Decision-Making Strategy for Digital Transformation: A Two-Year Analytical Study and Follow-Up Concerning Innovative Improvements in University e-Services

Universities worldwide strive to provide the best student services possible, particularly those that support student achievements and career goals. Therefore, academic advising continues to be a significant part of the student experience, one which universities need to fully understand in terms of its objectives, application processes, and required skill. As a result of significant technological improvements since the turn of the millennium, including expanding internet applications and digital transformations, universities have established computer information systems that support academic advising and course registration services. This study examined the effects of modifications to the electronic academic advising and course registration systems at King Abdulaziz University in 2018, and then again in 2020, following a university-wide system failure in 2018 resulting from a demand overload. In 2018, a preliminary statistical analysis and student feedback survey were conducted by the authors to measure student satisfaction with the online portal On-Demand University Services (ODUS Plus). In addition to recommendations suggested by the 2018 analysis such as balancing the load distribution of the university’s network, organizational (i.e., non-technical) solutions, rules, and regulations were adjusted such as progressive course registration that prioritized those expected to graduate first. The survey and analysis were repeated by the authors in 2020 to assess improvements in student satisfaction. As a result of the changes, the investigation revealed improved student satisfaction with the performance of ODUS Plus and network access. Overall, students were significantly more satisfied in 2020 than in 2018. This research shows that some technical challenges can be resolved using re-engineered processes and organizational solutions.


Introduction
The primary objective of this paper is to investigate how changes made to the electronic educational systems of higher education institutions may affect institutional decisionmaking strategies. In addition, this study contributes to the ongoing academic and practical discussions concerning the efficacy of digital transformation of higher education institutions based on the latest artificial intelligence developments.
While this study evaluated some changes made to the electronic education system used by King Abdulaziz University, a leading public university in Saudi Arabia, this study is an attempt to stay abreast of recent developments to improve the educational process, especially in terms of digital transformation, which may be central both to enhancing the process and in decision-making.
The university stage is considered the essential link in the educational chain due to its contributions to developing the student's critical thinking skills as well as their resilience The university stage is considered the essential link in the educational chain due to its contributions to developing the student's critical thinking skills as well as their resilience when faced with the challenges of working life. From this perspective, it is critical to provide students with the skills, capabilities, and relevant work experience to assist them as they take their first steps into their career.
The significant increase in students and the expansion of specialties, especially the scientific disciplines, as well as the diversity of student backgrounds have led to increasing innovations in methods and processes concerning academic advising in universities around the world, including King Abdulaziz University. The virtual space has become a critical part of the direction communication between universities and students that allows academic advising to commence regardless of time or location.
King Abdulaziz University has integrated academic advising services and electronic systems that were designed with the latest programming languages, databases, and software for its website applications. Furthermore, providing innovative, interactive advising services to university students provides a gateway to the university's digital educational offerings through their online portal [1][2][3][4][5][6][7][8][9].
After studying best practices at universities in developed countries, we found that students benefit from a university's efficient implementation of distance education systems, as well as a shift in purpose concerning academic advising. Rather than the traditional concept of academic advising as an educational guide, academic advising can expand its role to effectively, and virtually, support students by providing both guidance and an interactive, supportive community. For clarity, this is not the same process as eadvising, which relies on electronic data, advising instructions and protocols, and a study plan to facilitate the traditional academic process between advisors and students. This novel, interactive academic advising promotes communication between the student and the advisor, the student and other students, and the advisor and other advisors.
The academic advising process at King Abdulaziz University is an integrated approach that enables all associated parties (e.g., students, advisors, faculty members, and university management) to interact and communicate electronically. It promotes the integration of both male and female campuses in the academic environment, so they can reach the highest levels of academic achievement. Therefore, developing students' skills academically, intellectually, and socially is required for attaining the university's education goals. The electronic academic advising system at King Abdulaziz University has numerous key features that connect students and academic advisors for various educational purposes, the most essential of which are shown in Figure 1.  Taking King Abdulaziz University as a case study, we investigated how changes to the online processes involved in academic advising and course registration impact student satisfaction and their decision-making process.
This research offers insights on practical outcomes when considering similar changes for leaders in higher education institutions (HEIs). It also provides technical solutions for typical problems that can be easily resolved with re-engineered processes and organizing solutions and may be used in the future as a reference for the development and implementation of online course registration and virtual academic advising processes.

