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Advances in Social Sciences Research Journal – Vol. 11, No. 8
Publication Date: August 25, 2024
DOI:10.14738/assrj.118.17459.
Alshammari, M. F., Yusoff, R. C. M., & Abas, H. (2024). A Systematic Literature Review of Factors Influencing Satisfaction and
Continuance Intention to Use E-Learning Systems in Higher Education. Advances in Social Sciences Research Journal, 11(8). 326-
346.
Services for Science and Education – United Kingdom
A Systematic Literature Review of Factors Influencing
Satisfaction and Continuance Intention to Use E-Learning Systems
in Higher Education
Maha Farhan Alshammari
Creative Artificial Intelligence Department,
Faculty of Artificial Intelligence, Universiti Teknologi Malaysia
Rasimah Che Mohd Yusoff
Creative Artificial Intelligence Department,
Faculty of Artificial Intelligence, Universiti Teknologi Malaysia
Hafiza Abas
Creative Artificial Intelligence Department,
Faculty of Artificial Intelligence, Universiti Teknologi Malaysia
ABSTRACT
Electronic learning (e-learning) allows students to access course materials without
time or location constraints. While much research has focused on e-learning
adoption or acceptance, there is a lack of studies examining students' satisfaction
with Continuance Intention (CI) to continue using e-learning systems. This paper
conducts a systematic review of existing research on this topic within higher
education. An analysis of 34 articles identifies 42 factors influencing students’
satisfaction with CI. The review outlines the models and constructs used in the
studies, revealing that many employed the Expectation Confirmation Model (ECM)
to assess satisfaction. Key constructs consistently found to significantly impact
students' satisfaction with CI include System Quality, Information Quality, Self- efficacy, Service Quality, Perceived Ease of Use, and Perceived Enjoyment.
Keywords: E-Learning, Continuance Intention, Satisfaction, Higher Education, Systematic
literature review.
INTRODUCTION
E-learning systems are modern approaches to sharing knowledge in educational settings. These
digital platforms allow for the delivery of lectures, coursework and evaluations in higher
education institutions without the need for a physical presence. The rapid growth of e-learning
tools and technologies stands out as one of the most significant advancements in higher
education, offering considerable benefits that enhance traditional learning methods [1, 2]. With
the rapid advancement of educational technology, e-learning systems have seen widespread
adoption in academic environments [3, 4]. Despite the many advantages these systems bring,
understanding the elements that affect student satisfaction is crucial for their long-term success
[5-7].
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Alshammari, M. F., Yusoff, R. C. M., & Abas, H. (2024). A Systematic Literature Review of Factors Influencing Satisfaction and Continuance Intention
to Use E-Learning Systems in Higher Education. Advances in Social Sciences Research Journal, 11(8). 326-346.
URL: http://dx.doi.org/10.14738/assrj.118.17459
Many students are not satisfied with the e-learning system and expressed a preference for the
face-to-face method, as the absence of the instructor in the e-learning environment often leads
to confusion [8-10]. This lack of intention to continue using the e-learning system was
highlighted in normal situations and became more pronounced by the end of the COVID-19
pandemic [11]. During the pandemic, the use of the e-learning system was mandatory, but
afterward, students showed decreased interest in using it voluntarily and did not develop
positive intentions to use it. Studying the factors that affect students' satisfaction to continue
using the e-learning system is crucial, as the success of such systems depends on students'
willingness to continue use them. E-learning systems cost universities a lot of money and effort
to implement and operate. Accordingly, students' refusal to continue use these systems can lead
to system failure and result in a waste of time, effort, and financial resources for the university.
A range of educational technologies has been explored, such as mobile learning [8, 12, 13].
Nonetheless, research on these specific technologies is less prevalent than studies examining
the factors that influence the satisfaction for CI in e-learning system. Literature on satisfaction
and CI has grown rapidly in recent years, a thorough, Systematic Literature Review (SLR) is
missing. One study reviewed the factors affecting students’ and instructors’ continuance
satisfaction and limited to the year 2017 [14-16]. While prior studies have studied instructors'
perspectives, they have not explored students' viewpoints regarding the primary factors
influencing their satisfaction for continuance intention with e-learning systems [17, 18].
