Measuring the Mediating Roles of E-Trust and E-Satisfaction in the Relationship between E-Service Quality and E-Loyalty: A Structural Modeling Approach

Abstract

Despite a plethora of research studies on service quality, in general, and electronic (e) service quality, in particular, studies often focus on either understating the dimensions of e-service quality or its direct relationship with other variables such as customer e-satisfaction, e-trust, and e-loyalty. This study developed a comprehensive theoretical model to examine both direct and indirect influence of e-service quality on the e-loyalty of hotel customers in their usage of online travel agencies (OTAs). The research examines the mediating effect of e-trust and e-satisfaction in the relationship between e-service quality and e-loyalty. The research adopted a quantitative approach through a questionnaire survey for data collection from hotel customers who adopt OTAs for choosing their hotel services. Data collected from the questionnaires were analyzed using structural equation modeling (SEM). The results confirmed all direct relationships among e-service quality, e-trust, e-satisfaction, and e-loyalty. However, the results did not confirm a mediating role of either e-trust or e-satisfaction in the relationship between e-service quality and e-loyalty. Nonetheless, the results interestingly showed that both variables (trust and e-satisfaction) have a mediating role in the relationship between e-service quality and e-loyalty. The results of this research have numerous implications for senior tourism marketers, as well as marketing scholars.

1. Introduction

Electronic (e) and/or digital marketing has become the supreme marketing channel adopted by most hotel companies as a response to the transformation toward e-commerce to attract their customers and meet their needs [1]. One of these marketing channels that hotels adopt is online travel agencies (OTAs). These OTAs are like conventional travel agencies, albeit through cyberspace; hence, all transactions are undertaken online [2]. These OTAs plays a mediating role between hotel customers and hotel companies. They work as a trusted a service consultant and broker to offer hospitality and travel options for their customers online [2]. OTAs provide online booking services and/or travel reviews.

Hotel companies comprehensively depend upon these OTAs to reach a wider group of customers [3,4,5], despite the high commission required by these OTAs, which could be up to 40% of the sales [6]. There is a price parity clause between hotel companies and OTAs confirming that hotels should not offer lower prices for customers on other marketing channels. This makes OTAs a better option for customers, especially with a variety of choices, which makes hotel selection a cool experience for customers [7]. Online targeting of customers has allowed OTAs to understand customers’ buying habits, as well as their buying needs [8]. Recent research studies [4,5] showed that the sales of hotels through OTAs can reach up to 90% of the total hotel sales, especially for resort hotels.

The relationship among quality of service, customer buying intention, trust, satisfaction, and loyalty is well established in the marketing literature [9,10,11,12] and is clear in the hotel industry [13,14]. E-service quality has drawn the attention of marketing and management researchers for decades; hence, its dimensions and consequences have been widely examined [15,16]. More specifically, research has examined the relationship among e-service quality, trust, and customer satisfaction, on one hand [17], and e-service quality, e-satisfaction, and e-loyalty, on the other hand [18]. Additionally, studies [19,20,21] have examined the relationship among e-service quality, e-trust, e-satisfaction, and e-loyalty of online shoppers on different websites. Nonetheless, studies on the role of both e-trust and e-satisfaction in the relationship between e-service quality and e-loyalty of hotel customers through their usage of OTAs remain limited. This study is an attempt to bridge this knowledge gap and examine this relationship.

This research provides a comprehensive model to examine the direct relationship between e-service quality and e-loyalty of hotel customers, and their indirect relationship mediated by e-trust and e-satisfaction. The research examines the antecedents of hotel customer e-loyalty by focusing on the three dimensions of e-service quality. It also examines the attributes that customers value when evaluating the e-service quality of OTAs. Additionally, the research examines the mediating role of both e-trust and e-satisfaction in the relationship between e-service quality and e-loyalty of hotel customers through their usage of OTAs. This research extends the service marketing literature, especially in the tourism and hotel context, and gives answers about the nature of the relationships (direct/indirect) among e-service quality, e-trust, e-satisfaction, and e-loyalty of OTA customers when booking hotel services.

The relationships among service quality, buying intention, trust, customer satisfaction, and loyalty has been examined extensively in the literature; however, studies on the simultaneous mediating effects of both e-trust and e-satisfaction in these relationship remain limited, especially in the context of the hotel industry through the usage of OTAs. This paper is an attempt to bridge this knowledge gap. Furthermore, the current study suggests a comprehensive and holistic framework to test not only the direct impact of e-service quality on customer e-loyalty in the hotel industry context, but also the indirect effects mediated by e-trust and e-satisfaction.

To achieve the purpose of this research, the structure of the current paper is as follows: Section 1 introduces the research and highlights the gap in the research, as well as its objectives; Section 2 presents the research framework by defining the research constructs and presenting the research hypotheses; Section 3 presents the research methods including the study measures and data collection approach; Section 4 analyzes the collected data and shows the research findings; Section 5 discusses the results and the research implications; Section 6 concludes the study and highlights its limitations.

