0.360) (Table VIII). Therefore, in addition to confirming hypotheses concerning causal relations (-H7), it can be said that the model has predictive potential.
Result of discriminant analysis
To test H9, a discriminant analysis was carried out. The results (Table IX) allow us to confirm that there are some significant differences in the responses directly provided by young people for reasons of gender, as evidenced by the levels of self-value (SV) and canonical correlation (CC), and because the level of the Wilks’ Lambda indicator (WL), which moves away from 1. In addition, the significance is high (p⩽0.005).
Based on the above data, it was found that men, unlike women, state that they are accustomed to buying online (SU3), value the perceived reputation of the firm or the site (RI1) and, to a lesser extent, are willing to repeat the purchase (LO2). Women tend to value more than men, the ease and speed of the site (DE3), the information they write and share other people (IN1) and the real and tangible character of the product (PR2). Consequently, H9 is not confirmed, since there are differences in the responses between sexes, although these differences are not excessively significant (Table X).
With respect to H10 regarding gender differences, a multi-group analysis was performed using the analytical method called PLS-MGA (Hair et al., 2014). The results obtained using 5,000 cases and significance level of 0.05 are shown in Table XI.
Taking into account that the path differences, with p⩽0.05 and p⩾0.95, are considered significant, H10 is not confirmed, since most of the causal relationships between latent variables (path) are significantly different between men and women (Table XII).
5. Discussion and conclusions
This paper is a new contribution to the online context of tourism destinations providing a better understanding, both theoretically and practically, of the process of forming e-loyalty among young people, responding with it to the needs and concerns of other authors (Gonçalves et al., 2016; Winnie, 2014; Bilgihan, 2016).
It is concluded that e-commerce is the prevailing form of commerce whether carried out through websites, social or mobile phone networks, therefore tourism firms must continue to strive to develop online loyalty among their customers (Elkhani et al., 2014; Winnie, 2014).
Additionally, researchers and practitioners should research in greater depth the variables that affect e-loyalty in tourism. This is due to the benefits of e-loyalty, the increasing competition of the online market and the greater demands of tourist users (Sobihah et al., 2015; Wu and Hsu, 2015). There are several factors to be considered that are specific to online purchases of tourism products such as the inexistence of face-to-face interactions in e-commerce, as well as the insecurity, complex and intangible nature of the sector itself, and of the products and services that are marketed (Martínez et al., 2016).
Thus, tourism firms must properly manage the variables involved in the formation of e-loyalty, bearing in mind that they do not have full control over some of them. Such management should be carried out on the premise that in the online context, young people constitute the segment of the least loyal consumers, but with the highest potential consumption and influence. In addition, this segment is highly predisposed to carrying out leisure and tourism activities, using e-commerce and regularly using the internet, social networks and mobile phones (Jing et al., 2015).
In this paper, a causal model has been generated that is clear and largely approachable at a practical level by tourism managers, as far as most of the controllable variables are concerned. This model has generated three large groups of variables that must be adequately managed by firms (see Figure 2), which include external, internal and output variables.
The “external” variables are that that facilitate or precede loyalty and are characterised by initiating the chain of effects that leads to e-loyalty and to which tourism managers must pay special strategic attention. Thus, to orientate themselves to the online market and the digital consumer, tourism firms should take into account the users when designing their websites and, therefore, integrate information technology professionals with marketing and tourism professionals.
Likewise, tourism firms should, as far as possible, attend to all actions and messages that online tourist users send to and receive from firms and their websites, which could negatively affect the reputation and image as these have an influence on the value and quality of the service perceived by consumers (Çoban, 2012). Moreover, it should be ensured that messages and ratings by other users, on the site or on others, are the most appropriate owing to their influence on young people’s perceptions about the value and quality of service as demonstrated.
