Master Thesis
MSc. Marketing, Marketing Management
Faculty of Economics and Business
Department of Marketing
The role of knowledge, privacy concerns, and e-trust to
create loyalty in the online travel market: A moderated
mediation model
June 2020
By Erika Christodoulou
S4042271
Dimostheni Voutira, 17
3066, Limassol, Cyprus
e.christodoulou.1@student.rug.nl
+31622179576
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The role of knowledge, privacy concerns, and e-trust to
create loyalty in the online travel market: A moderated
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Abstract:
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Table of Contents:
1. Introduction ...6
2. Theoretical framework ...8
2.1 Loyalty in the online travel market ... 8
2.2 Trust and loyalty in the online travel market ... 9
2.3 Knowledge of e-commerce and establishing loyalty in the online travel market; The mediating role of e-trust ... 10
2.4 Privacy concerns and loyalty in the online travel market... 12
2.5 A privacy paradox: The moderating effect of privacy concerns on the e-trust –loyalty relationship ... 13
2.6 Conceptual model ... 14
3. Methodology ... 14
3.1 Questionnaire and Procedure ... 14
3.2 Measures ... 16
3.3 Pilot test and content validity... 17
3.4 Sampling and questionnaire administration ... 17
4. Data analysis ... 18
4.1 Data Cleaning ... 18
4.2 Socio-demographics ... 18
4.3 Analysis ... 19
5. Results ... 20
5.1 Checking reliability and validity of knowledge, e-trust, privacy concerns, and e-loyalty ... 20
5.2 Assumptions check ... 23
5.3 Regression analysis for knowledge of commerce, trust and privacy concerns on e-loyalty ... 24
5.4 E-trust as the mediator between knowledge of e-commerce and e-loyalty ... 24
5.5 Privacy concerns as the moderator between e-trust and e-loyalty ... 26
6. Discussion and Conclusion ... 27
6.1 Conclusions ... 27
6.2 Managerial Implications ... 29
6.3 Limitations and recommendations for further research ... 31
7. References ... 32
8. Appendices ... 40
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8.2 Appendix B: Constructs and references of the questionnaire ... 46
8.3 Appendix C: Socio-demographics... 48
8.4 Appendix D: Collinearity check ... 49
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1. Introduction
During the past years, people have turned their attention into online shopping, which offers convenience and the opportunity to compare products’ prices and characteristics (Lee, Denizci & Law, 2013). 4.54 billion people around the world choose to purchase their products online (Statista, 2020). One of the most popular online industries is the travel and tourism market. Travel websites like Booking.com and Expedia.com belong to the top-visited websites globally (SimilarWeb, 2020). Forecasts show that the online travel market will grow significantly from $570.25 billion for 2017 to $1,134.55 billion by 2023 (MarketWatch, 2020). Therefore, online travel shopping is an essential market in the online world.
The continuous growth of the online travel business has resulted in increased competition. Due to the alternative attractiveness, customers can easily switch from one e-retailer to the other (Singh & Rosengren, 2020), diminishing in this way, the likelihood of commitment to only one. To encounter this competition, developing customer e-loyalty is essential. E-loyalty can be expressed in multiple ways such as creating referrals, willingness to pay more, or repurchasing intention on the same websites (Gruen, Osmonbekov & Czaplewski, 2006; Srinivasan, Anderson & Ponnavolu, 2002; Hu, Kandampully & Juwaheer, 2009; Kim, Jin & Swinney, 2009). Companies usually focus on attracting more customers while there is not much effort on maintaining the existing customers, which is the crucial element to create loyalty and strong relations with them (Reichheld & Schefter, 2000). For an e-retailer, owning a loyal customer's portfolio can lead to profitability and sustainable competitive advantage (Carayannis et al., 2017; O’Connor & Kelly, 2017; Teo, 2005). The present study aims to examine the factors that affect customers’ loyalty in the online travel industry.
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Knowledge of e-commerce refers to the extent to which an individual holds the adequate experience and skills to use the Internet in general, and electronic commerce in specific (e.g., Baek, 2014; Hargittai & Marwick, 2016). Knowledge in this study refers to a broad concept, including the customers’ knowledge concerning general skills on technological aspects for using the Internet (e.g., Baek, 2014; Holloway, Wang, & Parish, 2005). Also, knowledge entails the experience of an individual in following specific strategies to make purchases online, such as using incognito pages or encryption of e-mails (Baek, 2014; Suh & Han, 2003). Prior research illustrated that the more knowledge customers have in purchasing online, the more likely they are in developing trustworthiness (Bart et al., 2005; Yoon, 2002). Furthermore, previous research supported that knowledge on where to browse and also on technology issues can affect purchase intention in e-commerce and loyalty (Baek, 2014). The current study aims to investigate the extent to which knowledge of e-commerce can lead to e-loyalty through the substantial use of e-trust in the online travel industry.
A fundamental factor that creates negative purchase intention is privacy concerns (Lwin, Wirtz & Williams, 2007). When privacy is essential for an individual, it can affect the intention to not only purchase online, but mostly, to become loyal to a specific e-retailer (Cui, Lin & Qu, 2018; Hu et al., 2009; Kim, Jin, & Swinney, 2009). The more influential customers’ concern about their privacy, the less likely they are to become loyalists (Alfonzan et al., 2020). However, the literature suggests that privacy concerns are for some customers strong and for others non-existent, referring to the “privacy paradox” (Gerber, Gerber & Volkamer, 2018). Hence, it is vital to understand how privacy concerns set boundary conditions for the relationship between e-trust and e-loyalty.
