Acceptance of e-learning in higher education: the role of task-technology fit with the information systems success model
Open accessAbstract
The COVID-19 global epidemic has compelled higher education institutions to reconsider their teaching methods. Because of this public health emergency, universities in higher education have adopted e-learning techniques as a solution to face-to-face education. Thus, e-learning has emerged as a critical technology in education at higher education institutions. Nonetheless, the effectiveness of e-learning systems is largely dependent on students' adoption of such systems. The study aims to evaluate the usefulness of task-technology fit (TTF) with the information system success model (ISSM) in perceiving students' adoption of e-learning with the goal of encouraging them to adopt e-learning in the context of higher education. The study employed a quantitative approach, and a theoretical model was evaluated with proposed hypotheses to find the relationships between the constructs. A questionnaire based on TTF and ISSM was distributed among the students, and 260 valid responses were received using a sample random sampling technique. Data was analyzed with the help of SPSS and Partial Least Squares-Structural Equation Modeling (PLS-SEM). After analyzing the data, it was found that perceived ease of use, perceived usefulness, system use, and task technology fit of e-learning are positively and significantly influenced by system quality, information quality, perceived enjoyment, technology characteristics, and task characteristics. The results of TTF and ISSM on system use show a positive effect on e-learning benefits in educational institutions, with all male and female students completely satisfied with the use of e-learning systems. As a result, we advise students to use e-learning systems for educational purposes and should have motivated them to do so through lecturers at higher-level educational institutions.
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Written from the paper’s full text.
TL;DR
This study integrates task-technology fit (TTF) with the information systems success model (ISSM) to explain undergraduate students’ e-learning acceptance at King Faisal University. Using PLS-SEM on 260 valid survey responses, it finds all 18 hypothesized paths supported, with TTF showing the strongest effect on e-learning benefit.
Key findings
- All 18 hypotheses were accepted. TTF had the strongest positive relationship with e-learning benefit (β = 0.643, t = 16.457, p = 0.000), followed by PEOU → TTF (β = 0.441, t = 8.580) and PU → system use (β = 0.371, t = 4.706).
- Information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics positively predicted PEOU and PU; for example, IQ → PEOU β = 0.284, t = 4.409; IQ → PU β = 0.256, t = 4.238; PE → PEOU β = 0.232, t = 3.562; PE → PU β = 0.241, t = 3.619.
- System use positively predicted e-learning benefit (β = 0.310, t = 7.260), and PEOU, PU, and system use also predicted TTF (β = 0.441, 0.339, and 0.140, respectively).
- The measurement model reported Cronbach’s alpha and composite reliability above 0.7 and AVE above 0.5 for the constructs.
Methods
- Quantitative survey design testing an integrated TTF + ISSM model with perceived enjoyment as an external factor; a 5-point Likert questionnaire (described as 50 questions in the conclusion) was administered physically and online to King Faisal University students.
- 275 questionnaires were distributed, yielding 260 valid responses after excluding 15 incomplete/unanswered questionnaires, a 94.5% response rate; participants were 187 male (71.9%) and 73 female (28.1%), ages 18–46, across management, engineering, science and technology, social science, and other fields.
- Data were analyzed with SPSS and SmartPLS 3.3.3 using PLS-SEM; the measurement model assessed reliability and validity, and the structural model used bootstrapping with 5000 resamples.
Limitations
- Stated: data came from one university, which may affect generalizability; the scope was restricted to two possibilities regarding prior e-learning expertise; possible extrinsic variables related to system use and TTF were not considered; future research should include more participants with varied majors.
- Evident: the sampling description is inconsistent—the abstract says random sampling, while the methodology also says convenience sampling—and the sample is heavily male (71.9%), which may limit representativeness.
- Evident: the study uses a self-report questionnaire collected at the start of the 2020–2021 academic year, so causal claims are not established by the design.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Claims the paper makes
0 of 37 cited sources checked · 0 supported · 0 flagged
- Findingown data
The study's analysis found that system quality, information quality, perceived enjoyment, technology characteristics, and task characteristics positively and significantly influenced perceived ease of use, perceived usefulness, system use, and task-technology fit of e-learning.
“After analyzing the data, it was found that perceived ease of use, perceived usefulness, system use, and task technology fit of e-learning are positively and significantly influenced by system quality, information quality, perceived enjoyment, technology characteristics, and task characteristics.”
Quote in the paperSubstantial claim - Findingown data
The results of TTF and ISSM on system use show a positive effect on e-learning benefits in educational institutions.
“The results of TTF and ISSM on system use show a positive effect on e-learning benefits in educational institutions”
Quote in the paperSubstantial claim - Findingreasoning
All male and female students were completely satisfied with the use of e-learning systems.
“with all male and female students completely satisfied with the use of e-learning systems.”
Quote in the paperNot a substantial claim - Findingown data
Technology characteristics positively predict perceived ease of use (β = 0.154, t = 2.341, p = 0.023).
“| Technology characteristics➔Perceived ease of use | H7 | 0.154 | 2.341 | 0.023 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
System quality positively predicts perceived ease of use (β = 0.173, t = 3.365, p = 0.001).
“| System Quality➔Perceived ease of use | H3 | 0.173 | 3.365 | 0.001 | Accepted |”
Quote in the paperSubstantial claim
- Findingown data
Perceived ease of use positively predicts perceived usefulness (β = 0.131, t = 2.157, p = 0.036).
“| Perceived ease of use➔Perceived usefulness | H11 | 0.131 | 2.157 | 0.036 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Perceived enjoyment positively predicts perceived ease of use (β = 0.232, t = 3.562, p = 0.001).
“| Perceived enjoyment➔Perceived ease of use | H5 | 0.232 | 3.562 | 0.001 | Accepted |”
Quote in the paperNot a substantial claim - Findingown data
Perceived ease of use positively predicts system use (β = 0.268, t = 3.134, p = 0.003).
“| Perceived ease of use➔System Use | H12 | 0.268 | 3.134 | 0.003 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Perceived usefulness positively predicts system use (β = 0.371, t = 4.706, p < 0.001).
“| Perceived usefulness➔System Use | H14 | 0.371 | 4.706 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Task characteristics positively predict perceived ease of use (β = 0.151, t = 2.604, p = 0.012).
“| Task characteristics➔Perceived ease of use | H9 | 0.151 | 2.604 | 0.012 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Perceived enjoyment positively predicts perceived usefulness (β = 0.241, t = 3.619, p = 0.001).
“| Perceived enjoyment➔Perceived usefulness | H6 | 0.241 | 3.619 | 0.001 | Accepted |”
Quote in the paperNot a substantial claim - Findingown data
Task-technology fit positively predicts e-learning benefit (β = 0.643, t = 16.457, p < 0.001).
“| Task-technology fit➔E-learning Benefit | H18 | 0.643 | 16.457 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Information quality positively predicts perceived usefulness (β = 0.256, t = 4.238, p < 0.001).
“| Information Quality➔Perceived usefulness | H2 | 0.256 | 4.238 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Task characteristics positively predict perceived usefulness (β = 0.090, t = 2.164, p = 0.035).
“| Task characteristics➔Perceived usefulness | H10 | 0.090 | 2.164 | 0.035 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Information quality positively predicts perceived ease of use (β = 0.284, t = 4.409, p < 0.001).
“| Information Quality➔Perceived ease of use | H1 | 0.284 | 4.409 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
System use positively predicts task-technology fit (β = 0.140, t = 3.180, p = 0.003).
“| System Use➔Task-technology fit | H16 | 0.140 | 3.180 | 0.003 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
System quality positively predicts perceived usefulness (β = 0.193, t = 4.398, p < 0.001).
“| System Quality➔Perceived usefulness | H4 | 0.193 | 4.398 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
System use positively predicts e-learning benefit (β = 0.310, t = 7.260, p < 0.001).
