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Article

Understanding the Antecedents of Use of E-Commerce and Consumers’ E-Loyalty in Saudi Arabia Amid the COVID-19 Pandemic

by
Fahad Ali Algamash
1,
Munir Shehu Mashi
2,* and
Mohammad Nurul Alam
3
1
Administrative and Technology Department, Applied College, King Khalid University, Abha 62529, Saudi Arabia
2
Department of Business Management, Federal University Dutsin-Ma, Dutsin-Ma 821101, Nigeria
3
Department of Management, Faculty of Business Administration, University of Tabuk, Tabuk 47512, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 14894; https://doi.org/10.3390/su142214894
Submission received: 5 October 2022 / Revised: 3 November 2022 / Accepted: 3 November 2022 / Published: 11 November 2022
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Abstract

:
Customer loyalty is a critical factor for any business’ long-term profitability and growth. Despite the rapid expansion and prominence of e-commerce, businesses still face challenges in attaining customers’ e-loyalty. The objective of this paper is to uncover the numerous factors that have an impact on e-loyalty. The data were gathered from 334 students from various universities in Saudi Arabia. With the aid of structural equation modeling (SEM), the hypotheses were examined. The findings support the links between effect expectation, social influence, and facilitating conditions on the use of e-commerce (UEC). However, the linkage between perceived risk and performance expectations on the UEC is not supported. In addition, the UEC predicts e-commerce satisfaction, and e-commerce satisfaction predicts e-loyalty. The paper also supports the UEC and e-commerce satisfaction as mediators. Importantly, the e-commerce experience is supported as a moderator of the connection between e-commerce satisfaction and e-loyalty. The paper recommends that online businesses assess the three constructs of effect expectation, social influence, and facilitating condition that are positively associated with the UEC. Various implications, both theoretical and practical, are highlighted.

1. Introduction

The most intriguing breakthrough in information and communication technology in recent years is the internet [1,2]. With improved internet access, in Saudi Arabia, the use of online shopping services is increasing locally. According to a study conducted by the Boston Consulting Group and Meta Platforms, the Saudi e-commerce market will reach SAR 13.3 billion by 2025, about 60% from 2019 to 2020, amid a recovery in the digital boom accelerated during the COVID-19 pandemic. The report also revealed that 14.26% of Saudi Arabia’s inhabitants are regular users, or 3.5 million internet users participate in e-commerce. The report’s findings show that Saudi Arabia has emerged as a suitable market for e-commerce activities among developing countries in the Middle East [1]; roughly 77% of Saudi consumers are shopping online more frequently now than they were before the COVID-19 pandemic. Thus, COVID-19 has been a great accelerator of digitalization.
In recent years, e-commerce has grown exponentially in the international markets. According to research [3,4], when a seller advertises a product on a website, the customer expresses approval, considers the product’s features, prices, and shipping options, buys an intriguing item, and then completes the checkout process. Adapting these products to specific markets and target customer groups increases online retail sales and reduces the cost of updates accessed by customers. This demonstrates the important role that e-commerce plays in determining whether online retailers can use innovative technologies to add value for their customers [3].
Coronavirus (COVID-19) has affected the nature of products being used and has had a significant impact on many products by cutting the global supply chain, despite having little effect on others, such as protective equipment and pharmaceutical products [5]. People are staying indoors, making purchases, studying, and operating from home as a result of the social isolation due to this virus, which has resulted in e-commerce for groceries at Walmart to grow by 74%. Moreover, media usage has increased during this period; Facebook and Google possess updated features to connect 44 individuals at once, as demonstrated by Facebook’s introduction of Messenger, in opposition to Zoom. Google too has released an updated version [6].
Developing and managing customer loyalty is directly connected to the profitability and continuing growth of a company [7]. Loyal customers visit their favorite websites twice as often as non-loyal customers, and loyal customers spend more money on buying products repeatedly [8]. Analysts estimate that between 35 and 40 percent of sales revenue on e-commerce websites comes from recurring users [9]. As a result, it is unsurprising that customer loyalty has been identified as a critical asset for e-retailers. According to [10], the high cost of acquiring new e-customers can result in unprofitable customer relationships for up to three years. As a result, it is extremely beneficial to determine the antecedents of customer loyalty in the Saudi Arabian context.
Many prior studies on e-commerce have been carried out in Western countries regarding the widespread use of e-commerce (UEC) and consumer loyalty [4,11]. However, these research studies were carried out in developed countries, where e-commerce is already flourishing, whereas such research has been scarce in emerging economies. Furthermore, the UEC and e-commerce satisfaction have not been examined as mediators concretely. Furthermore, in developing countries, where the majority of e-customers are risk avoiders, it is extremely important in shaping customer attitudes toward e-commerce [12]. Previously, only a few empirical studies have looked at how e-satisfaction affects e-loyalty [1,13]. However, research on the impact of the UEC and e-commerce satisfaction on e-loyalty is still scarce. Against this backdrop, the current study bridges the gap by considering how the UEC predicts e-commerce satisfaction, ultimately leading to an individual’s e-loyalty, especially during the COVID-19 pandemic [11].
This study offers many contributions: First, it investigates the role of perceived risk, performance expectation, effect expectation, social influence, and facilitating conditions in addition to the UEC and e-commerce satisfaction in customer e-loyalty in one model. Second, the goal of this research is to propose and test a model of the e-loyalty development process for online shopping regarding perceived risk, performance expectation, effect expectation, social influence, and facilitating conditions, attempting to develop a broader holistic framework (Refer to Figure 1). This study closes this research gap in Saudi Arabia. Thus, the objectives of this study are: (1) To examine the direct relationship between perceived risk, performance expectation, effect expectation, social influence, and facilitating conditions on the UEC. (2) To test the direct relationship between the UEC and e-commerce satisfaction. (3) To investigate the direct link between e-commerce satisfaction and e-loyalty. (4) To determine the role of e-commerce as a mediator in the link between perceived risk, performance expectation, effect expectation, social influence, and facilitating conditions on e-commerce satisfaction. (5) To test the mediating effect of e-commerce satisfaction on the relationship between the UEC and e-loyalty. (6) To test the moderating effect of e-commerce experience on the relationship between the UEC and e-commerce satisfaction. (7) To examine the moderating effect of e-commerce experience on the relationship between e-commerce satisfaction and e-loyalty.