Therefore, it is useful to critically assess the extant literature and clarify what we already know
and what we need to know regarding the theories and factors affecting the student’s
satisfaction for CI to use e- learning system in higher education institutions.
This study conducts a systematic literature review (SLR) with two primary objectives: first, to
provide a comprehensive analysis of the existing literature on satisfaction and Continuance
Intention (CI) in e-learning environments; and second, to identify the models and factors that
are most commonly used to predict student satisfaction with CI. The following sections discuss
related work, methodology, results, and discussion, and conclude with the study's conclusions,
limitations, and recommendations for future research.
RELATED WORK
Continuance Intention
Bhattacherjee [19] suggested that continued technology usage is a temporal phenomenon,
reliant on initial perceptions and intentions associated to technology continuance. Although
users might have the intention to act a certain way, their behavior doesn't always align with
those intentions. Therefore, it is suggested that future research should investigate the role of
satisfaction in motivating students to continue using e-learning systems in higher education
environments.
METHODOLOGY
The systematic literature review (SLR) in this study adheres to the procedure outlined by
Budgen, Kitchenham [20], which involves three main stages: planning, conducting, and
reporting (Fig. 1).
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Fig. 1:The systematic literature review process (Kitchenham and Charters, 2007)
Identifying Existing SLRs to Assess the Need for Review
To establish the need for this systematic literature review (SLR), an initial survey of existing
literature was conducted. The preliminary investigation highlighted several studies focusing on
various aspects of e-learning in higher education, such as technological integration,
instructional strategies, and learner engagement. However, a gap was identified in
comprehensive reviews specifically examining the factors that affect student satisfaction and
their intention to continue using e-learning systems. While some studies touched on these
factors, they did not provide a holistic overview or delve deeply into the theories and models
used to predict these outcomes. This gap underscores the necessity for a systematic review to
consolidate existing knowledge and identify key factors and theoretical frameworks, thereby
contributing to the field's academic and practical understanding.
Research Questions
To provide a comprehensive overview of the current literature, the following research
questions were developed:
1. What are the factors that influence student satisfaction regarding their intention to
continue using e-learning systems?
2. What theories or models are used to predict student satisfaction concerning their
intention to continue using e-learning systems?
Review Strategy Protocol: Searching, Selecting, and Synthesizing
The systematic review process followed a structured protocol designed to ensure thoroughness
and reliability. The primary databases used for the search were Scopus and Web of Science
(WOS), chosen for their extensive coverage of high-quality, peer-reviewed academic
publications. The search strategy included primary keywords such as "Satisfaction" AND ("e- learning continuance intention" OR "online learning continuance intention") AND ("University"
OR "academic") in all possible combinations. Articles published between 2015 and 2023 were
considered to ensure the inclusion of recent developments in the field.
Inclusion criteria required articles to be open access and directly relevant to the scope of the
study, focusing on factors affecting satisfaction and continuance intention in e-learning systems
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Alshammari, M. F., Yusoff, R. C. M., & Abas, H. (2024). A Systematic Literature Review of Factors Influencing Satisfaction and Continuance Intention
to Use E-Learning Systems in Higher Education. Advances in Social Sciences Research Journal, 11(8). 326-346.
URL: http://dx.doi.org/10.14738/assrj.118.17459
within higher education. Exclusion criteria ruled out articles not relevant to these focus areas.
The initial search yielded a substantial number of articles, which were then screened based on
their titles and abstracts. Relevant articles were retrieved for full-text review to further assess
their alignment with the research questions and methodological quality.