“| System Use➔E-learning Benefit | H17 | 0.310 | 7.260 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Technology characteristics positively predict perceived usefulness (β = 0.148, t = 2.893, p = 0.006).
“| Technology characteristics➔Perceived usefulness | H8 | 0.148 | 2.893 | 0.006 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Perceived usefulness positively predicts task-technology fit (β = 0.339, t = 6.074, p < 0.001).
“| Perceived usefulness➔Task-technology fit | H15 | 0.339 | 6.074 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingown data
Information quality (IQ), system quality (SQ), perceived enjoyment (PE), task characteristics (TC), and the quality of the eLearning content were critical indicators that contributed to perceived ease of use (PEOU) and perceived usefulness (PU).
“Variables such as IQ, SQ, PE, TC, and the quality of the eLearning content were also identified as critical indicators that contributed to PEOU and PU.”
Quote in the paperSubstantial claim - Findingown data
Perceived ease of use positively predicts task-technology fit (β = 0.441, t = 8.580, p < 0.001).
“| Perceived ease of use➔Task-technology fit | H13 | 0.441 | 8.580 | 0.000 | Accepted |”
Quote in the paperSubstantial claim - Findingnone given
Participants' evaluations of the usefulness of e-learning increase when e-learning is rated as user-friendly.
“participants' evaluations of the usefulness of e-learning increase when e-learning is rated as user-friendly.”
Quote in the paperSubstantial claim - Findingown data
Perceived ease of use has a bigger effect on task-technology fit than system use.
“In contrast, PEOU has a bigger effect on task-technology fit than system use, which has an effect on e-learning.”
Quote in the paperSubstantial claim - Study design and conductcitation
The sample size of 260 is adequate for quantitative research, given the recommended minimum of 200.
“According to Krejcie and Morgan (1970) and Hair et al. (2010), who claimed the minimal sample size for quantitative research is (N = 200), the sample size of this study (N = 260) is adequate in light of this.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Krejcie and Morgan (1970)
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- cites Hair et al. (2010)
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- Premisecitation
Abdullah and Ward reported a substantial and positive relationship between perceived enjoyment and perceived usefulness for e-learning in eight out of eight studies (100%).
“The same is true between PE and PU, as is evident from eight out of eight studies (100%).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [85] Abdullah, 2016, Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors, Comput. Hum. Behav. source
Not checked in the free run.
- Premisecitation
Information quality is a vital and crucial aspect in determining the efficacy of information and e-learning systems.
“Information quality is a vital and crucial aspect in determining the efficacy of information and e-learning systems due to the crucial role information plays in accomplishing educational goals and the enormous obstacles brought on by low IQ [16].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Al-Rahmi, 2021, The influence of information system success and technology acceptance model on social media factors in education, Sustain. Times
Not checked in the free run.
- Premisecitation
Earlier studies found that perceived enjoyment had a substantial impact on perceived ease of use and perceived usefulness for e-learning.
“Earlier studies, according to Abdullah and Ward [84,85], found that PE had a substantial impact on the PEOU and PU for e-learning.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [84] Al-Adwan, 2021, Developing a holistic success model for sustainable e-learning: a structural equation modeling approach, Sustain. Times
Not checked in the free run.
- cites [85] Abdullah, 2016, Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors, Comput. Hum. Behav. source
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- Premisecitation
Abdullah and Ward reported a substantial and positive relationship between perceived enjoyment and perceived ease of use for e-learning in eight out of eleven research studies.
“Moreover, Abdullah and Ward [85] reported a substantial and optimistic relationship between PE and PEOU for e-learning in eight out of eleven research studies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [85] Abdullah, 2016, Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors, Comput. Hum. Behav. source
Not checked in the free run.
- Premisecitation
Perceived enjoyment is essential in assessing the adoption of e-learning.
“Perceived enjoyment is essential in assessing the adoption of e-learning.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [84] Al-Adwan, 2021, Developing a holistic success model for sustainable e-learning: a structural equation modeling approach, Sustain. Times
Not checked in the free run.
- cites [85] Abdullah, 2016, Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors, Comput. Hum. Behav. source
Not checked in the free run.
- Study design and conductown data
The measurement model has satisfactory reliability, with Cronbach's alpha and composite reliability above 0.7.
“As presented in Table 2 [113,114], both of these characteristics have satisfactory values above the given threshold of 0.7, as permitted by law.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [113] Gefen, 2000, Structural equation modeling and regression: guidelines for research practice, Commun. Assoc. Inf. Syst.
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- cites [114] Kannan, 2005, Just in time, total quality management, and supply chain management: understanding their linkages and impact on business performance, Omega source
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- Study design and conductown data
The HTMT ratio indicated discriminant validity, with construct values below the threshold of 0.85.
“Table 5 shows the HTMT ratio results. It shows that the construct’s values for each do not surpass the threshold, which is 0.85. This signifies the validity of the discriminants [116].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [116] Henseler, 2015, A new criterion for assessing discriminant validity in variance-based structural equation modeling, J. Acad. Market. Sci. source
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- Interpretationreasoning
The TTF model has shown to be a useful tool for understanding students' intentions as they embrace and use e-learning as the medium of e-learning advantage.
“The TTF model has shown to be a useful tool for understanding students' intentions as they embrace and use e-learning as the medium of e-learning advantage.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
- Interpretationown data
The benefits of e-learning are primarily dependent on the individual's own views, familiarity, talents, and self-assurance, more so than on their personality or other people's views.
“The results show that the benefits of e-learning are primarily dependent on the individual’s own views, familiarity, talents, and self-assurance, more so than on their personality or other people’s views.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Interpretationreasoning
The findings suggest an extended task-technology fit and ISSM model for the use of e-learning systems to enhance student teaching and learning performance.
“The findings suggest an extended TSK-technology fit and ISSM model for the use of e-learning systems to enhance student teaching and learning performance.”
Quote in the paperSubstantial claim - Interpretationown data
By correctly responding to student inquiries and simultaneously enhancing the quality of the information through which students can obtain information, the system and information quality may also assist students in their learning.
“By correctly responding to student inquiries and simultaneously enhancing the quality of the information through which students can obtain information, the system and information quality may also assist students in their learning.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
- Interpretationown data
Task-technology fit with ISSM criteria such as information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics were important in forecasting technology benefit.
“In addition, task-technology-fit with ISSM criteria such as information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics were found to be important in forecasting technology benefit in the current study.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Interpretationreasoning
Factors with significant effects have a positive influence on e-learning indirectly, through perceived ease of use and perceived usefulness.
“Those with significant effects have a positive influence on e-learning indirectly, through perceived ease of use and perceived usefulness.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Interpretationown data
The practical results provide strong support for the integrative approach between task-technology fit theory and the information systems success model (ISSM).
“Although only the task-technology fit with the information systems success model method and one other variable were used in this study, the practical results thus provide strong support for the integrative approach between Tsk-technology fit theory and the ISSM model.”
Quote in the paperSubstantial claim - Study design and conductreasoning
No significant errors were found with the measurement model's reliability and validity.
“No significant errors were found with the developed measurement model’s reliability and validity; hence, proceeding with its evaluation is considered reliable.”
Quote in the paperSubstantial claim - Study design and conductcitation
PLS-SEM was the appropriate analytical tool for this study.
“Hence, PLS-SEM was the appropriate tool for this study.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [111] Roldán, 2012, Variance-based structural equation modeling: guidelines for using partial least squares in information systems research
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- cites [112] Barclay, 1995, The determinants of corporate leverage and dividend policies, J. Appl. Corp. Financ. source
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- Interpretationcitation
The study's findings corroborate prior findings that information quality has a substantial impact on perceived ease of use and perceived usefulness.
“The study’s findings corroborate those of [117,118], who found that the quality of the information had a substantial impact on PEOU and perceived usefulness.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Study design and conductreasoning
The study's volunteers came from only one university, which may limit the generalizability of the results.