2. Literature Review and Theoretical Framework

2.1. E-Loyalty

Oliver [14] defines loyalty as “a deeply held commitment to re-buy or re-patronize a preferred product/service consistently in the future, resulting in repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior”. Neal [15] defines customer loyalty as a percentage of the total number of shoppers in that category. The frequency with which shoppers choose the same product or service assumes that the category conveniently offers additional matching goods or services. In another definition of loyalty used by loyalty researchers, the existence of multiple definitions indicates that loyalty remains the subject of on-going research, and its components help scholars and professionals to understand the various implications of loyalty [16].
The significance of e-loyalty has been emphasized in the literature on online purchases. E-loyalty is commonly characterized as a customer’s enthusiastic attitude and devotion to an online merchant, which leads to repeat-buying behavior [17]. As a result, e-loyal customers increase the business’s profitability via the long-term low cost of attracting commitment from existing customers [1,18]. E-loyal customers are those who are willing to pay a premium rather than the lowest price [1]. They also tend to refer new customers to the online retailer, which provides a plethora of potential profit streams [17]. Furthermore, e-loyal customers can spend more and serve at lower operating costs than new customers [1]. The cost of building electronic loyalty is higher than traditional physical retailers, but post-relationship profit growth accelerates even faster [19].
The concept of e-loyalty has been discussed widely in various papers, including continuance intention [20,21], re-purchase intention [22,23], patronize intention [24,25], commitment [26], stickiness [27], and word of mouth [22]. Fu and Parks [28] distinguished three methods of loyalty: behavioral, integrated, and attitudinal. The behavioral observes customers’ proclivity to replication and continue previous purchases, whereas the attitudinal describes the psychological involvement of customers, and a sense of altruism toward a specific product or service [16]. The integrated approach combines both behavioral and attitudinal approaches to create fresh ideas about loyalty. There is widespread acceptance that assessing loyalty must take into account both behavioral and attitudinal characteristics [16].

2.2. Perceived Risk and UEC

Attitude toward using technology (ATUT), also known as user behavior, is defined as “a person’s overall affective reaction to using technology” [29]. As a result, the UEC refers to a person’s total emotional response to using e-commerce technology [30].
According to Lee, Hsieh, and Hsu [31], perceived risk is widely regarded as a significant barrier to workers’ intentions to use particular technology. Potential insecurity resulting from key-in data errors, software crashes, connection instability, and privacy loss can all be viewed as a risk [30]. Furthermore, internet crimes, such as internet hacking and threats, may cause customers to notice unauthorized access to their financial-related accounts, resulting in significant monetary loss. As a result, customers may decide to forego the potential benefits of using e-commerce, although e-commerce is easy and beneficial.
Previous research has found that perceived risk has a negative influence on actual user behavior [32,33,34]. In this study, customers will use e-commerce if it is perceived as a safe trading platform, safe from hackers, and free from avoidable financial risk. Therefore, the following hypothesis is proposed.
H1. 
Perceived Risk is negatively related to the UEC.

2.3. Performance Expectation and UEC

Performance expectancy refers to how much customers believe e-commerce improves their transaction experience, which influences their future intentions [35,36]. According to Bhattacherjee [37], when users believe that a system is beneficial to them, they will be satisfied and wish to continue using it.
The effects of different levels of experience on the relationship of performance expectancy with satisfaction, such as the effects of effort expectancy, require further investigation [38]. Previous research has found that performance expectations have a positive effect on actual use behavior [39,40,41]. According to the findings of earlier studies, customers believe that using e-commerce will improve their purchase efficiency.
E-commerce is regarded as a useful tool for increasing satisfaction, increasing the convenience of making the purchase, and allowing customers to complete business transactions in less time. Thus, performance expectation has been conceptualized as the degree to which customers believe that e-commerce will improve their transaction performance. As a result, the following hypothesis is proposed.
H2. 
Performance expectation is positively related to the UEC.

2.4. Effect Expectation and UEC

Effort expectancy refers to customers’ belief that performing e-commerce requires no big effort [29]. When customers use a website to search for information and make purchases, the amount of effort they must expend influences their decision to use the e-commerce system. For example, utilizing excessive security measures to win over customers’ trust by the accessible retailer makes the website more difficult to use, lowering customer satisfaction [42,43,44]. In the context of online banking, effort expectancy was reported to be linked to behavioral intentions to use e-commerce [45], and the relationship between ease of use and satisfaction [46].
Previous research has discovered that effort expectancy has a positive influence on actual use behavior [45,47]. According to the earlier findings, customers believe that learning and using e-commerce will make their transactions easier. In this regard, e-commerce is regarded as a simple and understandable tool that is easier to use and learn, necessitates less transaction time, and is straightforward [45]. As a result, the variable on effort expectancy has been conceptualized as the degree of customers based on the belief that e-commerce is simple to learn and apply. Therefore, the following hypothesis is proposed.
H3. 
Effect expectation is positively related to the UEC.

