Purpose This empirical research developed and tested a multimodel framework for generative AI adoption in higher education settings in general and in B‐schools in particular. This empirical research integrated the constructs from Task‐Technology Fit, Diffusion of Innovation, Technology–Organization–Environment, Technology Acceptance Model, and the extended Unified Theory of Acceptance and Use of Technology model to develop a thorough approach to understand and adopt generative AI in higher educational settings. Design/Methodology/Approach A structured questionnaire was used to gather data from faculty and students at different B‐schools in Hyderabad as part of the study′s quantitative design. IBM SPSS was used to conduct the exploratory and confirmatory factor analyses in order to identify the structural relationships between the constructs. Structural equation modeling was used to test the hypotheses. Findings The interrelationships among the theoretical constructs confirmed that the Technology Acceptance Model, UTAUT2, Task Technology Fit, and Diffusion of Innovation positively and significantly impact generative AI adoption intentions in the higher education settings. Surprisingly, technology–organization–environment factors are significant but have a negative relationship with the adoption of generative AI. Tools. Generative AI adoption positively and significantly impacts pedagogical transformation. However, trust partially mediated the nexus among adoption intentions and pedagogical transformation. Research Limitations/Implications This empirical research adopted a cross‐sectional approach, collecting data at a single point of time from various sources. This research contributes to technology adoption theory by demonstrating how multiple theoretical frameworks interact in relation to generative AI adoption while providing practical guidance for enhancing implementation through multiple frameworks. Practical Implications Replacing rigid governance with agile frameworks that allow safe AI experimentation while ensuring ethical compliance in the educational institutions. To support meaningful AI integration in teaching, give training that improves both technical skills and moral awareness. Encourage responsible use of AI by encouraging people to think for themselves instead of relying too much on automated outputs. Make rules that can change to keep up with new ideas while also protecting privacy, integrity, and ethics.
Prasad et al. (Thu,) studied this question.
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