The Role of Digital Maturity in Achieving the Quality of E-Services: An Analytical Study at Al-Rafidain Bank – Fallujah Branch

1. Research problem

As a result of the changes taking place in the digital ecosystem and the increasing adoption of technology in service delivery, banks face a pressing problem: the development of digital systems with the components of quality and efficiency.

One modern concept, echoing digital transformation within a system, is “Digital Maturity.” This indicates the effectiveness of a system in optimizing its resources, particularly in terms of the quality of e-services. Despite technical progress, there is a digital service gap in several Iraqi banks. This narrows down to the level of the e-services rendered. This raises pertinent issues regarding the digital service gap and its service components, such as service speed, service accuracy, service security, and customer satisfaction.

Thus, the research issue arises from the primary question:

“What is the impact of digital maturity on the quality of e-services at Al-Rafidain Bank – Fallujah Branch?”

This primary question splits into several sub-questions, such as:

2. Research significance

The relevance of this research is reflected in two dimensions:

Figure 1. The hypothetical research framework.

First: Digital maturity

1.1 The Concept of Digital Maturity

Profound changes across societies have been brought about by rapid developments in digital maturity. For instance, the integration of technology into business processes is believed to improve business efficiency, enhance organizational resilience, transform management practices, and improve value delivery to users. With the shift toward digitalization, the growth of available data has been staggering, enabling organizations to pinpoint strengths and weaknesses and act on corrections with minimal delay (Ahmat, et al., 2024: 3399). Gupta (2025:5) notes that the provision of digital innovation and the advancement of modern technologies within institutionally beneficial yet consumer protective parameters are fundamental in advancing public and consumer interest policymaking. This is done through the creation of basic frameworks and general policies derived from societal value systems and orientations that help minimize the intangible and unintended adverse outcomes of rapid technological advancement. In addition, solving the issue of digital absence is not only about the digital divide, but also a matter of strategic importance in its own right, especially to enable small and medium enterprises (SMEs) in resource-dependent economies to function effectively in the transformations to come (Sánchez-Ortiz et al., 2025: 13). Thus, digital maturity is mostly understood as the result of a process that is unfolding rather than a static (Nielsen et al., 2024: 196).

Several definitions of digital maturity have been presented in the literature. Table 1 outlines the selected definitions in chronological order.

The two researchers defined digital maturity “It is a measure of the organization’s capability of effectively adopting and utilizing digital technologies to advance its operations, provide value-adding services, promote innovations, and adapt to revolutions in the digital world. Incorporates the cultural aspect of technology, digital tools, structural organization, and the visions and perceptions of the employees and the top management of the organization into biotechnology.”

1.2 Importance of Digital Maturity to Organizational Development

Establishing the level of digital transformation maturity is of primary importance to the institution because it allows situational analysis and gap identification, which in turn allows the identification of the best suited transformation approach. In this step, organizational agility is reinforced, and the institution is positioned for sustainable long-term growth, especially when all dimensions of maturity are considered in the evaluation (Haryanti et al. 2023, 2). Determining the level of digital maturity makes it possible to establish the difference between the actual and desired set of capabilities, and allows the institution to define strategic objectives. The underlying sophistication model continues to prove meaningful to organizations because its elements can be tailored to a variety of functions, including the assessment of goals, discretion, performance, and model transparency (Merdin et al., 2023: 263). They continue to validate frameworks that facilitate the transition of services to digital mode while simultaneously lowering operational costs, enhancing productivity, and improving data and information quality (Gerald et al., 2017). More than just enabling organizations to strategize and make plans to achieve a higher level of information age sophistication, digital maturity models provide a yardstick against which organizations may gauge their performance and make comparisons internally and externally (Ochoa-Urrego & Peña, 2020:2). They strengthen productivity and competitiveness and help the top echelons of management articulate strategic goals (Heidenwolf et al., 2025: 5902).

Tonder et al. (2024:112) accentuates the benefits of digital maturity models:

Digitally mature organizations substantially outperform other organizations in their industry, boasting, on average, 6-9% more revenue per employee and 9-26% greater profitability than their competitors (Thordsen & Bick, 2023: 967). They simplify the understanding of the organization’s level of digitization and enhance organizational competence and performance improvement (Sándor & Ákos, 2022: 59).

