Website quality has emerged as kind of strategic imperative across industries, with the Internet basically changing how organizations and customers meet, forcing firms to treat websites as main distribution channels.^11,13 However, the literature is not much clear, like there is some conceptual fuzziness about what “website quality” actually consists of in terms of dimensions, some authors suggest a simple three-part scheme (usability, content, and organization),¹⁴ whereas others push for wider structures that add design, reliability, responsiveness, and information accuracy.^2,15 As this no one really agrees, cross-study comparisons become a bit messy, and whatever generalization you want to make is harder to justify.

On top of that, the empirical results about which quality facets matter most are also mixed. Scott and Franssen et al. leaned toward content richness as the most important, however, Stevko et al.^16,17 instead prioritized structural soundness and reliability. These differences could mean the weight of each dimension shifts depending on setting, but only a few studies look carefully at the moderating drivers that might explain why effects diverge.

More crucially, most of the earlier work has been carried out in developed-country contexts, so there is this noticeable shortfall for developing economies. In those places tech infrastructure, digital literacy, and what consumers expect can vary a lot, or so it seems. Al-dweeri et al.¹⁸ reported that website usability and organizational structure are positively related to e-loyalty, but whether those findings travel over to emerging markets—where internet access is limited and digital maturity is uneven—is still not settled. The existing body of work, overall, has mostly treated website quality, without really considering the broader environment.

E-loyalty, basically customers sustained, positive feelings and the habit of coming back, again and again, to digital platforms ^7,19 is now kind of a main worry for academics and people in practice. Even so, the existing research feels a bit messy, because there are several inconsistencies and gaps that really need a closer look. There is still this ongoing argument about whether cognitive factors or affective ones matter more for e-loyalty. Some studies focus on satisfaction and trust as mental, kind of “pre-steps”,^3,4 while others stress emotional attachment, and the overall experiential quality.²⁰ Because of this theoretical fragmentation, it becomes difficult to build one consistent way of explaining how e-loyalty forms in the first place.

Additionally, the relationship between online loyalty and offline loyalty is not clear enough. Some work implies that brand loyalty can flow from one channel to another,²¹ however other researchers argue that e-loyalty is, in fact, its own thing and needs to be conceptualized separately. Their reasoning includes lower switching costs, sharper price transparency, and less face-to-face interpersonal interaction once people move into digital environments.²² Of special note, the antecedents of e-loyalty in banking settings have mostly been investigated within Western economies. Miguens and Vazquez¹² reported that e-satisfaction, e-trust, and switching barriers have direct effects on e-loyalty in developed banking markets, whereas Kaya et al. (2019)⁴ observed meaningful links between e-service quality, e-satisfaction, and e-loyalty in an emerging economy. However, they did not clearly spell out how organizational knowledge capabilities might moderate those relationships.

The literature has disproportionately put emphasis on the direct effects of service quality on e-loyalty, but it has payed limited attention to organizational processes that could help or maybe even disrupt this connection. Even though Karim et al. and Dianat et al.^10,11 did recognize the part of electronic customer relationship management, in steering e-loyalty, these studies didn’t really go through, in a systematic manner, how knowledge-based capabilities team up with website quality in order to produce loyalty outcomes. This kind of gap is especially concerning, because banks in developing countries often operate with scarce resources for improving their web presence and they have to figure out leverage points that squeeze the most return from digital investments.

Customer knowledge management is considering as sort of an evolution, from the classic knowledge management and the customer relationship management paradigms, but with a more strategic angle, where customer insights are integrated into organizational processes.²³ Even so, the CKM literature has, despite the conceptual appeal, a lot of conceptual confusion about what it includes and where it ends. The papers often separate four types of knowledge, knowledge about consumers (so demographics and psychographic profiles), knowledge for consumers (product and service information), knowledge from consumers (customer-generated ideas and feedback), and knowledge among consumers (peer-to-peer exchanges), however, they still do not really spell out how these types differ in terms of effects on organizational outcomes. Because of that, the operational definitions vary, and empirical synthesis becomes awkward or inconsistent.²⁴ When it comes to results on CKM consequences, the empirical findings are mixed. Some scholars show positive effects on innovation, satisfaction, and loyalty,^6,25 while others warn that leaning too heavily on customer knowledge may push organizations toward incremental improvements, instead of more radical innovation, or may generate lock-in effects that actually limit organizational learning.⁵

Sitting on top of the DeLone and McLean IS Success Model and Relationship Marketing Theory, this section develops hypotheses about how website quality dimensions, customer knowledge management (CKM), and e-loyalty relate, kind of linked up.