As for the “internal” variables, these have greater influence on satisfaction and loyalty as reflected by Chen and Wang (2016). The internal variables that most require attention when targeting the youth segment are: website design, value and quality of service offered, as well as information and presentation of products. Likewise, it is recommended that efforts be made to generate user-friendly websites to generate positive perceptions regarding the tangibility and realism of products, the availability of products and the value of promotions (Li et al., 2015). To the extent that consumers perceive high value and effective and quality service, tourism products will also be perceived positively (Chen, Chang and Lee, 2015), and it will be more feasible to meet the expectations of young people and form loyalty (Jiang et al., 2016).
Tourism firms, following the above, should continue to investigate the perceptions and expectations of their customers so as to satisfy them and only in this way, will online tourist users be satisfied, repeat the purchase on the same site or recommend it to other consumers. From the results of the descriptive analysis, it is concluded that young people make tourism purchases online in a moderate way, highlighting in first place purchases through the web, followed by mobile phones and, lastly, through social networks. This may be due to the limited resources of young people, or because, due to the age of the segment studied, their online experience is still scarce. However, these statements by young people about their consumption neither contradict their potential consumption and influence (Nusair et al., 2011, 2013), nor the characteristics conducive to e-commerce that this segment has (Gurtner and Soyez, 2016).
Furthermore, it is useful for firms to know and take into account that, as well as the causal relationships of the proposed model, young people’s assessments about the variables analysed in this study oscillate around 70 per cent, including satisfaction and loyalty. This suggests that young people have a favourable attitude and predisposition towards e-commerce, which, in turn, favours firms’ efforts to promote consumption and loyalty within the framework of the model’s variables.
Although studies on the formation of offline loyalty in tourism have demonstrated there are not large differences between men and women in a sample similar to that of this study (Martínez, 2014; Martínez et al., 2016), in this online context, there are some gender differences in the responses given by young people, although these differences are not very significant. These differences suggest a pattern of online shopping behaviour related to satisfaction and e-loyalty that is more related to the search for security by women and a greater fit to their own lifestyles for men. Given that there are also some gender differences in the causal relationships of the proposed model, firms should study and understand these differences in more detail to assess the possibility of conducting differential online actions directed at men and women, in order to promote online shopping behaviour, satisfaction and loyalty of the segment.
Limitations and future lines of research