2.5. Social Influence and UEC

This construct is based on social support theory [48], which holds that instrumental, informational, and emotional support can motivate users to engage in positive behavior. This theory has been applied in several studies in e-commerce research to assess the impact of various factors on user behavior. For example, Liang, Ho, Li, and Turban [49] submitted that social support has a positive influence on users’ intentions to use e-commerce. Similarly, Hajli [50] reported that social support provided by information sharing increased trust, which improved purchase intentions on e-commerce platforms.
Social support may influence customer decision making and purchase intentions on e-commerce sites. As a result, earlier studies believe that social support via online media activities can influence trust, satisfaction, and purchase intentions on e-commerce platforms [51]. Furthermore, the quality aspect reflected in surface credibility may have an impact on purchase intentions. These activities facilitate user-to-user interactions about products or services via user-generated content, influenced by the e-retailer. Users can seek information and suggestions before shopping by participating in these activities, as well as reviewing the recommendations and comments of other online users [51]. Previous research has submitted that social influence has a positive effect on actual use behavior ([45,51,52]. In this study, it is expected that customers will use e-commerce if they are encouraged by others, influenced by family or peers, learn about others’ learning experiences, or believe that customers that use e-commerce have higher prestige. As a result, the following hypothesis is proposed.
H4. 
Social influence is positively related to the UEC.

2.6. Facilitating Condition and UEC

Facilitating conditions are described as facilitating factors that enhance and symbolize the individual’s view of having the organizational and technical architecture required to facilitate the use of information management [29]. This is a provision of assistance for users in the areas of computer hardware and software required to operate on e-commerce, e-commerce compatibility with other systems, and people who utilize e-commerce. The facilitating condition element was utilized in the UTAUT model [29].
According to previous research, the facilitating condition has a positive influence on actual use behavior ([45,51,52]; customers will use e-commerce if the system has adequate resources and capabilities, access to government resources, good support from web-store service providers, guidance from marketplace operators, and assistance from marketplace specialized instructors. As a result, the concept of facilitating condition is conceptualized as how customers perceive the availability of technical infrastructure and organizational facilities to support the UEC. Therefore, the following hypothesis is proposed.
H5. 
Facilitating condition is positively related to the UEC.

2.7. UEC and E-commerce Satisfaction

The UEC refers to the customer’s decision to purchase from an online website after weighing all of the factors that the customer considers important. Customers’ behavior can usually be predicted by their purchase intention, so it is critical to study this relationship [53,54]. Many authors have investigated the factors that influence purchase intent and discovered that satisfaction is extremely important. For example, Lee and Lin [55] and Tzeng, Ertz, Jo, and Sarigöllü [56] determined a strong association between customer satisfaction and purchase intent in their empirical studies. Similarly, Thakur, Ali, Gai, and Qiu [57] found that satisfaction has a significant impact on purchase intention in a study on the interrelationships between hotel website quality, perceived flow, customer satisfaction, and purchase intentions. Therefore, the following hypothesis is formulated.
H6. 
UEC is positively related to e-commerce satisfaction.

2.8. E-Commerce Satisfaction and E-Loyalty

Satisfaction is a measure of how delightful or not the level of fulfilment of a customer is [58]. This can result in customers’ intentions to re-purchase [55]. Customer satisfaction in the context of e-commerce is known as e-satisfaction [59,60]. Anderson and Srinivasan [61] defined e-satisfaction as the customer’s satisfaction with their previous e-commerce purchasing experience. E-commerce needs to improve customer satisfaction to retain customer e-loyalty [55]. According to Ribbink et al. [62], customer satisfaction with e-commerce is very important in deciding whether or not to continue the relationship with e-commerce. Customers who are satisfied with their previous purchases are more likely to re-purchase from the same e-commerce site [59]. Dissatisfied customers, on the other hand, are more likely to decline any programs for building closer relationships through e-commerce and to take steps to wean themselves off of it.
E-loyalty is defined as a consumer’s favorable attitude toward an online seller, which results in repeat purchase behavior [1,14,15,16,18]. Consumers intend to return to a website and repurchase from the same e-retailer [18]. Loyal customers are more frequent buyers than new customers. This commitment generates a substantial profit for online sellers by lowering the cost of acquiring new customers [18]. Loyal customers not only purchase products and services from e-retailers, but they also help to bring in new customers through positive word-of-mouth marketing [14,15].
Loyal customers have been discovered to be considerate even when things go wrong and, interestingly, to be willing to pay more [4]. E-retailers identify and recognize their loyal customers and take steps to meet their specific needs to maintain a mutually beneficial relationship [4]. Several studies have revealed the impact of e-satisfaction on e-loyalty [55,62,63]. It has been assumed that e-satisfaction is a natural precursor to e-loyalty [18,55,62]. Thus, we hypothesized the following.
H7. 
E-commerce satisfaction is positively related to e-loyalty.

