Understanding behavioral intentions of UG and PG students in Tier II Indian cities toward AI-technology adoption in higher education: a mixed-methods approach using the SOR model

This study explores the factors influencing AI-Technology adoption among undergraduate and postgraduate students in Tier II cities of Punjab and Haryana, India. A mixed-method approach combined quantitative data from 598 respondents with qualitative insights from personal interviews. Stimulus-Organism-Response (SOR) theory was applied to explain the students’ adoption behavior, and Structural Equation Modeling (SEM) was employed for data analysis. The model demonstrated an explanatory power of 78.5 percent, with social influence emerging as the most critical determinant of behavioral adoption intentions, followed by facilitating conditions. Additionally, the study found that perceived usefulness and students’ attitudes mediated the relationship between perceived credibility and behavioral intentions. The study may lack generalizability, given the diverse backgrounds of students in Tier II cities concerning their understanding of online education and smartphone usage. Future research could incorporate constructs such as risk, learnability, and trialability to enhance the model’s explanatory power. However, this study provides crucial insights for AI companies and policymakers aiming to increase the adoption and utilization of AI-Technology in India. This underscores the importance of building trust in online education and promoting these platforms using targeted marketing strategies. This study contributes significantly to the literature on technology adoption, explicitly focusing on undergraduate and postgraduate students in Tier II Indian cities. This is one of the few empirical studies in this area within the Indian context and offers valuable insights that could help explain technology adoption in online education in other developing countries.