Collecting, Selecting, Assessing Quality, and Extracting Data from Articles
After completing the planning stage, keywords were applied to five different databases:
Emerald, Science Direct, IEEE, Springer, and Scopus. The search in these databases yielded over
1,000 articles related to the keywords. Duplicate articles were excluded, and each article's
abstract was reviewed to determine its relevance to IS continuance intention and satisfaction,
as well as its inclusion of a theoretical model and quantitative data for validation. This process
identified 34 relevant articles from the five databases: Emerald (3), IEEE (4), Science Direct (5),
Springer (11), and Scopus (11). Fig. 2 illustrates the systematic review process flowchart for
reviewing the literature on e-learning and satisfaction for continuance intention. To ensure
comprehensive coverage, the number of publications obtained from each database was
carefully recorded. After conducting the automated search with the refined keywords, a manual
search was also performed to capture any potentially missed primary studies. This involved
scanning the citation and reference lists of the articles obtained from the automated search,
ensuring a thorough collection of relevant studies. Throughout the article collection and
selection process, each article was rigorously examined based on its title, abstract, and full text,
following the inclusion and exclusion criteria detailed in Table 1. The primary aim was to
identify significant factors influencing satisfaction and the intention to continue using e- learning systems in higher education. Consequently, articles were included only if they directly
addressed this objective. Studies unrelated to e-learning or those focusing on different
perspectives, such as factors influencing teachers' intentions, were excluded. Furthermore,
studies that did not evaluate the associations between variables and only offered descriptive
analysis were excluded. For an article to be considered, it had to be available in full text. During
the article collection and selection process, results were compared to ensure no relevant
studies were missed. Ultimately, 34 publications were identified as pertinent to this research.
The selected articles then underwent a rigorous quality assessment based on specific criteria:
➢ QA1: Is the topic of the study relevant to satisfaction and continuance intention in e- learning systems?
➢ QA2: Is the study context clear, including a description of the sample and setting?
➢ QA3: Is the research methodology clearly explained?
➢ QA4: Is the data collection procedure adequately described?
➢ QA5: Is the data analysis method clearly detailed?
Each criterion was scored on a scale from 0 to 1, with 1 indicating full satisfaction, 0.5 indicating
partial satisfaction, and 0 indicating no satisfaction. Studies that scored 3 or higher were
classified as high quality, while those with scores between 1 and 3 were categorized as medium
quality. Studies scoring below 1 were classified as low quality and excluded from the review.
The quality assessment was conducted by two researchers, with oversight from an experienced
researcher to ensure consistency. All 34 selected studies passed the quality assessment with
high scores as illustrated in (Table 2). These studies included 19 journal articles and 15
conference proceedings, all of which were peer-reviewed. As a result, no additional studies
were excluded during the quality assessment stage. The selection process (Fig. 2) shows the
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number of publications obtained and excluded at each step, resulting in the final selection of 34
relevant publications for data extraction and synthesis.
Table 1: Inclusion and Exclusion Criteria for Selection of Articles.
Inclusion Criteria Exclusion Criteria
Papers that employ inferential statistical methods to
analyse data and determine the impact of identified
factors.
Papers that do not investigate the effects of
factors on e-learning satisfaction and continuance
intention.
Papers available in full text. Articles that are not available in full text or are only
available as abstracts.
Only one copy of duplicated studies will be
included.
Table 2:Quality assessment results of the 33 selected studies.
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It explores customer satisfaction and post-purchase behaviour by focusing on the cognitive
evaluation of the alignment between pre-existing expectations and actual experiences. This
theory suggests that customers' intention to repurchase is primarily influenced by their
satisfaction with previous product or service usage as illustrated in (Fig.3).
Expectation Confirmation Model (ECM):
Drawing inspiration Oliver's Expectation confirmation theory, Bhattacherjee [19] adapted this
model for information systems. The Expectation Confirmation Model (ECM) is a conceptual
framework proposed to understand the factors influencing satisfaction and continued usage
intentions in various contexts, particularly in consumer behaviour and information systems
(Fig.4). The ECM focuses on how individuals' initial expectations about a product, service, or
system are confirmed or disconfirmed by their actual experiences, leading to subsequent
satisfaction or dissatisfaction. This model suggests that satisfaction and continued usage
intentions are primarily determined by the extent to which actual experiences align with initial
expectations [21-23] (Table 3) presents the definitions of ECM variables.
Fig. 4: Expectation Confirmation Model (Bhattacherjee, 2001).
Table 3: Definitions of ECM variables
Construct Definitions
Perceived
Usefulness
Users' expectations of perceived benefits from utilising any products or services
based on IT or technology [24].
Satisfaction Feelings of users once their expectations of using technology or IS have been
confirmed [19].