Literature Review
Universities worldwide rely heavily on online services for registration and academic advising as well as for other services. This shift has expanded and has become a predominant feature of respected universities worldwide, even before the COVID-19 pandemic. As a term, "advising" or "academic advising" or, sometimes, "student advising", can have variations in their meanings, as each definition has its particular nuance or focus. For example, Crookston [10] tackled advising from a student development approach and, therefore, viewed academic advising as "a teaching function based on a negotiated agreement between the student and the teacher in which varying degrees of learning by both parties to the transaction are the product." Abernathy and Engelland [11] viewed advising as serving to "provide the primary guidance and direction a student needs to navigate through the college course selection and registration process successfully." Therefore, we inferred that e-advising could be considered a technology-supported version of academic advising.
While universities must address student dissatisfaction with advising and registration processes regardless of the efforts made by advisors to accommodate student needs, Laghari [12] suggested that from a student's perspective the registration process involved choosing which courses they should take. However, from the administration's perspective, it involved keeping records up to date to create class lists and organize other academic activities. Therefore, a robust system for advising and registration is required.
Accordingly, automated advising systems have been developed to successfully assist students in course selection during online registration. Since academic advising has been shown to improve academic performance, Brdesee et al. [6] suggested that universities could engage students' passion for social media to deliver advising services and develop an integrated framework that offered academic advising via social networks. Meanwhile, within the same context, Afify and Nasr [13] proposed a web-based program for academic advising to enhance the overall advising experience that focused on the methodologies used to develop the system, as they considered advising as more of a process than a product. The study's premise was that the online advising and registration system should detect student problems and, consequently, provide them with solutions. The proposed program, which was built using the Ruby programming language and PostgreSQL's open-source database system, stressed the significance of the academic advising system as a web-based application and recommended further developments for advising programs using more complex systems.
Pasquini and Steele [14] asserted that artificial intelligence (AI) could support academic advising and other student services with a focus on the impact of AI interactions on all involved parties. To make well-informed decisions and offer students more comprehensive support, technology should underpin advising and registration programs. The study also stressed that stakeholders' input regarding these issues must be prioritized before the implementation of any new or improved student support technologies, as the stakeholders directly engage with students and have increased awareness concerning how AI could enhance students' experience in academic advising and registration processes. This view was also shared by Henderson and Goodridge [15]. They maintained that the process of academic advising in higher education has many inefficiencies, including high student loads, redundant issue handling, and long waiting periods, among other issues. Henderson and Goodridge proposed an intelligent web-based application to provide e-advising services. The study suggested that using AI to facilitate academic advising without any human involvement improved the efficiency and transparency of the advising process in higher education. It was recommended that students who had problems beyond the scope of the online system could communicate with human academic advisors using online methods such as e-mail.
Furthermore, Estevez et al. [16] pointed out that course registration systems often posed usability problems for users. Therefore, the researchers proposed an enhanced model for course registration that improved usability and increased user satisfaction. The study concluded that designing the right solution for a university's needs reduced the time involved in registration and improved student satisfaction. While maintaining that academic advising has a crucial role in resolving academic issues for students as well as in boosting their satisfaction and loyalty, Assiri et al. [2] examined other technologies that have been used in academic advising. They recommended creating robust AI-based e-advising systems while stressing the need to address the challenges and drawbacks in e-advising systems, including how to deal individually with students with special issues, the problems associated with information overload, the need for a holistic approach that encompasses advising goals (i.e., competencies, goals and interests), and system speed and response times, among others. The study also found that the e-advising systems must be integrated with universities' student records databases.
Academic advising is concerned mainly with registration issues. Students contact their academic advisors when they face complex registration problems, usually for reasons such as early graduation, changing majors, transferring course credits, and studying abroad. Since the mid-1990s, universities have, for the most part, computerized their registration systems. The link between academic advising and registration encourages research on assessing the validity, reliability, and usability of higher education electronic advising and registration systems. In a study conducted in Saudi Arabia, specifically on King Abdulaziz University system, Noaman et al. [8] maintained that universities with a large number of students suffered a massive burden in terms of academic advising provisions for students and found that the e-academic advising approach adopted by King Abdulaziz University improved the advising process. The researchers suggested further developments of such e-services. Furthermore, Eckroth and Anderson [17] proposed an academic advising system named "Tarot", which employed planning engines that considered unexpected scenarios for course registration.
Tchouakeu et al. [18] argued that one of the key academic objectives should be making the registration process "more convenient and easier to achieve". Their study considered the "e-Lion" system (now "LionPATH"), used in Pennsylvania State University, illustrating how the system succeeded in increasing efficiency and satisfaction. Meanwhile, Liu et al. [19] proposed a model system for university student registration as an information management system that functions as a registration tool and offers statistics and analysis for all inputs. The researchers recommended upgrading and expanding the proposed system to boost the e-registration process further. Likewise, Almazmomi and Alhassan [20] maintained that usability assessments reflected user satisfaction. Their study investigated optical character recognition (OCRS) and subsequently proposed a model to measure OCRS usability, taking King Abdulaziz University's e-platform (ODUS plus) as a case study. Their study concluded that the student satisfaction level was second only to the importance of the system design.
Abalorio and Cerna [21] proposed the course evaluation generator (CEG) model to address registration and advising problems using (OCR). This allowed the system to read directly from the student's academic transcript. The study proved the feasibility of crediting courses from the scanned image, providing that the right policy and text imaging tools were used. Furthermore, the researchers proposed the integration of more sophisticated AI and machine learning in e-registration systems.
Iatrellis et al. [22] investigated e-advising systems by examining the literature on experimental case studies conducted from 2008 to 2017. After reviewing and analyzing scores of studies, the researchers concluded that there were three predominant focuses in the empirical research on academic advising systems, namely pedagogy-oriented issues, learning analytics, and educational data mining, while observing that the three issues were not completely autonomous as there was frequent overlap. The study recommended that universities needed effective academic advising software to augment the relationship between students and their advisors, noting that the existing software cannot and should not replace human advisors. The researchers also suggested more interdisciplinary research into learning science, psychology, pedagogy, and computer science in terms of e-advising systems.
Kalamkarian et al. [23] discussed the ongoing efforts to leverage e-advising technologies to redesign the e-advising and registration systems in three HEIs in the United States. The study recommended that other HEIs redesign their e-advising tools so they can be customized for technology-mediated advising to further support their students.
Pelletier and Hutt [24] maintained that although advisors had more technological solutions, they still needed to advance the use of technology and develop new mindsets and skills for a successful transformation. The researchers recommended introducing more training for advisors; nevertheless, operations at the institutional level were also highlighted. The researchers underlined that shareholders needed to go beyond comprehending the features of new technologies and that they must understand how the new technologies operate so that they can apply them strategically. Donaldson et al. [25] examined academic advisors' perspectives on enhanced advising programs, wherein they found that for an improved advising experience, students needed to participate and engage in constructing a proactive educational plan.
After reviewing the literature, and based on the experiences of several universities regarding e-advising services, there is an opportunity to improve higher education eadvising and e-registration systems using advanced technology.
To attain this objective, this study developed two questionnaires that would be issued two years apart to measure the development of electronic registration and academic advising services in a university. The two questionnaires were delivered in 2018 and 2020 to the affiliates of King Abdulaziz University, a leading university in Saudi Arabia. The questionnaires aimed to compare the satisfaction levels of students regarding course registrations and academic advising.