2.9. The mediating role of Customer UEC

Previous research has suggested that effort expectation, effect expectation, social influence, perceived risk and performance expectation and facilitating condition has an indirect and positive impact on e-satisfaction via the mediating effect of e-commerce [1,14,15,16,18]. In this study, it is anticipated that these constructs will influence customers to adopt e-commerce and consequently influence their e-satisfaction. Therefore, the following hypotheses are formulated:
H8. 
UEC mediates the relationship between perceived risk and e-commerce satisfaction.
H9. 
UEC mediates the relationship between performance expectation and e-commerce satisfaction.
H10. 
UEC mediates the relationship between affect expectation and e-commerce satisfaction.
H11. 
UEC mediates the relationship between social influence and e-commerce satisfaction.
H12. 
UEC mediates the relationship between facilitating conditions and e-commerce satisfaction.

2.10. Mediating Role of E-Commerce Satisfaction

A customer’s loyalty or intent of loyalty is usually formed when the customer shows a positive attitude toward the vendor, product, or brand, and more importantly, this leads to repetitive buying behavior [16]. Previous studies have also found that increased electronic satisfaction leads to increased electronic loyalty and repurchase motivation [64]. In particular, a retail banking survey conducted by Vun, Harun, Lily, and Lasuin [65] highlighted the role of e-satisfaction as an intermediary. According to their research, customer satisfaction acts as a moderator for the relationship between quality of service and service loyalty. This study also stimulated future research on the role of satisfaction as an intermediary when the order of relationships between quality of service, customer satisfaction, and service loyalty is still unclear. As a result, the following hypothesis is made.
H13. 
E-commerce satisfaction mediates the relationship between the UEC and e-loyalty.

2.11. Moderating Role of E-Commerce Experience

An examination of the e-commerce literature reveals a lack of consensus on the definition of an online experience. The literature contains several terms for online experiences, such as “online customer experience” [66], “website experience” [67], and “online purchase experience” [45,67]. These distinctions are due to the nature of the experiences being described. For example, one school of thought takes a process-oriented approach and defines the online experience as “the personal interpretation of the online shopping process as a result of the customer’s interaction with various touch points during online shopping” [68]. Khalifa and Liu [69] argue that experience moderates the impact of satisfaction on repurchase intention. The purpose of this paper is to contribute to this research by examining the role of e-commerce experience in Saudi Arabia’s typical e-commerce model. This leads to the following research hypothesis.
H14. 
The relationship between the UEC and e-commerce satisfaction will be stronger when the level of customer e-commerce experience is high.
H15. 
The relationship between e-commerce satisfaction and e-loyalty will be stronger when the level of customer e-commerce experience is high.

2.12. Theoretical Background

The current paper is founded on the unified theory of acceptance and use of technology (UTAUT) [29]. Performance expectancy, facilitation conditions, social influence, and effort expectancy are the four main constructs that influence the UEC or behavioral intention in the UTAUT framework. According to [29], performance expectancy means that using online technology assists consumers in performing specific tasks (e.g., information search and other tasks in purchasing). The perception of a consumer’s control over behavior is reflected in the facilitation conditions. The consumer’s belief in the influence of others who believe they should use an online platform, such as blogs or websites, is referred to as social influence. The level of ease associated with the use of online platforms is represented by effort expectancy [29,70]. These constructs have had varying degrees of influence on customers’ intention to use e-commerce services.

3. Sample and Procedure

The study design is empirical and confirmatory. A questionnaire was given to a group of students in Saudi Arabia. Saudi university students were selected as the study sample for a variety of reasons. For example, it includes the authors’ belief that college students are good subjects for research studies in e-commerce because they have free access to the internet and can use it to communicate. Students often purchase e-commerce-friendly products, such as books and CDs. E-commerce and online behavioral research commonly use student samples and educational environments [1]. The survey was conducted by mailing questionnaires to two universities in Saudi Arabia and distributing the questionnaires to university students by email and in person. To ensure the reliability of the responses, the author instructed each subject to select one of her most frequently used websites and asked the participant a series of questions to determine the appropriate prompt to search for services. A total of 334 students completed questionnaires and used them for this study.

3.1. Measures

The current study’s questionnaire was adapted from previous studies. All responses to questions were graded on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). Performance expectation was measured using six items adapted from Venkatesh et al. [29]. Sampled items include: “I find that e-commerce is useful to me” and “Using e-commerce enables me to accomplish transactions more quickly”. Effort expectancy was measured using six items adapted from Venkatesh et al. [29]. Sampled items include: “Learning to use the e-commerce would be easy for me” and “It would be easy for me to become skillful using e-commerce”. Social influence was measured using six items adapted from Venkatesh et al. [29]. Sampled items include: “I feel people around me would encourage me to use e-commerce” and “People who are important to me would think that I should use e-commerce”. Facilitating conditions were measured using six items adapted from Venkatesh et al. [29] Sampled items include: “I have the resources necessary to use the e-commerce” and “Guidance was available to me in the selection of the e-commerce platforms”. Perceived risk was measured using six items adapted from Venkatesh et al. [29]. Sampled items include: “I would not feel safe providing my information to an e-commerce website”. The UEC was measured using six items adapted from Venkatesh et al. [29]. Sampled items include: “Using the e-commerce is a good idea” and I find using the e-commerce to be enjoyable”. E-commerce satisfaction was measured using three items adapted from Hsu et al. [71]. Sampled items include: “I am satisfied with the online shopping experience” and “I am pleased with the online shopping experience. The e-commerce experience was measured using three items adapted from Hao Balaji, and Kok Wei [72]. Sampled items include: “Online retailer’s website is convenient as it allows me to know and compare the products” and “The online retailer’s website creates a shopping experience that is appealing and enjoyable”. Customer loyalty was measured using four items adapted from Ribbink et al. [62]. Sampled items include: “I will recommend this online company to other people” and “I would recommend this company’s website to others”.