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Furthermore, investigating the moderator role of privacy concerns in the relationship between trust and loyalty in the online travel market will help to understand when privacy concerns become less important, that is, the boundary condition under which e-trust impacts e-loyalty most. The knowledge of this research can provide online travel companies with valuable information on how to increase their portfolio of loyal customers leading in establishing a competitive advantage in their businesses. In conclusion, the research aims to answer the following research questions: RQ1: To what extent does e-trust mediates the knowledge of e-commerce and e-loyalty relationship? RQ2: To what extent do privacy concerns moderate the relation between e-trust and e-loyalty?
2. Theoretical framework
2.1 Loyalty in the online travel market
Each company in the online market targets on developing loyal relationships with its customers as e-loyalty is strongly correlated with revenues (Carayannis et al., 2017; O’Connor & Kelly, 2017; Teo, 2005) and can be the baseline to grow and establish a company in the market. Typically, loyal customers are translated into people who spend a significant amount of money on a specific company, and therefore support their profitability in the long run (Lacey & Morgan, 2007). When purchasing online, customers may face significant drawbacks, such as “information asymmetries” (Alfonzan et al., 2020: 10). Besides, when being online, customers are not able to interact directly with the retailers, which makes loyalty a big hurdle (Luarn & Lin, 2003). McCall and McMahon (2016: 112), highlighted the importance of e-loyalty and argued that “loyal customers offer businesses a steady customer base, more frequent purchase cycles, higher profit margins, and advocates who volitionally market the firm to prospective customers.” Thus, the challenge for e-companies is to understand the factors resulting in e-loyalty to follow specific strategies that will result in corporate success.
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to revisit a specific website over the time. In the past, customer loyalty has been explored from both sides (Oppermann, 2000). However, when investigating the travel business, it is a common practice to study the attitudinal perspective (Yoon & Uysal, 2005). As a result, this study includes three attitudinal sub-dimensions that express e-loyalty: create referrals (Gruen et al., 2006), willingness to pay more for services (Srinivasan et al., 2002) and repurchase intention (Hu et al., 2009; Kim et al., 2009). Each of these sub-dimensions will be explained briefly, showing why they are considered essential parts of “true” e-loyalty.
Firstly, when customers are loyal, they are more likely to recommend a company. Referral intention is the probability of a customer to recommend a company to others (Mukherjee & Nath, 2007; Yoon & Uysal, 2005). Interestingly, customer referrals, also known as word of mouth (WOM), show an individual’s commitment towards a company (Tran & Strutton, 2020). Once customers are satisfied with their experience and become loyalists with an e-retailer, they are more likely to share their experiences with others and recommend the relevant company (Reichheld & Schefter, 2000). Secondly, loyalists are willing to pay more (WTP) for services and are more likely to “upgrade” for a product or service that they already know (Jones & Taylor, 2007), thereby making it an essential sub-dimension of “true” loyalty as well. Thirdly, repurchase intention (RI) is described as an individual’s judgment about choosing the same company, considering his/her current situation and conditions (Lacey & Morgan, 2007). Oliver (1999) argued that loyalty relates to the commitment of an individual towards a specific product or service, which leads to repeat purchases. This commitment holds when the customer is exposed to more attractive alternatives by competitors, leading to resistance of switching.
It is essential to understand how e-loyalty is expressed through word of mouth, willingness to pay, and repurchase intention, but it is also essential to understand how e-loyalty is formed. Therefore, this study focuses on analyzing the predictors of e-loyalty in the online travel market. Specifically, it uses e-trust, knowledge of e-commerce, and privacy concerns. The ways the relations will be tested on predicting e-loyalty will be analyzed in the upcoming sections.
2.2 Trust and loyalty in the online travel market
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Milne, 2014), and it is related to online loyalty (Tran & Strutton, 2020). Moreover, the absence of e-trust might create resistance in purchasing online (Gefen & Straub, 2004; Pavlou, 2003), thereby confirming its relationship to online loyalty.
Establishing trust in the online market is even a more significant challenge than in a brick and mortar since there is a lack of direct interaction with the e-retailer. The absence of physical interaction creates uncertainty (Fortes & Rita, 2016). Hence, creating e-trust can pay off. When companies make an effort to create trustworthiness among their users, it is appreciable as it is the only way of interaction (Bart et al., 2005). Customers who feel that the online environment is safe will not worry about opportunistic behavior and will easier become loyal with all that entails. In contrast, during brick and mortar transactions, trustworthiness is not equally appreciable as interaction is controllable by customers, and trust is taken for granted (Bilgihan, 2016).
High perceived trust results in a more definite purchase intention, whereas weak perceived e-trust will prompt an offline purchase (Yoon, 2002). Despite being satisfied, customers who lack trustworthiness across a website will doubtfully turn into a buyer and, more importantly, a loyalist (Anderson & Srinivasan, 2003). Therefore, e-trust can reduce the uncertainty of an individual (Chaudhuri & Holbrook, 2001) and drive the likelihood of commitment, which more likely results in “true” loyalty (Reichheld & Schefter, 2000).