“To begin with, there was just one university from which volunteers for this study came. It might affect how broadly applicable the results are.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The uniqueness of task activities may determine whether task qualities have a bigger direct impact on perceived ease of use and perceived usefulness than technology attributes.
“This study suggests that the uniqueness of task activities may determine whether task qualities have a bigger direct impact on perceived ease of use and perceived usefulness than technology attributes.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Interpretationown data
Students perceive an e-learning system's usefulness in providing helpful functions for efficient learning when it offers high-quality functions to achieve learning goals and tasks and facilitate the learning process.
“Our research shows that students perceive an e-learning system’s usefulness in providing helpful functions for efficient learning when it offers high-quality functions to achieve learning goals and tasks and facilitate the learning process.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Study design and conductown data
The measurement model has convergent validity, with all loading factors and AVEs above 0.7 and 0.5.
“All of the loading factors and AVEs are greater than the recommended minimum values of 0.7 and 0.5, which indicates that the measurement model has convergent validity.”
Quote in the paperSubstantial claim - Interpretationown data
Task characteristics had a more significant impact on task-technology fit than technology characteristics.
“Notably, our research discovered that task characteristics had a more significant impact on task-technology-fit than technology characteristics, even though TC has been reported in some studies [40,88] to have a more significant effect on PEOU and PU than TC.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [40] Tam, 2016, Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective, Comput. Hum. Behav. source
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- cites [88] Afshan, 2016, Acceptance of mobile banking framework in Pakistan, Telematics Inf. source
Not checked in the free run.
- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Study design and conductown data
The Fornell-Larcker criterion indicated discriminant validity, with square roots of AVEs greater than their associations with other constructs.
“The findings show that the square roots of all AVEs are greater than their associations with other constructs [115].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [115] Fornell, 1981, Evaluating structural equation models with unobservable variables and measurement error, J. Mar. Res. source
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- May combine two claims. 11
- Classified as probably not a substantial claim. 26
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataTask-technology fit (TTF) had the greatest impact on e-learning benefits, followed by perceived ease of use (PEOU).
“Using partial least squares (PLS), it was found that TTF had the greatest impact on e-learning benefits, followed by PEOU.”
- Also stated in: § Conclusion · boosted
“Task-technology-fit in connection to system use, as determined by the COVID-19, strongly predicts the advantages of e-learning”
Quote in the paperSubstantial claim- Also stated in: § Conclusion · boosted
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe participants were given an introduction to the research before completing the questionnaire, and their participation was completely optional.
“The participants were given an introduction to the research before completing the questionnaire, and their participation was completely optional.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataAfter taking into consideration missing data and incomplete questionnaires, 15 questionnaires were omitted.
“After taking into consideration the missing data and questionnaires that were incomplete, 15 questionnaires were omitted.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationIn their extensive literature review, [73] discovered that "usage" had a moderate relationship with the system's benefits.
“In their extensive literature review, [73] discovered that “usage” had a moderate relationship with the system’s benefits.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [73] Shahzad, 2021, Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female, Qual. Quantity source
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Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataData was collected from 260 students, both online and manually, who were chosen at random from King Faisal University students.
“data was collected from 260 students, both online and manually, who were chosen at random from King Faisal University students.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrevious research has demonstrated the importance of the link between system utilization and advantages.
“Previous research has demonstrated the importance of the link between system utilization and advantages [35,79].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [35] Chen, 2012, Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan, Eval. Progr. Plann. source
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- cites [79] Garcia-Smith, 2013, Development and initial evaluation of the clinical information systems success model (CISSM), Int. J. Med. Inf. source
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Classified as probably not a substantial claim. May combine two claims.
PremisecitationDeLone and McLean suggested the ISSM as a way to gauge the success of IS in organizations and determine the net benefit.
“Delone and McLean [68] suggested the ISSM as a way to gauge the success of IS in organizations and determine the net benefit.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [68] DeLone, 1992, Information systems success: the quest for the dependent variable, Inf. Syst. Res. source
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationUsing e-learning to deliver training courses to employees has a direct and considerable impact on the business's net benefits.
“It has been demonstrated that using e-learning to deliver training courses to employees has a direct and considerable impact on the business’s net benefits [35,79].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [35] Chen, 2012, Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan, Eval. Progr. Plann. source
Not checked in the free run.
- cites [79] Garcia-Smith, 2013, Development and initial evaluation of the clinical information systems success model (CISSM), Int. J. Med. Inf. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEarlier studies have generally demonstrated the impact of task characteristics and technology characteristics on task-technology fit in a range of scenarios.
“Earlier studies have generally demonstrated the impact of task characteristics and technology characteristics on TTF in a range of scenarios [39,40,88].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [39] Goodhue, 1995, Task-technology fit and individual performance, MIS Q, Manag. Inf. Syst. source
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- cites [40] Tam, 2016, Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective, Comput. Hum. Behav. source
Not checked in the free run.
- cites [88] Afshan, 2016, Acceptance of mobile banking framework in Pakistan, Telematics Inf. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationPrevious e-learning research found that perceived usefulness significantly influences users' intention to use the e-learning system.
“Previous e-learning research found that PU significantly influences users' intention to use the e-learning system [99].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [99] Chu, 2016, With Good We Become Good: understanding e-learning adoption by theory of planned behavior and group influences, Comput. Educ. source
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAccording to Ref. [94], perceived ease of use is associated with a better elevation of behavioral intention to utilize it, both directly and indirectly.
“According to Ref. [94], PEOU is associated with a better elevation of behavioral intention to utilize it, both directly and indirectly.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [94] Cheng, 2012, The effects of organizational learning environment factors on e-learning acceptance, Comput. Educ. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisereasoningThe task-technology fit, which in turn affects users' performance and utilization, can be influenced by both task features and technological qualities.
“The task-technology fit, which in turn affects users' performance and utilization, can be influenced by both task features and technological qualities.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationThe key success elements for implementing ISSM to achieve net benefits are service, system, information quality, system utilization, and user happiness.
“According to the new model, the key success elements for implementing ISSM to achieve net benefits are service, system, information quality, system utilization, and user happiness.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [70] DeLone, 2016, Information systems success measurement, found, Trends® Inf. Syst.
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAccording to Ref. [91], task interdependence has a significant impact on subjective norm.
“According to Ref. [91], TI has a significant impact on the subjective norm.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [91] Kim, 2007, A structural equation modeling of the Internet acceptance in Korea, Electron, Commer. Res. Appl. source
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Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe participants were chosen from different departments and faculties using a convenience sampling technique.
“The participants were chosen from different departments and faculties using a convenience sampling technique.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningAn ethical clearance was obtained for the study (Ref. No. KFU-REC-2022-OCT-ETHICS267).
“Thus, an ethical clearance was obtained for this study (Ref. No. KFU-REC-2022-OCT-ETHICS267)”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe TTF concept contends that performance and acceptance are influenced by how well a task and the technology being used to complete it fit together.
“The task-technology fit (TTF) concept contends that performance and acceptance are influenced by how well a task and the technology being used to complete it “fit” together [39].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [39] Goodhue, 1995, Task-technology fit and individual performance, MIS Q, Manag. Inf. Syst. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThere were about 275 questions given, and 260 were answered, representing a 94.5% response rate.
“There were about 275 questions given, and the respondents replied to 260 of them, representing a 94.5% response rate.”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Study design and conductown dataThe cross-loading criteria indicated discriminant validity, with loading indicators of each construct higher than cross-loadings.
“Since the loading indicators of each construct are higher than the loadings of its corresponding constructs, the cross-loading criteria are portrayed in Table 3.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataTechnology characteristics and task characteristics are strongly influenced by students' perceived ease of use and perceived usefulness, which further influences their easefulness and usefulness of e-learning.
“In the same way, our study confirms that technology characteristics and task characteristics are strongly influenced by students' PEOU and perceived usefulness, which further influences their easefulness and usefulness of e-learning.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThe effective application of e-learning would assist educational institutions in overcoming some of the problems that occur in traditional classrooms.