Research Objectives
This study examined the effects of changes made to King Abdulaziz University's electronic educational system, specifically to their electronic academic advising and electronic course registration processes. Prior to 2018, the students used their accounts to add and drop courses with online support provided by their academic advisors, which meant that approximately 107,000 students and approximately 3000 academic advisors logged into the system simultaneously. The university network and data servers were unable to handle the load, which caused a system failure. A cascade of complications followed, where students enrolled for courses they did not yet need and others were unable to meet their required course load, among other consequential issues. Therefore, in 2018, a preliminary survey and a statistical analysis were conducted to measure student satisfaction with portal services.
A follow-up survey was conducted in 2020 to examine whether student satisfaction had improved over the previous two years. Based on the survey recommendations, improvements were implemented in work methods. The most important of which was the distribution of the demand across the networked electronic systems. The number of permissible credit hours and other adjustments were used to determine when electronic registration could commence and balance the network load.
This study, therefore, used statistical analysis to examine the data collected from the questionnaire. As a result of the digital solutions implemented by the university, this study analyzed the improvements in course registration as well as in academic advising processes in 2020, as compared with 2018.
Therefore, statistical hypotheses about the extent to which digital transformation had improved registration and academic advising were derived to address this research goal. Six statistical hypotheses were formed, as follows: Hypothesis 1 (H1). Improved digital solutions have reduced the pressure on the ODUS Plus electronic system and made network access easier.
Hypothesis 2 (H2). Student satisfaction increased as a result of improvements to the electronic systems that permitted course registration for last-year students first.

Hypothesis 3 (H3)
. Improved digital solutions have increased student satisfaction in course registration, allowing the male campus students to register at alternate times other than those reserved for the female campus.

Hypothesis 4 (H4).
Modifications to the electronic procedures have increased student satisfaction with the university's role in academic advising and course registration. Initially, descriptive analysis was utilized to calculate the repetitions and ratios of study variables, as discussed in the analysis section, and determine student trends regarding these variables. In addition, some evidentiary approaches were used to determine the improvements in student satisfaction apart from the variances in student satisfaction in 2020 based on various factors such as campus, campus location, and college, among others.