1.3 Dimensions of Digital Maturity

As elaborated by the two researchers with respect to their individual views and spheres of activity, Digital Maturity goes beyond simpl technology; it includes managerial facets that reflect modifications to services, processes, skills, cultural aspects, and overall capabilities. At its most fundamental stage, digital maturity is confined to a continuous cycle of organizational learning that enables the firm to respond to the requirements of the hypercompetitive digital ecosystem (Brodny & Tutak, 2021: 1-38). By review ing the existing literature, the authors isolated four fundamental dimensions through which an organization’s degree of digital maturity can be assessed. What follows is an elaborate account of each dimension in terms of its focus and importance.

Second: E-Service quality

2.1 The Concept of E-Service Quality

Quality in dictionary form is defined as the nature, condition, or standard of something. The more abstract form of this definition, however, suggests that it does not speak of the specific dimensions of space and time. Quality is an indefinite and relevant phenomenon. Contemporary settings demand more focus and attention for achievement (Afshari & Shokrollahi, 2025: 3). In this case, public service is the government’s responsibility to citizens, where it centers on regulation, control, offering services, and facilities for the sake of the public (Julaeha et al., 2023: 3231). According to Mothey et al. (2025: 90), e-service quality refers to the delivery of products and services through the internet, smart devices, and digital channels. The quality of these services is defined by their ability to meet customer expectations in terms of their efficiency, reliability, security, and ease of use. E-service quality is typically measured by comparing customers’ expectations with the services they receive. This reflects the service’s ability to meet its needs. E-services represent an evolution of traditional services, and their quality is assessed based on the effectiveness and efficiency with which they are delivered via online platforms (Utama & Trisnawati, 2024: 177 & Husien et al., 2019: 23).

The researchers present a selection of definitions and concepts from scholars and authors that reflect various intellectual and philosophical perspectives on the topic, as shown in Table 2 below:

The two researchers characterized the quality of electronic services as “the measuring of the efficiency and effectiveness of the services rendered through digital channels, in fulfilling user expectations, and in relation to the ease of use, security, reliability, responsiveness, and confidentiality.”

2.2 Importance of E-Service Quality

E-service quality is very important, especiall in the banking industry, because customers are able to receive services in a timely manner and improve operational efficiency, thus leading to higher customer satisfaction (Asawawibul et al., 2025: 353). This is an indicator of the responsiveness of the banking system to a changing environment. E-service quality is crucial to the competitiveness of a digital business, and includes the level of responsiveness and ease of use of the system, system security, and overall system clarity. Increasing or obtaining customer satisfaction virtually guarantees customer loyalty (Asawawibul et al., 2025). E-service quality also increases value perception and lowers costs and benefits from the use of AI and tracking systems to increase visibility and efficiency (Asawawibul et al., 2025). These benefits include reduction of the costs associated with the systems low AI effectiveness at low levels in the system (Asawawibul et al., 2025, Al-Rawi et al., 2021). The use of e-systems has proven capable of fulfilling their promises in multiple situations (Aulawi et al., 2025: 92). E-service quality is also associated with customer satisfaction because the technology and new systems adopted help improve the efficiency of service delivery (Rahahleh et al., 2020: 2760). Having any transaction anytime or anywhere through remote e-banking services helps reduce expenses while simultaneously enhancing the banking relationship with the customer (Subahudin & Shahrom, 2023: 35–36). Loyalty is a function of customer satisfaction, and its quality is a source of competitive advantage and business sustainability (Mohamed et al., 2025: 1056). E-banking also helps empower bank employees to offer more effective and efficient services, and customers to transact remotely, privately, and with a high degree of banking security (Afshari & Shokrollahi, 2025: 1).

2.3 Dimensions of E-Service Quality

E-service quality dimensions, as defined by the two scholars, owe to the different approaches each takes to the dimensions relative to their areas of study. The sub-components or standards e-service quality dimensions stem from the decomposed overall construct into definable and comparable units. Theprimary dimensions include responsiveness, ease of use, confidentiality, trust and security, and reliability. Each dimension is defined by a certain set of indices. Disagreements among scholars on the definition of dimensions arise from differences in the theoretical perspective upon which the discussion is anchored, the type of industry, and the socio-technical environment. Some scholars, for example, add the dimensions of system efficiency, visualization, or personalization, while others opt to collapse redundant (Mothey et al., 2025: 90).

In this case, the authors mentioned five distinct and validated dimensions of e-service quality and constructed them from previous literature, which are summarized as follows:

First: The study population

The study population for this research comprised managers and staff of the Al Rafidain Bank – Fallujah Branch because of their active participation in employing digital processes and providing clients with e-services. A purposive sampling method of choosing 102 members from the bank’s management and employees at different administrative and operational levels was used. The sample was stratified across a variety of demographic characteristics such as sex, age, educational attainment, duration of employment, and circle of position in such a way as to mirror the bank’s structure. This will enable a better assessment of the association between a bank’s level of digital maturity and the quality of the electronic services offered.