3.2. Data Analysis Method

Data were analyzed using IBM SPSS Statistics version 24.0 and SmartPLS version 3.2.7 [73]. The dispersion-based PLS-SEM approach was chosen because it can handle the complex model used in the proposed model of this study. In addition, PLS-SEM was chosen for its ability to simultaneously estimate causal relationships between all latent structures while dealing with structural model measurement errors [73]. Moreover, PLS-SEM is best suited for this study, as our study is descriptive [74]. Accordingly, the measurement model was evaluated separately before the structural model was evaluated. In addition, a common method variance test was performed to check the data quality and consistency of the structural model before performing the PLS-SEM analysis.

3.3. Common-Method Variance Bias Test

This study uses a Harman [75] one-factor test to determine the potential for systematic variance bias between variables. The researchers followed the guidelines and approaches of Podsakoff et al. [76] when performing the Harman [75] one-factor test. To this end, all items on the measurement scale were entered into principal component analysis using varimax rotation to allow the identification of all signs of a single factor from factor analysis. The results extracted five different factors from all items measuring composition, and the rotations converged in six iterations. The first factor accounted for 21.45%, which is less than the 50% proposed by Podsakoff et al. [76]. Based on these results, it can be concluded that this study does not suffer from deviations from conventional methods.

3.4. Respondents’ Profile

Table 1 below reports the results of the respondent’s profiles, including their gender, age group, course, and family income. With regards to gender, the majority are male (66.5%). Their age demonstrates that a majority are under 25 years (88.6%). Their course demonstrates that the majority are undergraduate students (79.9%). Their family income shows that the majority are within less than 5000 (56.3%).

3.5. Mean Values, and Correlation of the Study Variables

All of the constructs’ descriptive statistics and intercorrelations are listed in Table 2. All of the variables were found to have a substantial correlation with e-loyalty. With regards to the mean value, performance expectation has the lowest mean value (2.5714), while social influence has the highest mean value 3.8591.

3.6. Evaluation of Measurement Model (Outer Model)

Using the guidelines of Hair et al. [74] and Henseler et al. [77], all constructs were evaluated for reliability and validity in evaluating the reflection measurement model. The results showed that all configurations had factor loading values in the range of 0.70 to 0.90. In addition, all configurations were evaluated for composite reliability (CR) scores greater than the criticality level of 0.70 proposed by Cohen [78]. The mean extract variance (AVE) for all configurations was also higher than that for Hair et al. [74] who proposed a critical value of 0.50 (2021). The full validity and reliability results for all configurations are shown in Table 3 and Figure 2.
In addition, the HTMT ratio of the correlation was calculated. This is a modern tool for analyzing the discriminative validity of configurations. As a rule of thumb, an HTMT score above 0.85 indicates a potential problem with the validity of the discrimination [74]. All HTMT scores in this study were all below the 0.85 threshold, indicating that the validity of the discrimination was acceptable (see Table 4).

3.7. Assessment of Structural (Inner) Model

As depicted in Figure 2 and reported in Table 5, the R2 values of the model show that the model has acceptable explanatory significance. Perceived risk, performance expectation, effect expectation, social influence, and facilitating condition explained 48.1% variance in the UEC, while the UEC and e-commerce experience explained 14.8% variance in e-commerce satisfaction. Further, e-commerce experience and e-commerce satisfaction explained 19.5% variance in e-loyalty.
The Q2 test developed by Stone-Geisser [79] was used to assess the predictive relevance of a structural model. As a general rule, if the Q2 value is greater than zero, it indicates that the structural model has predictive relevance [74]. Our model’s Q2 values were 0.115 for e-commerce satisfaction, 0.126 for e-loyalty, and 0.290 for UEC, respectively, which supports the study’s underlying assumption that the endogenous constructs have strong predictive relevance. Furthermore, each construct was evaluated for the possibility of collinearity. Our findings show that collinearity is not an issue in our study because of the multicollinearity such that values do not exceed the cut-off value of 10.00 as recommended by Hair et al. [74] (refer to Table 5). Further, we calculated the effect size (f2). The values of f2 0.02, 0.15, and 0.35 appeared as small, medium, or large [31]. Finally, the model fits were tested using the model-fitting parameters recommended using PLS [80]. The standardized root mean square residual (SRMR = 0.044) was lower than 0.08, and the normed fit index (NFI = 0.914) was higher than 0.90.

3.8. Hypotheses Testing

It was found that the relationship between perceived risk and UEC (β = −0.01; t-value = 0.385; p = 0.385) is not significant. Thus, H1 is not supported. Similarly, it was found that the relationship between performance expectation and UEC (β = 0.045; t-value = 1.039; p = 0.151) is not significant. Thus, H2 is not supported. With regards to H3, which is the relationship between effect expectation and UEC (β = 0.219; t-value = 5.512; p = 0.000), it is supported. The proposed relationship between social influence and UEC (β = 0.158; t-value = 3.319; p = 0.001) is also significant, and thus H4 is supported. Additionally, a strong positive relationship of (β = 0.581; t-value = 16.410; p = 0.000) between facilitating condition and UEC provides support for H5. The findings of the SEM analysis support H6, demonstrating a significant and positive direct relationship (β = 0.201; t-value = 5.103; p = 0.000) between UEC and E-commerce satisfaction. Lastly, the findings support H7, demonstrating a significant and positive direct relationship (β = 0.669; t-value = 3.284; p = 0.001) between e-commerce satisfaction and e-loyalty. Table 6 and Figure 3 provide a concise summary of these findings.