2. The Research Theoretical Framework

2.1. Defining the Research Constructs

Service quality (SQ) is often defined by marketing scholars depending on the expectancy disconfirmation paradigm (EDP) [22]. It refers to the level of perceived services (P) compared to expected services (E) [9,10,11,12]. Hence, the equation can be written as SQ = P − E. Service quality is crucial for customer satisfaction, business accomplishment, and survival [12,18]. E-service quality originated from the work of marketing scholars, who introduced the concept of service quality, such as Parasuraman and his coworkers [9,10,11,12,15,16]. E-service quality was defined as customer assessment of the extent to which the seller provides efficient, effective, and appropriate e-service on the website and in the delivery of the service [18]. E-service quality can also be defined as customer judgments relating to the quality of service delivered in a virtual environment [23].

In response to e-commerce and customer direction toward e-commerce, several researchers have developed different instruments to assess e-service quality. For example, Zeithaml et al. [24] first introduced the concept of service quality and its dimensions, as well as a conceptual framework for e-SQ. Loiacono et al. [25] introduced a scale for measuring the quality of websites called “WebQual”. In the same vein, Yoo and Donthu [26] developed “SITEQUAL” to assess the website interactivity. However, these two scales failed to pay sufficient attention to customer interaction in a virtual environment and focused mainly on the quality of the websites [27]. Wolfinbarger and Gilly [28] introduced a scale for measuring e-retail service quality called “eTailQ”. E-retail service quality involves customers’ attitude toward the website, as well as their satisfaction and loyalty. Bauet et al. [29] developed another transaction process-based scale for measuring business-to-customer service quality in online shopping called “eTransQual”. Parasuraman et al. [16] presented “E-S-QUAL”, which is a multiple-item scale for evaluating e-service quality. The scale has four dimensions: efficiency, fulfillment, system availability, and privacy [16].

Customer satisfaction refers to the feeling of pleasure or displeasure following a comparison between perceived performance and expected performance [11]. In the same line, customer satisfaction is defined as an evaluation of whether the perceived product or service meets the customer’s expectation and needs [30]. Furthermore, customer satisfaction is seen as a positive and affective state resulting from the evaluation of all aspects of services provided by the seller [31]. There is a general consensus in the marketing literature that satisfaction is a direct outcome of service quality [9,10,11,12].

According to the Merriam-Webster dictionary [32] trust is defined as “one in which confidence is placed” and also “dependence on something future or contingent”. Research often refers to trust as the readiness to rely on an exchange with someone with confidence; hence, there is a mutually beneficial gain [33]. Hence, trust has a combination of cognitive and affective dimensions [34]. Trust was confirmed as a crucial factor in maintaining a strong and long-term relationship with customers [35]. There is evidence that trust is a predictor of customer loyalty, and loyalty can be achieved through customer trust [36].

2.2. The Relationship among E-Service Quality, E-Satisfaction, and E-Trust

Research (e.g., [37,38]) on the relationship among e-service quality, e-satisfaction, and e-trust showed a positive influence of the dimensions of e-service quality on both e-satisfaction and e-trust of the customer. The study of Kundu and Datta [37] on internet banking customers showed that e-service quality has a strong association with e-satisfaction and e-trust. Additionally, Kassim and Abdullah [38] found that service quality has a significant impact on satisfaction and trust in an e-commerce setting, even in two countries with different cultures, i.e., Malaysian and Qatari. As discussed earlier regarding the different dimensions of e-service quality [16], this research adopted the three dimensions of e-service quality, i.e., efficiency, system availability, and privacy protection, because they are the most accepted dimensions of measuring service quality among scholars in the e-commerce context [18]. Efficiency refers to the ease and speed of the website, while system availability refers to technical functioning of the website [16]. The privacy protection dimension refers to the safety and protection that a website offers to protect customer information. The study of Yen and Lu [18] drew on EDP to examine the influence of these three dimensions of e-service quality on e-satisfaction among auctioneers and found that the three dimensions positively influence e-service quality. We also expect that the dimensions of e-service quality affect e-trust, as there is evidence that e-service quality influences e-trust [39]. Additionally, the authors of [40] showed that e-trust is related to the efficiency and user-friendliness dimensions of service quality. On the basis of these arguments and as pictured in Figure 1, the following hypotheses are proposed:

Hypothesis 1 (H1):

Website efficiency has a positive effect on customer e-satisfaction.

Hypothesis 2 (H2):

Website privacy protection has a positive effect on customer e-satisfaction.

Hypothesis 3 (H3):

Website availability has a positive effect on customer e-satisfaction.

Hypothesis 4 (H4):

Website efficiency has a positive effect on customer e-trust.

Hypothesis 5 (H5):

Website privacy protection has a positive effect on customer e-trust.