As mentioned above, many studies have investigated the effect of e-trust on e-loyalty. What has not been well-examined yet, is whether the relationship between e-trust and e-loyalty works in the same way or differently in the online travel market. Based on the research above, we hypothesize that the higher the perceived trust in the online travel market, the higher the prospective of e-loyalty across the e-travel company, forming the following hypothesis:
Hypothesis 1: E-trust is positively related to e-loyalty in the online travel market.
2.3 Knowledge of e-commerce and establishing loyalty in the online travel market;
The mediating role of e-trust
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level of knowledge in the online world and across e-commerce (Hargittai & Marwick, 2016). Conversely, lack of technical knowledge or computer anxiety show weak knowledge, leading to higher perceived uncertainty and can impact online purchase intention (Gerber et al., 2018). As purchase intentions are part of loyalty, knowledge seems to affect loyalty as well.
Knowledge of commerce is partially created through the experience of an individual in the e-commerce. Customers are sometimes aware of using such techniques because of previous experience they or their network have gone through (Hargittai & Marwick, 2016). By experiencing a transaction to a specific website or platform, a user gains the adequate knowledge on how to protect personal data or on which data to disclose.
Cook and Coupey (1998) argued that users with more considerable experience in online purchasing are better at getting valuable information about the products of their interest in the e-commerce rather than in brick and mortar. In line with this idea, Broekhuizen and Huizingh (2009) claimed that users with more expertise who have been through the learning process are now knowledgeable enough to know where to search for the information needed. These customers are more likely to become loyal as the relationship with the website has been strengthened. The speculation is whether knowledge of e-commerce, which is formed by skills and experience, can lead to direct e-loyalty or whether other factors should hold to establish e-loyalty in the online travel industry. The present study will investigate whether knowledge of e-commerce can influence e-loyalty via e-trust.
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shows the intention/behavior of a customer – mostly in an indirect way. The indirect effect here is expected by using e-trust as the intermediate factor, which is a more specific construct. Therefore, we hypothesize that knowledge of commerce can lead to loyalty through the influence of e-trust. That is:
Hypothesis 2: The relation between knowledge of e-commerce and e-loyalty is mediated by e-trust.
2.4 Privacy concerns and loyalty in the online travel market
With the growing use of the Internet, customers appear to be very careful about their online purchase behavior. When customers visit websites in order to purchase a product or a service, they need to feel that they have “the right to be alone” (Wang, Lee & Wang, 1998: 63), referred to as “privacy”. The significance of privacy has been studied in the past (e.g., Bart et al., 2005; Lwin et al., 2007).
When purchasing online, privacy can take many forms. One of them is the fear people feel that their information may be mishandled or lost (Limbu, Wolf, & Lunsford, 2011). This information includes exclusive or sensitive data that are important to people. Sensitive information is a very distinct type of information, which entails sharing data that can harm someone in a financial or social level. For example, the full name of an individual might be less sensitive, whereas the credit card details are very sensitive information (Mothersbaugh et al., 2012).
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2.5 A privacy paradox: The moderating effect of privacy concerns on the e-trust –
loyalty relationship
Customers’ privacy concerns do not always reflect their purchasing behavior (Gerber et al., 2018). Despite showing concerns, an individual can still disclose information and proceed to a transaction, creating a dichotomy, the so-called “privacy paradox” (e.g., Hargittai & Marwick, 2016). The paradox refers to the phenomenon in which “on the one hand, users express concerns about the handling of their data and report a desire to protect their data, whereas, at the same time, they… rarely make an effort to protect their data actively…” (Gerber et al., 2018: 227). Descriptive information also endorses this idea; 57% of European users are worried about the safety of their data, while only 25% of the users take the time to read in full the terms and conditions when signing up on an online website or while making a purchase (Gerber et al., 2018).
Prior research has tried to explain the paradox between privacy concerns and actual behavior and found evidence on when privacy concerns assuage in the online market (Martin & Murphy, 2017). For instance, customers are more willing to disclose personal data even if they are concerned about their privacy when they are offered something in return, including financial discounts or easy check-outs (Gerber et al., 2018). Disclosing information is considered as the “cost,” whereas getting financial discounts is the “benefit” offered in return (Gabisch & Milne, 2014). According to Martin and Murphy (2017), when the perceived benefits exceed the costs, people consciously prefer to give their data despite their concerns on privacy. The “cost versus benefit” concept can positively impact the negative nature of privacy concerns towards a purchase intention.
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Hypothesis 3: Privacy concerns moderate the relation between e-trust and e-loyalty in such a way that they become less important once a strong level of perceived e-trust across a travel-related website is established.
2.6 Conceptual model
Figure 1 shows the conceptual model, including the hypotheses of the current paper. The specific model used for analyzing and interpreting the results, so to confirm (reject) the hypotheses below.
3. Methodology
The main goal of the research was to examine how e-trust can play a mediating role between knowledge of e-commerce and e-loyalty, and the moderating role of privacy concerns on the online trust-loyalty relationship. The present study focuses on the online travel market, specifically.
3.1 Questionnaire and Procedure
In order to test the hypotheses, quantitative research was necessary for the specific study (Malhotra, 2009). Thus, an online questionnaire was designed and distributed to the target group.
H1 E-loyalty Privacy concerns H3 E-trust Knowledge of e-commerce H2
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The questionnaire was in a structured-direct format, which is considered as the most popular data collection method (Malhotra 2009). The questionnaire is provided in Appendix A.