2.4.1 Website Quality and E-Loyalty

The relationship between website quality and e-loyalty is already pretty well-known, but the results still feel fragmented across the different quality dimensions. In earlier work, researchers treated website quality like it was one overall thing.^26,27 Later, studies broke quality into separate pieces, such as content, design, organization, and ease of use. Dumbrell and Steele²⁸ showed that content quality has a significant effect on e-loyalty, and that result was echoed by Boateng²⁹ and Valacherry and Pakkeerappa,³⁰ who also found that both content and design drive e-satisfaction and loyalty. Casaló et al.³¹ and Lee and Kozar³² carried these insights further by adding organizational structure and design, and Shankar et al.³³ put extra weight on ease of use as well as site organization. Gibbert et al.²³ and Mocanu³⁴ in the same direction strengthened the idea that content and design quality matter a lot.

However, the relative importance of each dimension does not stay constant across studies, which hints at contingency effects that are not examined enough. Additionally, a big share of the evidence comes from developed economies, so it may not transfer cleanly to developing-country banking contexts, you know. Even so, the overall weight of evidence points toward positive relationships, so:

2.4.2 Website Quality and Customer Knowledge Management

Emerging evidence suggests website characteristics meaningfully influence CKM processes, like really. Gebert et al.³⁵ found that content and organizational quality enhance customer retention and even deeper customer understanding, in practice. Zanjani et al.³⁶ addressed the role of website usability in CKM, while Taherparvar et al.³⁷ and Rastgar et al.³⁸ looked at how website design and organization make it easier for knowledge acquisition about customers as well as from customers. Still, the literature feels underdeveloped when it comes to the separate effects of particular quality dimensions on different CKM types, and the causal direction is still quite unclear, mainly because the studies are cross-sectional. Even with those limits, the theoretical logic backs the following:

2.4.3 Customer Knowledge Management and E-Loyalty

The CKM-e-loyalty relation is theoretically rooted in relationship marketing, it argues that knowledge capabilities support relational results via personalization, and careful trust building. Garg and Jain³⁹; Chou et al.⁴⁰ showed that website usability, reliability, and content can make people revisit, and that this leads to e-loyalty, all of it, even if not always said directly depends on effective CKM. Still, earlier studies were a bit uneven on whether CKM works as an antecedent or as a moderator, so there is this notable theoretical hole. Even so, companies that manage customer knowledge more effectively should form tighter relational bonds:

A convenient sampling method has been employed for the primary collection of data. The subjects of this study were consumers of Rafidain Bank in Nineveh; random samples were selected from different locations. The survey was conducted from October 2023 to December 2023, spanning three months. After screening the questionnaire and sending it to 550 consumers, 311 valid responses were received. This is a large enough sample size for PLS-SEM. Data analysis yielded meaningful results. Another part of the questionnaire asked the sample representatives to provide their age, gender, education, and profession. In the second part, participants have given their assessment of the research constructs. For this study, non-probabilistic simple random sampling was employed to select the target sample, as it was deemed the most suitable approach for this research. Additionally, the researcher faced unique time pressures to collect data and comply with consumers’ requirements simultaneously.

For this study, the questionnaire serves as the primary research tool. The original form of the questionnaire was written in Arabic. After that, it has been translated into English. Based on the variables examined in the study, a research tool was created. The tool incorporates elements from earlier research, modified to meet the needs of the present study. The survey was broken down into four primary sections. The first section (1) includes information concerning demographics, and the second section (2) includes additional information. Comprises survey questions about the quality of the information on his website. The third part (3) consists of survey questions about E-Loyalty, and the fourth part (4) consists of survey questions about Customer Knowledge Management.