The main limitation of this study has been related to the number of variables and measurement indicators that, according to the literature, influence e-loyalty. Therefore, as a future line of research, we suggest studies that include other variables in the formation of e-loyalty.
In relation to the age segments, it might be interesting to compare the formation of e-loyalty between different generational segments. Likewise, it would be of interest to analyse in more depth what the gender differences found in direct responses and in causal relations may be due to. Additionally, it would be interesting to conduct a study of online loyalty by differentiating whether the purchasing process is carried out on social networks, mobile telephony or websites.
Figures
Variables related to quality website design
| Variable | Facilitates | Authors |
|---|---|---|
| Utility, ease and speed | Improved performance | Wu and Hsu (2015) |
| Interactivity | Reciprocal communication | Cyr et al. |
| Participation | Co-create value | Chen and Wang (2016) |
| Personalisation | Receive services and register preferences | Winnie (2014) |
| Aesthetics | Capture attention and experience | Yang et al. (2009) |
| Security reliability | Privacy, confidentiality | Jiang et al. (2016) |
| Positive experience | States of flow | Bilgihan (2016) |
| Brakes to change | Information, discounts | Kim et al. |
Source: Own elaboration from the literature
Sample details
| Degree year/gender | Men | Women | Total (%) |
|---|---|---|---|
| 1st | 82 | 105 | 187 |
| 2nd | 63 | 77 | 140 |
| 3rd | 45 | 51 | 96 |
| 4th | 34 | 40 | 74 |
| Total (%) | 224 (45%) | 273 (55%) | 497 (100%) |
Source: Own elaboration
Online consumption data
| Method | Min. | Max. | Sum | % |
|---|---|---|---|---|
| Using the web | 1 | 5 | 1,480 | 59.56 |
| Using social networks | 1 | 5 | 832 | 33.48 |
| Using mobile phones | 1 | 5 | 1,079 | 43.42 |
Notes: n=497. The percentage refers to the maximum value that the item would have reached if the entire sample had given the item the highest score (5)
Source: Own elaboration
Descriptive statistics
| Latent variable | Min. | Max. | Sum | % | Average | DT | |
|---|---|---|---|---|---|---|---|
| Subject (SU) | SU1 | 1 | 5 | 1,526 | 61.41 | 3.07 | 1.05 |
| SU2 | 1 | 5 | 1,729 | 69.58 | 3.48 | 1.09 | |
| SU3 | 1 | 5 | 1,676 | 67.44 | 3.37 | 1.01 | |
| Design (DE) | DE1 | 1 | 5 | 1,484 | 59.72 | 2.99 | 0.88 |
| DE2 | 1 | 5 | 1,803 | 72.55 | 3.63 | 1.01 | |
| DE3 | 2 | 5 | 1,961 | 78.91 | 3.95 | 0.87 | |
| Reputation and image (RI) | RI1 | 2 | 5 | 1,905 | 76.66 | 3.83 | 0.87 |
| RI2 | 1 | 5 | 1,927 | 77.55 | 3.88 | 0.92 | |
| Information (IN) | IN1 | 1 | 5 | 1,555 | 62.58 | 3.13 | 1.08 |
| IN2 | 1 | 5 | 1,799 | 72.39 | 3.62 | 0.96 | |
| Firm (FI) | FI1 | 1 | 5 | 2,091 | 84.14 | 4.21 | 1.03 |
| FI2 | 1 | 5 | 1,935 | 77.87 | 3.89 | 0.91 | |
| FI3 | 1 | 5 | 1,995 | 80.28 | 4.01 | 0.98 | |
| Product (PR) | PR1 | 1 | 5 | 1,973 | 79.40 | 3.97 | 0.97 |
| PR2 | 1 | 5 | 1,935 | 77.87 | 3.89 | 0.96 | |
| PR3 | 1 | 5 | 1,955 | 78.67 | 3.93 | 0.97 | |
| Satisfaction (SA) | SA1 | 1 | 5 | 1,644 | 66.16 | 3.31 | 1.20 |
| SA2 | 1 | 5 | 1,700 | 68.41 | 3.42 | 1.17 | |
| Loyalty (LO) | LO1 | 1 | 5 | 1,708 | 68.73 | 3.44 | 1.14 |
| LO2 | 1 | 5 | 1,758 | 70.74 | 3.54 | 1.19 |
Source: Own elaboration
Measurement model: basic data
| Latent variable | Items | Loading λ | CR | AVE |
