The Impact of User Satisfaction in the Use of E-Learning Systems in Higher Education: A CB-SEM Approach

: The primary objective of this research is to thoroughly investigate the intricate dynamics and collective influence of essential elements within the e-learning domain. Examining the interdependencies and combined effects of technology system quality, information quality, and support services on user satisfaction. This study investigates the key factors affecting user satisfaction in e-learning at Universitas Negeri Makassar, focusing on the roles of Information Quality, System Quality, and Service Quality. Survey data from 231 diverse students were analyzed using a Likert scale questionnaire, with Structural Equation Modeling via IBM AMOS. The findings aim to reveal how these quality dimensions impact user satisfaction, potentially guiding enhancements in e-learning system design. A comprehensive study examining e-learning systems conclusively found that System Quality, Information Quality, and Service Quality are pivotal factors influencing user satisfaction. Improving system functionality, ensuring the accuracy and relevance of information, and delivering high-quality service were all significantly correlated with higher satisfaction levels among users. This underscores the critical need for educational institutions to prioritize these aspects to enhance the e-learning experience. The research presents strong evidence that educational institutions can significantly boost user satisfaction by focusing on the quality of the system, information, and services provided in e-learning platforms. These findings provide actionable insights for decision-makers in the education sector, suggesting that investments in these areas will likely yield positive outcomes in user engagement and satisfaction with e-learning systems.


Introduction
Technology-based education has rapidly evolved in recent years, with e-learning becoming one of the increasingly popular approaches to providing broader access to education [1]- [3].E-learning allows students to learn flexibly, access materials online, and interact with instructors and fellow students without being constrained by geographical limitations [4], [5].This presents significant potential for improving educational accessibility, particularly for those facing geographical constraints or other limitations in participating in traditional education.
One of the significant challenges in e-learning is ensuring student satisfaction with their learning experience.Factors such as the quality of the technology systems, the quality of the provided information, and the quality of support services can significantly impact student satisfaction [6], [7].Technology systems' quality encompasses access speed, platform stability, and userfriendly interfaces.Information quality involves the accuracy and relevance of the provided learning materials.Meanwhile, service quality includes the responsiveness of technical support and the availability of assistance to students.
Although there has been research exploring these aspects individually, there is still a need for a deeper understanding of how these factors collectively influence student satisfaction in e-learning.Therefore, this research aims to fill this knowledge gap by analyzing the combined impact of system quality, information quality, and support services on student satisfaction in e-learning [8], [9].Thus, this study is expected to provide deeper insights and practical recommendations for educational institutions and e-learning platform developers to enhance the elearning experience for students [10], [11].
Several prior studies have examined various aspects related to student satisfaction in e-learning.These findings underscore the significance of user-friendly technology systems, relevant and accurate information quality, and responsive, high-quality support services in enhancing the e-learning experience [12]- [16].Previous research identified that the technology systems deployed in elearning platforms considerably impact student satisfaction.Systems that are easy to use, intuitive interfaces and stable performance can elevate student satisfaction levels [17], [18].Furthermore, the quality of information presented in e-learning also plays a pivotal role.Relevant, accurate, and up-to-date learning materials can enhance students' understanding, subsequently increasing satisfaction [12].Equally vital are responsive and top-tier support services.Swift and efficient technical support and assistance for students facing challenges can amplify student satisfaction levels regarding the e-learning platform [19], [20].
In addition to these factors, fundamental theories and frameworks have been employed in previous research to elucidate student satisfaction with e-learning.The Technology Acceptance Model (TAM), proposed by Davis in 1989, concentrates on users' acceptance and adoption of technology.According to TAM, perceptions about a technology's usefulness and ease of use will influence users' intention to utilize it.In e-learning, technology deemed easy and beneficial by students can enhance their satisfaction [17].Research related to student satisfaction in the realm of e-learning has evolved alongside advancements in information technology.Initially, this research primarily centers on the technical and functional aspects of e-learning platforms.At this stage, the primary focus was on developing e-learning platforms that could operate seamlessly, efficiently deliver learning materials, and provide essential tools for online instruction and learning [6].The overarching goal was to ensure that the deployed technology could effectively support learning content delivery.
With the evolution of the concept of customer satisfaction, research is increasingly focusing on the factors that influence students' perceptions of the quality of the e-learning experience.This includes understanding how students interact with technology, how they perceive the quality of the information provided, and to what extent support services meet their needs.Management theories, such as the Technology Acceptance Model (TAM) introduced by Davis in 1989, are becoming more widespread in e-learning research.TAM helps explain why students accept or reject e-learning technology by focusing on perceptions of its usefulness and ease of use [17].Consequently, e-learning research has shifted from technical approaches towards a more user-oriented and overall service quality-oriented approach.
In the context of this research, e-learning is increasingly understood as an ecosystem that involves technical factors, information quality, system quality, and service quality.This helps research understand that these factors synergize and mutually influence each other to shape the student experience, ultimately affecting their satisfaction.Thus, recent research in e-learning is more oriented toward understanding how these factors work together to create a better e-learning experience and how educational institutions and e-learning platform developers can leverage this understanding to enhance the quality of technology-based education.
While much research has been conducted on the factors influencing student satisfaction in e-learning, a significant research gap remains.One such gap is the lack of understanding of how technology systems, information, and services interact and affect student satisfaction [21].This research aims to fill this gap by conducting a comprehensive analysis of the combined impact of these factors on student satisfaction in the e-learning context.A significant research gap in student satisfaction in elearning lies in a deep understanding of the interplay of critical factors, namely technology systems, information, and services, and how this interaction affects student satisfaction.Previous research has identified the crucial roles of these factors, but there is still a need to bridge this gap in greater detail.
E-learning is a highly diverse environment, encompassing various types of educational institutions, elearning platforms, and subjects [11].Therefore, a research gap exists in understanding how these factors may operate differently in various e-learning contexts.Some previous studies may not have used comprehensive measurement tools to assess these factors and their impacts on student satisfaction.There is potential to develop better methods and instruments for measuring the quality of technology systems, information quality, and support services in more detailed and accurate ways.By understanding these gaps, this research aims to fill knowledge voids by conducting a comprehensive analysis of the interactions and combined impacts of critical factors in the context of e-learning.
Through a deeper understanding of the relationship between technology system quality, information quality, and support services, this research will provide valuable insights and practical solutions for developing better elearning.

