Factors Influencing the Adoption of IoT for E-Learning in Higher Educational Institutes in Developing Countries
Open accessAbstract
The internet of things (IoT) is an emerging paradigm of educational applications and innovative technology in the current era. While capabilities are increasing day by day, there are still many limitations and challenges to utilizing these technologies within E-Learning in higher educational institutes (HEIs). The IoT is well-implemented in the United States of America (USA), United Kingdom (UK), Japan, and China but not in developing countries, including Saudi Arabia, Malaysia, Pakistan, Bangladesh, etc. Few studies have investigated the adoption of IoT in E-Learning within developing countries. Therefore, this research aims to examine the factors influencing IoT adoption for E-Learning to be utilized in HEIs. Further, an adoption model is proposed for IoT-based E-Learning in the contexts of developing countries and provides recommendations for enhancing the IoT adoption for E-Learning in HEIs. The IoT-based E-Learning model categorizes these influencing factors into four groups: individual, organizational, environmental, and technological. Influencing factors are compared along with a detailed description in order to determine which factors should be prioritized for efficient IoT-based E-Learning in HEIs. We identify the privacy (27%), infrastructure readiness (24%), financial constraints (24%), ease of use (20%), support of faculty (18%), interaction (15%), attitude (14%), and network and data security (14%), as the significant E-Learning influencing factors on IoT adoption in HEIs. These findings from the researcher's perspective will show that the national culture has a significant role in the individual, organizational, technological, and environmental behavior toward using new technology in developing countries.
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Sorted by citations
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“ALPs have been found to increase student engagement through personalized challenges and feedback based on the learner’s real-time performance, keeping them motivated and on track [116,117].”
- Authors
- Madni, S. H. H., Ali, J., Husnain, H. A., Masum, M. H., Mustafa, S., Shuja, J., Maray, M., & Hosseini, S.
- Affiliations
- University of Technology Malaysia (MY)
- Saudi Electronic University (SA)
- COMSATS University Islamabad (PK)
- King Khalid University (SA)
- Tecnológico de Monterrey (MX)
- Corresponding author
- Hosseini, Samira
- ORCID
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2022-07-08
- Volume
- 13
- Pages
- 915596-915596
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC9305662
- OpenAlex
- W4284974382
- Cited by
- 58
- References
- 101
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 13 · 2023: 11
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Universiti Teknologi Malaysia
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Technology-Enhanced Education Studies
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Technology
- Keywords
- Internet of Things, online learning, technology adoption, higher education, developing countries, adoption model, privacy, infrastructure readiness, financial constraints, ease of use, faculty support, national culture, technology acceptance
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.
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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