Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education
Abstract 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 fac...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | International Journal of Educational Technology in Higher Education |
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| Online Access: | https://doi.org/10.1186/s41239-025-00506-4 |
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| _version_ | 1823861762092433408 |
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| author | Maria Ijaz Baig Elaheh Yadegaridehkordi |
| author_facet | Maria Ijaz Baig Elaheh Yadegaridehkordi |
| author_sort | Maria Ijaz Baig |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d4be899fad40494192704fd424b45f23 |
| institution | Kabale University |
| issn | 2365-9440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Educational Technology in Higher Education |
| spelling | doaj-art-d4be899fad40494192704fd424b45f232025-02-09T12:49:46ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402025-02-0122112310.1186/s41239-025-00506-4Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher educationMaria Ijaz Baig0Elaheh Yadegaridehkordi1Department of Information Systems, Faculty of Computer Science and Information Technology, University of MalayaCollege of Information and Communications Technology, School of Engineering and Technology, CQUniversityAbstract 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.https://doi.org/10.1186/s41239-025-00506-4Generative artificial intelligence (GenAI)Continuous intentionHigher educationExpectation confirmationand Academic staff satisfaction |
| spellingShingle | Maria Ijaz Baig Elaheh Yadegaridehkordi Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education International Journal of Educational Technology in Higher Education Generative artificial intelligence (GenAI) Continuous intention Higher education Expectation confirmation and Academic staff satisfaction |
| title | Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education |
| title_full | Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education |
| title_fullStr | Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education |
| title_full_unstemmed | Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education |
| title_short | Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education |
| title_sort | factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence genai in higher education |
| topic | Generative artificial intelligence (GenAI) Continuous intention Higher education Expectation confirmation and Academic staff satisfaction |
| url | https://doi.org/10.1186/s41239-025-00506-4 |
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