Hypothesis 6 (H6):

Website availability has a positive effect on customer e-trust.

2.3. The Relationship among E-Trust, E-Satisfaction, and E-Loyalty

The authors of [41] confirmed a positive association between trust and satisfaction. In the e-commerce context, this notion was also the case, since a direct positive influence of e-trust on e-satisfaction was confirmed among internet banking users [37]. Research also confirmed that e-trust is a key detrainment of e-satisfaction [41]. Additionally, e-trust was found to have a significant influence on the e-loyalty of the customer in different cultures [38]. The same study showed that this e-loyalty was confirmed by word of mouth, which is an antecedent of other behavioral intentions such as repeat visits or repurchase intentions. On the basis of these findings, that the following hypotheses can also be proposed:

Hypothesis 7 (H7):

Customers’ e-trust has a positive effect on their e-satisfaction.

Hypothesis 8 (H8):

Customers’ e-trust has a positive effect on their e-loyalty.

2.4. The Relationship between E-Satisfaction and E-Loyalty

The relationship between customers’ satisfaction and their loyalty is well established in the marketing literature [11,12]. The literature confirmed that customer satisfaction directly and positively influences customer loyalty. In the e-commerce context, this notion was supported by the work of many researchers [18,38]. Yen and Lu [18] found that the e-satisfaction of auctioneers is positively correlated with customer e-loyalty. Additionally, Kassim and Abdullah [38] confirmed a significant direct influence of e-satisfaction on e-loyalty in different cultures. On this basis, that the following hypothesis can be proposed:

Hypothesis 9 (H9):

Customers’ e-satisfaction has a positive effect on their e-loyalty.

2.5. The Mediating Role of E-Trust and E-Satisfaction in the Relationship between E-Service Quality and E-Loyalty

The above discussion of the literature (see, for instance, [18,20,37,38]) confirmed the direct relationships among e-service quality, e-trust, e-satisfaction, and e-loyalty. These factors are all related to one another. Additionally, e-trust was found to have a partial mediating role in the relationship between the dimensions of e-service quality and e-satisfaction among internet bankers [37]. To the best of the researchers’ knowledge, no published research examined the mediating role of e-trust and e-satisfaction in the relationship between the three dimensions of e-service quality and customer e-loyalty, especially in relation to the use of OTA services by hotel customers. This research is an attempt to examine this relationship. In this research, we hypothesize that both e-trust and e-satisfaction have a mediating effect (either full or partial mediation) on the relationship between the three dimensions of e-service quality and customer e-loyalty. Our assumption is made on the basis of the expectancy disconfirmation theory, which confirms that satisfaction is an outcome of perceived service quality, which also leads to other consequences, such as customer loyalty. However, the provision of service quality does not always ensure customer loyalty; hence, satisfaction could play a role in this relationship [40]. This research, thus, proposes the following hypotheses:

Hypothesis 10 (H10):

Customers’ e-trust plays a mediating role in the relationship between the three dimensions of e-service quality and e-satisfaction.

Hypothesis 11 (H11):

Customers’ e-trust plays a mediating role in the relationship between the three dimensions of e-service quality and e-loyalty.

Hypothesis 12 (H12):

Customers’ e-satisfaction plays a mediating role in the relationship between the three dimensions of e-service quality and e-loyalty.

3. Methodology

3.1. Study Measures

We developed the study measures after a rigorous review of the related literature. This procedure extracted six factors, each of which had its own associated set of items, which were modified so that they were appropriate for the context of the study. The measures were created using a Likert-type scale with five points, with 1 representing “strongly disagree” and 5 representing “strongly agree. Of the six extracted measures, three were related to e-service quality. The scale developed by Parasuraman et al. [16] and Bauer et al. [29] was employed to measure e-service quality as a multidimensional construct with three dimensions: efficiency (four items, α = 0.901), privacy (four items, α = 0.910), and system availability (four items, α = 0.943). Sample items include “online hotel bookings websites are well organized”, “online hotel bookings websites do not share my personal information with other sites”, and “online hotel bookings websites are always available”, respectively. Similarly, four reflective variables were employed to measure e-satisfaction following the suggestions of Oliver [22] and Bhattacherjee, and Premkumar [42]. A sample item includes “I am satisfied with the experience of using online hotel bookings websites”. To operationalize e-trust, three items developed by Chu and Kim [43] and Yeh and Choi [44] were employed in our study. A sample item includes “I trust the online hotel bookings websites”. Lastly, the scale of e-loyalty (four items) developed by Parasuraman et al. [16] and Pavlou and Gefen [45] was employed in this study. A sample item includes “I will continue purchasing from online hotel bookings websites”.