The study employed a mostly quantity approach, and the sample was chosen through simple random sampling, which efforts each member of the population gets a fair chance of selection. This is meant to help keep objectivity up and reduce bias, which in turn supports generalizability too. The sampling frame came from lists of active customers of Iraqi commercial banks, specifically those who regularly use electronic services. Even with these benefits, the method still brings a few caveats, like the tricky part of getting a complete sampling frame and the risk of non-response, yet steps were taken such as continuous follow-up, and also using multiple distribution channels to lessen the impact. The questionnaire was developed by applying a 5-point Likert scale to evaluate and explore the opinions provided by individuals. There are five levels on the rating scale, including “Strongly Disagree” (1), “Disagree” (2), “Neutral” (3), “Agree” (4), and “Strongly Agree” (5). The questionnaire consisted of 42 items. It included: (A) 18 items of^41,42 relating to Website Quality with four dimensions: Content Quality (five items: e.g., “the website easy navigation and readability of information on the website”); Design quality (five items: e.g., “the website has been designed to ensure effective search functions and to attractive consumers”); Organizing Quality (four items: e.g., “the website is well-structure and the ability of quick access the needed information”); Example of use (four items: e.g., “the website loads quickly and works very well technically”) (B) 8 items of^4,18 associated with the E-Loyalty (e.g., “I used to recommend this site to other people” “I am a regular visitor of this sit”) and (C) 16 items of^27,29 relating to Customer Knowledge Management (e.g., “The bank used to make surveys in to know more about consumers satisfaction” “The bank reacts quickly and flexibly to customer inquiries.”). The survey was given out in May 2025. As a valuable method for gathering data and information on this topic, the survey employed a self-administered questionnaire. Questionnaires can be sent to multiple samples using the self-administered method, thereby reducing interview costs and minimizing interview bias. The survey was created in Google Forms and was designed to take no more than 10 minutes to complete. A total of 410 questionnaires were distributed, with 311 responses, for a response rate of 76%.

The current study included a group of adult participants. Informed verbal consent was obtained prior to data collection. No minors participated in the study; therefore, parental consent was not required. The sample of the study consisted of a random group of bank customers who had been previously informed of the research objectives and their participation. Because the questionnaire was electronic and anonymous, no written consent was required from respondents, and the questionnaire did not collect any personal information or pose any risks.

Before proceeding to analyze the relationships between variables, it is necessary to first describe the demographic characteristics of the study sample, as this information provides a deeper understanding of the context in which the data was collected, and contributes to a more accurate interpretation of the results. Table 1 presents the characteristics of the respondents targeted in this work.

To highlight the variables used in this work, the author has developed a specific model to support the study’s orientations. Figure 1 below refers to the said model.

In this section, several tests were adopted to ensure the quality criteria of variables:

Table 2 presents the kurtosis and skewness measures used to determine whether the data are typically distributed. According to the scale, data are considered normally distributed if the skewness value falls within the range of ±2 and the kurtosis value fall within the range of ±7. According to the results mentioned in Table 2 below, the data are typically distributed. Note that Smart Pls can be applied to normally or abnormally distributed data.

Table 3 below explained the convergent validity of the measurement, using both Cronbach’s Alpha and CR, which are adopted as measures of the internal consistency reliability of the research variables. As the value of Composite Reliability should be between 0.7–0. 99 As for Cronbach’s Alpha, it is assumed that it is limited to 0.6–0. 99. By reading the table above, the scale used to measure the variables is reliable and carries great credibility because all values are within the acceptable range. The AVE index, which indicates the degree of variance explained by each variable in the study, is shown in Table 3. AVE states that the relationship of each variable to itself must be greater than the relationship of the same variable with other variables in the matrix. The Factor Loadings indicator is concerned with determining the suitability of the questions or items used to measure each of the research variables. It also indicates that these elements are suitable for measuring a variable without others. Referring to the data from the current research, the Factor Loadings for all indicators are greater than 0.5, indicating the validity of the questions used to measure each variable.

Whereas the VIF indicator has been used to measure the linear relationship between the independent variables, it is assumed that there are no such relationships between the independent variables. To be sure, the values of VIF are confined between 0.10 and 10.00 for each independent variable. Referring to the data in Table 4, it is clear that there are no significant relationships between the independent variables, indicating that the current research data are sound and ready for analysis using the SmartPLS program.

According to Table 4 above, the Fornell-Larcker criterion is employed to verify the discriminant validity of the measurement models. The square root of the mean variance extracted by the item itself must be greater than the correlation between the item and any other item within the construction.