Research Approach
Quantitative approaches in scientific research rely heavily on strategies such as experiments and surveys, which allow researchers to collect statistical data to answer research questions empirically [22].This approach emphasizes the importance of causation, where researchers try to identify causal relationships between the variables they observe [23].To simplify data complexity, variable reduction is essential in quantitative analysis [24].Quantitative research involves formulating hypotheses that can be tested empirically [25].This hypothesis serves as a framework for directing research and provides a basis for data collection and analysis.Finally, this research requires the formulation of specific research questions to be answered [26].These questions guide the research design and collect relevant data.

Sample and Data Collection
This study utilized a sample of 231 respondents as research subjects, chosen with meticulous attention to reflecting the diversity of the population pertinent to the study.This deliberate selection ensured robust representation and more broadly applicable results.Data collection was carried out via a survey employing a carefully constructed questionnaire.The questionnaire was randomly distributed to every student at the Universitas Negeri Makassar, and we ensured that respondents had ample time to provide comprehensive responses.The data collected through this questionnaire is the foundation for our research analysis and findings.We meticulously designed the questionnaire to optimize the efficiency and effectiveness of data collection.It comprises questions tailored to elicit pertinent information aligned with our research objectives.The phrasing of these questions was rigorously crafted to prevent bias and ensure data accuracy.

Research Instrument
A variable is a contract whose properties have been assigned a value as a number or concept with two or more values on a continuum.The value of a variable can be expressed with numbers or words.Variables can also be interpreted as symptoms of something that will be the object of research.Based on the relationship, variables are divided into two: (1) Independent variables influence or cause other variables.The independent variables in this research are Information Quality, System Quality, and Service Quality; and (2) The dependent variable is a variable that is influenced or caused by other variables.The dependent variable in this research is User Satisfaction.

Table 1. Research Instrument
Variables

Kode Construct Sources
System Quality SYQ1 How responsive is this e-learning system to your commands?[12] SYQ2 What is the level of ease of access to this e-learning system?[27] SYQ3 How easily can you overcome technical problems when using e-learning systems?[28] SYQ4 What is the user interface's suitability level in the e-learning system?[29] Information Quality