“The effective application of e-learning would assist educational institutions in overcoming some of the problems that occur in traditional classrooms.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenE-learning is one of the key elements of task-technology fit.
“E-learning is one of the key elements of task-technology fit.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataIndividuals have a high level of trust in the e-learning system in terms of information quality, system quality, perceived enjoyment, and technological characteristics.
“In fact, individuals have a high level of trust in the e-learning system in terms of information quality, system quality, perceived enjoyment, and technological characteristics.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe study contributes to the current acceptance of information in e-learning by academics in educational institutions, as well as to theory in task-technology fit and information systems success model adoption.
“As a result, the study contributes to the current acceptance of information in e-learning by academics in educational institutions, as well as to theory in task-technology fit and information systems success model adoption.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
May combine two claims.
InterpretationreasoningE-learners were more engaged in utilizing the e-learning system, which raised their reported ease of use and perceived usefulness when they believed the e-learning system could deliver important knowledge for their employment.
“Our research reveals that e-learners were more engaged in utilizing the e-learning system, which thus raised their reported ease of use and perceived usefulness when they believed the e-learning system could deliver important knowledge for their employment.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenThe ISSM model is the best one for comprehending information quality and system quality in enhancing perceived utility, system use, and task-technology fit.
“It has been established that the ISSM model is the best one for comprehending information quality, system quality in enhancing perceived utility, system use, and task-technology fit.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationcitationThis is a significant theoretical advance over earlier ISSM and TTF investigations, which did not examine the impact of perceived usability, perceived usefulness, system use, and task-technology fit on the applied ethical aim.
“This is a significant theoretical advance over earlier ISSM and TTF investigations, which did not examine the impact of perceived usability, perceived usefulness, system use, and task-technology fit on the applied ethical aim [85,107,108].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [85] Abdullah, 2016, Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors, Comput. Hum. Behav. source
Not checked in the free run.
- cites [107] Humida, 2022, Predicting behavioral intention to use e-learning system: a case-study in Begum Rokeya University, Rangpur, Bangladesh, Educ. Inf. Technol. source
Not checked in the free run.
- cites [108] Alyoussef, 2021, Massive open online course (Moocs) acceptance: the role of task-technology fit (ttf) for higher education sustainability, Sustain. Times
Not checked in the free run.
- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationE-learning attracts learners and improves their perceived usability and convenience of use due to the use of video, action, and text type, as well as the speed and capacity of the system.
“E-learning attracts learners and improves their perceived usability and convenience of use due to the use of video, action, and text type, as well as the speed and capacity of the system.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [119], [120], [121]
Not checked in the free run.
May combine two claims.
InterpretationreasoningInformation quality, system quality, perceived enjoyment, technology characteristics, task characteristics, perceived ease of use, perceived usefulness, task-technology fit, system use, and e-learning benefit are all factors to consider in e-learning benefit.
“Given that TTF and system use both have a positive influence on e-learning benefit, as a result, it can be stated that information quality, system quality, perceived enjoyment, technology characteristics, task characteristics, perceived ease of use, perceived usefulness, task-technology-fit, system quality, and e-learning benefit are all factors to consider.”
Quote in the paperSubstantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe model of student acceptance for online learning created in this study can be applied to actual evaluations of e-learning programs created to promote system use.
“Therefore, the model of student acceptance for online learning created in this study can be applied to actual evaluations of e-learning programs created to promote system use.”
Quote in the paperNot a substantial claim- Rests on an assumption: The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
- Rests on an assumption: The findings can be generalized to higher education in Saudi Arabia.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe scope of the analysis was restricted to two possibilities regarding prior expertise in e-learning.
“Second, the scope of the analysis was restricted to two possibilities regarding prior expertise in e-learning.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The effectiveness of e-learning systems is largely dependent on students' adoption of such systems.
“Nonetheless, the effectiveness of e-learning systems is largely dependent on students' adoption of such systems.”
The paper relies on this without support.
There is a scarcity of research on university students' intentions to adopt e-learning during the global epidemic that uses TTF and ISSM with perceived enjoyment as an external factor, especially in developing countries such as education.
“Moreover, there is a scarcity of research on university students' intentions to adopt e-learning during the global epidemic that uses the TTF and ISSM models with perceived enjoyment as an external factor of the model, especially in developing countries such as education, where e-learning was not widely used at universities prior to the pandemic.”
The paper relies on this without support.
E-learning was not widely used at universities prior to the pandemic in developing countries such as education.
“where e-learning was not widely used at universities prior to the pandemic.”
The paper relies on this without support.
TTF has been the subject of research in a number of contexts, but e-learning has received relatively less attention.
“TTF has been the subject of research in a number of contexts, but e-learning has received relatively less attention.”
The paper relies on this without support.
Up to this point, it has not been determined if or how well a solid task-technology fit will affect a user’s adoption of e-learning.
“Up to this point, it hasn’t been determined if or how well a solid task-technology fit will affect a user’s adoption of e-learning.”
The paper relies on this without support.
The TTF model does not take into account social dynamics in the context of e-learning.
“The TTF model does not take into account social dynamics in the context of e-learning, which would limit its potential to forecast the use of social networking technology.”
The paper relies on this without support.
The sample of 260 students from King Faisal University is representative of students in higher education more broadly.
“260 randomly chosen students from King Faisal University who were enrolled in bachelors, masters, and PhD programs, both domestically and internationally, were the source of the data.”
The paper relies on this without support.
The findings can be generalized to higher education in Saudi Arabia.
“The five e-learning benefit dimensions information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics were used as potential constructs that might affect how easy and useful an e-learning system is to use in Saudi Arabia as well as how well it fits into the system as a whole and how well it fits into the task-technology fit.”
The paper relies on this without support.
The respondents answered the questionnaire honestly and accurately.
“The questionnaire was distributed physically, and respondents were asked to return it once completed.”
The paper relies on this without support.
This is the first study to look into the acceptance of eLearning benefits among undergraduate students during the COVID-19 epidemic.
“Furthermore, this is the first study to look into the acceptance of eLearning benefits among undergraduate students during the COVID-19 epidemic.”
The paper relies on this without support.
e-learning was widely used as a teaching medium during the COVID-19 epidemic, particularly among students in higher education.
“In conclusion, e-learning was widely used as a teaching medium during the COVID-19 epidemic, particularly among students in higher education.”
The paper relies on this without support.
A few studies have examined the use of e-learning during pandemics like COVID-19.
“However, a few studies have examined the use of e-learning during pandemics like COVID-19.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The task-technology fit theory suggests that the adoption of technology is influenced by individual and organizational factors.
“The task-technology fit theory also suggests that the adoption of technology is influenced by other factors, such as individual and organizational factors (Goodhue and Thompson, 1995; Lin, 2014; Alyoussef, 2023).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Alyoussef, 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Sampling method is described as both convenience and random
MajorThe methods section says participants were chosen using convenience sampling, but also describes the students supplying the data as randomly chosen.
“The participants were chosen from different departments and faculties using a convenience sampling technique.”
“260 randomly chosen students from King Faisal University who were enrolled in bachelors, masters, and PhD programs, both domestically and internationally, were the source of the data.”
Ethics approval date follows the stated study period
MajorThe paper says approval was required before the work was carried out, but identifies an October 2022 ethics clearance while stating that the research was conducted at the start of the 2020-2021 academic year.
“any research work must first receive approval before being carried out. Thus, an ethical clearance was obtained for this study (Ref. No. KFU-REC-2022-OCT-ETHICS267)”
“This research was conducted at the start of the academic year 2020–2021.”
H4 p-value differs from Table 6
MinorThe discussion assigns a p-value of 0.001 to both H3 and H4, but Table 6 gives 0.000 for H4.
“the system quality variable strongly supports the H3 and H4 hypotheses, confirming them with a p-value of (0.001).”