Confirmation User expectations and IS or technology performance correlation [19].
Continuance
intention
User’s intention to continue using IS or technology [19].
Constructs for Predicting Satisfaction for CI
Analysis of Construct:
The constructs used to predict students' satisfaction with CI in e-learning systems varied across
studies, as shown in Table 4. Satisfaction was frequently employed as a construct, often linked
to continuance intention. For example, Abdullah, Arokiyasamy [25] found that satisfaction
significantly influenced students' CI towards remote learning. Similarly, Cheng [26] identified
interaction and course quality as significant predictors of both satisfaction and continuance
intention in e-learning systems.
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Alshammari, M. F., Yusoff, R. C. M., & Abas, H. (2024). A Systematic Literature Review of Factors Influencing Satisfaction and Continuance Intention
to Use E-Learning Systems in Higher Education. Advances in Social Sciences Research Journal, 11(8). 326-346.
URL: http://dx.doi.org/10.14738/assrj.118.17459
Self-efficacy and perceived usefulness were also commonly examined constructs. Al-Emran,
Arpaci [8] integrated these constructs into their model, demonstrating their impact on the
continuous intention to use m-learning. The study highlighted that higher self-efficacy and
perceived usefulness lead to a greater intention to continue using the system.
Moreover, constructs related to the quality of the e-learning experience, such as system quality
and information quality, were crucial in several studies. Ali, Puah [27] emphasized that system
quality directly affects students' satisfaction and commitment to e-learning services. Similarly,
Mishra, Shukla [28] found that information quality, among other factors, influenced students'
continuance intention to use e-learning platforms.
Social influence and facilitating conditions were significant in some models. For instance,
Venkatesh, Morris [24] included these constructs in their UTAUT model, which was adapted by
various studies to fit the e-learning context. Ngah, Kamalrulzaman [29], Li, Wang [30]
demonstrated the importance of facilitating conditions and social influence in predicting
students' behavioural intention to use e-learning systems.
Table 4: Summary of Reviewed Studies on Factors Influencing Satisfaction for CI to Use
E-Learning Systems.
Study
ID
Author Location Methods Theories Used Proposed Factors Influence
Satisfaction for CI
ER-1 Ali et al.,
(2022)
Pakistan Sample data of 359
university students, data
analyzed using SEM
- System quality, course material
and instructor quality, support
service quality, course website
quality
ER-2 Momen et al.,
2023
Bangladesh 450 students who used
online classes, data
analyzed using SEM
Disconfirmation
theory
Technological advancement,
Internet infrastructure,
convenience, resource
ER-3 Gantasala et
al., 2022
India 198 students, analysis
using SEM (AMOS
software)
- Quality of Learning Experience
(QLE)
ER-4 Rahmania et
al., 2022
Indonesia 151 students, analyzed
in SMART PLS using SEM
ECM Self-efficacy, PU, confirmation
ER-5 Nikou 2021 UK Survey of 211 university
students, data analyzed
using SEM
TAM, ECM PU, confirmation
ER-6 Clary et al.,
2022
USA 386 students, data
analyzed using SEM
Social cognitive
theory, social
cognitive career
theory
Perceived support, perceived
compatibility, self-efficacy
ER-7 Um, N., & Jang,
A. (2021)
South Korea 236 college students - Interactions between students
and instructor, teaching
presence, self-management of
learning, academic self-efficacy
ER-8 Shanshan S.;
Wenfei L.
(2022)
Chinese
University
306 students, data
analyzed using SEM
ECM, Task technology
fit, Flow theory
Confirmation, factors from
both SERVQUAL and Quality
Matters
ER-9 Tseng et al.,
2022
USA 369 students - Online student connectedness,
performance proficiency
ER-10 Li et al., 2022 China 452 university students,
data analyzed using SEM
ECM, TPB PU, confirmation, perceived
enjoyment
ER-11 Ngah et al.,
2022
Malaysia 300 university students,
data analyzed using SEM
SOR System quality
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ER-12 Yang et al.,
2022
China 320 students, data
analyzed using SEM
ECM Performance expectancy,
intrinsic motivation,
confirmation
ER-13 Abdullah et al.,
2022
- 253 students, data
analyzed using SEM
- Online feedback, online future
relevance, online interaction,
online teaching effectiveness,
personal well-being
ER-14 Alzahrani and
Seth (2021)
UK 200 students, data
analyzed using SEM
Social cognitive
theory, ECM, ISSM
Information quality, self- efficacy
ER-15 Pozón-López
et al., 2021
University of
Granada,
Spain
184 students, data
analyzed using SEM
TAM, self- determination
theory, SERVQUAL
Course quality, entertainment
ER-16 Laifa et al.,
2023
Algeria 217 students, data
analyzed using SEM
TAM Perceived ease of use, PU
ER-17 Albelbisi et al.
