Adoption of Artificial Intelligence-based e-Learning Platforms in Institutes of Excellence: extending UTUAT with Technophobia, Technophilia, Content Quality, and Functional Quality.

Published: 19 October 2023| Version 1 | DOI: 10.17632/n69rdbph73.1
Sougato Das


This research study aims to enhance our understanding of the factors influencing the adoption of AI-ELP by extending the Unified Theory of Acceptance and Use of Technology (UTAUT). The extended model incorporates technophobia, technophilia, content quality, and functional quality. Through a comprehensive review of the existing literature, this study proposes an augmented conceptual framework that considers the unique psychological tendencies of users toward technology (technophobia and technophilia) and the quality aspects of the AI-ELP platform (content quality and functional quality). By incorporating these additional factors, this research provides a more holistic understanding of users’ acceptance and use of AI-ELP. This study employed quantitative research methods, including surveys and statistical analysis, to collect and analyze data from research scholars at IIT, Kharagpur. These findings will contribute to theoretical advancements and practical implications by shedding light on the complex dynamics of adopting AI-ELP. The research outcomes are expected to guide educational practitioners and policymakers in developing strategies to address user concerns, enhance the quality of content and platform functionality, and promote the effective adoption and use of AI-ELP in educational settings.


Steps to reproduce

The questionnaire used a five-point Likert scale (strongly disagree to strongly agree) to gage opinions on AI-CRM implementation, comprising 42 questions. The questionnaire was administered to research scholars at IIT Kharagpur. Data collection occurred over a two-month period, from March 2023 to April 2023, during which the authors personally distributed the questionnaires and ensured accurate recording of responses. We used partial least squares structural equation modeling (PLS-SEM) as our analysis method. The PLS-SEM was chosen because of its adaptability to complicated and exploratory research models. PLS-SEM provided the flexibility needed to investigate the numerous links between many variables within the conceptual framework as we delved into the complicated dynamics of technology adoption. We used SMARTPLS4 software to perform the PLS-SEM analysis.. It is appropriate for real-world research because of its robustness with non-normal and small-sample data. Regular updates and an active user community keep it at the cutting-edge of PLS-SEM methodology. SmartPLS4 is a dependable, accessible, and effective tool for PLS-SEM analysis that researchers of all skill levels use.


Artificial Intelligence, Technology Adoption