Table 3 shows the distribution of the study sample by gender, where males represented 81.4% of the total sample, dominating over females, representing 18.6%. This indicates that males constituted the majority of the study population.

Table 4 shows the age-based stratification of study participants. The age cohort of 41-50 years comes first with 41.2%, the highest proportion among the benchmark sample categories. This suggests that most of the participants had ample professional experience and job stability. The 31-40 years age group comes next with 31.4%, indicative of moderately experienced and high-performing professionals. The 50 years and older group makes up 26.5% and is typically regarded as someone with extensive professional experience and considerable administrative and professional competencies. The less than 30 years group was the least represented with 1.0%, signaling the dearth of active youth participation in the study population.

Table 5 shows the distribution of respondents according to their level of education. Most respondents resume bachelor degrees at the highest value of 71.6%. This means that the majority of respondents had sufficient education to undertake the administrative and technical tasks at hand. The second category, which pertains to secondary school qualifications, constituted 14.7%. This suggests that there is a segment of the study population that comprises employees with basic qualifications. We have respondents with higher diplomas accounting for 6.9%, postgraduate masters at 5.9%, and with the lowest value, doctorate holders at 1.0%. This suggests a scant new presence of highly scholarly employees in the sample domain. This, in turn, indicates a higher concentration on the routine than on scholarly or highly academic work in the case organization that is being investigated.

As shown in Table 6, the composition of this sample correlates with job titles. The largest segment in this segment is the “other” category, with a 56.9% value. This suggests that operational and/or technical positions, which are not classified as higher administrative positions, were held by the majority of respondents. This is in line with the hierarchical structure of the organization, which has much membership in operational roles. The second largest group in the organization is classified as unit supervisors at 24.5%, which suggests that these employees have minimal supervisory responsibilities. Division supervisors represent 12.7% of the sample, while department managers have a very low rank of 5.9%. This indicates that the sample is dominated by low and middle administrative level positions compared to the small availability of sample members in high level administrative leadership positions.

Table 7 demonstrates the sample’s distribution based on the years of service of each respondent. The figures indicate that the highest percentage per respondent is 33.3% in the 11 to 15 years category. This implies that a considerable number of respondents possess a moderate to substantial level of experience that allows them to efficiently understand the work environment and work requirements. The second highest percentage is 25.5% of respondents who have served for 26 years and above and are classified as long serving employees who are professionally stable and possess a well-developed understanding of the institution. The five years or less group holds 18.6%, in which there are a significant number of employees or even new recruits within the organization. The 16 20, 6 10, and 21 25 year age groups obtained 11.8%, 6.9%, and 3.9%, respectively. This indicates that the 21 to 25 years group had the lowest representation. These results indicate how the study population is temporally characterized by its diversity in relation to professional experience as well as a clear focus in the medium and above service years, suggesting organizational maturity and a well-developed base of institutional knowledge.

This table ( Table 8) includes the study terms, variables, and dimensions, specifying the number of questions allocated to measure each dimension and its corresponding code. This classification aims to provide a clear methodological framework for measuring the main variables in the research.

Second: Testing the normal distribution of the data

In choosing the most suitable statistical approach for data analysis, the author focused on the normality of the distribution using Skewness and Kurtosis coefficients. The use of skewness and kurtosis as benchmarks is one of the first steps in statistical analysis to ascertain the degree of normality of a distribution. The distribution of values in skewness and kurtosis that range from (±1.96) depicts a lack of normality divergence more than normality at a significancelevel of 0.05. This enhances the sustenance of parametric methodologies, which are based on the normality of data and have higher power in determining relationships or differences among study variables. However, the more skewed or kurtotic the values are, the more they are not normal, and thus, the use of non-parametric methods is more appropriate. The statistical analysis results in Table 9 show that every value is contained in the result range. (±1.96) indicates a normal distribution and non-weakness in the degree of repetition of the data. This is a strong indication of the episode of unsound parametric statistical methods, which were not compiled in later analytical phases of the study. As a result, the data of the processes ensure that the primary assumption of the parametric methods used in these phases is fulfilled.