The questionnaire was sent via personal network e-mail, provided with a specific link. It was also shared on multiple social media platforms such as LinkedIn and Twitter of the personal network of the researcher, mainly focusing on people between 18-56 (and over) years old, who purchase their flight tickets, or book hotel accommodation online. Participants who did not choose the Internet for these purposes (n=10, 2.8%) were excluded from the main part of the questionnaire. The format was initially in English, so the knowledge of the specific language was required. However, since the researcher comes from a country where the Greek is the native language, for “convenience sampling” purposes (Malhotra, 2009), the questionnaire was translated into Greek as well. The translation from Greek to English was made by two independent postgraduate students and then translated back in English to ensure the validity of the questions. The two versions were compared with the original format, and any invalid questions were resolved for the final version.
The questionnaire consisted of three sections: (a) introduction explaining the goals of the study, (b) socio-demographic indicators which helped to define the profile of the respondents, and, (c) necessary information to measure each construct of the conceptual framework (López-Miguens & Vázquez, 2017). In order to get insights about users' attitude and be able to make the tests across the online travel market, four well-known travel websites were used as examples. Currently, several travel-related websites are available. For this study, Booking.com, Airbnb.com, Eastday.com, and Expedia.com were used since they are ranked among the top ten websites on travel and tourism worldwide (SimilarWeb, 2020). However, participants also had the choice to express their preferences toward a different website and provide the name of it. The chosen preferred travel website was then shown as the default choice for the main part of the questionnaire. We ensured that all participants were exposed to their preferred travel website, making the questions as personally relevant as possible.
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or they did not prefer any of them, they could choose their preference and answer the questions based on it. The second part of section (c) included questions on the main constructs of the study (see measures below).
3.2 Measures
The study's main constructs were measured on a 5-point Likert scale ranging from “1= strongly disagree” to “5= strongly agree”. To ensure content validity, we used constructs that were already used in prior studies. The only adjustments that were made regarded as necessary changes to fit the specific model and goals. All measures were chosen based on specific criteria. Firstly, the measures should be relevant to the goal of a study. Secondly, the items should be straightforward and readable for two reasons: (1) to enable the researcher to adjust them based on this study and (2) to enable the participants with no experience or knowledge to easily understand them (Cui et al., 2018).
A summary of the measures included in each construct of the study are provided below. For a detailed description of the items included in each construct and the scholars the items were based on, see Appendix B.
E-loyalty. The outcome variable of the present study, e-loyalty, was measured with nine items, adapted from prior studies (e.g., Chaudhuri & Holbrook, 2001; Jones & Taylor, 2007; Luarn & Lin, 2003; Nadeem et al., 2015; Zeithaml, Berry & Parasuraman, 1996).
Privacy concerns. For measuring privacy concerns, seven items were used adopted from past studies (e.g., Cheung& Lee, 2006; Flavían & Guinalíu, 2006; Kim et al., 2011; Quach, Thaichon & Jebarajakirthy, 2016; Xu et al., 2011).
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3.3 Pilot test and content validity
The questionnaire was initially distributed to 20 people in order to undertake a pilot test. From those, all of them took the questionnaire and provided feedback to the researcher (response rate: 100%). Among the 20 participants, 11 participants checked the English version and nine the Greek version. The procedure lasted one day, starting from Monday, April 27th, and ending on Tuesday, April 28th. The respondents were asked to identify any misunderstandings as well as checking the flow, the design, and the structure. By doing so, inconsistencies and constructs that were not understandable by the average participant were resolved. Specifically, one question in part (c) was rephrased in the Greek version as it perceived as non-easily understandable by two respondents. Also, two spelling errors in part (c) in the English version were detected by one respondent and adjusted by the researcher. The feedback was provided to the researcher by online contact, comments, and, in some cases, via phone. The 20 respondents of the pilot test were not considered during the sampling of the questionnaire as the goal of this test was not to answer the questionnaire, but rather check it.
3.4 Sampling and questionnaire administration
After the completion of the pilot test, the official distribution and collection of participants started. We used a convenience sampling strategy by approaching fellow students at the University of Groningen and the researcher’s social media network (Malhotra, 2009). We used a snowball sampling strategy to collect as many respondents as possible by asking the targeted respondents to share the survey link with their social networks. Participants were collected via social media platforms, online platforms, and via the close network of the researcher (friends and siblings) who also distributed the questionnaire to their network. By doing so, we ensured that the collection of data was distributed to relevant people who are users of the Internet in general as no printed form of the questionnaire was distributed. Hence, the approach to target specific internet users for this study provided us with valid participants who were more likely to use the Internet for travel shopping.
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extreme observations (see 4.1). After the data cleaning, a total of 350 respondents were used for the final analysis (valid response rate: 91.38%).
4. Data analysis
In order to analyze and report the results from the data collected, we used the IBM SPSS Statistics 26 program, which offers a variety of alternatives in order to test our hypotheses.
4.1 Data Cleaning
Before analyzing and interpreting the results, it was necessary to check the data and, where necessary, apply a data cleaning. While taking the questionnaire, participants had the right to accept or reject taking it as well as withdraw it at any time before the completion. Of 383 responses, 13 participants did not complete the whole questionnaire and were therefore excluded from the sample. Also, nine respondents (response rate: 2.40%) did not consent to participate in the questionnaire and were also excluded from the dataset. Another requirement for considering a participant valid was the use of travel-related websites when considering traveling. Of the 361 remaining participants, ten respondents (response rate: 2.80%) did not use such websites. Since the whole study based on the use and engagement of specific websites in the online travel market, they considered as invalid respondents and were excluded from the sample. A part of the questionnaire included a question regards the preference towards a specific website. The participants could choose among four displayed websites but could also choose other websites of their choice. In this case, they should specify the name of the website. In total, ten respondents (response rate: 2.80%) chose other and provided a name, but one chose other and did not provide the name of the website. Thus, this respondent was excluded from the sample. After the data cleaning, a total of 350 respondents used as a valid sample for the following analyses (response rate: 91.38%).