Materials and Methods
To confirm the feasibility of using student and academic advisor data to analyze a university's performance in advising services and provide an additional tool for decisionmakers, the authors decided to employ a case study framework to understand university digital transformation. One of the biggest Saudi Arabian universities was selected, particularly the university's Deanship of Admission and Registration information system. The system has more than 170,000 users. The system is dynamic, responding to thousands of requests by students, advisors, and admins every hour. Its efficiency ensures that the activities involved in academic advising (AA) have a broader impact through online services.
Data on the activities of the Deanship of Admissions and Registration (DAR) system were obtained through a distributed online survey to students on two occasions, in 2018 and 2020. The authors designed one survey format, which was used for each survey period. A sample of 1100 students was surveyed in 2018 and 2020, and some responses were eliminated while other specific questions were skipped. Only three university campus locations were used, and some were excluded due to a lack of response.
The questionnaire included issues related to electronic registration and academic advising services provided by the university. The first section eliminated typical students such as external or graduate students, as it was designed for undergraduate students. The following survey section requested the student's demographic information including age group, campus, and others. This introductory section was followed by a series of questions related to online services, such as the satisfaction level on performance, availability, and reliability of e-services. They responded using a typical 5-point Likert scale, and there were other scales used based on the question aims.
The survey was submitted to the DAR Twitter account, which had more than 340,000 followers, and it was also sent to student email accounts. The questionnaire was available for ten days. All records were transferred to an Excel spreadsheet and kept securely on the university servers. The data were used only for research purposes. The following tests were applied in the inference analysis [26][27][28][29][30]: 1.
Calculating the ratio between two independent proportions to see if the percentage of difficulties students faced in 2020 was lower than in 2018.

2.
A two-sample t-test was used to assess how student perceptions of study variables improved from 2018 to 2020. 3.
The one-way analysis of variance (ANOVA) test was used to analyze the differences between student feedback on study variables in 2020 according to a range of factors, such as campus, campus location, college, and others. 4.
The correlation link matrix between study variables was calculated to determine the relationship between these variables. 5.
It should be noted that a level of significance of 0.05 was adopted in all tests.
Therefore, this research involving structured data was considered quantitative research. An SPSS package was used to analyze the obtained data. The hypotheses mentioned in the analysis section were addressed in an effort to contribute to the existing literature.

Validity & Reliability Tests
After a questionnaire is produced, it is critical to assess its validity, and Al-Bannaa [28] has suggested that validity should measure a trait or phenomena. Subjective validity was used to verify the validity of the questionnaire, which was calculated by finding the square root of consistency (Cronbach's alpha).
The reliability of a questionnaire indicates that it will produce similar results when used under similar conditions. There are numerous approaches to ensure reliability, and Cronbach's alpha was employed in the current study to ensure the consistency of the questionnaire. The validity for the study years was 79% in 2018 and 77% in 2020 while the reliability results for the same years were 89% and 88%, respectively. These resulted indicated that the tool's validity and reliability across two years were high; therefore, the tool was deemed both valid and reliable [29].