To certify the questionnaire’s reliability and clarity, 12 business school faculty members and 20 online shoppers were invited to complete it. No changes to the questionnaire’s content were made as a result of this procedure. In the introduction of the study questionnaire, we declared that all the collected information would be kept e anonymous and confidential. Due to the self-reporting nature of the employed study questionnaire, common method variance (CMV) could be an issue [46]. Harman’s single-factor analysis was conducted to address CMV, with the extracted factors fixed to the value of 1.00 with no rotation method in SPSS exploratory factor analysis (EFA). As only one factor was extracted to explain 37% (>50%) of the variance, CMV was not a concern [46].

3.2. Data Collection

A questionnaire was directed to a random sample of 700 hotel customers who used OTAs when buying their hotel services in Saudi Arabia’s Eastern Province. The Eastern Province is the Kingdom’s largest province and is extremely popular with domestic tourists due to its long, beautiful Persian Gulf coasts. During March and April 2022, the questionnaire was distributed to the targeted sample. Twenty-five enumerators were recruited and trained to distribute and collect the designed questionnaire. The enumerators were able to survey 700 online shoppers; 680 questionnaires were retained, while 10 surveys were removed due to missing data, producing 670 valid surveys for further data analysis, with a response rate of around 95%. The current study sample size of 670 surveys is acceptable and deemed to be suitable for SEM analysis as it satisfies Nunnally’s [47] recommendation of at least 10 replies per each employed question (our study had 27 variables, generating a suggested minimum sample size of 270); it also fulfilled Hair et al.’s [48] conditions of at least 100 to 150 replies to produce acceptable estimations of MLE (“maximum likelihood estimation”). Moreover, as introduced by Krejcie and Morgan [49], if the total population exceeds 1,000,000, the lowest required sample size should be at least 384. Taking into account the above, our study sample size of 670 exceeded all recommendations. An independent t-test sample method was performed to assess the mean of early and late responses. Nonresponse bias was not a problem in our study, as no significant differences (p > 0.05) were found [50].

4. Data Analysis

4.1. Descriptive Statistics

The majority of the respondents (75%) were female and aged between 21 to 45 (70%). Approximately 80% of those who participated were university graduates and working in governmental organizations (60%). Some descriptive statistics of the study participants are presented in

Table 1. Descriptive statistics (M, SD, skewness, and kurtosis values).

Abbrev.	Items	M	SD	Skewness	Kurtosis
E-service quality (efficiency dimension) (Parasuraman et al., 2005 [16]; Bauer et al., 2006 [29]) (α = 0.901)
Effic_1	Information at online hotel booking websites is relevant	3.57	1.040	−1.139	0.991
Effic_2	Online hotel booking websites are well organized	3.66	1.065	−1.324	1.232
Effic_3	Online hotel booking websites are simple to use	3.65	1.043	−1.272	1.168
Effic_4	Online hotel booking websites enable me to complete a transaction quickly	3.69	0.878	−1.018	1.685
E-service quality (privacy dimension) (Wolfinbarger and Gilly, 2003 [29]; Parasuraman et al., 2005 [16]) (α = 0.910)
Priv_1	Online hotel booking websites have adequate security features	3.84	1.043	−1.142	0.854
Priv_2	Online hotel booking websites do not share my personal information with other sites	3.84	0.937	−0.873	0.245
Priv_3	Online hotel booking websites protect my personal information from unauthorized access	3.88	1.002	0.334	1.235
Priv_4	Online hotel booking websites protect information about my transactions	3.94	0.888	−0.931	0.541
E-service quality (system availability dimension) (Parasuraman et al., 2005 [16]) (α = 0.943)
Avail_1	Online hotel booking websites are always available	3.90	0.775	−0.902	1.517
Avail_2	Online hotel booking websites launch and run right away	3.79	0.759	−0.801	1.709
Avail_3	Online hotel booking websites do not crash	3.81	0.718	−0.559	0.876
Avail_4	Pages at online hotel booking websites do not freeze after I enter my bidding information	3.81	0.858	−0.817	0.592
E-satisfaction (Oliver, 1980 [22]; Bhattacherjee and Premkumar, 2004 [42]) (α = 0.952)
e_stas_1	I am satisfied with the experience of using online hotel booking websites	3.75	0.872	−0.601	0.409
e_stas_2	I am pleased with the experience of using online hotel booking websites	3.68	0.927	−0.670	0.483
e_stas_3	I am delighted with the experience of using online hotel booking websites	3.68	0.900	−0.673	0.598
e_stas_4	My feelings about using online hotel booking websites are good	3.75	0.891	−0.537	0.155
E-trust (Chu and Kim 2011 [43]; Yeh and Choi 2011 [44]) (α = 0.90)
e_trst_1	I trust in the online hotel booking websites	3.53	0.902	−0.512	0.383
e_trst_2	I have confidence in the online hotel booking websites	3.64	0.865	−0.840	0.603
e_trst_3	I can believe in the online hotel booking websites	3.78	0.944	−0.582	0.052
E-loyalty intention (Parasuraman et al., 2005 [16]; Pavlou and Gefen, 2004 [45]) (α = 0.962)
e_loy_1	I would recommend online hotel booking websites to someone who seeks my advice	3.50	1.081	−0.659	−0.289
e_loy_2	I say positive things about online hotel booking websites	3.48	1.045	−0.740	−0.121
e_loy_3	I intend to continue purchasing from online hotel booking websites	3.43	1.090	−0.623	−0.384
e_loy_4	I will continue purchasing from online hotel booking websites	3.50	1.054	−0.726	−0.145