3.9. Tests for Mediation

This study uses a nonparametric bootstrap method to assess the importance of mediation effects [74]. Next, we evaluated the mediation effect. To assess the importance of Henseler et al. [77] and Preacher and Hayes [81] using the PLS-SEM approach, a bootstrapping procedure was used following the non-parametric path modeling approach. Table 7 shows the beta values for the indirect effects. Based on the findings of indirect effect, H8 (β = −0.003; t-value = 0.267; p = 0.395) is not supported. H9 (β = 0.009; t-value = 0.858; p = 0.196) is not supported. H10 (β = 0.044; t-value = 3.551; p = 0.000), H11 (β = 0.032; t-value = 2.350; p = 0.010), H12 (β = 0.117; t-value 4.985; p = 0.000), H13 (β = 0.135; t-value = 2.552; p = 0.006) are all supported because the t-values exceed the critical value of 1.645 at the 95% level of significance, and the bias-correlated and accelerated confidence interval does not straddle a zero in between (i.e., when the lower limit has a negative and the upper bound has a positive sign) [82]. Variance accounted for (VAF) determines the size of the indirect effect concerning the total effect (i.e., direct effect + indirect effect) [74].
H14: E-commerce experience moderates the relationship between the UEC and e-commerce satisfaction such that the relationship is stronger when the level of customer e-commerce experience is high (β = −0.024, t = 0.235, p =0.407). This hypothesis is not supported (refer to Table 8). H15: E-commerce experience moderates the relationship between e-commerce satisfaction and e-loyalty such that the relationship is stronger when the level of e-commerce experience is high (β = 0.077, t = 1.699, p = 0.046). Hence, Hypothesis 14 is supported. Consequently, Figure 4 depicts that the relationship between e-commerce satisfaction and e-loyalty is more positive and stronger for customers high in e-commerce experience than for customers low in e-commerce experience. This indicates that e-loyalty increases when both e-commerce satisfaction and e-commerce experience are high (refer to Figure 4).

4. Discussion

The purpose of this research is to uncover the various factors that influence e-loyalty. The hypotheses were tested using structural equation modeling (SEM). The findings support the relationships between effect expectation, social influence, and facilitating conditions on the UEC. As a result, it can be concluded that effect expectation, social influence, and facilitating conditions have a significant positive effect on the intended UEC. This finding is in line with prior studies [42,43,44,51]. This finding could be explained by the fact that the organizations that used online platforms in Saudi Arabia are providing adequate technical support for online shopping systems. Promotional conditions are a major factor influencing the intent of consumers to purchase products and services through online shopping in Saudi Arabia. Further, the rationale for this finding of social influence predicting the UEC could be that the positive impact of the respondents’ friends and family on the UEC may have a significant effect on their intention to use e-commerce in this context.
Surprisingly, the relationship between perceived risk and UEC and performance expectations on the UEC is not supported. As a result, this finding does not support the UTAUT model that predicts the connection between perceived risk and performance expectation on online shopping intention. The rational justification for this finding could be that internet users in Saudi Arabia may not perceive e-commerce to be simple to use. Additionally, they may likely perceive online shopping to be risky, especially since many hackers are present on many sites [65].
In addition, the UEC predicts e-commerce satisfaction, and e-commerce satisfaction predicts e-loyalty. This important finding is in line with the earlier studies [53,54,55]. The paper also supports the UEC and e-commerce satisfaction as mediators. Importantly, e-commerce experience was found to moderate the relationship between e-commerce satisfaction and e-loyalty. It is an important finding that the e-commerce experience aspect should not be ignored in ensuring e-loyalty among customers.

4.1. Theoretical Implications

The study also extends the UTAUT model by incorporating the effect expectation, social influence, and facilitating condition on the UEC, e-commerce satisfaction, and e-loyalty construct and restructures the variable interrelationships. According to the findings, the paper also supports the UEC and e-commerce satisfaction as mediators. Importantly, e-commerce experience was found to moderate the relationship between e-commerce satisfaction and e-loyalty.
Based on the focus of this study, which is to determine the antecedents of e-commerce, e-commerce satisfaction, and e-loyalty in Saudi Arabia using the UTAUT research framework, this research expanded the theory that can be used in the e-commerce context. As a result, this research contributes to the development of a comprehensive integrated model for the UEC to improve customers’ loyalty.