IQ1
How accurate and reliable are the learning materials in this e-learning system?[30] IQ2 How does this e-learning system present information clearly and easily understand?[31] IQ3 What is your perception of the novelty and completeness of the information in this e-learning?[32] Services Quality SEQ1 Is the guidance provided when experiencing difficulties in e-learning adequate?[33] SEQ2 What is the quality of user communication services and e-learning support teams? [12] SEQ3 How responsive and professional is the technical support service? [34] SEQ4 How easy is it to interact in the e-learning system with instructors and fellow users?[35] Variables Kode Construct Sources User Satisfaction US1 Are you satisfied with the features of this e-learning system overall?[30] US2 What is your level of satisfaction with your academic performance after using this system?[36] US3 Does using this e-learning system positively contribute to increasing your knowledge and skills?[37] US4 Do you feel this e-learning system provides an opportunity to learn more interactively?[37] The questionnaire utilized in this study aims to categorize students based on Information Quality, System Quality, Service Quality, and User Satisfaction.To measure these variables, we employed the Likert scale guidelines, widely used to assess individuals' attitudes, opinions, and perceptions regarding specific social phenomena, as determined by the researcher.In this context, the research variables include Information Quality, System Quality, Service Quality, and User Satisfaction.With the Likert scale, these variables are translated into measurable indicators.Each item in the instrument offers a range of responses, from very positive to very negative, expressed in words.The answer options used in this instrument are Strongly Agree (5), Agree (4), Neutral or Neither Agree (3), Disagree (2), and Strongly Disagree (1).

Data Analysis
In this study, we utilized Confirmatory Factor Analysis (CFA) and the Covariance-Based Structural Equation Modelling (CB-SEM) method to comprehensively examine the relationships between the variables outlined in our previously established conceptual model.CB-SEM represents a robust statistical tool employed to explore connections between latent variables and observed data in research.As elucidated by [38], CB-SEM empowers researchers to scrutinize hypotheses generated from a conceptual model.This methodology seamlessly integrates Confirmatory Factor Analysis, which evaluates the validity and reliability of latent variables, with path analysis to delve into the causal relationships among these variables [39].In this analysis, the initial step involves estimating the measurement model to assess the construct's reliability and validity.Subsequently, we estimate the structural model to test the hypothesized relationships between variables.Structural Equation Model (SEM) analysis was conducted using the IBM AMOS program as the data processing application to analyze the primary data obtained.

System Quality
System quality pertains to the accessibility and responsiveness of a system, ensuring it effectively addresses user inquiries and promptly caters to their needs, aligning with user expectations [40].It gauges the extent to which system users perceive specific systems as userfriendly, comprehensible, learnable, and enjoyable [41].
Evaluating system quality commonly involves assessing the reliability of inherent system attributes, encompassing system performance and system and user interfaces.This investigation focuses on how the quality of a system intricately links to a student's e-learning experience, encompassing user-friendliness, learnability, accessibility, and rapid response times.Moreover, the system's quality significantly influences user contentment and system utilization [42].User-friendly performance, denoted as a system's operational trait [43], correlates with easily navigable, comprehensible, and graspable systems.It extends to adaptability, reflecting a system's effective response to changing circumstances [44].Essential aspects of system usage encompass time savings, redundancy reduction, and heightened productivity [45].Consequently, the timeliness factor also emerges as a determinant impacting system quality.
H1: There is a significant influence between system quality and user satisfaction.

Information Quality
Information quality pertains to the caliber of the results generated by the information system [30], [46], [47].Essentially, it encapsulates the worth of the information output emanating from the system [48].This discernment underscores that the evaluation of information quality revolves around the outcomes engendered by the system, alongside their value to the user.Information quality assessment encompasses a triad of criteria: precision, punctual delivery, and relevance of information.Evaluating information quality interlinks with the information the system can generate, spanning transactional data and comprehensive reporting [49], [50].
H2: There is a significant influence between Information quality and user satisfaction.

Services Quality
Service quality within higher education encompasses the discernible variance between students' expectations and actual encounters within the higher education context [51].It pertains to the comprehensive assistance furnished by service providers within the information system domain, encompassing contributions from internal and external entities affiliated with the organization.In pursuing educational objectives, service quality is pivotal in enhancing the educational experience via online platforms [52].It fortifies competitive advantage by introducing distinctive elements that augment user contentment.Several pivotal facets that serve as service quality indicators within the e-learning landscape encompass administrative and support mechanisms, instructor competence, precision, specialized resources, and security provisions [53].Cultivating commendable service quality positively influences user satisfaction and system utilization [54].To advance the quality of information system services in education, a pivotal approach involves enhancing e-learning services, achieved through evaluative processes rooted in students' experiences and perceptions.
H3: There is a significant influence between Service quality and user satisfaction.