. The data mean (M) values were between 3.33 and 3.87, and the standard deviation (SD) scores ranged from 0.187 to 1.239, providing statistical evidence that our study data were more normally distributed and less focused on the mean score [51]. Additionally, the skewness and kurtosis statistics are presented in Table 1; no values were found to be greater than −2 or +2, suggesting that the data had a normal distribution curve [48].

4.2. Results of Confirmatory Factor Analysis (CFA)

To determine the validity and reliability of the employed scale, all the study’s independent and dependent dimensions, as well as their corresponding reflective items, were exposed to CFA with the AMOS program and MLE (“maximum likelihood estimation”) techniques. As proposed by Kline [52], Haire et al. [48], Bryman and Cramer [51], Anderson, and Gerbing [53], and Fornell and Larcker [54], several GoF (“goodness-of-fit”) metrics were adopted to assess the theoretical model’s fit to the empirically collected data, including “normed chi-square”, comparative fit index” (CFI), “Tucker–Lewis index” (TLI), and “root-mean-square error approximation” (RMSEA). The GoF results proved that the CFA demonstrated sufficient and acceptable fit as presented in

Table 2. Results of convergent and discriminant validity.

Factors and Items	Loading	CR	AVE	MSV	1	2	3	4	5	6
1 Efficiency (α = 0.901)		0.961	0.862	0.235	0.928
Effic_1	0.903
Effic_2	0.957
Effic_3	0.969
Effic_4	0.881
2 Privacy (α = 0.910)		0.916	0.732	0.243	0.485	0.856
Priv_1	0.896
Priv_2	0.913
Priv_3	0.799
Priv_4	0.809
3 System availability (α = 0.943)		0.942	0.804	0.243	0.476	0.493	0.897
Avail_1	0.820
Avail_2	0.944
Avail_3	0.954
Avail_4	0.862
4 E-satisfaction (α = 0.952)		0.951	0.830	0.393	0.457	0.346	0.402	0.911
e_stas_1	0.875
e_stas_2	0.944
e_stas_3	0.944
e_stas_4	0.878
5 E-trust (α = 0.90)		0.904	0.759	0.154	0.133	0.192	0.197	0.386	0.871
e_trst_1	0.846
e_trst_2	0.956
e_trst_3	0.805
6 E-loyalty intention (α = 0.962)		0.963	0.866	0.393	0.379	0.116	0.183	0.627	0.392	0.931
e_loy_1	0.930
e_loy_2	0.959
e_loy_3	0.942
e_loy_4	0.891

Model GOF: χ² (215, N = 670) = 956.535, p < 0.001, normed χ² = 4.449, RMSEA = 0.0162, SRMR = 0.0202, CFI = 0.976, TLI = 0.976, NFI = 0.975, PCFI = 0.681, and PNFI = 0.689. CR: composite reliability; AVE: average variance extracted; MSV: maximum shared value; diagonal values: the square root of AVE for each dimension; below diagonal values: intercorrelation between dimensions.

. The scale reliability was evaluated with Cronbach’s alpha (α) values and “composite reliability” (CR). Table 2 displays the CR values for the six employed dimensions: efficiency (α = 0.901, CR = 0.961), privacy (α = 0.910, CR = 0.916), system availability (α = 0.943, CR = 0.942), e-satisfaction (α = 0.952, CR = 0.951), e-trust (α = 0.90, CR = 0.904), and e-loyalty (α = 0.962, CR = 0.963). All “α” and “CR” scores surpassed the recommended threshold of 0.7 as indicated by Fornell and Larcker [54], implying that our employed data had an acceptable internal consistency.

Additionally, the study measure had adequate convergent validity for two reasons as suggested by Kline [52]. First, all the SFL (“standardized factor loadings”) were found to be statistically significant with high factor loading exceeding 0.7 (Hair et al. [48]), as illustrated in Table 2. Second, the AVE (“average variance extracted”) scores for all employed six factors (efficiency (0.862), privacy (0.735), system availability (0.804), e-satisfaction (0.830), e-trust (0.759), and e-loyalty (0.866)) exceeded the recommended threshold of 0.50, displaying an acceptable and satisfactory convergent validity [48].