“| System Quality➔Perceived usefulness | H4 | 0.193 | 4.398 | 0.000 | Accepted |”
H14 path result differs between the text and Table 6
MinorThe text reports a coefficient of 0.441 and t-value of 8.580 for H14, while Table 6 reports 0.371 and 4.706 for the same perceived-usefulness-to-system-use path.
“perceived usefulness (H14) (= 0.441; t = 8.580)”
“| Perceived usefulness➔System Use | H14 | 0.371 | 4.706 | 0.000 | Accepted |”
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314 · see citing works
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190 · see citing works
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · see citing works
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541 · see citing works
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
(2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045748 · Cited by 11,791 · see citing works
Information Systems Success: The Quest for the Dependent Variable
DeLone, W. H., & McLean, E. R. (1992) · Information Systems Research · doi:10.1287/isre.3.1.60 · Cited by 10,697 · see citing works
Structural Equation Modeling and Regression: Guidelines for Research Practice
Gefen, D., Straub, D., & Boudreau, M.-C. (2000) · Communications of the Association for Information Systems · doi:10.17705/1cais.00407 · Cited by 6,416 · see citing works
Task-Technology Fit and Individual Performance
Goodhue, D. L., & Thompson, R. L. (1995) · MIS Quarterly · doi:10.2307/249689 · Cited by 6,006 · see citing works
e-Learning, online learning, and distance learning environments: Are they the same?
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011) · The Internet and Higher Education · doi:10.1016/j.iheduc.2010.10.001 · Cited by 2,042 · see citing works
Extending the technology acceptance model with task–technology fit constructs
Dishaw, M. T., & Strong, D. M. (1999) · Information & Management · doi:10.1016/s0378-7206(98)00101-3 · Cited by 1,772 · see citing works
Evaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350 · see citing works
Abdullah, F., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.036 · Cited by 986 · cited 5× · see citing works
“Earlier studies, according to Abdullah and Ward [84,85], found that PE had a substantial impact on the PEOU and PU for e-learning.”
BackgroundResultInfluentialCritical success factors in online education
Voléry, T., & Lord, D. (2000) · International Journal of Educational Management · doi:10.1108/09513540010344731 · Cited by 944 · see citing works
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101565 · Cited by 895 · see citing works
Variance-Based Structural Equation Modeling
Roldán, J. L., & Sánchez‐Franco, M. J. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-0179-6.ch010 · Cited by 881 · see citing works
Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S. A., Rashaideh, H., & Alharbi, H. F. (2021) · Journal of Computing in Higher Education · doi:10.1007/s12528-021-09274-2 · Cited by 832 · see citing works
Effect of COVID-19 on the Performance of Grade 12 Students: Implications for STEM Education
Sintema, E. J. (2020) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/7893 · Cited by 813 · see citing works
Instructional quality of Massive Open Online Courses (MOOCs)
Margaryan, A., Bianco, M., & Littlejohn, A. H. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.08.005 · Cited by 794 · see citing works
Park, S. Y., Nam, M., & Cha, S.-B. (2011) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2011.01229.x · Cited by 773 · see citing works
Students’ perception of online learning during the COVID-19 pandemic
Bączek, M. M., Zagańczyk-Bączek, M., Szpringer, M., Jaroszyński, A., & Wożakowska−Kapłon, B. (2021) · Medicine · doi:10.1097/md.0000000000024821 · Cited by 725 · see citing works
KANNAN, V (2005) · Omega · doi:10.1016/j.omega.2004.03.012 · Cited by 720 · see citing works
E-learning success determinants: Brazilian empirical study
Cidral, W., Oliveira, T., Felice, M. D., & Aparício, M. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.12.001 · Cited by 664 · see citing works
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2939467 · Cited by 651 · see citing works
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020) · Quality & Quantity · doi:10.1007/s11135-020-01028-z · Cited by 647 · see citing works
Alqahtani, A. Y., & Rajkhan, A. A. (2020) · Education Sciences · doi:10.3390/educsci10090216 · Cited by 620 · see citing works
Šumak, B., Heričko, M., & Pušnik, M. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.08.005 · Cited by 602 · see citing works
A task–technology fit view of learning management system impact
McGill, T. J., & Klobas, J. E. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.10.002 · Cited by 555 · see citing works
The determinants of corporate leverage and dividend policies
Barclay, M. J., Smith, C. W., & Watts, R. L. (1995) · Journal of applied corporate finance · doi:10.1111/j.1745-6622.1995.tb00259.x · Cited by 555 · see citing works
Turnbull, D., Chugh, R., & Luck, J. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10633-w · Cited by 529 · see citing works
Chang, C., Hajiyev, J., & Su, C.-R. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.04.010 · Cited by 479 · see citing works
Information Systems Success Measurement
DeLone, W. H., & McLean, E. R. (2016) · Foundations and Trends® in Information Systems · doi:10.1561/2900000005 · Cited by 479 · see citing works
Lin, W.-S., & Wang, C. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.07.008 · Cited by 474 · see citing works
COVID-19 and E-Learning: the Challenges of Students in Tertiary Institutions
Aboagye, E. T., Yawson, J. A., & Appiah, K. N. (2020) · Social Education Research · doi:10.37256/ser.212021422 · Cited by 445 · see citing works
Pal, D., & Vanijja, V. (2020) · Children and Youth Services Review · doi:10.1016/j.childyouth.2020.105535 · Cited by 440 · see citing works
Mailizar, M., Burg, D., & Maulina, S. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10557-5 · Cited by 420 · see citing works
A model for measuring e-learning systems success in universities
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.028 · Cited by 409 · see citing works
Tam, C., & Oliveira, T. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.016 · Cited by 383 · see citing works
Al‐Samarraie, H., & Saeed, N. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.05.016 · Cited by 378 · see citing works
Acceptance of mobile banking framework in Pakistan
Afshan, S., & Sharif, A. (2016) · Telematics and Informatics · doi:10.1016/j.tele.2015.09.005 · Cited by 376 · see citing works
Al‐Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2017) · Studies in Higher Education · doi:10.1080/03075079.2017.1298088 · Cited by 348 · see citing works
Reviewing and exploring innovative ubiquitous learning tools in higher education
Aljawarneh, S. A. (2020) · Journal of Computing in Higher Education · doi:10.1007/s12528-019-09207-0 · Cited by 333 · see citing works
Chen, H.-R., & Tseng, H.-F. (2011) · Evaluation and Program Planning · doi:10.1016/j.evalprogplan.2011.11.007 · Cited by 323 · see citing works
Chow, M. C. M., Herold, D. K., Choo, T.-M., & Chan, K. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.05.011 · Cited by 320 · see citing works
Determinants of perceived usefulness of e-learning systems
Alsabawy, A. Y., Cater‐Steel, A., & Soar, J. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.065 · Cited by 297 · see citing works
Distance learning in the era of COVID-19
Schneider, S. L. (2020) · Archives of Dermatological Research · doi:10.1007/s00403-020-02088-9 · Cited by 297 · see citing works
Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.09.009 · Cited by 289 · see citing works
Antecedents and consequences of e-learning acceptance
Cheng, Y. (2010) · Information Systems Journal · doi:10.1111/j.1365-2575.2010.00356.x · Cited by 280 · see citing works
Gelles, L. A., Lord, S. M., Hoople, G. D., Chen, D. A., & Mejia, J. A. (2020) · Education Sciences · doi:10.3390/educsci10110304 · Cited by 277 · see citing works
