Examining the Use of Generative Artificial Intelligence in Higher Education and Its Impact on Social Sustainability: An Integrated Model of UTAUT2 and T-EESST

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dc.contributor.advisorDr Mostafa Al-Emran
dc.contributor.authorQUNNEIS, SAI’DAH MAHMOUD
dc.date.accessioned2025-05-01T10:43:31Z
dc.date.available2025-05-01T10:43:31Z
dc.date.issued2024-11
dc.description.abstractIn this research, the author assesses and evaluates the degree to which the higher education (HE) sector has embraced generative artificial intelligence (GenAI), primarily focusing on the adoption and application of GenAI and its implications for social sustainability. The aim of this research entails the proposing and advancing of a model that enhances the adoption of GenAI in an educational setting that is based on Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST) and the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. This research employs structural equation modelling (SEM), in conjunction with artificial neural networks (ANNs), to examine the main constructs driving the intention to use GenAI and the nature of its impact on social sustainability (SS). The research’s results indicate four factors that significantly impact the adoption of GenAI in HE—habit (HB), hedonic motivation (HM), performance expectancy (PE), and perceived trust (PT)—essentially highlighting intrinsic motivators. However, effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), and perceived risk (PR) were not statistically significant. Furthermore, the study demonstrates that adopting GenAI has a significant impact on social sustainability (SS) in education by promoting equitable, inclusive, and lifelong learning, as well as enhancing societal well-being. These outcomes provide essential awareness for policymakers and educational institutions, establishing a basis for developing socially sustainable learning environments that leverage the revolutionary capabilities of GenAI. Keywords: Generative AI, higher education, technology acceptance, UTAUT2, T-EESST, SEM, ANN, habit, hedonic motivation, perceived trust, social sustainability, perceived risk, performance expectancy, effort expectancy.
dc.identifier.other22002683
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/2914
dc.language.isoen
dc.publisherThe British University in Dubai (BUiD)
dc.subjectgenerative AI, higher education, technology acceptance, UTAUT2, T-EESST, SEM, ANN, habit, hedonic motivation, perceived trust, social sustainability, perceived risk, performance expectancy, effort expectancy
dc.titleExamining the Use of Generative Artificial Intelligence in Higher Education and Its Impact on Social Sustainability: An Integrated Model of UTAUT2 and T-EESST
dc.typeDissertation
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