Lastly, the scale’s discriminant validity was confirmed for two reasons as introduced by Hair et al. [48], Bryman and Cramer [51], and Anderson and Gerbing [53]. First, the MSV (“maximum shared variance”) scores for all dimensions were found to be lower than the AVE scores, as presented in Table 2: efficiency (AVE = 0.862, MSV = 0.235), privacy (AVE = 0.735, MSV = 0.243), system availability (AVE = 0.804, MSV = 0.243), e-satisfaction (AVE = 0.830, MSV = 0.393), e-trust (AVE = 0.759, MSV = 0.154), and e-loyalty (AVE = 0.866, MSV = 0.393). Second, the AVE square-root values for the six employed factors in the bold diagonal values exceeded the values of intercorrelated dimensions located below the bold diagonal values (as illustrated in Table 2).

4.3. Results of Structural Equation Modeling (SEM)

In our study, we conducted a two-step confirmatory strategy, in which, in the first stage, we developed the conceptual framework based on previous studies’ findings. In the second stage, we collected actual primary data to detect if the conceptual framework matched the primary data [53]. The conceptual, theoretical developed framework was rejected or accepted according to several GoF conditions as previously explained in the CFA section. SEM was employed in our study as the main data analysis method, not only for its ability to test complicated models while taking into consideration the measurement error, but also for its ability to simultaneously test multiple direct and indirect relationships between different independent and dependent laten variables in one model [48,53].

The SEM output provided evidence that the structural conceptual model fit the primary data well: χ² (221, N = 670) = 1047.089, p < 0.001, normed χ² = 4.738, RMSEA = 0.024, SRMR = 0.0169, CFI = 0.954, TLI = 0.966, NFI = 0.955, PCFI = 0.689, and PNFI = 0.682 (as presented in

Table 3. The results of the structural proposed model.

	Hypotheses			Beta (β)	CR (t-Value)	R2	Results of Hypotheses
H1	Efficiency		E-satisfaction	0.21 ***	4.508		Supported
H2	Privacy protection		E-satisfaction	0.20 ***	4.454		Supported
H3	System availability		E-satisfaction	0.19 **	2.812		Supported
H4	Efficiency		E-trust	0.39 ***	8.745		Supported
H5	Privacy protection		E-trust	0.42 ***	10.090		Supported
H6	System availability		E-trust	0.35 ***	8.729		Supported
H7	E-trust		E-satisfaction	0.52 ***	12.691		Supported
H8	E-trust		E-loyalty	0.25 ***	3.249		Supported
H9	E-satisfaction		E-loyalty	0.54 ***	12.446		Supported
E-loyalty						0.54

Model GoF: χ² (221, N = 670) = 1047.089, p < 0.001, normed χ² = 4.738, RMSEA = 0.024, SRMR = 0.0169, CFI = 0.954, TLI = 0.966, NFI = 0.955, PCFI = 0.689, and PNFI = 0.682. ** p < 0.01; *** p < 0.001.

). The proposed and justified hypotheses were then assessed later after achieving a satisfactory model fit. The justified hypotheses in our study are pictured in Figure 2, where each path drawn denotes a specific hypothesis.

Our study tested nine main hypotheses. The SEM output revealed that efficiency (as a dimension of e-service quality) had a positive and significant impact on e-satisfaction (β = 0.21, t-value = 4.508, p < 0.001) and e-trust (β = 0.39, t-value = 8.745, p < 0.001); thus, hypotheses 1 and 4 were supported. Similarly, privacy protection (as a dimension of e-service quality) was found to have a statistically significant and positive impact on e- satisfaction (β = 0.20, t-value = 4.454, p < 0.001) and e-trust (β = 0.42, t-value = 10.090, p < 0.001); thus, hypotheses 2 and 5 were supported. Likewise, the SEM calculations demonstrated that system availability (as a dimension of e-service quality) positively impacted e-satisfaction (β = 0.19, t-value = 2.812, p < 0.01) and e-trust (β = 0.35, t-value = 8.729, p < 0.001); thus, hypotheses 3 and 6 were confirmed. E-trust, in return, as pictured in Figure 2, was found to have a positive and significant impact on e-satisfaction (β = 0.52, t-value = 12.691, p < 0.01) and e-loyalty (β = 0.25, t-value = 3.249, p < 0.01); hence, hypotheses 7 and 8 were supported. Lastly, e satisfaction positively and significantly impacted e-loyalty (β = 0.52, t-value = 12.691, p < 0.01); therefore, hypothesis 9 was supported.

To examine the mediation impacts of e-satisfaction and e-trust in the relationship between e-service quality (efficiency, privacy, and system availability) and e-loyalty, all direct and indirect path coefficients in the SEM results were inspected following the suggestions from (1) Kelloway [55] for partial and full mediation criteria, (2) Zhao et al. [56] for competitive mediation and complementary mediation conditions, and (3) SEM-specific standardized indirect estimates.