(2021)
Malaysia 276 students, data
analyzed using SEM
Self-regulated
learning theory, ISSM
System quality, information
quality, service quality
ER-18 Nugroho et al.,
2019
Indonesia 180 participants, data
analyzed using SEM
ISSM System quality, information
quality, service quality
ER-19 Garg and
Sharma (2020)
India 150 participants, data
analyzed using SEM
TAM, ECM Ease of use, course content
ER-20 Rekha et al.,
(2023)
India 250 students, data
analyzed using SEM
ECT, ECM Confirmation, enjoyment,
perceived openness, perceived
usefulness, perceived
reputation
ER-21 Al-Emran et
al., 2020
UAE 220 university students,
data analyzed using SEM
TAM, ECM, TPB PU, confirmation
ER-22 Dai et al., 2020 China Survey of 221 Chinese
higher institutions
MOOC users, data
analyzed using SEM and
CFA
ECM, Curiosity theory Confirmation
ER-23 Rajeh et al.,
2021
KSA 260 students, data
analyzed using SEM
TPB, ECM Expectation, confirmation
ER-24 Suzianti and
Paramadini.
(2021)
Indonesia Teacher (190
participants), data
analyzed using SEM
ECM, ISSM Confirmation, information
quality, system quality,
sociability quality, teachers’
self-efficacy
ER-25 Ye J-H, Lee Y-S
and He Z
(2022)
China 200 students, data
analyzed using SEM
ECM Expectancy beliefs
ER-26 Rabaa'i et al.,
2021
Kuwait 300 students, data
analyzed using SEM
ECM Confirmation, system
interactivity, performance
expectancy
ER-27 Al-Adwan et
al., 2022
Jordan 280 students, data
analyzed using SEM
ISSM Service quality, system quality,
information quality, self- directed learning
ER-28 Prasetya et al.
(2021)
Indonesia 325 students, data
analyzed using SEM
ECM PU, confirmation, self-efficacy
ER-29 Mishra et al.,
(2022)
India, Qatar,
France, and
the UK
360 students, data
analyzed using SEM
TCT, ISSM PU, PEOU, confirmation,
enjoyment
ER-30 Barreto et al.,
(2021)
Jordan, China,
Spain
240 students, data
analyzed using SEM
ECM PU, confirmation
ER-31 Gu et al. (2021) China 290 students, data
analyzed using SEM
ECM, ISSM PU, confirmation
ER-32 Daneji et al.,
(2019)
Malaysia 314 students, data
analyzed using SEM
ECM PU, confirmation
ER-33 Tawafak et al.,
(2019)
Oman 250 students, data
analyzed using SEM
TAM, ECM PU, perceived ease of use
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Unified Theory of Acceptance and Use of
Technology (UTAUT)
ER-29 1
Analysis of the Construct Findings:
There are 34 final papers with initially 93 factors influencing satisfaction and CI, not consider
the factors for basic ECM model (Perceived Usefulness, Satisfaction, Confirmation and
Continuance intention). After removed the duplication, we identified 42 factors. Table 6. shows
the list of factors that affect student’s satisfaction for CI to use e-learning.