|---|---|---|---|---|
| Subject (SU) | SU1: computer and website knowledge | 0.710 | 0.762 | 0.517 |
| SU2: the website fits in with my lifestyle | 0.708 | |||
| SU3: be accustomed to buying online on the site | 0.739 | |||
| Design (DE) | DE1: make the website interactive and allow participation | 0.724 | 0.797 | 0.567 |
| DE2: security and reliability of website | 0.756 | |||
| DE3: website is quick and easy to use | 0.778 | |||
| Reputation and image (RI) | RI1: perceived reputation of the firm | 0.820 | 0.813 | 0.685 |
| RI2: image I have of the firm and website | 0.835 | |||
| Information (IN) | IN1: information from other people about the website | 0.709 | 0.772 | 0.632 |
| IN2: information received by other means | 0.872 | |||
| Firm (FI) | FI1: on the website I receive more than I give | 0.806 | 0.848 | 0.651 |
| FI2: the firm provides a good service through the website | 0.764 | |||
| FI3: the firm complies effectively as promised on the site | 0.849 | |||
| Product (PR) | PR1: offers and promotions on certain products | 0.710 | 0.815 | 0.596 |
| PR2: real and tangible character of products | 0.831 | |||
| PR3: variety and availability of products | 0.770 | |||
| Satisfaction(SA) | SA1: my expectations have been met | 0.945 | 0.946 | 0.898 |
| SA2: I have perceived and felt satisfaction | 0.950 | |||
| Loyalty (LO) | LO1: I would recommend the site to other people | 0.944 | 0.931 | 0.872 |
| LO2: I would repeat the purchase on the same site | 0.923 |
Source: Fuente: own elaboration
Disciminant validity: Fornell Larcker criteria
| SU | DE | RI | IN | FI | PR | SA | LO | |
|---|---|---|---|---|---|---|---|---|
| SU | 0.719 | |||||||
| DE | 0.440 | 0.753 | ||||||
| RI | 0.133 | 0.068 | 0.828 | |||||
| IN | 0.329 | 0.250 | 0.334 | 0.795 | ||||
| FI | 0.326 | 0.374 | 0.425 | 0.425 | 0.807 | |||
| PR | 0.236 | 0.443 | 0.290 | 0.312 | 0.539 | 0.772 | ||
| SA | 0.209 | 0.115 | 0.142 | 0.122 | 0.188 | 0.286 | 0.948 | |
| LO | 0.180 | 0.188 | 0.099 | 0.093 | 0.081 | 0.248 | 0.882 | 0.934 |
Source: Own elaboration
Cross-factor loadings
| Latent variable | SU | DE | RI | IN | FI | PR | SA | LO | |
|---|---|---|---|---|---|---|---|---|---|
| Subject (SU) | SU1 | 0.710 | 0.221 | 0.157 | 0.149 | 0.196 | 0.008 | 0.212 | 0.234 |
| SU2 | 0.708 | 0.299 | 0.288 | 0.473 | 0.406 | 0.348 | 0.097 | 0.110 | |
| SU3 | 0.739 | 0.387 | −0.088 | 0.105 | 0.125 | 0.126 | 0.158 | 0.085 | |
| Design (DE) | DE1 | 0.348 | 0.724 | 0.103 | 0.135 | 0.166 | 0.313 | −0.018 | 0.060 |
| DE2 | 0.262 | 0.756 | 0.019 | 0.249 | 0.302 | 0.391 | 0.156 | 0.213 | |
| DE3 | 0.381 | 0.778 | 0.032 | 0.182 | 0.374 | 0.298 | 0.122 | 0.152 | |
| Reputation and image (RI) | RI1 | 0.112 | 0.081 | 0.835 | 0.321 | 0.358 | 0.226 | 0.111 | 0.095 |
| RI2 | 0.108 | 0.030 | 0.820 | 0.230 | 0.345 | 0.255 | 0.124 | 0.070 | |
| Information (IN) | IN1 | 0.310 | 0.119 | 0.203 | 0.709 | 0.272 | 0.211 | 0.105 | 0.112 |
| IN2 | 0.234 | 0.258 | 0.315 | 0.872 | 0.391 | 0.280 | 0.093 | 0.049 | |
| Firm (FI) | FI1 | 0.168 | 0.213 | 0.286 | 0.253 | 0.806 | 0.400 | 0.106 | 0.011 |
| FI2 | 0.254 | 0.380 | 0.326 | 0.300 | 0.764 | 0.478 | 0.065 | 0.007 | |
| FI3 | 0.344 | 0.299 | 0.402 | 0.453 | 0.849 | 0.424 | 0.266 | 0.162 | |
| Product (PR) | PR1 | 0.121 | 0.312 | 0.261 | 0.195 | 0.324 | 0.710 | 0.189 | 0.090 |