Characteristic of Respondents
This study collected data from 231 respondents to explore various aspects of them.It evaluated the gender distribution among the respondents.The research results indicated that most were males, comprising more than half of the respondents.Although the difference was not statistically significant, it was quite noticeable.There were five different age groups; the 20-year-old age group was the largest.This may suggest that this age group was the most heavily involved in the study.We also examined the faculties or majors chosen by the "Engineering" faculty highest respondents, followed by the "Mathematical Psychology and Natural Sciences" faculty.This could indicate the interests and distribution of respondents across various fields of study.

Goodness of Fit (GOF) Test
The goodness-of-fit analysis evaluates how much the proposed model fits the empirical data.Hu and Bentler explain that goodness-of-fit analysis provides information about the model's suitability to observational data [55].
A concise set of indicators effectively reveals the relationship between latent variables, provided values stay below a specified threshold.Correlation strength is measured by a loading factor exceeding 0.50, indicating that the reflective construct in the structural model surpasses the threshold, making missing latent variables unnecessary.When evaluating the measurement model's quality, consider validity and reliability.In Table 4, the Construct Reliability (CR) value from the SEM study of the measurement approach exceeds 0.70, signifying reliability across all models and instilling confidence in their applicability.The Average Variance Extracted (AVE) reflects the construct variance represented by the latent variable.To ensure robust convergent validity, aim for a threshold of 0.50 or higher [76].
From Table 4, findings confirm positive outcomes, with the AVE indicating excellent validity within the structural model.This suggests latent explanatory variables account for over half the variance in average indicators, further validating the model's reliability.