Hernández-Sellés, N., Carril, P. C. M., & Sanmamed, M. G. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.04.012 · Cited by 257 · see citing works
An Empirical Investigation of Knowledge Management Systems' Success
Halawi, L., McCarthy, R. V., & Aronson, J. E. (2008) · Journal of Computer Information Systems · doi:10.1080/08874417.2008.11646014 · Cited by 250 · see citing works
Chu, T.-H., & Chen, Y. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.09.013 · Cited by 250 · see citing works
Al‐Adwan, A. S., Albelbisi, N. A., Hujran, O., Al-Rahmi, W. M., & Alkhalifah, A. (2021) · Sustainability · doi:10.3390/su13169453 · Cited by 246 · see citing works
Yang, M. H., Shao, Z., Liu, Q., & Liu, C. (2017) · Educational Technology Research and Development · doi:10.1007/s11423-017-9513-6 · Cited by 232 · see citing works
Blockchain Technology Adoption in Smart Learning Environments
Ullah, N., Al-Rahmi, W. M., Alzahrani, A. I., Alfarraj, O., & Alblehai, F. (2021) · Sustainability · doi:10.3390/su13041801 · Cited by 211 · see citing works
Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021) · Sustainability · doi:10.3390/su13147770 · Cited by 197 · see citing works
Jogezai, N. A., Baloch, F. A., Jaffar, M., Shah, T., Khilji, G. K., & Bashir, S. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06781 · Cited by 195 · see citing works
Yang, F., Ren, L., & Gu, C. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10562 · Cited by 193 · see citing works
A model of organizational employees’ e-learning systems acceptance
Lee, Y., Hsieh, Y. G., & Ma, C.-Y. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.09.005 · Cited by 181 · see citing works
Al-Maatouk, Q., Othman, M. S., Aldraiweesh, A., Alturki, U., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2020) · IEEE Access · doi:10.1109/access.2020.2990420 · Cited by 177 · see citing works
Çalışır, F., Gümüşsoy, Ç. A., Bayraktaroglu, A. E., & Karaali, D. (2014) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.20548 · Cited by 176 · see citing works
Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. K. (2017) · Behaviour and Information Technology · doi:10.1080/0144929x.2017.1340973 · Cited by 176 · see citing works
Rajab, K. D. (2018) · IEEE Access · doi:10.1109/access.2018.2800164 · Cited by 169 · see citing works
Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Al-Rahmi, W. M. (2021) · Sustainability · doi:10.3390/su13137074 · Cited by 169 · see citing works
Formal to informal learning with IT: research challenges and issues for e‐learning
Cox, M. (2012) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2012.00483.x · Cited by 160 · see citing works
Online vs. traditional learning in teacher education: a comparison of student progress
Hurlbut, A. R. (2018) · American Journal of Distance Education · doi:10.1080/08923647.2018.1509265 · Cited by 159 · see citing works
Exploring the Factors Affecting Mobile Learning for Sustainability in Higher Education
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al‐Adwan, A. S. (2021) · Sustainability · doi:10.3390/su13147893 · Cited by 159 · see citing works
Alismaiel, O. A., Cifuentes‐Faura, J., & Al-Rahmi, W. M. (2022) · Sustainability · doi:10.3390/su141811134 · Cited by 158 · see citing works
The effects of organizational learning environment factors on e-learning acceptance
Cheng, B. H., Wang, M., Moormann, J., Olaniran, B. A., & Chen, N. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.10.014 · Cited by 155 · see citing works
An empirical study on behavioural intention to reuse e‐learning systems in rural China
Li, Y., Duan, Y., Fu, Z., & Alford, P. (2011) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2011.01261.x · Cited by 147 · see citing works
Al‐Adwan, A. S., Yaseen, H., Alsoud, A. R., Abousweilem, F., & Al-Rahmi, W. M. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10758-y · Cited by 145 · see citing works
Fauzi, M. A. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09433 · Cited by 143 · see citing works
Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019) · IEEE Access · doi:10.1109/access.2019.2928142 · Cited by 141 · see citing works
Kim, T. (., Suh, Y. K., Lee, G., & Choi, B. G. (2010) · International Journal of Tourism Research · doi:10.1002/jtr.787 · Cited by 139 · see citing works
Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10707-9 · Cited by 133 · see citing works
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Mulyono, H., Suryoputro, G., & Jamil, S. R. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07853 · Cited by 131 · see citing works
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Mhouti, A. E., Erradi, M., & Nasseh, A. (2017) · Education and Information Technologies · doi:10.1007/s10639-017-9642-x · Cited by 129 · see citing works
Durodolu, O. O., & Olumide, O. (2016) · Lincoln (University of Nebraska) · Cited by 126 · see citing works
Yawson, D. E., & Yamoah, F. A. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05519 · Cited by 122 · see citing works
Is more always better? Investigating the task-technology fit theory in an online user context
Aljukhadar, M., Sénécal, S., & Nantel, J. A. (2014) · Information & Management · doi:10.1016/j.im.2013.10.003 · Cited by 120 · see citing works
Yekefallah, L., Namdar, P., Panahi, R., & Dehghankar, L. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07628 · Cited by 119 · see citing works
Hong, J., Tai, K., Hwang, M., Kuo, Y.-C., & Chen, J.-S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.08.044 · Cited by 117 · see citing works
Oyediran, W. O., Omoare, A. M., Owoyemi, M. A., Adejobi, A. O., & Fasasi, R. B. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05457 · Cited by 112 · see citing works
E-Learning Acceptance: The Role of Task–Technology Fit as Sustainability in Higher Education
Alyoussef, I. Y. (2021) · Sustainability · doi:10.3390/su13116450 · Cited by 111 · see citing works
Self-citationA structural equation modeling of the Internet acceptance in Korea
Kim, B. G., Park, S. C., & Lee, K. J. (2006) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2006.08.005 · Cited by 105 · see citing works
How does task-technology fit influence cloud-based e-learning continuance and impact?
Cheng, Y. (2019) · Education + Training · doi:10.1108/et-09-2018-0203 · Cited by 104 · see citing works
User continuance intention to use cloud storage service
Yang, H., & Lin, S.-L. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.05.057 · Cited by 101 · see citing works
Kim, K., Trimi, S., Park, H., & Rhee, S. (2012) · Decision Sciences Journal of Innovative Education · doi:10.1111/j.1540-4609.2012.00360.x · Cited by 101 · see citing works
Development and initial evaluation of the Clinical Information Systems Success Model (CISSM)
García-Smith, D., & Effken, J. A. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2013.01.011 · Cited by 100 · see citing works
The Role of Perceived System Quality as Educators’ Motivation to Continue E-learning System Use
Islam, A. K. M. N. (2012) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00037 · Cited by 92 · see citing works
A proposed model of e-learning tools acceptance among university students in developing countries
Valencia-Arías, A., Naffah, S. C., & Bermúdez-Hernández, J. (2018) · Education and Information Technologies · doi:10.1007/s10639-018-9815-2 · Cited by 91 · see citing works
Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alyoussef, I. Y., & Crawford, J. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1003007 · Cited by 80 · see citing works
Self-citationAlyoussef, I. Y. (2021) · Sustainability · doi:10.3390/su13137374 · Cited by 79 · see citing works
Self-citationA Model of Factors Affecting Cyber Bullying Behaviors Among University Students
Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Aljarboa, N. A., Kamin, Y. B., & Moafa, F. A. (2019) · IEEE Access · doi:10.1109/access.2018.2881292 · Cited by 78 · see citing works