4.2 Socio-demographics
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answered the questionnaire in Greek versus those in English, but no significant differences or abnormalities were detected concerning socio-demographics, so we reported the figures for one group (n=350). The majority of the respondents were female at 67% (n=236), following male (n=112) 32%, and 1% of the respondents preferred not to specify their gender (n=2). The questionnaire had a restriction in the age as only 18 years old, and above could participate. The age of respondents ranged between 18 and 62, with a mean of 26 years old (SD=6.39). The majority of the respondents had completed a bachelor’s degree at 49.10% (n=172), following by a master’s degree at 36.30% (n=127).
More than half of the respondents were currently students (n=195, 55.70%), while 40.30% (n=141) were employed. As a result of the 350 respondents, there was a variety of characteristics among the sample. During the collection data, convenience and snowball sampling methods were used, which raises the expectation of young people who vary among bachelor’s and master’s degrees. Because of the study's online traveling context, the specific group of people is a representative sample as they are likely to use the Internet for booking flight tickets or accommodation. Thus, the population of interest for the study was achieved. All figures are summed up in Appendix C.
4.3 Analysis
To analyze the responses from the collected data, we used the IBM SPSS Statistics 26 program. It is worth noting that at first, we distinguished respondents into two groups based on the language selected during the data collection so to check if there were significant differences among those who responded in Greek versus the English language. So, we ran separate analyses, but no significant changes were detected. Therefore, we decided to analyze and report the results for the whole sample combined in one group, as explained below (n=350).
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In the sequence, we reported the correlations and the Variance Inflation test (VIF) to check whether there was multicollinearity among the factors. We wanted the factors to correlate so to show a connection between one another, but not high collinearity as it would be a problem. A very high collinearity would mean that two constructs are interpreted almost in the same way and one of them should be removed from the analysis. Expressly, a VIF higher than 4 indicated moderate multicollinearity, whereas VIF higher than 10 indicated high multicollinearity (Malhotra, 2009). After computing the theoretical constructs, we tested the main hypotheses. For this, we initially performed a linear regression. We tested how each independent variable related to e-loyalty. Hence, we conducted a multiple linear regression, including privacy concerns, knowledge of e-commerce, and e-trust as the independent variables. From this analysis, we tested Hypothesis 1. Additionally, we performed a moderation and a mediation analysis using Hayes’ PROCESS macro (2017) so to test the remaining hypotheses. More specifically, for Hypothesis 2, we used a mediation analysis (model 4) testing for the effect between knowledge of e-commerce, e-trust, and e-loyalty. To further validate these results, we interpreted e-loyalty as a function of three sub-dimensions: WOM, WTP, and RI. Thus, we ran three additional mediation analyses, each focusing on a sub-dimension of e-loyalty.
Lastly, for testing Hypothesis 3, we used model 1 of Hayes’ PROCESS macro (2017), testing for the moderating effect of privacy concerns (moderator) on the relationship between e-trust (independent variable) and e-loyalty (dependent variable). We further validated Hypothesis 3 by running three distinct moderation analyses for the sub-dimensions of e-loyalty (WOM, WTP, and RI).
5.
Results
All the results from the relevant analyses are provided in the section below.
5.1 Checking reliability and validity of knowledge, trust, privacy concerns, and
e-loyalty
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that the variables are uncorrelated in the population held. The KMO was 0.63, which is higher than the recommended 0.50, and Bartlett’s Analysis of sphericity was statistically significant (p<0.001). Therefore, the two conditions held, and the factor analysis considered as appropriate for the current study.
We chose the Principal Components Analysis (PCA), which is the most common method as it considered the total variance in a dataset (Malhotra, 2009). We included those factors with eigenvalues higher than 1 (Malhotra, 2009). The PCA showed four factors with a total variance explained of 48.16%. For a better understanding of each measure's correlations to a specific factor, we used a varimax rotation. Items that had loadings lower than 0.40 were excluded from the factor analysis. At this point, we removed two privacy concerns items: “I am concerned that my personal information might get shared with third parties without my permission.” and “I am concerned that transactions are not always secure.” The knowledge item “I know less than most users about using the internet for travel purposes” did also not meet the 0.40 criterium and was therefore removed from the main analyses.
Before finalizing the factors, we ran a reliability analysis for each factor, including the remaining items for each theoretical construct. Hence, we ran four different reliability tests to confirm the internal validity of knowledge, e-trust, privacy concerns, and e-loyalty. The validity is confirmed once each test exports a Cronbach’s alpha higher than 0.60. All the factors showed an acceptable internal consistency: privacy concerns (α=0.78), e-trust (α=0.77), e-loyalty (α=0.80) and knowledge of e-commerce (α=0.78), hereby supporting the reliability of the measures. The final factor loadings, as extracted with the PCA (varimax rotation), eigenvalues/percentage of explained variance, and Cronbach’s alphas are shown in Table 1.
