GENERATIVE AI ADOPTION AMONG LECTURERS IN HIGHER EDUCATON: A REFINED DOI–TTF FRAMEWORK INCORPORATING SOCIAL AND COGNITIVE DYNAMICS
Abstract
This study develops a refined theoretical model to explain lecturers’ intentions to adopt generative AI in higher education. It extends the combined perspectives of the Diffusion of Innovation (DOI) and Task-Technology Fit (TTF) frameworks. The model includes technological, social, and cognitive factors that shape lecturers’ decisions when adopting new technologies. Data were collected from 246 lecturers across Indonesian universities and analyzed using Structural Equation Modeling (SEM). The results show that lecturers’ perceptions of the benefits of generative AI strongly influence their intention to adopt it. These benefits include its relative advantage and its intelligent functions. The results also show that these perceived benefits shape social expectations in academic environments. However, concerns about privacy and perceived risks weaken these social influences, reducing the likelihood of adoption. The study also finds a gender difference. Male lecturers are more influenced by social factors than female lecturers. This study contributes to theory by refining the DOI–TTF framework. It explains how technological perceptions, cognitive judgments, and social pressures work together to influence adoption decisions. This topic has received limited attention in earlier studies. The practical implications highlight the importance of peer support, clear institutional guidelines, and targeted training to promote responsible and effective adoption of AI in higher education.
Keywords
Generative AI, Higher Education, SEM, Lecturer, Adoption
Ethical Statement
References
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