| PR2 | 0.280 | 0.376 | 0.274 | 0.303 | 0.521 | 0.831 | 0.268 | 0.260 | |
| PR3 | 0.114 | 0.334 | 0.131 | 0.208 | 0.372 | 0.770 | 0.194 | 0.198 | |
| Satisfaction (SA) | SA1 | 0.205 | 0.058 | 0.120 | 0.118 | 0.195 | 0.260 | 0.945 | 0.815 |
| SA2 | 0.192 | 0.157 | 0.148 | 0.113 | 0.162 | 0.282 | 0.950 | 0.857 | |
| Loyalty (LO) | LO1 | 0.247 | 0.178 | 0.137 | 0.100 | 0.095 | 0.245 | 0.881 | 0.944 |
| LO2 | 0.076 | 0.173 | 0.042 | 0.072 | 0.054 | 0.216 | 0.758 | 0.923 |
Source: Own elaboration
Effects, significance and confirmation of hypotheses
| Latent variable | Path (β) | T | p-values | CH |
|---|---|---|---|---|
| H1 Subject (SU) →Design (DE) | 0.440 | 13.205 | 0.000 | YES |
| H2 Reputation and image (RI)→Firm (FI) | 0.318 | 7.169 | 0.000 | YES |
| H3 Information (IN)→Firm (FI) | 0.319 | 10.048 | 0.000 | YES |
| H4 Design (DE)→Product (PR) | 0.281 | 6.483 | 0.000 | YES |
| H5 Firm (FI)→Product (PR) | 0.434 | 11.128 | 0.000 | YES |
| H6 Product (PR)→Satisfaction (SA) | 0.286 | 5.789 | 0.000 | YES |
| H7 Satisfaction (SA)→Loyalty (LO) | 0.882 | 85.225 | 0.000 | YES |
Source: Own elaboration
R2. Q2 and GoF test indicators
| Construct | R2 | AVE | Q2a |
|---|---|---|---|
| Design (DE) | 0.194 | 0.567 | 0.107 |
| Firm (FI) | 0.271 | 0.651 | 0.170 |
| Product (PR) | 0.359 | 0.596 | 0.205 |
| Satisfaction (SA) | 0.192 | 0.898 | 0.151 |
| Loyalty (LE) | 0.779 | 0.872 | 0.670 |
| Average | 0.359 | 0.717 | − |
| GoF | 0.507 | ||
Note: aThis test is a measurement of the degree to which the observed values are reproduced by the model and by its estimated parameters
Discriminant analysis, basic data
| Results by sex | Centroids | ||||
|---|---|---|---|---|---|
| AV | CC | LW | Sig. | H | M |
| 0.336 | 0.502 | 0.748 | 0.000 | 0.649 | −0.516 |
Source: Own elaboration
Discriminant analysis, standardised coefficients
| Items | SC | Items | SC |
|---|---|---|---|
| SU1 | 0.280 | FI1 | 0.143 |
| SU2 | 0.236 | FI2 | 0.168 |
| SU3 | 0.557 | FI3 | −0.183 |
| DE1 | 0.236 | PR1 | −0.260 |
| DE2 | 0.087 | PR2 | −0.723 |
| DE3 | −0.464 | PR3 | 0.000 |
| RI1 | 0.558 | SA1 | 0.080 |
| RI2 | 0.221 | SA2 | 0.112 |
| IN1 | −0.438 | LO1 | −0.237 |
| IN2 | 0.166 | LO2 | 0.365 |
Source: Own elaboration
PLS-MGA multi-group analysis (men and women)
| Latent variable | Dif. path (β) (H-M) | p-values |
|---|---|---|
| H1 Subject (SU) →Design (DE) | 0.263 | 1.000 |
| H2 Reputation and image (RI)→Firm (FI) | 0.141 | 0.966 |
| H3 Information (IN)→Firm (FM) | 0.233 | 0.000 |
| H4 Design (DE)→Product (PR) | 0.198 | 0.016 |
| H5 Firm (FI)→Product (PR) | 0.013 | 0.582 |
| H6 Product (PR)→Satisfaction (SA) | 0.302 | 0.002 |
| H7 Satisfaction (SA)→Loyalty (LE) | 0.049 | 0.008 |
Source: Own elaboration
Notes
In this paper, the term e-loyalty is used. Consumer e-loyalty is studied in a global framework, i.e. considering consumer buying behaviour regardless of whether through websites, social networks or mobile phones. The “website” is the specific link that the consumer is using at a given time.
It has been verified that, regardless of studies or academic year, there are no significant differences in the responses of young people of the same age as shown by Martínez (2014) using subjects and variables similar to those included in this study.
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