Students' Characteristics
This section calculates the descriptive statistics for the study variables such as the ratios and the repetitions for the years 2018 and 2020 to determine the categories in which the majority of student responses were clustered and estimate student satisfaction rates across study variables. Table 1 shows the distribution of student responses at different levels between 2018 and 2020 survey periods. We noted that second-year students were 24.82% more responsive in 2018 while third-year students were 28.73% more responsive in 2020.  Table 2 shows the distribution of student numbers by campus between 2018 and 2020 survey periods. During the two periods, we noted that the responses of the female campus students increased significantly, as compared with the male campus.  Table 3 shows the distribution of student numbers by campus location between 2018 and 2020 survey periods. All student responses came from three campus locations, mainly in Sulaymaniyah, Rabigh, and Al-Marjan. During the two periods, we note that more than 90% of the responses came from the main campus location in Sulaymaniyah.  Table 4 shows the distribution of student numbers by college between 2018 and 2020 survey periods. Colleges with fewer than 30 responses were integrated into another category. In 2018, the Faculty of Arts and Humanities had the highest response rate at 23.91% while the highest response rate in 2020 came from the Faculty of Science at 29%.  Table 5 and Figure 2 display the distribution of student numbers based on the number of academic hours completed between 2018 and 2020, which were classified into five categories. Figure 1 shows how this information was represented graphically. In 2018, the responses from students with 61-90 academic hours were 37.55% greater than those from other groups in 2020.  Table 5 and Figure 2 display the distribution of student numbers based on the number of academic hours completed between 2018 and 2020, which were classified into five categories. Figure 1 shows how this information was represented graphically. In 2018, the responses from students with 61-90 academic hours were 37.55% greater than those from other groups in 2020.    Table 6 shows the distribution of student numbers according to the student's grade point average (GPA) between the years 2018 and 2020. Their GPA was divided into five main categories, from less than 2 to 4.5 and above. It should be noted that no category with fewer than two responses was found. These data were graphically represented, as shown in Figure 3. The 3.75-4.49 category had the highest response rate at 43.45% and 41.91% in 2018 and 2020, respectively.  Table 6 shows the distribution of student numbers according to the student's grade point average (GPA) between the years 2018 and 2020. Their GPA was divided into five main categories, from less than 2 to 4.5 and above. It should be noted that no category with fewer than two responses was found. These data were graphically represented, as shown in Figure 3. The 3.75-4.49 category had the highest response rate at 43.45% and 41.91% in 2018 and 2020, respectively.   Table 7 displays the distribution of student numbers based on the problems they reported encountering during course registration in 2018 and 2020. The most prevalent problem faced by students in 2018 was the system failure of ODUS Plus and the subsequent difficulty accessing the network, which 58.73% of students reported. However, this percentage decreased to 19.18% in 2020. However, despite the decreased ratio in 2020, the   Table 7 displays the distribution of student numbers based on the problems they reported encountering during course registration in 2018 and 2020. The most prevalent problem faced by students in 2018 was the system failure of ODUS Plus and the subsequent difficulty accessing the network, which 58.73% of students reported. However, this percentage decreased to 19.18% in 2020. However, despite the decreased ratio in 2020, the list of responses was mostly negative, which indicated a significant improvement in those services after two years. This suggested that by 2020 the procedures to make course registration easier had yielded this decreased ratio. The following discussion includes our analysis of the non-technical improvements the university had made, which were included in the student survey. Table 8 depicts the distribution of student numbers based on their feedback regarding the progressive registration procedure that the university implemented between 2018 and 2020, wherein the last-year students (i.e., those expected to graduate within the next two semesters) accessed course registration first; once they had completed their registrations, third-year students could access the registration system, and so on. The registration process in 2020 was more streamlined than it had been in 2018. We found that the approval rating in 2018 was 56.73% (strongly approve and approve). In 2020, student approval rose to 62.18%. These data are graphically depicted in Figure 4.   Table 9 shows the distribution of student numbers based on their feedback on allowing course registration for the male campus on days other than those reserved for the female campus. We noted that the approval rate during 2018 (strongly approve and approve) was 58%, and the approval rate among students increased in 2020 to 80%. This change led to a streamlined registration process in 2020, as compared with 2018, with approval ratings increasing by 22% between 2018 and 2020. These data are graphically represented in Figure 5.   Table 9 shows the distribution of student numbers based on their feedback on allowing course registration for the male campus on days other than those reserved for the female campus. We noted that the approval rate during 2018 (strongly approve and approve) was 58%, and the approval rate among students increased in 2020 to 80%. This change led to a streamlined registration process in 2020, as compared with 2018, with approval ratings increasing by 22% between 2018 and 2020. These data are graphically represented in Figure 5.   Table 10 displays the distribution of student numbers based on their views on the university's role in academic advising and course registration between 2018 and 2020. We noted that the approval percentage in 2018 was 33.9% (strongly agree and agree), whereas it was 51% in 2020. In 2020, students were more satisfied with the university's academic advising and course registration than they were in 2018.  Table 11 depicts the distribution of student numbers based on their feedback on the DAR's role in academic advising and course registration between 2018 and 2020. We noted that in 2018, the approval percentage (strongly agree and agree) was 17.55%, whereas, in 2020, it was 65.64%. Therefore, we estimated that in 2020, student satisfaction  Table 10 displays the distribution of student numbers based on their views on the university's role in academic advising and course registration between 2018 and 2020. We noted that the approval percentage in 2018 was 33.9% (strongly agree and agree), whereas it was 51% in 2020. In 2020, students were more satisfied with the university's academic advising and course registration than they were in 2018.  Table 11 depicts the distribution of student numbers based on their feedback on the DAR's role in academic advising and course registration between 2018 and 2020. We noted that in 2018, the approval percentage (strongly agree and agree) was 17.55%, whereas, in 2020, it was 65.64%. Therefore, we estimated that in 2020, student satisfaction with the DAR's role in academic advising, and course registration was much higher than in 2018.  Table 12 shows five statements that were used to assess student satisfaction with their academic advisor as well as their responses to various expressions and the overall focal point/axis level for 2018 while Table 13 shows the outcomes for 2020. As shown in Table 12, the majority of the axis expressions and the focal point/axes, in general, were neutral in 2018. Students were satisfied with all expressions, as well as the overall focal point/axes, according to the 2020 findings, as shown in Figure 6.

Data Analysis: Inference Statistics
This section details the testing of our statistical hypotheses concerning the extent to which digital transformation has improved registration and academic advising and the statistically significant differences between student feedback on registration status and academic advising by campus, college, academic year, campus location, number of approved academic hours, and GPA. It also aims at looking into certain links between variables.
Hypothesis 1. Changes in digital transformation methods have reduced the pressure on the ODUS Plus electronic system and made network access easier.