The results of the statistical analysis presented in Table 9 indicate that all values fall within the acceptable range (±1.96), suggesting that the data are normally distributed and exhibit a high degree of consistency. This is a strong indication of the soundness of the data distribution and supports the use of parametric statistical methods in subsequent analytical stages, as one of their fundamental assumptions is fulfilled.

Third: Reliability using split-half method

To measure the reliability of the instrument, the researcher used the Split-Half Reliability method As shown in the Table 10, which is popularly used to estimate the internal consistency of a scale. The Spearman-Brown coefficient was calculated to be 0.839, whereas the result obtained using the Guttman Split-Half was 0.839. These results are interesting. Indeed, such values are suggestive of relatively high internal consistency, proving the effectiveness of the instrument within different times or circumstances. Thus, such consistency demonstrates the instrument’s reliability, which in turn provides grounds on which to use it in analytical statistics for this study.

Fourth: Sample adequacy

Analyzing the sample is the first step in factor analysis and can be performed using the KMO technique. If the value was greater than 0.50, the sample was considered appropriate for analysis. Moreover, correlations can be tested using Bartlett’s Test of Sphericity. If the P-value is less than 0.05, it means that the variables have significant relationships, thus confirming that the data are appropriate for factor analysis. These indicators are illustrated in more detail in the Results section, and the results are shown in Table 11.

The results from the analysis in the context of Table 11 show that the KMO values with respect to the variables (Digital Maturity and Quality of Electronic Services) were (0.788 and 0.842) respectively, well above the minimum 0.50 required in Kaiser’s classification. This suggests that the sample size was adequate and suitable for the analysis. In addition, Bartlett’s test results for the variables in the study were (1663.370 and 2691.022) respectively, and the level of significance was 0.000, which is much lower than the required 0.05. This means that the correlation matrix is not an identity matrix, and that the variables are interrelated to a considerable degree. Therefore, the sample size can be considered adequate, which is a good indicator for further statistical analysis.

Fifth: Measurement quality assessment

Research Model

The previous chapter has shown us the results concerning testing both the validity and reliability of the dimensions concerning our research model. From Figure 2. The independent variable, Digital Maturity, has four major dimensions: TC, which represents Technological Culture, Technological Techniques (TT), Organizational Structures (OS), and vision and perceptions (VP) of a company. On the other hand, the dependent variable, Quality of Electronic Services, has five main dimensions: Reliability (REL), Responsiveness (RES), Trust and outspoken Security (TOS), Ease of Use (EOU), and Confidentiality (C). All the results presented in the table demonstrate satisfactory outcomes concerning Composite Reliability (CR), with a minimum threshold of 0.70. The ranges of 0.755 and 0.913 demonstrate an above-average internal consistency, along with the reliability of the measuring instruments. Most metrics suggest that uniform and non-uniform standardized alpha metrics suggest that the value of 0.70, which serves as a lower limit for standard metrics, was exceeded with single dimensions ranging from 0.757 to 0.913, proving the self-consistency responses of the dimensions, along with their outcome stability. The AVE results are also commendable. The set parameter in the AVE was 0.50, and the results of the metrics set were between 0.521 and 0.693, indicating satisfactory convergent validity. This suggests that the dimensions comprising each construct account for sufficient variances of their respective constructs; hence, one can confidently say that all reliability and validity measures remained within reasonable bounds, indicating that the quality of the scales and instruments used in the research were sufficient and that the research was trustworthy. Furthermore, the sub-dimensions of each variable sufficiently accounted for the total variance of the primary variables, which increased the validity of the analytical model in assessing the relationship between Digital Maturity and Quality of Electronic Services.

Figure 2. The complete research model.

Source: Output from SmartPLS v.4 software.

Note: The numbers within the circles represent Cronbach’s Alpha coefficients.

As illustrated in Table 12, the item factor loadings for the items were between (0.601), the lower limit, and (0.918), the upper limit, and all were above the accepted minimum cut off value of (0.40). This indicates that the items accurately represented the theoretical dimensions pertaining to them. Additionally, the (t) values ranged between (5.713) and (11.128) at a (P = 0.000) significance level, which is evidence that all relationships among items and dimensions are present and are sufficiently strong. These findings indicate a high level of construct and internal consistency, demonstrating the strong representation of the dimensions of digital maturity and e-service quality, and the high efficiency of the analytical model used in the research.

Sixth: Research variable for analysis is

Trust and assurance ranked fifth and had a mean of (3.939) and a CV of (17.60%), which suggests that employees are more relaxed when using the bank’s e-services. However, there is still a need to enhance measures to safeguard sensitive information and transactions from potential cyber threats.