Table 1: Variables used for measurements
Construct Measurement items Loading Factor
Eigenvalue / % of variance Cronbach’s alpha Privacy concerns
I believe that my personal information will be kept private and confidential after reading the privacy statements.
.78 2.82/
11.29%
.78
I believe that the privacy statements provided are an effective way to demonstrate
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I believe that transactions are always safe. .71 I think it only collects the necessary amount
of personal information.
.36
I believe that my privacy is guaranteed. .74
E-trust I believe that, overall, it is reliable. .70 2.86/ 11.44%
.77 I believe it always fulfills its obligations. .59
I believe it provides enough safeguards to make me feel comfortable using it.
.79
I feel confident that encryption and other technological advances create a trustworthy environment.
.48
I feel safe sharing any personal information that I am asked to (e.g., e-mail address, phone number, credit card details).
.73
E-loyalty I recommend it to those who ask me for advice.
.75 3.44/
13.77%
.80
I say positive things to friends and family. .68 I consider it as my first option for
travel-related purposes.
.74
I will continue to use it even if I have to pay to get access to this service.
.77
I will always prefer it even if the alternative companies have slightly better offers.
.82
I consider buying from this website compared to its competitors, even if the prices are higher on it.
.79
I intend to choose it as my e-service provider for travel-related purposes.
.65
I am more likely to purchase from this rather than a competitive travel website.
.73
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Knowledge of e-commerce
I know where I can get help for problems when something goes wrong on the Internet.
.67 2.92/
11.67%
.78
I have adequate knowledge to protect my personal information when using online websites.
.71
I have adequate knowledge in online transactions to feel confident to do this.
.74
I consider myself knowledgeable about good search techniques on the Internet.
.71
I know how to find what I am looking for online.
.57
I am familiar with practices such as using firewalls, deleting cookies, or using encryption on e-mails.
.70
Before running the regression analysis, we performed some tests to detect if there was a multicollinearity issue. No issues were detected from the correlation matrix, with correlations fluctuating within the acceptable range between 0.07 (privacy concerns - knowledge of e-commerce) and 0.56 (privacy concerns - e-trust). For cross-validation purposes, we also checked the VIF scores. The values for e-trust, knowledge of e-commerce, and privacy concerns were 1.53, 1.05, and 1.46, respectively. Since the numbers are below four, there was no issue of multicollinearity (see Appendix D).
5.2 Assumptions check
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5.3 Regression analysis for knowledge of e-commerce, e-trust and privacy concerns
on e-loyalty
We ran a multiple linear regression, including knowledge, e-trust, and privacy concerns as independent variables and e-loyalty as the dependent variable to check for the fit of the model and how the variables are related to each other. The overall model was significant, F (3, 35) = 27.38 (p<.001). The results showed that all three variables positively and significantly contributed to the explanatory power of the model. E-trust (B=0.34, p<.001) was the strongest predictor, followed by knowledge of e-commerce (B=0.11, p<.01) and privacy concerns (B=0.10, p=0.100). The results showed that a change in e-trust affects e-loyalty the most, whereas, for knowledge of e-commerce and privacy concerns, the effect on e-loyalty is weaker. These results are in line with Hypothesis 1, that is, the more customers trust a travel-website, the more likely they are to show commitment towards it.
5.4 E-trust as the mediator between knowledge of e-commerce and e-loyalty
We ran a mediation analysis using PROCESS macro model 4 of Hayes (2017) to test Hypothesis 2. Knowledge of e-commerce was the independent variable, e-trust the mediator, and e-loyalty the outcome variable. After running the mediation, we assessed the results based on Baron and Kenny’s (1986) following criteria: (1) the effect of knowledge of e-commerce on the outcome variable, namely 𝑐 had to be significant, (2) the effect of knowledge of e-commerce on e-trust, namely 𝑎 had to be significant, (3) the effect of e-trust on the outcome variable, namely 𝑏 had to be significant, and (4) including e-trust had to make the effect of knowledge of e-commerce on e-loyalty, namely 𝑐′, either non-significant or lower its effect substantially, so to argue that there is a full or partial
mediation, respectively.
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significant predictor of e-loyalty after controlling e-trust, although, to a lesser extent indicating a partial mediation. Hayes’ (2017) mediation procedure illustrated that the indirect effects of knowledge of e-commerce on e-loyalty through the effect of e-trust (ab paths) were significant, as the 95% level of confidence did not include zero. The indirect coefficient indicated that e-trust mediated the knowledge of e-commerce and e-loyalty relationship, (B=0.08, SE=0.03, CI [0.04, 0.14]), hereby supporting Hypothesis 2.
To further validate the results above, we ran three additional mediation analyses distinguishing e-loyalty into three different sub-dimensions. The first mediation analysis included WOM as the outcome variable, the second included WTP, and the third RI. Knowledge of e-commerce remained the independent variable, and e-trust was used as the mediator in all three analyses. The results showed that the positive effect of knowledge of e-commerce on e-trust remains the same for all the analyses (a path: B=0.21, p<0.001). The b path was also significant: results showed that e-trust was a significant predictor of WOM (b1 path: B=0.40, p<0.001), WTP (b2 path: B=0.17, p<0.001) and RI (b3 path: B= 0.32, p<0.001) in the online travel market. The knowledge of e-commerce directly influences WOM (c1 path: B=0.22, p<0.001), insignificantly influences WTP (c2 path: B=0.03, p=0.620) and significantly influences RI (c3 path: B=0.16, p<0.001).