Hypothesis Test Result
Table 5 indicates the analysis results indicating a significant relationship between the tested factors and User Satisfaction.System Quality is confirmed to have a positive and significant relationship with User Satisfaction (β = 0.385, p<0.01).This implies that enhancing system quality positively influences the level of user satisfaction.These findings support the first hypothesis (H1), which posits that System Quality affects User Satisfaction.Secondly, Information Quality also significantly impacts User Satisfaction (β = 0.524, p<0.01).This suggests that improving information quality increases user satisfaction, aligning with the second hypothesis (H2).Thirdly, Service Quality notably substantially impacts User Satisfaction (β = 0.652, p<0.001).This underscores that elevating service quality considerably positively affects user satisfaction levels, reinforcing the third hypothesis (H3).These results strongly emphasize the significance of these factors in enhancing user satisfaction.
These results demonstrate a highly significant relationship between System Quality, Information Quality, and Service Quality and their impact on User Satisfaction.Each factor exhibits a robust positive influence on User Satisfaction, as evidenced by the substantial estimated values (β) and low probability values.These findings support this study's three primary hypotheses (H1, H2, and H3).Based on these results, it can be confidently concluded that investing in and actively improving System Quality, Information Quality, and Service Quality can be regarded as an effective strategy for enhancing User Satisfaction within an organizational or corporate context.These findings offer valuable guidance for decisionmakers striving to enhance user experience and satisfaction.
The influence of information quality on user satisfaction is crucial in the e-learning context.E-learning employs information and communication technology to deliver information and remote education to users.In an e-learning environment, good information quality can contribute to user satisfaction and the effectiveness of the learning process.Therefore, this research investigates the significant effect of information quality on e-learning satisfaction.
Previous research has identified aspects of information that potentially influence user satisfaction in e-learning.Common aspects related to elearning information quality include accuracy, clarity, relevance, timeliness, and availability of information.Studies also indicate that these factors contribute to users' perceptions of information quality and, consequently, to their satisfaction.
A related study by Hong et al. investigated the effect of information quality on user satisfaction in the e-learning context at higher education institutions.The results revealed that aspects of information quality, such as accuracy, clarity, and relevance, significantly and positively influence e-learning user satisfaction [77].
Other research by R. Wang et al. and X. Wang et al. also explored the influence of information quality on user satisfaction on e-learning platforms.They found that factors like the timeliness and availability of information significantly contribute to e-learning user satisfaction.Elearning users tend to be more satisfied when they access current and well-available information [78], [79].
A study by Huang et al. explored the relationship between information quality, user satisfaction, and learning effectiveness in e-learning systems in Taiwan.Information quality, including clarity, relevance, and availability, positively impacts user satisfaction and learning effectiveness [80].Although the study by Wu & Wang did not focus specifically on e-learning, it centered on measuring the success of a knowledge management system (KMS).This research provides valuable insights into factors affecting user satisfaction, associating it with information quality [81].The findings revealed that information quality significantly affects user satisfaction in the KMS context.Research by Liu et al. examined the influence of information quality in the context of elearning technology acceptance.High-quality information, including clarity and relevance, can positively impact user experience and enhance e-learning technology acceptance [82].
Through this research, a better understanding of the relationship between information quality and user satisfaction in the e-learning context is expected.The findings can offer recommendations for educational institutions and e-learning platform providers to enhance the quality of information they provide, thereby increasing user satisfaction and remote learning effectiveness.E-learning system quality is paramount in enhancing user satisfaction, as underscored by multiple studies and user experience factors.The accessibility and intuitiveness of an e-learning system stand as pivotal elements in this regard.Students appreciate a straightforward platform, enabling them to swiftly access materials, connect with peers and instructors, and easily harness available features.Such systems minimize technical challenges, providing a smooth and efficient learning experience.
Content availability and reliability are foundational for any e-learning system.Students' satisfaction levels soar When they can seamlessly access current, comprehensive, and relevant learning materials.Reliability in delivering these materials and ensuring consistent system functionality is equally critical.This gives students the confidence to depend on the platform for their educational needs.
Interactive and collaborative elements also serve to elevate the e-learning experience.Features that promote discussions, virtual classes, or collaboration platforms foster richer interactions, permitting students to exchange ideas, offer feedback, and form valuable social bonds.These engagements enhance user satisfaction by making learning more dynamic and interconnected.
The e-learning system's ability to provide prompt and constructive feedback is also a significant determinant of user satisfaction.Students value timely feedback on assignments, exams, and queries, as it clarifies their progress and areas for improvement.Quick responses cater to academic needs and symbolize institutional support, further heightening satisfaction.
Multiple studies corroborate these observations.Wu et al. emphasized the importance of e-learning system quality aspects like response speed, interface design, and feature relevance in influencing user satisfaction [83].They have pinpointed factors such as ease of use, system responsiveness, and technical support instrumental to elearning user contentment [42], [84] and echoed these sentiments, emphasizing system reliability, content availability, and interactive features as crucial drivers of user satisfaction [85].
In essence, an e-learning system's quality significantly determines user satisfaction.To offer an optimal learning experience, universities and platform providers must prioritize factors like ease of use, content availability, system reliability, and interactive capabilities.By these qualities, institutions significantly uplift user satisfaction in e-learning environments.
The influence of service quality on user satisfaction is a crucial factor in e-learning.E-learning is a learning method that employs information and communication technology to provide distance education to users.In an e-learning environment, good service quality can contribute to user satisfaction and the success of the elearning platform implementation.Therefore, this research aims to investigate the significant influence of service quality on e-learning user satisfaction.
The impact of service quality on user satisfaction in elearning at higher education institutions.E-learning users, namely were approached with a survey using a questionnaire as a data collection instrument.The results indicate service dimensions such as responsiveness, technological reliability, and instructor interaction positively and e-learning user satisfaction.Service responsiveness, such as quick responses to user queries and problems, enhances user satisfaction [86], [87].Technological reliability and interaction between instructors and users also contribute to user satisfaction [20], [88].
Another study revealed the influence of service quality on user satisfaction in e-learning platforms [20], [89].They found that factors like platform accessibility, instructor interaction, and availability of learning resources significantly contribute to e-learning user satisfaction.
Easy-to-use and intuitive platform accessibility, good interaction between instructors and users through discussion forums or support services, and ample availability of learning resources are crucial factors affecting user satisfaction.
From this research, it can be concluded that good service quality in the context of e-learning significantly affects user satisfaction.Service quality dimensions such as service responsiveness, technological reliability, instructor interaction, platform accessibility, and the availability of learning resources need to be considered and enhanced to improve e-learning user satisfaction.The recommendations from this research can assist educational institutions and e-learning platform providers in improving the quality of services they offer, thus enhancing user satisfaction and the effectiveness of distance learning.

Conclusion
The research highlights the pivotal role of System Quality, Information Quality, and Service Quality in enhancing user satisfaction with e-learning platforms.Institutions must prioritize the development of intuitive and reliable systems, deliver clear and relevant content, and ensure responsive and supportive service to users.The study suggests a comprehensive strategy for educational providers: invest in user-friendly technology, maintain up-to-date and accessible educational content, and provide effective support and interaction.These findings not only guide institutions in refining their e-learning offerings but also have broader implications for educational policy, suggesting a need for standards that uphold these quality dimensions.By focusing on these key areas, educational institutions can foster a more engaging and successful elearning experience for users.

Table 2 .
Characteristic of Respondents

Table 4 .
CR and AVE Test Result.

Table 5 .
Results of Hypothesis Testing