Seventh: Hypothesis testing

1. Main Hypothesis One

In Table 14 and Figure 3, the outcomes of the correlation analysis between the variables Digital Maturity and E-Service Quality are presented. The results showed that the correlation coefficient was (0.735), indicating a strong positive relationship between the variables. Furthermore, the calculated Z-value was (9.348), which was above the threshold of (1.96) at a significance level of (P = 0.000). Therefore, the alternative hypothesis is retained: Digital Maturity and e-service quality are correlated and the correlation is significant.

Figure 3. Correlation values between digital maturity dimensions and E-service quality.

The analysis clarifies that there is a positive correlation between Digital Maturity and E-Service Quality. This means that the greater the level of digital maturity in an organization, the greater the quality of the e-services offered. This suggests that an organization can achieve higher levels of digital culture, strong technological infrastructure, flexible organizational structures, and clear organizational visions to achieve an integrated balance between the technical and administrative levels. This would result in a higher operational efficiency, faster response, and better electronic service execution. Hence, digital maturity is an important factor in improving e-services by providing users with modern digital devices, advancing technological innovations, and enhancing user satisfaction with the digital services offered by the organization.

a. Sub-Hypothesis One

Tables 3 and 14 present the correlation analysis results between the “technological culture” dimension and the “quality of electronic services.” The correlation coefficient 0.596, indicating a strong positive relationship between the two variables. The calculated Z-value (6.835) exceeds the critical value (1.96) at a significance level of 0.000, which leads to acceptance of the alternative hypothesis.

This finding demonstrates that technological culture has a statistically significant correlation with quality of electronic services. This suggests that enhancing employees’ digital awareness and competencies directly contributes to improving the efficiency and quality of e-services and increasing beneficiary satisfaction.

b. Sub-Hypothesis Two

According to the data shown in Table 14 and Figure 3, there is a strong correlation between the “technological tools” dimension and the “quality of electronic services.” The correlation coefficient is 0.620, indicating a highly positive relationship. The calculated Z-value (7.214) was much greater than the critical value (1.96) at a significance level of 0.000, leading to the acceptance of the alternative hypothesis.

These results confirm the significant relationship between technological tools and e-service quality, emphasizing that the continuous development of digital systems and infrastructure plays a crucial role in enhancing service performance, efficiency, and user satisfaction.

c. Sub-Hypothesis Three

Table 14 and Figure 3 display the correlation results between the “organizational structures” dimension and “quality of electronic services.” The correlation coefficient was 0.634, indicating a strong positive association. The calculated Z-value (7.443) surpassed the critical threshold (1.96) at the significance level of 0.000, confirming the acceptance of the alternative hypothesis.

These results reveal a statistically significant relationship between organizational structure and e-service quality. They indicated that flexible, well-structured, and digitally supportive administrative frameworks enhance institutional adaptability and contribute to higher service efficiency and quality.

d. Sub-Hypothesis Four

Table 14 and Figure 3 display the outcomes of the correlation assessment of Visions and Outlook, and E-Service Quality. The correlation coefficient of the two variables was 0.650, which was considered strong. The Z-value of 7.714 was greater than the critical value of 1.96, with a level of significance of P = 0.000. Therefore, the second hypothesis is supported. The correlation between the vision and outlook dimensions and e-service quality is statistically significant.

This means being able to visualize a service in digital form, and proactively looking into the construction of digital services also improves the effectiveness and quality of electronic services.

2. Main Hypothesis Two

The calculated F-value associated with the influence of digital maturity on e-service quality, as presented in Table 15 and depicted in Figure 4, is (117.213). This figure is greater than the relevant critical F-value of (3.94) at a 0.05 significance level, thus strongly advocating the acceptance of the alternative hypothesis, which posits that digital maturity has a statistically significant effect on e-service quality. These results suggest that digital maturity can explain 53% of the variance in e-service quality. This suggests that institutions with greater digital maturity are more adept at integrating the technical and organizational infrastructure components necessary for the provision of high-quality services. In addition, the computed t-value for the digital maturity variable is (10.826), which is higher than the t-value of (1.984) at the significance level of 0.05, thus supporting the hypothesis that β is statistically different from zero. The regression coefficient (β = 0.681) suggests that an increase in digital maturity by one unit is associated with a 68% increase in the level of electronic services provided.