Kim, B., & Park, M. J. (2017) · Information Technology for Development · doi:10.1080/02681102.2017.1312244 · Cited by 77 · see citing works
Human and social factors affecting the decision of students to accept e-learning
Olasina, G. (2018) · Interactive Learning Environments · doi:10.1080/10494820.2018.1474233 · Cited by 76 · see citing works
Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., & Almutairy, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.905968 · Cited by 76 · see citing works
Effects of realistic e-learning cases on students’ learning motivation during COVID-19
Rahm, A., Töllner, M., Hubert, M. O., Klein, K., Wehling, C., Sauer, T., Hennemann, H., Hein, S. J., Κender, Ζ., Günther, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249425 · Cited by 69 · see citing works
Continuance Intention of E-Learning: The Condition and Its Connection with Open Innovation
Suzianti, A., & Paramadini, S. A. (2021) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc7010097 · Cited by 66 · see citing works
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Azhari, F. A., & Ming, L. C. (2015) · Indian Journal of Pharmaceutical Education and Research · doi:10.5530/ijper.49.4.2 · Cited by 65 · see citing works
Li, C., He, L., & Wong, I. A. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10462-x · Cited by 65 · see citing works
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Ulla, M. B., & Perales, W. F. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e08317 · Cited by 60 · see citing works
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Özkan, S., & Koseler, R. (2009) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2009.5350590 · Cited by 58 · see citing works
Al-Rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015) · Research Journal of Applied Sciences, Engineering and Technology · doi:10.19026/rjaset.9.2608 · Cited by 55 · see citing works
Costley, J., & Lange, C. H. (2017) · Interactive Technology and Smart Education · doi:10.1108/itse-08-2016-0025 · Cited by 55 · see citing works
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Moafa, F. A., Ahmad, K., Al-Rahmi, W. M., Yahaya, N., Kamin, Y., & Alamri, M. M. (2018) · IEEE Access · doi:10.1109/access.2018.2866525 · Cited by 53 · see citing works
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Alamri, M. M., Al-Rahmi, W. M., Yahaya, N., Al-Rahmi, A. M., Abualrejal, H. M., Zeki, A. M., & Al-Maatouk, Q. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f1043.0986s319 · Cited by 53 · see citing works
Gupta, M. M., Jankie, S., Pancholi, S. S., Talukdar, D., Sahu, P. K., & Sa, B. (2020) · Education Sciences · doi:10.3390/educsci10120352 · Cited by 53 · see citing works
Selviandro, N., & Hasibuan, Z. A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36818-9_20 · Cited by 52 · see citing works
Divayana, D. G. H., Suyasa, P. W. A., & Widiartini, N. K. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06347 · Cited by 51 · see citing works
Yi, Y. J., You, S., & Bae, B. J. (2016) · Library Hi Tech · doi:10.1108/lht-04-2016-0038 · Cited by 50 · see citing works
Shahriar, S. H. B., Arafat, S., Sultana, N., Akter, S., Khan, M. M. R., Nur, J. M. E. H., & Khan, S. I. (2021) · AAOU Journal/AAOU journal · doi:10.1108/aaouj-02-2021-0025 · Cited by 48 · see citing works
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Al-Shargabi, B., Sabri, O., & Aljawarneh, S. A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.723 · Cited by 38 · see citing works
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Zhang, T., Wang, W. Y. C., Lin, Y. C., & Tai, L.-H. (2014) · Behaviour and Information Technology · doi:10.1080/0144929x.2014.964319 · Cited by 33 · see citing works
Al-Kahtani, N. K., Almurayh, A., Subbarayalu, A. V., Sebastian, T., Alkahtani, H. K., & Aljabri, D. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10898 · Cited by 33 · see citing works
Ramli, M. F., Majid, M., & Badyalina, B. (2020) · International Journal of Learning and Development · doi:10.5296/ijld.v10i4.17921 · Cited by 32 · see citing works
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Salahshouri, A., Eslami, K., Boostani, H., Zahiri, M., Jahani, S., Arjmand, R., Heydarabadi, A. B., & Dehaghi, B. F. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e08984 · Cited by 32 · see citing works
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Pour, M. J., Hosseinzadeh, M., Azar, M. B., & Taheri, F. (2017) · Kybernetes · doi:10.1108/k-02-2017-0060 · Cited by 29 · see citing works
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Cheng, W.-Y. T., & Chen, C.-C. (2015) · International Journal of e-Education e-Business e-Management and e-Learning · doi:10.17706/ijeeee.2015.5.4.212-228 · Cited by 27 · see citing works
Measuring e-learning systems success: Implementing D & M is success model
Sandjojo, N., & Wahyuningrum, T. (2015) · International Conference on Interactive Digital Media (ICIDM) · doi:10.1109/idm.2015.7516343 · Cited by 23 · see citing works
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Chinyamurindi, W., & Shava, H. (2015) · South African journal of information management · doi:10.4102/sajim.v17i1.635 · Cited by 19 · see citing works
Awareness of electronic crimes related to E-learning among students at the University of Jordan
Ayyoub, H. Y., AlAhmad, A. A., Al‐Serhan, A., Al-Abdallat, M., Al-Muheisen, E., Boshmaf, H., Abu-Taleb, Y. A., Alqudah, Y. O., & Alshamaila, Y. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10897 · Cited by 11 · see citing works
Perceptions on Service Quality and Ease-of-Use: Evidence from Malaysian Distance Learners
Hilmi, M. F., Pawanchik, S., & Mustapha, Y. A. A. (2012) · Cited by 8 · see citing works
O’Reilly, C. A. (1980) · Academy of Management Proceedings · doi:10.5465/ambpp.1980.4976197 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Klopping, 2004, Extending the technology acceptance model and the task-technology fit model to consumer e-consumer, Inf. Technol. Learn. Perform J., 22, 35
- Sathishkumar, 2020, E-Learning during lockdown of covid-19 pandemic: a global perspective, Int. J. Control Autom., 13, 1088
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Lestari, L. W., & Setyasto, N. (2025) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v11i3.10420 · Cited by 9 · see citing works
Alkhuwaylidee, A. R. (2025) · Computers · doi:10.3390/computers14050176 · Cited by 9 · see citing works
Hằng, N. P. T., Nguyen, N. T. T., & Huynh, T. B. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01043-3 · Cited by 9 · see citing works
Basri, I. Y., Giatman, M., Mukhaiyar, R., Taali, T., Samala, A. D., & Dewi, I. P. (2025) · TEM Journal · doi:10.18421/tem142-63 · Cited by 8 · see citing works
Amrani, M. A., Al-Tayar, B., Saeed, M. A., Ghaleb, A. M., Hebah, H. A., & Al-nour, H. A. (2023) · Albaydha University Journal · doi:10.56807/buj.v5i4.494 · Cited by 8 · see citing works
Ismanto, E., Herlandy, P. B., & Rahmadani, R. (2024) · JURNAL FASILKOM · doi:10.37859/jf.v14i1.6882 · Cited by 7 · see citing works
Su, N., Mamun, A. A., Reza, M. N. H., Yang, Q. Y., & Masud, M. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12794-w · Cited by 7 · see citing works
Determinants of donation intentions in Malaysia
Kamarudin, M. K., Mustaffa, F. N. A., Ganti, N., Norzilan, N. I. M., Zainan, N. I. N., & Khidzir, M. (2024) · International Journal of Social Economics · doi:10.1108/ijse-04-2023-0291 · Cited by 7 · see citing works
The impact of Edtech: strengths and opportunities
Shustova, I., Polevaya, E., & Ivanov, A. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202345806012 · Cited by 7 · see citing works
Towards a new definition of e-learning and m-learning
Chat, M., Zghıda, N., Ouaali, A. A., Youlyouz, A., Amiri, E. M., & Janati‐Idrissi, R. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025281 · Cited by 6 · see citing works
Seyum, A. A., Wang, S., Zhang, N., & Wang, L. (2025) · South African Journal of Economic and Management Sciences · doi:10.4102/sajems.v28i1.6034 · Cited by 6 · see citing works