Data Analysis: Inference Statistics
This section details the testing of our statistical hypotheses concerning the extent to which digital transformation has improved registration and academic advising and the statistically significant differences between student feedback on registration status and academic advising by campus, college, academic year, campus location, number of approved academic hours, and GPA. It also aims at looking into certain links between variables. Hypothesis 1. Changes in digital transformation methods have reduced the pressure on the ODUS Plus electronic system and made network access easier.
This hypothesis sought to find out whether changes in digital transformation methods have lessened the strain on the ODUS Plus electronic system as well as the challenges of connecting to the network in 2020 versus 2018. Table 14 displays the results of the independent proportions test in 2018 and 2020, which showed that accessing the network was more difficult in 2018 as a result of digital transformation. However, the modifications relieved the burden on the ODUS Plus electronic system in 2020, which was also reflected in the results. This hypothesis sought to find out whether improvements to the electronic systems increased student satisfaction by permitting course registration for students who were projected to graduate first. Table 15 displays the independent samples test results for 2018 and 2020, showing that, due to improvements to the electronic systems, students were more satisfied with a progressive course registration procedure in 2020 than the previous procedure used in 2018.
Hypothesis 3. Improved digital solutions have increased student satisfaction in course registration, allowing the male campus students to register at alternate times other than those reserved for the female campus. This hypothesis sought to find out whether improvements to digital transformation procedures enhanced student satisfaction in 2020 by permitting course registration for the male campus on days other than those reserved for female students in 2020, as compared with 2018. Table 15 shows the results of the independent samples t-test in 2018 and 2020, which indicated that as a result of changes, students were more satisfied with allowing course registration for the male campus on days other than those reserved for female students in 2020, as compared with 2018.
Hypothesis 4. Modifications to the electronic procedures have increased student satisfaction with the university's role in academic advising and course registration.
The purpose of this hypothesis was to see if changes to the electronic procedures enhanced student satisfaction with the university's participation in academic advising and course registration in 2020, as compared with 2018. Table 15 shows the results of the independent samples test in 2018 and 2020, which showed that students had a higher level of satisfaction with the university's role in academic advising and course registration in 2020 than they did in 2018.
Hypothesis 5. Student satisfaction with the role of the DAR in academic advising and course registration has increased as a result of the improved digital solutions. Table 15 shows the results of the independent samples test in 2018 and 2020, which indicated that students were more satisfied with the role of the DAR in academic advising and course registration in 2020 as compared to 2018. Hypothesis 6. Modifications to the electronic procedures have increased student satisfaction with the role of their academic advisor.
The purpose of this hypothesis was to see if changes to the electronic procedures increased student satisfaction with the role of their academic advisor in 2020, as compared with 2018. Table 15 illustrates the results of the independent samples test in 2018 and 2020, indicating that, as a result of improvements, students were more satisfied with the role of their academic advisor in 2020, as compared to 2018.
Next, we examined if there were variations in student satisfaction in 2020 based on a set of parameters. We deduced from prior findings that digital transformation improvements increased student satisfaction with the five study factors (as listed in the following section) connected to course registration and academic advising in 2020, as compared with 2018. The parameters for this analysis were as follows: (1) campus, (2) student year level, (3) campus location, (4) college, (5) approved academic hours, and (6) GPA.

1.
Examine the value implications of variations in satisfaction levels with the study variables by campus: The purpose of this analysis was to see if there were any value differences between students from male and female campuses and their levels of satisfaction with the following five study variables: First: Progressive course registration starting with last-year students. Second: Opening course registration for male campus students on days other than those reserved for the female campus. Third: Role of the affiliated college in academic advising and course registration. Fourth: Role of the DAR in academic advising and course registration. Fifth: Academic advisor role Table 16 shows descriptive data, whereas Table 17 shows the t-test findings (independent samples). Student value feedback differed on the availability of course registration for those expected to graduate first and the availability of course registration for male campus students on days other than those of the female campus students, where male campus students had a higher satisfaction score than female students.

2.
Examine the implications of disparities in satisfaction levels with the study variables by student year level: The purpose was to determine the extent to which, as a result of digital transformation, there were value variations in the degree of satisfaction by student year level from the five study variables for 2020. Table 18 shows no value disparities in the perspectives of students with different variations on the role of the DAR, enrollment in academic advising, course registration, and the academic advisor. The results of the ANOVA test confirmed that there were value differences between the feedback of students of different grades regarding the progressive course registration for those expected to graduate first, allowing course registration for male campus students on days other than those reserved for the female campus, and the role of the affiliated college in academic advising and course registration.
The least significant difference (LSD) test was used to determine which variables were responsible for these differences. It was discovered that the expected-to-graduate and fourth-year students were responsible for the value discrepancies in progressive course registration, as they were more satisfied than the other students. As for the availability of course registration for the male campus on days other than those for the female campus, the reason for the value differences was that the expected graduate students were less satisfied than the different student year levels.
In addition, those expecting to graduate were less satisfied with the rest of the courses, resulting in value inequalities in student feedback regarding the affiliated college's role in academic advising and course registration.

3.
Examine the value implications of disparities in satisfaction levels with the study variables by campus location: The purpose was to determine the extent to which digital transformation resulted from value disparities in the degree of satisfaction by campus location in 2020 based on the five study variables. Table 19 shows that there were no value differences between student feedback in terms of campus locations on allowing the expected to graduate students to register for courses first, the availability of course registration for the male campus on days other than those reserved for the female campus, the college's role in academic advising, the registration of courses, and ultimately the role of the DAR in academic advising and course registration, and academic advisor.