Kelloway [55] contended that, in order to support the full mediation, only the indirect paths should be significant and the direct paths (from exogenous dimensions to the final endogenous dimensions) should be insignificant. Alternatively, if it is discovered that both the direct and the indirect paths are significant, then it is possible to support the notion of partial mediation. Zhao et al. [56] went one step further to determine whether the significant effects that were found were positive or negative. More specifically, complementary mediation should be supported if all paths (direct and indirect) are found to be significant with the same signs, while competitive mediation should be supported if different significant signs (positive and negative) emerge in the model. As pictured in Figure 2 and presented in Table 3, the SEM results indicated that all the tested direct and indirect paths were positive and significant; consequently, partial [55] and complementary mediation [56] of e-satisfaction and e-trust could be confirmed between e-service quality and e-loyalty. Thus, hypotheses 10–12 were supported.

The previous conclusion was further confirmed by calculating the specific indirect estimation from Amos calculations to detect the mediation impacts of e-trust and e-satisfaction in the relationships between e-service quality dimensions and e-loyalty. Among the 12 possible specific indirect paths from e-service quality dimensions (efficiency, privacy, and system availability) to e-loyalty through e-satisfaction and e-trust, as shown in

Table 4. Specific indirect effects from AMOS calculations.

Indirect Path	Unstandardized Estimate	Lower	Upper	p-Value	Standardized Estimate
Efficiency e-trust e-satisfaction	0.006	0.016	0.031	0.639	0.007
Efficiency e-trust e-satisfaction e-loyalty	0.394	0.311	0.423	0.001	0.307 ***
Efficiency e-trust e-loyalty	0.004	0.010	0.027	0.632	0.004
Efficiency e-satisfaction e-loyalty	0.193	0.046	0.148	0.081	0.083
Privacy-protection e-trust e-satisfaction	0.032	0.009	0.061	0.070	0.041
Privacy-protection e-trust e-satisfaction e-loyalty	0.384	0.317	0.461	0.001	0.341 ***
Privacy-protection e-trust e-loyalty	0.024	0.009	0.046	0.071	0.023
Privacy-protection e-satisfaction e-loyalty	0.036	0.004	0.078	0.138	0.034
System-availability e-trust e-satisfaction	0.046	0.020	0.086	0.056	0.040
System-availability e-trust e-satisfaction e-loyalty	0.389	0.315	0.464	0.001	0.330 ***
System-availability e-trust e-loyalty	0.035	0.013	0.070	0.065	0.022
System-availability e-satisfaction e-loyalty	0.148	0.093	0.211	0.071	0.095
E-trust e-satisfaction e-loyalty	0.383	0.256	0.503	0.001	0.321 ***

Significance of estimates: *** p < 0.001.

, only four indirect paths were found to be significant. Three significant paths included both e-trust and e-satisfaction as two simultaneous mediators, while the other significant path was from e-trust to e-loyalty through e-satisfaction. More specifically, as presented in Table 4, the specific indirect estimation from efficiency (as a dimension of e-service quality) to e-loyalty through e-trust and e-satisfaction had a lower (0.394) and an upper value (0.423) that established a significant (p > 001) standardized indirect estimate of 0.307. Similarly, the specific indirect estimation from privacy protection (as a dimension of e-service quality) to e-loyalty through e-trust and e-satisfaction had a lower (0.384) and an upper value (0.461) that formed a significant (p > 001) standardized indirect estimate of 0.341. Likewise, the specific indirect estimation from system availability (as a dimension of e-service quality) to e-loyalty through e-trust and e-satisfaction had a lower (0.389) and an upper value (0.464) that formed a significant (p > 001) standardized indirect estimate of 0.330. Lastly, the specific indirect estimation from e-trust to e-loyalty through e-satisfaction had a lower (0.383) and an upper value (0.503) that created a significant (p > 001) standardized estimate of 0.330. Therefore, the model showed good explanatory power as all latent variables explained 54% of the variance of e-loyalty intention (R² = 0.54), as shown in Table 3.

5. Discussion and Implications

This research was established to examine the direct relationship between the three dimensions of e-service quality (efficiency, privacy protection, and system availability) and e-loyalty of hotel customers using OTAs and the indirect relationship mediated by e-trust and e-satisfaction. As hypothesized, the results showed that efficiency had a significant positive impact on e-satisfaction and e-trust, which supports hypotheses 1 and 4. The results are consistent with the work of Kundu and Datta [37], who also found a direct significant positive influence of website efficiency as one of the e-service quality dimensions on both e-satisfaction and e-trust of internet bankers. However, this finding contradicts the work of Herington and Weaven [40], who found that, despite the importance of website efficiency, it is not a predictor of bankers’ e-satisfaction.

The results also support previous research [37] that privacy protection as a dimension of service quality was found to have a positive significant impact on e-satisfaction and e-trust, supporting hypotheses 2 and 5. Likewise, system availability had a significant positive impact on e- satisfaction and e-trust; thus, hypotheses 3 and 6 were confirmed. The results confirmed a direct positive and significant impact of e-trust on e-satisfaction and e-loyalty, thus supporting hypotheses 7 and 8. These findings support the work of Kassim and Abdullah [38], who confirmed the same results within different cultures. Furthermore, e-satisfaction was found to positively and significantly impact e-loyalty, thus supporting hypothesis 9 and research studies [18,38].