Figure 2: Statistical model of mediation between knowledge of
e-commerce, e-trust, and e-loyalty
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Knowledge of e-commerce remained a significant predictor after controlling the effect of the mediator on WOM (c’1 path: B=0.13, p<0.01) and on RI (c’3 path: B=0.09, p=0.070). In contrast, the effect of knowledge of e-commerce on WTP became insignificant after controlling for the mediation (c'2 path: B=-0.01, p=0.85). Hayes (2017) mediation procedure showed that the indirect effects of knowledge of e-commerce on WOM and RI through e-trust, were significant as the 95% confidence intervals did not include zero. On the other hand, the indirect effects of knowledge of e-commerce on WTP through the effect of e-trust were not significant as zero was included in the intervals. Hence, WOM and RI confirm the mediation effect, whereas there is no mediation regards WTP. In a nutshell, trust partially mediates the relationship between knowledge of commerce and e-loyalty as an overall construct as well as the two sub-dimensions of e-e-loyalty (WOM, RI), with this providing firm support for Hypothesis 2.
5.5 Privacy concerns as the moderator between e-trust and e-loyalty
For testing Hypothesis 3, PROCESS macro Hayes (2017) moderating procedure was performed. To analyze and interpret the hypothesis, we used e-trust as the independent variable, privacy concerns as the moderator, and e-loyalty as the outcome variable. As mentioned in chapter 4 of the analysis, we used model 1, 95% interval confidence, and 5000 bootstrap samples for percentile confidence intervals.
Hypothesis 3 investigated whether privacy concerns could moderate the relationship between e-trust and e-loyalty. The overall model explained 26.78% of the variance in e-loyalty (F (3, 346) =26.78, p<0.001). In line with previous findings, e-trust directly influences e-loyalty (B=0.39, p<0.001). This effect means that the higher the perceived e-trust, the more likely one is to become loyal online. Also, although not hypothesized, an insignificant effect for privacy concerns was observed: a change in privacy concerns do not affect the likelihood of e-loyalty (B=0.09, p=0.122). The main effect overrode the positive moderating effect of e-trust and privacy concerns on e-loyalty (B=0.07, p= 0.067).
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backfire only when perceived e-trust is relatively weak, resulting in less e-loyalty among customers. Hence, Hypothesis 3 is supported.
To further validate the results above, we ran three additional moderation analyses distinguishing e-loyalty into three different sub-dimensions. The first moderation analysis included WOM as the outcome variable, the second included WTP and the third RI. E-trust remained the independent variable, and privacy concerns were used as the moderator in all three analyses. Results showed that the main effect for e-trust on WOM (B=0.43, p<0.001) and RI (B=0.34, p<0.001) were significant. In contrast, the main effect of e-trust and WTP was not significant (B=0.08, p=0.230). Then the interaction effects between the moderator and each sub-dimension of e-loyalty were tested. For WOM, there was a significant interaction (B=0.08, p<0.05) as well as for WTP (B=0.07, p=0.070). However, there was no interaction effect between e-trust and privacy concerns on RI (B=0, p=0.910). By analyzing the conditional effects at high and low levels of privacy concerns, we found that for low privacy concerns, there was a weaker effect of e-trust on WOM (B=0.34, p<0.001), whereas for high privacy concerns the effect of e-trust became significantly stronger (B=0.51, p<0.001). Regarding the conditional effects of the moderation on the relation between e-trust and WTP, we found that the effect is significant only for those with high privacy concerns (B=0.15, p=0.070). Since the interaction effect for RI was not significant, there was no moderation effect, and no conditional effects were provided.
In conclusion, privacy concerns moderate the relationship between e-trust and e-loyalty as an overall construct and the two sub-dimensions of e-loyalty (WOM, WTP), thereby providing further support for Hypothesis 3.
6.
Discussion and Conclusion
The following section provides a general discussion of the results. Some managerial implications will be provided as well as limitations of the current study and recommendations for future research.
6.1 Conclusions
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& Straub, 2004; Pavlou, 2003), e-knowledge (e.g., Baek, 2014; Hargittai & Marwick, 2016), and privacy concerns (e.g., Fortes & Rita, 2016; Martin & Murphy, 2017) are essential for e-loyalty. However, less is known about how these factors may result in e-loyalty when interacting in specific directions. The present research aimed to investigate how knowledge of e-commerce can influence e-loyalty via e-trust. The study also focused on examining when privacy concerns backfire using a moderate model of privacy concerns in the relationship between e-trust and e-loyalty. Hence, we ran a mediated-moderation model in order to get an in-depth understanding of these relations. We also cross-validated the results by analyzing how the moderated-mediation models are influencing e-loyalty when three sub-dimensions express it; word of mouth, willingness to pay, and repurchase intention (Hu et al., 2009; Kim et al., 2009).
In line with Hypothesis 1, our findings showed that trust directly and positively influences e-loyalty. The stronger the level of trustworthiness of an individual across a travel-related website, the more perspectives he/she has in becoming a loyal customer. This finding supports previous evidence suggesting that the presence of e-trust in e-commerce is crucial to determining the likelihood of a customer to be committed across an e-business (e.g., Gefen & Straub, 2004; Pavlou, 2003; Tran & Strutton, 2020). It is also in line with Anderson and Srinivasan’s (2003) theory that e-trust is the most essential element for an individual so to become loyal across a website.