Table 6: Factors That Affect Satisfaction for Continuance Use of E-Learning
No. Satisfaction Factors Study IDs Frequency
1. System Quality ER-1, ER-11, ER-18, ER-17, ER-24, ER-27 6
2. Information Quality ER-14, ER-18, ER-17, ER-24, ER-27 5
3. Self-efficacy ER-6, ER-7, ER-14, ER-24, ER-28 5
4. Service Quality ER-1, ER-17, ER-18, ER-27 4
5. Perceived Ease of Use ER-16, ER-33 3
6. Perceived Enjoyment ER-20, ER-29 2
7. Course Material ER-1 1
8. Entertainment ER-1 1
9. Information Technology Support ER-1 1
10. Course Website Quality ER-1 1
11. Technological Advancement ER-2 1
12. Internet Infrastructure ER-2 1
13. Convenience ER-2 1
14. Resource ER-2 1
15. Quality of Learning Experience ER-3 1
16. Perceived Support ER-6 1
17. Perceived Compatibility ER-6 1
18. Interactions Between Students and Instructor ER-7 1
19. Teaching Presence ER-7 1
20. Self-management of Learning ER-7 1
21. Online Student Connectedness ER-9 1
22. Performance Proficiency ER-9 1
23. Performance Expectancy ER-12 1
24. Intrinsic Motivation ER-12 1
25. Online Feedback ER-13 1
26. Online Future Relevance ER-13 1
27. Online Interaction ER-13 1
28. Online Teaching Effectiveness ER-13 1
29. Personal Well-being ER-13 1
30. Course Quality ER-15 1
31. Perceived Value ER-18 1
32. Course Content ER-19 1
33. Perceived Openness ER-20 1
34. Perceived Reputation ER-20 1
35. Expectation ER-23 1
36. Sociability Quality ER-24 1
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shown to mediate the relationship between these constructs and continuance intention.
Albelbisi, Al-Adwan [39], Samed Al-Adwan, Nofal [40] highlighted the role of self-efficacy,
finding that students' confidence in their ability to use e-learning technologies positively
influences their satisfaction and continued use.
In terms of technology-related factors, constructs such as system quality, information quality,
and service quality have been critical in understanding e-learning CI. Studies by Pozón-López,
Higueras-Castillo [41], Laifa, Giglou [42] found that high-quality systems and services enhance
students' satisfaction and intention to use e-learning platforms. These findings underscore the
importance of reliable and user-friendly technological infrastructure in promoting the adoption
of educational technologies.
Despite the robust findings supporting the significance of these constructs, there are also
inconsistencies and gaps in the literature. For example, while some studies have found a
significant impact of system quality on CI, others, like Nugroho, Setyorini [43], reported no
direct effect. These contradictory results suggest the need for further research to explore the
conditions under which certain constructs become more or less influential.
The analysis also reveals the importance of understanding the interplay between different
constructs. For instance, Mishra, Shukla [28] demonstrated that while perceived ease of use had
a direct effect on behavioural intention, it also mediated the relationship between system
quality and intention. This finding suggests that constructs do not operate in isolation but
interact in complex ways to influence students' satisfaction and continued use of e-learning
technologies.
Overall, the analysis of construct findings indicates that while core constructs like perceived
usefulness and ease of use are consistently important, the broader context, including
environmental and learner-related factors, plays a significant role in shaping students'
satisfaction of e-learning. This comprehensive understanding can help educators and
technology developers create more effective and engaging e-learning environments that cater
to the diverse needs of students. As the field of e-learning continues to evolve, ongoing research
is essential to uncover new insights and refine existing models to better capture the
complexities of students CI in educational settings.
RESEARCH CONTEXTS
The context in which research on CI to use e-learning system is conducted significantly
influences the findings and implications of the studies. Various factors such as the participants'
demographics, educational level, prior experience with e-learning, and the specific e-learning
system being studied all play crucial roles. This section delves into these contextual elements
based on the reviewed literature.
The demographic characteristics of participants in studies on student’s satisfaction and CI to
use e-learning vary widely. Research by Ali, Puah [27], Momen, Sultana [32] predominantly
involved undergraduate students from different fields of study. Ali, Puah [27] focused on
students enrolled in finance courses, examining their satisfaction and commitment to e- learning during the COVID-19 pandemic. Similarly, Momen, Sultana [32] explored the
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employed by Suzianti and Paramadini [49] to determine significant differences between
groups.