4.2. Practical Implications

The study contributes significantly to management by presenting various methods for online businesses to foster their customers’ e-loyalty. According to the findings of this study, vendors should approach their online business with a relationship-oriented mindset. Online businesses can assess the three constructs of effect expectation, social influence, and facilitating condition, that are positively associated with the UEC. Online businesses must constantly deliver superior facilitating conditions and increase consumer happiness and satisfaction with every transaction to develop high levels of relational quality.
Satisfaction with the knowledge and engagement provided is insufficient to encourage users to share the website with others. Managers must understand the importance of satisfaction in providing this motivation to use e-commerce services. Before they are inspired to urge others to see the website, consumers must have faith in the information provided on it. As a result, managers must be vigilant in their pursuit of being up to date and not defrauding users.
The study has significant practical applications for internet marketers and merchants in Saudi Arabia. The study’s main finding confirms the impact of e-commerce consumer happiness, which then has an impact on online shoppers’ loyalty to a website. The paper assists e-retailers by providing an understanding of the dynamics of the impact of various antecedents on customer satisfaction with website-related services. Fast delivery times can increase customer satisfaction, as can a variety of delivery options that persuade customers to use a company’s products.
Firms should remember that effort expectancy is more effective on the customers’ intention to use e-commerce. As a result, businesses should invest in improving the usability and friendliness of the online shopping medium. Finally, regardless of the target market, social influence is a critical factor that e-vendors should always invest in. In a digital world, organizations need to constantly reform their strategies and seek development through innovation. Knowledge and intellectual capital related to online and mobile technologies are important business assets and sources of competitive advantage for organizations in competing for part of the digital-commerce pie. In this regard, the conclusions of the study may provide useful implications and insights that can be used in business technology to create and develop more effective and efficient user interfaces and improve the e-commerce user experience in the context of developing a competitive advantage in Saudi Arabia.

4.3. Limitations and Future Research

Though the research was carried out, several limitations can be noted, which can also be interpreted as recommendations for future research. First, the questionnaire could be expanded to include other countries, such as Nigeria and India, and compare the results to properly extend the findings. Second, the model could be examined concerning various fashion brands, as well as fashion products. Third, it would be interesting to conduct a longitudinal study to establish causation.

5. Conclusions

In conclusion, this paper supports various influences, i.e., effect expectation, social influence, and facilitating conditions on the UEC. In addition, the UEC predicts e-commerce satisfaction, and e-commerce satisfaction predicts e-loyalty. The paper also supports the UEC and e-commerce satisfaction as mediators. Importantly, e-commerce experience was found to moderate the relationship between e-commerce satisfaction and e-loyalty. Various theoretic and useful implications are highlighted. The paper recommends that online businesses can assess the three constructs of effect expectation, social influence, and facilitating condition that are positively associated with the UEC.