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effect on e-loyalty through the use of third constructs such as e-trust (Bilgihan, 2016; Toufaily et al., 2013; Swaminathan et al., 2018). Overall, our findings replicated previous evidence confirming that more skills and experience in the online commerce could significantly enhance the intention of a customer to become loyal across an e-travel website (Broekhuizen & Huizingh, 2009).
Moreover, we investigated Hypothesis 3 about the moderate effect of privacy concerns between e-trust and e-loyalty. Results regarding e-e-trust replicated previous findings of the current study, as it directly influences the likelihood of e-loyalty. Furthermore, the results on privacy concerns confirmed the expectations of when they backfire, resulting in e-loyalty. When privacy concerns exist for a customer, but the perceived e-trust is strong, the privacy concerns become of less importance, increasing the likelihood of commitment across the online travel market. In other words, once e-trust is ensured for a customer, privacy concerns do not matter to the same extent compared to when there is a lack of trustworthiness. We then cross-validated the results by using the three sub-dimensions of e-loyalty. The results showed that privacy concerns combined with high perceived e-trust result in commitment expressed by both higher intentions to create referrals and willingness to pay more for specific e-travel websites. Although, this does not apply to a customer’s repurchase intention, as trusting a travel-related website does not influence customers’ attitudes. These findings are in line with the theoretical background claiming that people may be worried about their privacy, showing skepticism on whether to disclose personal data or not in an online platform (Mothersbaugh et al., 2012), but this does not necessarily reflect on their actual attitude. Replicating previous studies, our findings give an explanation for this paradox. Specifically, we found that this dichotomy depends on the level of trustworthiness which becomes important so that it assuages privacy concerns regards sensitive information or skepticism on disclosing information and proceed to purchases. Consequently, there is an increased positive attitude towards the e-company and higher commitment perspectives.
6.2 Managerial Implications
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From a managerial perspective, e-retailers can establish e-loyalty based on continuously measuring customer perceived e-trust, so that management can take the appropriate actions when customers are not convinced about the trustworthiness across a travel-related website. It seems that e-trust rules the Internet (Reichheld & Schefter, 2000) as it plays a significantly important factor in any analysis that has been done in this study. Moreover, e-retailers can use the dimensions that indicate whether a customer is loyal or not (WOM, WTP, and RI) in order to understand their position in the online market but also take actions regards e-retailing activities vis-à-vis competitors to identify their comparative strengths and weaknesses. Strategies such as ensuring a safe environment can make the customers feel more comfortable while browsing in such websites. Similarly, encouraging confidence by providing safeguards for the encryption and technological advances can make customers feel safe enough to share personal information such as e-mail address, phone number, or credit card details.
Based on the results of the analyses, there are also distinct managerial implications on knowledge of e-commerce and privacy concerns. E-retailers can also take actions based on the effects between knowledge of e-commerce and e-trust on e-loyalty as they can contribute to enhancing the user-retailer relationship and increase the likelihood of commitment across a travel-related website. From the results of the analysis, primary attention should be given in the customer knowledge as prior experience and skills can impact the way a customer feels about such websites. For example, when a user knows where to find relevant information, how to proceed to a transaction, or how to resolve problems that might occur online, their perceived trustworthiness is positively influenced. Thus, e-companies must ensure that their customers gain both the adequate skills and have positive experiences when visiting their websites as these two elements drive their trustworthiness. Consequently, this attitude impacts whether they become loyalists.
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disclose personal information or increase the share of wallet across an e-company, showing a paradoxical attitude based on their concerns. By implementing such strategies, customers would feel confident to disclose such information, and they would believe that they are asked to share only relevant and necessary data. In the long run, these customers are likely to become loyal and will easily create referrals for acquiring new customers but will also show a higher willingness to spend more money on the specific websites.
In a highly competitive market like the online travel industry, e-retailers should concentrate their attention on establishing strategies that will make customers feel safe when browsing online to excel so to create but mostly maintain their competitive advantage.
6.3 Limitations and recommendations for further research
The purpose of this study was to explore the dimensions that form e-loyalty and propose a comprehensive overview of factors that can influence it in different ways. However, this study may have several limitations.
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several experiments, the cross-sectional process's current limitations would be addressed, resulting in a more comprehensive image of the predictors of e-loyalty under certain circumstances. Next, the current study used an attitudinal perspective to analyze how e-loyalty is formed and expressed through word of mouth, willingness to pay, and repurchase intention because it is a common method to use this technique in the travel and tourism industry (Yoon & Uysal, 2005). Although, many studies in the past used a behavioral context as well to conceptualize e-loyalty (Virto et al., 2019; Yoon & Uysal, 2005). Future research could address this limitation in replicating the current study by using both attitudinal and behavioral predictors so to provide similar (or different) insights on how trust, knowledge of commerce, and privacy concerns predict e-loyalty in the actual behavior.
Lastly, the current study found support for predicting e-loyalty in both the moderate and mediate models. Although it did not find support on when e-trust mediates the relationship between knowledge of e-commerce and willingness to pay more for products, and when privacy concerns moderate the relationship between e-trust and repurchase intention. Further studies might investigate them using alternative directions so to provide a better understanding on when willingness to pay more is predicted for knowledgeable customers but also when customers are more likely to make repeated purchases from the same websites when they are privacy concerned.
7.
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