Table 5 “below” and Figure 2 introduce the findings of the developed hypotheses. As shown in Figure 2, Organizing quality and Ease of Use quality have a positive effect on e-loyalty, with β = 0.224 (p < 0.01) and β = 0.244 (p < 0.01), respectively, supporting H3 and H4. In contrast, Content quality (β = 0.036, p < 0.01) and Design quality (β = 0.004, p < 0.01) are negatively related to e-loyalty; H1 and H2 are rejected. The researcher believes that the above results cannot be generalized, and further research is needed on this issue to expand the sample size and include a larger number of participants. In the other hand, Content quality, organizing quality, and Ease of use quality are positively related to Customer knowledge management with (β = 0.254, p < 0.01), (β = 0.342, p < 0.01), (β = 0.330, p < 0.01) supporting H5, H7, and H8. Contrary to expectations, Design quality doesn’t show a positive effect on Customer knowledge management, H6 is rejected. The path between Customer knowledge management and e-loyalty is significant (β = 0.429, p < 0.01), indicating that Customer knowledge management is positively related to e-loyalty (supporting H9).

As such, it is rational to conclude that the effect of website quality criteria is positively related to e-loyalty through the moderating effect of Customer knowledge management. In this respect, Customer knowledge management has played a key role as a moderator in this study, and H9 was supported. H1 and H2 are not supported because they do not fulfill the relationship with e-loyalty. The P-value indicates the likelihood of accepting or rejecting the hypotheses, as well as interpreting the relationship between the variables, which depends on the chosen significance level (typically 0.05, 0.01, or 0.0001). The table data indicates that all relationships between constructions are strong and have been accepted, except for H1 and H2. As for the T-statistic values, they are associated with P-values. The stronger the relationship (P < 0.000), the higher the T values. As for F2 values, it is used to measure the size of the effect of the independent variable on the dependent variable. If the F2 values are equal to or less than 0.02, it indicates a weak effect. If it equals 0.15, it indicates the average impact; if it equals 0.35 or more, it indicates a strong and influential impact.

First, as with any study, several research limitations of the current study should be considered. The current study was conducted in a specific region of the country (Iraq), represented by Nineveh Governorate, and therefore does not reflect the entire country. Similarly, the current work was carried out at the Rafidain Bank branch in Nineveh Governorate, so there may be differences in other branches of the bank located in different cities. Second, the sample size of the current study, comprising 311 participants, is considered acceptable but could be maximized for future generalizability. A larger sample size could be even more beneficial. Third, the study focused on the services provided by banks and did not address industrial companies with specific products in terms of testing website quality. Investigate the quality of industrial companies’ websites and examine their impact on other variables.

The results of the current study could enrich the body of knowledge. Understanding Iraqi bank customer behavior through the Internet involves designing a website and determining its quality standards, managing customer knowledge online, assessing the loyalty level of current customers, and maintaining this loyalty. Therefore, banks have the potential to manage customer knowledge, encompassing knowledge about customers, knowledge about customer interactions, and knowledge about customer preferences, through their websites. Furthermore, through website quality, banks can gain customer trust in the services provided and the resulting satisfaction and loyalty. Based on these results, banks can now improve the quality of their websites and incorporate advanced, modern standards to have a significant impact on increasing customer satisfaction, trust, and online loyalty.

The results of this study shed light on several important issues related to the concepts of Website Quality, E-loyalty, and Customer Knowledge Management. Not addressed in previous studies (Locally). First, the study confirms that website quality, especially its criteria features such as organization and ease of use, has a significant impact on launching E-loyalty through a direct relationship. In contrast, website criteria such as content and design have been shown to hurt e-loyalty.

In addition, this concept will be significantly enhanced when we use customer knowledge management as a moderator between Website Quality and E-loyalty (Indirect Impact). In the context of banks, e-loyalty is a significant challenge to reach. This is due to the establishment of the consumer’s mental image, which is crucial for the first interface design in marketing strategies, particularly in terms of ease of navigation and usability. Hierarchical multiple regression analysis showed that moderators of customer knowledge management partially moderating the satisfaction Relationship between website quality and E-Loyalty. Banks also need to ensure their websites are visually appealing and update the information and user guides provided.

The research presented in this article had limitations. The author deliberately selected the most common methods for evaluating websites for banks, focusing on improving methods for E-loyalty.

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Current Reviewer Status: ?

VERSION 1

PUBLISHED 14 Feb 2026

Reviewer Report 29 Apr 2026

Chathuni Jayasinghe, University of Kelaniya, Colombo, Sri Lanka 

Not Approved

VIEWS 0

Reviewer Report 11 Mar 2026

Sylvia Samuel, Universitas Pelita Harapan, Tangerang, Indonesia 

Not Approved

VIEWS 0