Figure 4. Testing the sub-hypotheses of digital maturity dimensions on E-service quality.

a. Sub-Hypothesis One

These results further reinforce the notion that digital maturity acts as a primary element underlying an institution’s ability to improve its performance and undergo effective digital transformation. The value of the effect of technological culture on per-e-service value quality ( Table 15) and its quantification on the graph ( Figure 5) depict the obtained F-value, which stood at 55.138. This value exceeds the critical F-value (3.94 at the 0.05 significant level), which in turn provides sufficient support in favor of the acceptance of the alternative hypothesis, which states that there is an effect of the technological culture dimension on e-service quality and the effect is statistically significant. The findings show that technological culture accounts for 34 percent of the variance in e-service qualit, which indicates its importance in improving the efficiency of performance and advancement of digital services on offer. The extracted t-value for the technological culture variable was 7.426, which is much greater than the critical value of 1.984 at 0.05. This confirms the significance of β, which had a regression coefficient of 0.441. This finding implies that technological culture increases the level of e-service quality by 44 percent for every unit increase. Overall, these results prove that improving digital culture among employees enhances their e-performance efficiency, which also improves users’ overall experience.

Figure 5. Effect analysis of the technological culture dimension on E-service quality.

As demonstrated in Table 15 and illustrated in Figure 6, the extracted F-value with respect to the impact of technological tools on e-service quality was (62.447). This is higher than the F critical value of (3.94) at the 0.05 significance level, thus providing adequate justification for the acceptance of the alternative hypothesis. This hypothesis suggests that the dimensions of technological tools have some impact on e-service quality.

Figure 6. Effect analysis of the technological tools dimension on E-service quality.

b. Sub-Hypothesis Two

The findings suggest that technological tools account for 37% of the variance in e-service quality, meaning that these tools are quite powerful in the context of system development and the improvement of digital infrastructure.

The extracted t-value corresponding to the technological tools was (7.902) and was also t-critical (1.984) at 0.05, thus confirming the significance of β. The regression coefficient (β = 0.514) suggests that the quality of e-services improved by 51% for every unit increase in the application of technological tools. This emphasizes the importance of digital tools in enhancing the operational efficiency and overall performance of the institution.

c. Sub-Hypothesis Three

Table 15 and Figure 7 show the extracted F-value for the effect of organizational structures on e-service quality, which was (67.247). This value is greater than the critical F-value of (3.94) at the significance level of 0.05, providing sufficient support for accepting the alternative hypothesis that states that There is a statistically significant effect of the organizational structures dimension on e-service quality. The results indicate that organizational structures explain 39% of the variance in e-service quality, reflecting their effectiveness in organizing the roles and responsibilities that support digital transformation. Moreover, the extracted t-value for the organizational structures variable was (8.200), which exceeded the critical value of (1.984) at a level of 0.05, confirming the significance of β. The regression coefficient was (β = 0.550), indicating that a one-unit increase in organizational structure leads to a 55% improvement in the quality of e-services.

Figure 7. Effect analysis of the organizational structures dimension on E-service quality.

This underscores the enhancement of organizational effort integration in supporting digital transformation and improving service efficiency.

d. Sub-Hypothesis Four

Based on the findings, technological tools are responsible for 37% of the variance in e-service quality, indicating that they are very effective in the context of system development and the enhancement of digital infrastructure. For the technological tools, the estimated t-value was (7.902) and t-critical was (1.984) at 0.05, which confirms the significance of β. The regression coefficient (β = 0.514) indicates that e-service quality improves with an increase in technological tool application by 51% for every unit increase. This further reinforces the effectiveness of technological tools in operationalizing institutions and improving productivity. The calculated F-value of the influence of visions and perceptions on e-service quality is (73.169), as presented in Table 15 and illustrated in Figure 8. This value exceeds the critical F-value of (3.94) at a significance level of 0.05. Thus, there is ample evidence to support the alternate hypothesis that claims there is an influence and that the visions and perceptions dimension has an influence on e-service quality. The data show that visions and perceptions account for 41% of the change in e-service quality, which demonstrates how much they cansteer digital change and foster more anticipative thinking about technology use. The t-value thatpertains to visions and perceptions is (8.554). Similar to the previous t-value discussed, this value is more than the 0.05 critical value of (1.984), which further lets us conclude that the hypothesis that β = 0 may be rejected. The value of the regression coefficient is (β = 0.477). This implies that an increase in vision and perception leads to an increase in e-service quality, and in this case, 47%. This result illustrates that a well-defined digital vision will manifest in enhanced organizational performance and service quality.

Figure 8. Effect analysis of the visions and perceptions dimension on E-service quality.

Third: Recommendations