This research uses a mixed-methods approach to identify predictors of Generative artificial intelligence (Gen-AI) adoption and usage among academics and educational researchers. It examines drivers and barriers to adoption and usage based on the diffusion of innovation theory (DIT) and the theory of planned behaviour (TPB).
A qualitative investigation was carried out by conducting interviews of academic researchers who used the Gen-AI tools such as ChatGPT. Based on the DIT, TPB and the qualitative analysis results, an integrated model was proposed and tested using survey data collected from academic researchers and analysed using partial least squares-structural equation modelling (PLS-SEM).
The study demonstrated that relative advantages and observability influence attitude and subjective norms, and these in turn impact behavioural intentions. Researchers' perception of relative advantage and their intentions to use Gen-AI tools were found to lead to positive usage behaviours. However, technical limitations and ethical concerns acted as key moderators between attitude and intention and subjective norms and intention, respectively. Mediation effects were also observed.
This study utilised TPB and DIT as its base models, and future research could incorporate additional constructs from other technology adoption theories. The study concentrated on researchers who had utilised Gen-AI tools and subsequently reported significant factors affecting its adoption and usage. Future studies should also consider the perspective of non-users of Gen-AI tools. Further, geographical focus was on researchers in India, future research should broaden the geographical scope.
The academic community must unite to develop ethical guidelines for using Gen-AI tools and plagiarism concerns in research. The focus should be on emphasising the importance of ethical usage of Gen-AI tools. This study highlights the need for establishing standards, tools and comprehensive guidelines for researchers to use such tools transparently within an ethical framework.
The study results can greatly enhance understanding of Gen-AI tools usage among researchers, particularly in light of concerns about its impact on research integrity and potential negative consequences for researchers.