With regard to the indirect paths from the three dimensions of e-service quality dimensions (efficiency, privacy, and system availability) to e-loyalty through e-satisfaction and e-trust, the results confirmed only four only significant indirect paths. The first three significant paths included both e-trust and e-satisfaction as two simultaneous mediators, while the other significant path was from e-trust to e-loyalty through e-satisfaction. However, each one of these two mediators failed to mediate the relationship between the three dimensions of e-service quality (efficiency, privacy, and system availability) and e-loyalty alone. Both variables have to exist to play a mediating role together.

The results of the current research have some implications for scholars. First, this research provided a comprehensive model, which highlights not only the direct influence of the dimensions of e-service quality on e-satisfaction, e-trust, and e-loyalty, but also the indirect role of both e-satisfaction and e-trust in the relationship between the three dimensions of e-service quality dimensions (efficiency, privacy, and system availability) and e-loyalty. Second, previous research (e.g., [18,37,38,41]) often focused on either the direct relationship or only one aspect of mediating roles, such as the mediating role of e-trust in the relationship between e-service quality and e-loyalty [37]. This research, however, examined the role of both e-trust and e-satisfaction together in the relationship between e-service quality and e-loyalty, which has not previously been studied to the best of the authors’ knowledge. Third, this research contributes to the academic body of literature indicating that despite each variable (e-satisfaction or e-trust) not playing a mediating role alone in the relationship between the dimensions of e-service quality and loyalty, they have a significant mediating effect together in this relationship.

This research also has implications for executive management in OTAs and hotels, especially marketing directors. First, the research not only shows the dimensions that managers should consider in ensuring e-service quality in their service provision, but also confirms how e-loyalty can be achieved, especially through e-satisfaction and e-trust. The research confirmed that these two factors are crucial for achieving e-loyalty. Second, e-service quality, e-satisfaction, and e-trust have to be prioritized by the management of OTAs to ensure the e-loyalty of their customers. Hence, it is essential managers pay attention to all these factors to retain customer e-loyalty.

6. Conclusions and Future Research Opportunities

The marketing literature has established relationships among quality of service, customer buying intention, trust, customer satisfaction, and customer loyalty; however, research papers on the mediating role of both e-trust and e-satisfaction in the relationship between e-service quality and e-loyalty in the hotel industry through their usage of OTAs remain limited. The current research paper was an attempt to bridge this knowledge gap and examine these relationships.

This study suggested a comprehensive and holistic framework to test the direct impact of e-service quality on customer e-loyalty in the hotel industry context and the indirect effects mediated by e-trust and e-satisfaction. The study tested the antecedents of hotel customer e-loyalty by focusing on the three dimensions of e-service quality (efficiency, privacy protection, and system availability). The study further examined the attributes that customers value when evaluating the e-service quality of OTAs. This research extends the service marketing literature, especially in the tourism and hotel context, on the relationship between e-service quality e-trust and e-satisfaction, as well as the e-loyalty of OTA customers when booking hotel services.

Data were distributed and collected from 670 hotel customers who booked their accommodation through OTAs. Convergent and discriminant validity was confirmed through running first-order CFA, and the direct and indirect relationships were subjected to SEM using Amos version 24 graphics to test the research hypotheses. As hypothesized, the SEM results gave evidence that the three dimensions of e-service quality (efficiency, privacy protection, and system availability) had a direct positive and statistically significant impact on both e-trust and e-satisfaction; in turn, e-trust and e-satisfaction had a direct positive statistically significant impact on e-loyalty. As shown in Figure 2 and Table 3, all direct and indirect relationships among the research variables were found to be positive and significant, evidencing that e-trust and e-satisfaction partially mediated the relationship between the dimensions of e-service quality and e-loyalty. The SEM method was further employed to detect the specific indirect effect estimation; interestingly, the only indirect paths found to be statistically significant involved both e-satisfaction and e-trust simultaneously mediating the relationship between the dimensions of e-service quality and e-loyalty. This implies that both dimensions should exist to obtain a better impact of e-service quality and e-loyalty among customers of OTAs.

The current study faced some limitations that can inspire researchers to conduct further studies. First, this study tested the effects of e-service quality dimensions (efficiency, privacy protection, and system availability) on e-loyalty through the mediating role of e-trust and e-satisfaction; however, other factors can be added and tested as moderators, such as hotel price, hotel review score, nationality, marital status, gender, and age. Second, the cross-sectional approach to data collection might have limited the accurate identification of causal effects between the research variables. Future studies may conduct a study with longitudinal data or collect data from different sources to validate or contradict the current study’s findings. Lastly, a multi-group approach can be further employed to compare these relationships in different distinct geographical or industry contexts.