The characteristics of the e-learning systems being studied also significantly impact research
findings. System quality, information quality, and service quality are frequently assessed to
determine their influence on user satisfaction and continuance intention. Studies by Ye, Lee
[50], A. Rabaa'i, Abu Almaati [51] focused on these quality dimensions, finding them to be
critical determinants of e-learning. Perceived ease of use and perceived enjoyment are other
essential constructs examined in many studies. Samed Al-Adwan, Nofal [40] highlighted the
importance of these factors in promoting positive user experiences and encouraging long-term
engagement with e-learning platforms. Similarly, Prasetya, Harnadi [52], Gu, Xu [53]
demonstrated that ease of use and enjoyment significantly influence students' behavioural
intentions.
Course material and entertainment value are also pivotal in determining students’ satisfaction
and CI. Studies like those by Daneji, Ayub [54], Tawafak, Malik [55] found that engaging and
well-designed course content enhances students' satisfaction and willingness to continue using
e-learning systems. This highlights the need for educational technologies to not only be
functional but also engaging and enjoyable. The COVID-19 pandemic has had a profound impact
on e-learning CI, as highlighted in studies by Cheng [26], Soria-Barreto, Ruiz-Campo [56]. The
sudden shift to remote learning forced educators and students to adapt quickly to new
technologies, often with little preparation or support. This context has underscored the
importance of system quality and user support in facilitating effective e-learning experiences.
Studies conducted during the pandemic, such as those by Ali, Puah [27], Momen, Sultana [32],
have provided valuable insights into the unique challenges and opportunities presented by
remote learning. These findings emphasize the need for robust and user-friendly e-learning
systems that can support students in diverse and challenging circumstances.
THEORETICAL AND PRACTICAL IMPLICATIONS
This study provides a comprehensive analysis of the models and factors influencing students'
satisfaction and continuance intention to use e-learning systems. By synthesizing findings from
a broad range of studies, this research offers valuable insights for both academics and
practitioners involved in the design, implementation, and evaluation of e-learning technologies.
From a theoretical perspective, the study highlights the predominant models used to predict e- learning satisfaction and CI, such as the Technology Acceptance Model (TAM) and the Unified
Theory of Acceptance and Use of Technology (UTAUT). These models have consistently
demonstrated that constructs like perceived usefulness and perceived ease of use are
significant predictors of students' satisfaction and CI of e-learning systems. Researchers can
utilize the summaries provided in this study to gain a clearer understanding of these models,
their constructs, and the methodologies employed in current research. This foundational
knowledge can serve as a basis for future studies aimed at exploring or extending current
research on e-learning satisfaction and CI predictors.
Furthermore, the study underscores the importance of considering a broader range of
constructs beyond the core components of TAM and UTAUT. Constructs such as perceived
enjoyment, satisfaction, and social influence have also been shown to significantly impact
students' satisfaction and CI of e-learning. These findings suggest that future research should
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Alshammari, M. F., Yusoff, R. C. M., & Abas, H. (2024). A Systematic Literature Review of Factors Influencing Satisfaction and Continuance Intention
to Use E-Learning Systems in Higher Education. Advances in Social Sciences Research Journal, 11(8). 326-346.
URL: http://dx.doi.org/10.14738/assrj.118.17459
adopt a more holistic approach, incorporating psychological and social factors to better
understand students' motivations and barriers to e-learning adoption. Additionally, the
contextual factors such as cultural differences, educational levels, and prior experience with
technology should be considered when developing and testing new models or extending
existing ones.
On the practical side, the findings of this study offer valuable insights for e-learning developers,
educational institutions, and instructors. The identification of key factors influencing e-learning
satisfaction and CI can guide developers in designing more user-centric e-learning systems. For
instance, ensuring that the e-learning platform is easy to use and enhances learning
performance can significantly increase students' satisfaction and CI. Developers should also
focus on creating enjoyable and interactive learning experiences, as perceived enjoyment has
been repeatedly shown to impact students' willingness to use e-learning systems.
Educational institutions and instructors can leverage the insights from this study to select and
implement e-learning tools that align with students' preferences and needs. By understanding
the factors that drive e-learning satisfaction and CI, institutions can make more informed
decisions about which technologies to adopt and how to integrate them into the curriculum.