Author Contributions

Conceptualization, F.A.A. and M.S.M.; methodology, F.A.A.; software, M.S.M.; validation, M.N.A. and F.A.A.; formal analysis, M.S.M.; investigation, F.A.A.; resources, F.A.A.; data curation, M.N.A.; writing—original draft preparation, M.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Measurement model.
Figure 2. Measurement model.
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Figure 3. Structural model with t-values.
Figure 3. Structural model with t-values.
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Figure 4. Interaction effect of e-commerce satisfaction and e-commerce experience on e-loyalty.
Figure 4. Interaction effect of e-commerce satisfaction and e-commerce experience on e-loyalty.
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Table
Table 1. Profile of respondent.
Table 1. Profile of respondent.
CharacteristicsFrequenciesPercentage
Course
Undergraduate26779.9
Post-graduate6720.1
Age
Up to 25 years29688.6
Above 25 years3811.4
Gender
Male22266.5
Female11233.5
Family income (per month, SAR)
Less than 500018856.3
25,000–40,000117350
More than 40,000298.70
Table
Table 2. Mean values, and correlation of the study variables.
Table 2. Mean values, and correlation of the study variables.
Variables123456789Mean
1 PR1 3.5534
2 PE−0.373 **1 2.5714
3 EE−0.221 **0.0851 3.0481
4 SI−0.0430.131 *0.156 **1 3.8591
5 ECS0.127 *0.114 *0.337 **0.256 **1 2.9457
6 E-Loyalty−0.020−0.0160.369 **0.217 **0.414 **1 2.9264
7 FC−0.1020.141 **0.134 *0.140 *0.189 **0.387 **1 3.4067
8 UEC−0.135 *0.133 *0.308 **0.260 **0.253 **0.353 **0.564 **1 3.3577
9 ECE−0.114 *0.233 **0.254 **0.129 *0.327 **0.280 **0.323 **0.339 **13.3811
NOTE: **. Correlation is significant at the 0.01 level (2-tailed), *. Correlation is significant at the 0.05 level (2-tailed), PR: perceived risk, PE: performance expectation, EE: effect expectation, SI: social influence, ECS: e-commerce satisfaction, FC: facilitating condition, UEC: UEC, ECE: e-commerce experience.
Table
Table 3. Results of CFA for the measurement model.
Table 3. Results of CFA for the measurement model.
VariableLoadingsCRAVE
E-commerce satisfaction 0.9320.821
ECSF10.966
ECSF20.769
ECSF30.969
Effect Expectation 0.8980.691
EE30.726
EE40.914
EE50.750
EE60.915
Facilitating Condition 0.9760.872
FC10.914
FC20.952
FC30.895
FC40.936
FC50.957
FC60.949
E-Loyalty 0.8920.676
LOY10.784
LOY20.683
LOY30.904
LOY40.896
E-commerce Experience 0.9670.907
OSE10.978
OSE20.916
OSE30.963
Performance Expectation 0.8300.502
PE10.642
PE20.846
PE30.526
PE40.638
PE50.836
Perceived Risk 0.9790.885
PR20.972
PR30.965
PR40.934
PR50.907
PR60.919
Social Influence 0.9190.656
SI10.823
SI20.870
SI30.783
SI40.784
SI50.817
SI60.778
UEC 0.9410.764
UECO20.642
UECO30.875
UECO40.945
UECO50.925
UECO60.944
NOTE: CR—composite reliability, AVE—average extract variance.
Table
Table 4. Discriminant validity: heterotrait–monotrait ratio (HTMT).
Table 4. Discriminant validity: heterotrait–monotrait ratio (HTMT).
Variable123456789
1 E-commerce Experience
2 E-commerce satisfaction0.358
3 E-Loyalty0.3150.481
4 Effect Expectation0.2060.2460.346
5 Facilitating Condition0.3370.2050.4290.095
6 Perceived Risk0.1180.1370.1090.1930.105
7 Performance Expectation0.2880.2080.1520.2280.1850.390
8 Social Influence0.1410.2790.2510.1730.1490.0850.181
9 UEC0.3650.2870.4050.3210.6010.1470.1870.286
Table
Table 5. Structural model assessment.
Table 5. Structural model assessment.
R Square R SquareR Square Adjusted
E-commerce satisfaction0.1480.143
E-Loyalty0.1950.191
UEC0.4810.473
f Square E-commerce satisfactionE-LoyaltyUEC
E-commerce Experience0.0720.030
E-commerce satisfaction 0.141
Effect Expectation 0.088
Facilitating Condition 0.620
Perceived Risk 0.000
Performance Expectation 0.003
Social Influence 0.046
UEC0.041
Inner VIF Values E-commerce satisfactionE-LoyaltyUEC
E-commerce Experience1.1431.127
E-commerce satisfaction 1.127
Effect Expectation 1.053
Facilitating Condition 1.049
Perceived Risk 1.124
Performance Expectation 1.128
Social Influence 1.050
UEC1.143
Fit SummaryTestValue
SRMR0.044
NFI0.914
Q-Square SSOSSEQ² (=1-SSE/SSO)
E-commerce satisfaction1002.000887.0000.115
E-Loyalty1336.0001167.2750.126
UEC1670.0001186.4930.290
Table
Table 6. Path coefficient (direct effect) results.
Table 6. Path coefficient (direct effect) results.
RelationshipsBeta
Value		T Valuep-Value5.0%
CI		95.0%
CI		Decision
H1 Perceived Risk -> UEC−0.010.2930.385−0.0960.054Not Supported
H2 Performance Expectation -> UEC0.0451.0390.151−0.0210.103Not Supported
H3 Effect Expectation -> UEC0.2195.5120.000 *0.1500.276Supported
H4 Social Influence -> UEC0.1583.3190.001 *0.0750.227Supported
H5 Facilitating Condition -> UEC0.58116.4100.000 *0.5270.644Supported
H6 UEC -> E-commerce satisfaction0.2005.1030.000 *0.1520.281Supported
H7 E-commerce satisfaction -> E-Loyalty0.6693.2840.001 *0.3051.032Supported
Note: * Significant at 0.01 (1-tailed), CI—confidence intervals.
Table
Table 7. Structural model assessment indirect (mediating) effect.
Table 7. Structural model assessment indirect (mediating) effect.
RelationshipsBetaT Valuep-Value5.0% CI95.0% CIDecision
H8: PR -> UEC -> ECS−0.0030.2670.395−0.0200.013Not Supported
H9: PE -> UEC -> ECS0.0090.8580.196−0.0050.028Not Supported
H10: EE -> UEC-> ECS0.0443.5510.000 *0.0310.072Supported
H11: SI -> UEC -> ECS0.0322.3500.010 *0.0140.054Supported
H12: FC -> UEC -> ECS0.1174.9850.000 *0.0920.168Supported
H13: UEC -> ECS -> E-Loyalty0.1352.5520.006 *0.0620.233Supported
Note: * Significant at 0.01 (1-tailed). CI—confidence intervals, PR—perceived risk, PE—performance expectation, EE—effect expectation, SI—social influence, ECS—e-commerce satisfaction, FC—facilitating condition, UEC = UEC.
Table
Table 8. Structural model assessment moderating effect.
Table 8. Structural model assessment moderating effect.
RelationshipsBetaT Valuep-Value5.0%95.0%Decision
H14: UEC*ECE -> ECS−0.0240.2350.407−0.2080.122Not Supported
H15: ECS*ECE -> E-Loyalty0.0771.6990.0460.1550.006Supported
Note: * Significant at 0.01 (1-tailed), CI—confidence intervals, ECS—e-commerce satisfaction, FC—facilitating condition, UEC = UEC, ECE—e-commerce experience.
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MDPI and ACS Style

Algamash, F.A.; Mashi, M.S.; Alam, M.N. Understanding the Antecedents of Use of E-Commerce and Consumers’ E-Loyalty in Saudi Arabia Amid the COVID-19 Pandemic. Sustainability 2022, 14, 14894. https://doi.org/10.3390/su142214894

AMA Style

Algamash FA, Mashi MS, Alam MN. Understanding the Antecedents of Use of E-Commerce and Consumers’ E-Loyalty in Saudi Arabia Amid the COVID-19 Pandemic. Sustainability. 2022; 14(22):14894. https://doi.org/10.3390/su142214894

Chicago/Turabian Style

Algamash, Fahad Ali, Munir Shehu Mashi, and Mohammad Nurul Alam. 2022. "Understanding the Antecedents of Use of E-Commerce and Consumers’ E-Loyalty in Saudi Arabia Amid the COVID-19 Pandemic" Sustainability 14, no. 22: 14894. https://doi.org/10.3390/su142214894

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