Maksum, H., Wagino, W., Purwanto, W., Muslim, M., Putra, R., & Koto, R. D. (2025) · TEM Journal · doi:10.18421/tem143-75 · Cited by 6 · see citing works
GPT Chat with Project-Based Learning in Learning: A Review
Yusuf, M. (2023) · JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION · doi:10.56778/jdlde.v2i5.257 · Cited by 6 · see citing works
MENINGKATKAN KETERLIBATAN MAHASISWA DALAM PEMBELAJARAN MELALUI PLATFORM MENTIMETER
Warsihna, J., Ramdani, Z., & Amri, A. (2024) · Jurnal Teknodik · doi:10.32550/teknodik.vi.1071 · Cited by 6 · see citing works
Linda, E., Rakhmawati, A., & Paidi, P. (2025) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v11i8.12140 · Cited by 5 · see citing works
Hardanti, K. N., Sutrisno, T., Saraswati, E., & Prastiwi, A. (2024) · Бизнес-информатика · doi:10.17323/2587-814x.2024.1.65.78 · Cited by 5 · see citing works
Huynh, A.-V., & Khoa, B. T. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-98476-1_24 · Cited by 5 · see citing works
Towards a Comprehensive Metaverse Forensic Framework Based on Technology Task Fit Model
AlMutawa, A., Ikuesan, R. A., & Said, H. (2024) · Future Internet · doi:10.3390/fi16120437 · Cited by 5 · see citing works
Lozada, P. B., Sarona, J. M., Marumas, D. G. M., Hasan, N.-N. B., Aidil-Karanain, F., & Alieto, E. O. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2671-4_4 · Cited by 4 · see citing works
Kumar, A., Mani, P. K. C., Atta, K., Omprakash, A., Maheshkumar, K., Atwa, H. S. M., Maruthy, K. N., & Padmavathi, R. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07582-0 · Cited by 4 · see citing works
Bensalah, F., Sadriddinovna, I. S., Terentev, D., Nikulushkin, A., Muracova, N., & Tsarev, R. Y. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00236-5_31 · Cited by 4 · see citing works
Nosrati, T., Nosrati, F., & Mashayekhi, M. (2025) · SAM Advanced Management Journal · doi:10.1108/samamj-07-2025-0041 · Cited by 4 · see citing works
Yang, W., Xie, Y., & Wu, X. (2025) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2025.104499 · Cited by 4 · see citing works
Zouair, N., Abou‐Shouk, M., Idriz, M., & Okleh, I. (2025) · Journal of Tourism and Services · doi:10.29036/tkk4ah55 · Cited by 4 · see citing works
Garmpis, A., Garmpis, S., Panagiotarou, A., Antonopoulou, H., & Halkiopoulos, C. (2026) · Societies · doi:10.3390/soc16010024 · Cited by 4 · see citing works
Canh, H., & Khoa, B. T. (2024) · Tạp chí Kinh tế và Phát triển · doi:10.33301/jed-05-2024-0048 · Cited by 4 · see citing works
Alamsyah, D. P., Lim, N., & Ruslie, D. D. (2025) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa67155.2025.11157151 · Cited by 3 · see citing works
Generative AI adoption in universities: how TAM–TTF and neuroticism influence sustained usage
Bawa, R. K., Jain, K., & Goel, P. (2025) · Interactive Technology and Smart Education · doi:10.1108/itse-02-2025-0030 · Cited by 3 · see citing works
Unlocking Healthcare Access Through Mobile Health
Ridwan, M. H., Aminah, S., Fuady, I., Koswara, I., Mutmainah, I., Maemunah, S., Elizabeth, R., Indarti, D. M., Djarwadi, D., Ma'rifah, D., et al. (2026) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.396959 · Cited by 3 · see citing works
Llacuna, F. D. G., Ong, A. K. S., & Young, M. N. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106471 · Cited by 3 · see citing works
Exploring synchronous, asynchronous, and conventional online courses in higher education
Moon, E., & Shin, W. S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0345955 · Cited by 3 · see citing works
Madjid, A., & Shodiq, S. F. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1758690 · Cited by 3 · see citing works
Yu, J. (2024) · Journal of Exercise Rehabilitation · doi:10.12965/jer.2346586.293 · Cited by 3 · see citing works
Evaluation Quality Of E-Learning X Using ISO/IEC 25010 Framework And Design Thinking Approach
Huda, B., Sediyono, E., Hartomo, K. D., Sembiring, I., Fauzi, A. Y., & Hananto, A. L. (2023) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact59844.2023.10455796 · Cited by 3 · see citing works
Sustainable Adoption of E-Learning in Romanian Universities after the COVID-19 Outbreak
Prioteasa, A.-L., Shuleski, D., Lazăr, L., Ciocoiu, C. N., & CHIVULESCU, F. A. (2024) · Sustainability · doi:10.3390/su16208795 · Cited by 3 · see citing works
Martín‐Navarro, A., Sancho, M. P. L., Szelągowski, M., & Medina-Garrido, J. A. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.364099 · Cited by 3 · see citing works
Lobo, J. (2025) · The Journal of Educators Online · doi:10.9743/jeo.2025.22.2.2 · Cited by 3 · see citing works
E-Learning and Organizational Support: Investigation of Quality and Behavior
Susanti, L., & Alamsyah, D. P. (2023) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic60109.2023.10382054 · Cited by 3 · see citing works
Pohan, S. D., Ali, I., Widiana, S. A., Supriyanto, S., & Mandaya, Y. W. (2025) · Journal of Advances in Information Systems and Technology · doi:10.15294/jaist.v7i1.20847 · Cited by 2 · see citing works
Web-Based Tuition Payment Information System Design Using Codeigniter at SMA Negeri 2 Bungku
Wulansari, L., Iskandar, A., Syahrul, S., Mansyur, M., Ramazanova, M., & Dian Saputri, S. (2025) · Ceddi Journal of Education · doi:10.56134/cje.v4i1.138 · Cited by 2 · see citing works
Abdulrab, M. (2025) · The Open Psychology Journal · doi:10.2174/0118743501416115251129091125 · Cited by 2 · see citing works
An, G. K., & Ngo, T. T. A. (2025) · Journal of Information Technology Education Research · doi:10.28945/5668 · Cited by 2 · see citing works
(2025) · Journal of Logistics Informatics and Service Science · doi:10.33168/jliss.2025.1103 · Cited by 2 · see citing works
Digital-Based Islamic Learning Strategy Management to Enhance Student Literacy
Priyantoro, D. E., Supriani, Y., Yusuf, M., & Nazriah, A. (2026) · Attaqwa Jurnal Ilmu Pendidikan Islam · doi:10.54069/attaqwa.v22i2.1211 · Cited by 2 · see citing works
Digitalizing Faculty Career Advancement: How SIM-PAK Quality Accelerates Academic Rank Promotion
Yoeliyanto, J., Yulianto, B., Trihantoyo, S., & Sa’diyah, D. F. (2025) · Business and Applied Management Journal. · doi:10.61987/bamj.v3i2.1984 · Cited by 2 · see citing works
Bragina, E. V. (2023) · Педагогика и просвещение · doi:10.7256/2454-0676.2023.3.43504 · Cited by 2 · see citing works
Role of Administrators in Blended learning in Higher Education Institution
Qassim, F. M., Jumanı, N. B., & Malik, S. (2023) · PJDOL · doi:10.30971/pjdol.v9i2.1896 · Cited by 2 · see citing works
(2024) · Journal of Logistics Informatics and Service Science · doi:10.33168/jliss.2024.0914 · Cited by 2 · see citing works
Huda, B., Hartomo, K. D., Nataliani, Y., & Sembiring, I. (2024) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia61827.2024.10761368 · Cited by 2 · see citing works
- Authors
- Alyoussef, I. Y.
- Affiliations
- King Faisal University (SA)
- Corresponding author
- Alyoussef, Ibrahim Youssef
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 9
- Issue
- 3
- Pages
- e13751
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC9938001
- OpenAlex
- W4321254258
- Cited by
- 181
- References
- 125
- Citations by year
- 2026: 45 · 2025: 53 · 2024: 59 · 2023: 23 · 2022: 1
- FWCI
- 46.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Heliyon (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research, King Saud University
- King Faisal University (2662)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Technology-Enhanced Education Studies
- Social Sciences › Education › Online and Blended Learning
- Keywords
- e-learning adoption, task-technology fit, DeLone and McLean model, university students, perceived ease of use, perceived usefulness, system quality, information quality, perceived enjoyment, technology characteristics, task characteristics, COVID-19 remote teaching, student acceptance of technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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