For example, incorporating elements of gamification and interactive content can make e- learning more appealing and effective for students. Instructors can also use these insights to
tailor their teaching strategies, ensuring that they support and enhance students' experiences
with e-learning technologies.
The study also emphasizes the importance of balancing educational goals with engaging and
enjoyable content. This balance is crucial for maintaining students' motivation and interest in
e-learning. Developers should strive to create e-learning platforms that are not only functional
and effective but also engaging and fun. This approach can lead to higher levels of student
satisfaction and increased continuance intention to use e-learning systems.
Moreover, the results of this study highlight the need for ongoing support and training for both
students and instructors to maximize the benefits of e-learning technologies. Institutions
should provide adequate resources and support to ensure that all users can effectively utilize
e-learning platforms. This support can include training sessions, user guides, and technical
assistance to help users overcome any challenges they may encounter.
CONCLUSION, LIMITATIONS, AND RECOMMENDATIONS FOR FUTURE RESEARCH
This paper identified a comprehensive analysis of factors influencing students' satisfaction for
continuance intention to use e-learning systems in higher education. By synthesizing findings
from various studies, this research highlights the predominant models used, such as the
Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of
Technology (UTAUT), to predict students’ satisfaction and CI. These models have consistently
shown that perceived usefulness and perceived ease of use are critical determinants of
students' satisfaction and CI of e-learning systems. Other significant constructs include
perceived enjoyment, satisfaction, and social influence. The study underscores that while TAM
and UTAUT are robust frameworks, integrating other theories related to learning and
technology design could provide a more comprehensive understanding of student’s satisfaction
and CI. Constructs from learning theories could offer deeper insights into the motivational and
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psychological aspects of e-learning, especially given the interactive nature of these systems.
Despite consistent findings for core constructs like perceived usefulness and ease of use, the
review revealed inconsistencies in the impact of other factors. These inconsistencies highlight
the need for further empirical research to validate and refine the determinants of e-learning CI.
This paper has several limitations. First, the scope of the review was constrained by the search
terms and inclusion criteria used. Despite efforts to construct a comprehensive search string,
some relevant articles may have been missed. Additionally, the review primarily focused on
studies involving students, the primary users of e-learning systems. However, the CI and use of
e-learning technologies are also influenced by other stakeholders such as educators,
administrators, and parents. Future research could benefit from broadening the scope to
include these perspectives. Another limitation is the reliance on published studies from specific
databases, which may introduce publication bias. The reviewed studies predominantly came
from well-known databases like Scopus and Web of Science, potentially overlooking relevant
research published elsewhere. The review also did not account for cultural and regional
differences in e-learning CI, an area that future research could explore.
Given these limitations, several suggestions for future research are proposed. Future studies
should integrate constructs from multiple theories to develop more holistic models of e- learning CI. Combining elements from TAM, UTAUT, learning theories could provide a richer
understanding of the factors influencing e-learning CI. Additionally, more research is needed
on under-studied constructs such as innovativeness, feedback, and relevance. These factors,
though less frequently examined, could offer valuable insights into the motivations and barriers
faced by students. Future research should also aim to include a broader range of participants,
including educators, administrators, and parents, to capture a more comprehensive view of e- learning CI. Understanding the perspectives of these stakeholders could provide valuable
insights into the broader ecosystem of e-learning adoption. Furthermore, studies should
consider the potential impact of cultural and regional differences on e-learning CI. Comparative
studies across different cultural contexts could reveal important variations in the factors
influencing e-learning adoption. Methodologically, future research could benefit from
employing diverse analytical techniques. While structural equation modelling (SEM) and
partial least squares (PLS) analysis are commonly used, incorporating other methods such as
mixed-methods approaches, longitudinal studies, and experimental designs could enhance the
robustness and depth of findings. Given the rapid advancements in e-learning technologies and
the increasing importance of online education, ongoing research is essential to keep pace with
these developments. Studies should continually update and refine existing models to ensure
they remain relevant and reflective of the current educational landscape. These efforts will be
crucial in advancing our understanding of e-learning CI and ensuring the effective
implementation of these technologies in educational settings.
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