Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling
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Chatterjee and Bhattacharjee (2020) demonstrated that performance expectancy, effort expectancy, and perceived risk significantly shape user attitudes toward AI technologies, which influence intention to continue using them.
“Chatterjee and Bhattacharjee (2020) demonstrated that constructs like performance expectancy, effort expectancy, and perceived risk significantly shape user attitudes toward AI technologies, which, in turn, influence their intention to continue using such technologies.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Chatterjee and Bhattacharjee 2020
Risk perceptions and privacy concerns exert a negative influence on attitudes, which reduce continued engagement.
“Notably, they found that risk perceptions and privacy concerns exert a negative influence on attitudes, which reduce continued engagement.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Chatterjee and Bhattacharjee 2020
User attitudes mediate the relationship between cognitive antecedents such as perceived benefits or risks and behavioral intentions.
“Building on the Theory of Reasoned Action (TRA), a substantial body of research has shown that user attitudes mediate the relationship between cognitive antecedentssuch as perceived benefits or risks-and behavioral intentions (Venkatesh et al. 2003;Chatterjee & Bhattacharjee 2020).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Chatterjee & Bhattacharjee 2020
Studies have shown that educators who are more literate in AI find it easier to adopt and interact with AI technologies because they are better able to understand and navigate the functionalities of these systems.
“Studies have shown that educators who are more literate in AI find it easier to adopt and interact with AI technologies because they are better able to understand and navigate the functionalities of these systems [53].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [53]
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“Chatterjee and Bhattacharjee (2020) demonstrated that constructs like performance expectancy, effort expectancy, and perceived risk significantly shape user attitudes toward AI technologies, which, in turn, influence their intention to continue using such technologies.”
- Authors
- Chatterjee, S., & Bhattacharjee, K. K.
- Affiliations
- Indian Institute of Technology Delhi (IN)
- Corresponding author
- Chatterjee, Sheshadri
- Venue
- Education and Information Technologies
- Published
- 2020-03-31
- Volume
- 25
- Issue
- 5
- Pages
- 3443-3463
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W3014502523
- Cited by
- 747
- References
- 71
- Citations by year
- 2026: 157 · 2025: 253 · 2024: 177 · 2023: 77 · 2022: 46 · 2021: 29 · 2020: 8
- FWCI
- 35.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Education and Information Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
- Keywords
- artificial intelligence adoption, higher education, UTAUT, technology acceptance, stakeholders, Indian higher education institutions, massification of higher education, educational technology, conceptual model
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