Evidence map›Paper›PMID 36845042›Full record

ArticleHeliyon2023

Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model.

Ibrahim Youssef Alyoussef

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Ibrahim Youssef AlyoussefFaculty of Education, Education Technology Department, King Faisal University, Al Ahsa, 31982, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

E-learningE-learning benefitInformation qualityPerceived enjoyment

Identifiers

PMID36845042
PMCPMC9938001

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.