4.
Examine the value disparities in satisfaction levels with the study variables among college students: The purpose was to determine the extent to which, due to digital transformation, there were value disparities in the degree of satisfaction by college affiliation regarding the five study factors in 2020. The LSD test was used to determine which college affiliations were correlated to the value differences in progressive course registration for those expected to graduate first, and we found that the Faculty of Science was the origin of the value discrepancies in progressive course registration for those expected to graduate first, as it had the lowest satisfaction average, as compared with the other colleges, see Table 20. We should also mention that the Faculty of Computers and Information Technology was connected to the value differences in allowing male campus students to register for courses on days other than those reserved for female campus students, as well as the academic advisor, where it had the highest satisfaction levels among the colleges.

5.
Examine the value disparities in satisfaction levels with the study variables according to the number of approved academic hours: The purpose was to determine how much of a difference there was in value satisfaction for the five research variables in 2020 due to digital transformation, as measured by the number of approved academic hours, see Table 21. The goal was to see how much of a difference there was in value satisfaction for the five research variables in 2020 as a result of digital transformation, as measured by approved academic hours. Tables 22 and 23 show that there were value differences in the student feedback based on the number of approved academic hours with regard to progressive course registration for those expected to graduate first, availability of course enrollment for male campus students on days other than the female campus, and the affiliated college's role in academic advising and course registration. However, there were no value differences between the rest of the variables according to the number of approved academic hours. The LSD test was used to determine which college affiliations were connected to these differences. It was discovered that a category of more than 120 h was connected to the value differences between the degree of student satisfaction in terms of allowing course registration to be available to those expected to graduate first, where it had the highest average. This group was also correlated with the value differences between the degree of students' satisfaction in terms of the role of the college in academic advising and course registration, where it had the lowest average.
A class of 91-120 academic hours triggered value discrepancies between the students' degree of satisfaction with this variable, which had the highest average, as compared with the rest of the categories, in terms of availability of course registration for male campus students on days other than those intended for female campus students.

6.
Examine the value disparities in satisfaction levels with the study variables according to GPA: The purpose was to determine the extent of value disparities in satisfaction levels according to GPA from the five study factors in 2020 due to digital transformation. Table 22 shows that there were value differences between student feedback and GPA with progressive course registration for those expected to graduate first, allowing course registration for male campus students on days other than those intended for female campus students, and the academic advisor, while there were no value differences with allowing course registration for those expected to graduate first.
Students with a 4.5 GPA or higher showed value disparities in their attitudes about the availability of course registration for those expected to graduate first. According to the LSD test, which was used to establish which institutions were accountable for the irregularities, it had the highest average. However, the 2.0-2.74 GPA group was connected to the value disparities between the student perspectives on course registration for male campus students on days other than those intended for female campus students, as well as the academic advisor, which had the lowest average among the study rate categories.

7.
Examination of the relationship between the research variables: The purpose was to determine the degree to which the five research variables were correlated. Pearson's correlation coefficients and their semantic levels are shown in Table 24 (between brackets). Table 24 shows that ejective correlations between distinct variables were statistically significant. This graph depicts the relationship between satisfaction and various criteria as a result of digital transformation and the implications for course registration and academic advising.

Discussion and Outcomes
As a result of modifications and enhancements to the electronic academic advising and course registration systems at King Abdulaziz University:

1.
Balancing the demand for the ODUS Plus electronic system improved network access.

2.
Students were more satisfied with progressive course registration for those expected to graduate first in 2020 than they were in 2018.

3.
Student satisfaction with allowing course registration for the male campus students on days other than those allotted for the female campus increased in 2020, as compared with 2018.

4.
In 2020, students were more satisfied with the university's role in academic advising and course registration than they were in 2018.

5.
In 2020, students were more satisfied with the DAR's role in academic advising and course registration than they were in 2018. 6.
Student satisfaction with the role of the academic advisor in 2020 was higher than it was in 2018.

