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Cited by 458Technology Adoption and User BehaviourArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour

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Ivanov, Stanislav, Soliman, Mohammad, Tuomi, Aarni, Alkathiri, Nasser Alhamar, Al-Alawi, Alamir N.·2024·Technology in Society
DOI 10.1016/j.techsoc.2024.102521
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Abstract

Drawing on the Theory of Planned Behaviour (TPB), this study investigates the relationship between the perceived benefits, strengths, weaknesses, and risks of generative AI (GenAI) tools and the fundamental factors of the TPB model (i.e., attitude, subjective norms, and perceived behavioural control). The study also investigates the structural association between the TPB variables and intention to use GenAI tools, and how the latter might affect the actual usage of GenAI tools in higher education. The paper adopts a quantitative approach, relying on an anonymous self-administered online questionnaire to gather primary data from 130 lecturers and 168 students in higher education institutions (HEIs) in several countries, and PLS-SEM for data analysis. The results indicate that although lecturers' and students' perceptions of the risks and weaknesses of GenAI tools differ, the perceived strengths and advantages of GenAI technologies have a significant and positive impact on their attitudes, subjective norms, and perceived behavioural control. The TPB core variables positively and significantly impact lecturers' and students’ intentions to use GenAI tools, which in turn significantly and positively impact their adoption of such tools. This paper advances theory by outlining the factors shaping the adoption of GenAI technologies in HEIs. It provides stakeholders with a variety of managerial and policy implications for how to formulate suitable rules and regulations to utilise the…

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Research works printed but not linked: 2

  1. Vogler, 2024, 2023 – an AI university space Odyssey, ROBONOMICS: J. Autom. Econ., 5, 55
  2. Gilson, 2022, How well does ChatGPT do when taking the medical licensing exams? The implications of large language models for medical education and knowledge assessment, medRxiv, 1

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Metadata

Authors
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N.
Affiliations
  • Varna University of Management (BG)
  • University of Technology and Applied Sciences-Salalah (OM)
  • Haaga-Helia University of Applied Sciences (FI)
  • University of Technology and Applied Sciences — Ibri (OM)
Corresponding author
Ivanov, Stanislav
ORCID
Venue
Technology in Society
Publisher
Elsevier BV
Published
2024-06
Volume
77
Pages
102521
ISSN
0160-791X (ISSN-L)
DOI
10.1016/j.techsoc.2024.102521
OpenAlex
W4393149408
Cited by
397
References
59
Citations by year
2026: 168 · 2025: 186 · 2024: 43
FWCI
148.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Technology in Society (publishedVersion, cc-by)
  • Theseus (Ammattikorkeakoulujen) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI adoption, higher education, theory of planned behavior, perceived benefits, risk perception, attitudes, subjective norms, perceived behavioral control, intention to use, lecturers, students, AI regulation

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