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Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education

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Publicado en:International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 5
Publicado:
Springer Nature B.V.
Materias:
Higher education
Generative artificial intelligence
Ethical standards
Colleges & universities
University faculty
Attitude surveys
Ethics
Influence
Education policy
Policy making
Artificial intelligence
Teaching
Privacy
Polls & surveys
Academic achievement
Learning
Satisfaction
Teachers
Academic staff
Structural equation modeling
Learning outcomes
Correlation
Job Satisfaction
Intention
Structural Equation Models
Least Squares Statistics
Expectation
Social Influences
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Resumen:Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.
ISSN:2365-9440
1698-580X
DOI:10.1186/s41239-025-00506-4
Fuente:Political Science Database