7.
Students' perspectives on the role of the affiliated college and the DAR in academic advising and the role of the academic advisor differed significantly in 2020. There were also value differences in student feedback regarding the availability of course registration for those expected to graduate first and the availability of course registration for the male campus students on days other than those reserved for the female campus students. In fact, the satisfaction score of the male campus students was higher than the female campus.
In 2020, there were value differences between student feedback in the different student year levels regarding the availability of course registration for those expected to graduate first, allowing course registration for male campus students on days other than female campus students, and the role of the college in academic advising and course registration. The value disparities were connected to the expected to graduate and fourth-year students as they reported more satisfaction than the other students, and the availability of course registration for the male campus on distinct days from the female campus. The results also showed that fourth-year students and those expected to graduate were connected to the value discrepancies, as they were less satisfied than the rest of the student year levels regarding the availability of course registration for male campus students on days other than those reserved for female campus students. In addition, expected graduate students were less satisfied with the university's academic advising and course registration than the rest of the student year levels, resulting in value inequalities in student grades concerning the university's role in academic advising and course registration.
There were value discrepancies between the different college students' perspectives concerning the five study factors. The data also reveal that the Faculty of Science, which had the lowest average among the colleges, was related to the value dissimilarities in prioritizing course registration for the expected graduate students. We also noted that the Faculty of Law was connected to value differences regarding the role of the university in academic advising and course registration, as well as the role of the DAR in academic advising and course registration, where it had the lowest average satisfaction levels among the colleges. We should also mention that the Faculty of Computers and Information Technology was connected to the value disparities in allowing male campus students to register for courses on days other than those reserved to the female campus students, as well as the academic advisor, where it had the highest average of all the colleges.
There were value differences in student feedback based on the number of approved academic hours regarding progressive course registration for those expected to graduate first, allowing male campus students to register for courses on days other than those reserved for female campus students, and the college's role in academic advising and course registration, while there were no value differences between the remainder of the factors according to the number of approved academic hours.
The findings also revealed that the category of more than 120 h was connected to the value differences between student satisfaction levels in terms of the progressive course registration for those expected to graduate first, which had the highest average. The same category was also connected to value differences between student satisfaction levels in terms of the university's role in academic advancement, which had the lowest average. As for the availability of course registration for male campus students on days other than those reserved for female campus students, a class of 91-120 academic hours was connected to value differences between the students' degree of satisfaction with this variable, which had the highest average, as compared to the rest of the categories.
There were value differences between student feedback based on their diverse GPAs regarding progressive course registration for those expected to graduate first, allowing male campus students to register for courses on days other than those reserved to the female campus students, as well as the academic advisor, while there were no value differences between the rest of the variables.
The results also reveal that the 4.5 GPA group and above was connected to the value differences between student feedback regarding progressive course registration for those expected to graduate first, with the highest average, while the 2.0-2.74 GPA group was connected to value differences between student feedback regarding the availability of course registration for the male campus students on days other than those reserved to the female campus students, as well as the academic advisor, which had the lowest average, as compared to the rest of the GPA categories.
The presence of statistically significant ejective correlations among several variables depicted the relationship between satisfaction and many variables and the implications for course registration and academic advising as a result of digital transformation.

Conclusions
Higher education institutions seek to modernize and develop study programs and systems. Universities such as King Abdulaziz University have been interested in developing digital solutions that promote academic counseling and advising for students since they have been shown to be a valuable asset in the educational process. The researchers compared student satisfaction across two surveys, one from 2018 and the other from 2020, to determine any variations, specifically in student satisfaction regarding academic advising and electronic course registration.
The investigation discovered that modifications in digital transformation procedures resulted in balancing the demand on the ODUS Plus electronic system and alleviated the problems accessing the network that occurred in 2018. This study examined student satisfaction with changes in digital transformation methods and advances in many parts of academic advising and course registration, including progressive course registration for those expected to graduate first This research is intended to broaden other universities' research both inside Saudi Arabia and around the world. The questionnaire that was designed for this study was shown to be reliable and valid and, therefore, may be of use in future research under similar conditions. This research asserted that some technical challenges were easily resolved using modifications and digital solutions. This may provide a reference for the development of online platforms for academic advising and course registration.

Academic and Practical Implications, and Limitations
The research provided insights for decision-makers in the educational institutions that wish to enhance their performance in academia. Universities and HEIs seek efficient academic performance in each aspect. Academic advising (AA) is one effective aspect that positively impacts student outcomes in areas such as enrollment, course registration, academic accountability, and career success. The supporting information systems that facilitate AA should accelerate their development to support these goals.
However, implementing novel systems requires investigation into their impact on student performance and satisfaction. Therefore, this research indicated the benefit of examining AA systems to understand the profound learning process more fully. Otherwise, student retention rates may be negatively affected. This would harm not-for-profit and for-profit educational institutions alike. The authors believe that this research area can promote the development of more efficient and effective advising services for both students and HEIs.
HEI leaders may find this research useful in practical applications. Similar investigations can be applied to understand student satisfaction level before and after modifications to digital or other solutions to assure better organizational performance. The authors recommend that AI and machine learning be used to analyze and understand the classification of inquiry topics and the interactions received from beneficiaries in the future. This can be done by analyzing similar calculations from other universities to verify and confirm the findings. By its sample nature, this research was limited to one selected university; thus, the researchers also suggest that this study could be expanded in the future. Funding: This research was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge the DSR for its technical and financial support. The author is also thankful to King Abdulaziz University of Saudi Arabia for supporting this research by providing the research environment, data, and funds.
Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.
Data Availability Statement: Secured and private datasets were analyzed in this study.

Conflicts of Interest:
The authors declare no conflict of interest.