Decoding user readiness for sustainable AI adoption: A behavioural approach through technology readiness segmentation (TRS) - Munmun Ghosh (Sustainable Futures, 2025)
Artificial intelligence (AI) is rapidly transforming how individuals and organisations interact with technology in their everyday lives. AI systems' adaptive, intelligent, and autonomous capabilities significantly differ from those of traditional technological innovations. As AI-built systems increasingly take over our everyday lives, we must understand and gauge the factors influencing their adoption, ensuring an inclusive and sustainable technology uptake.The study examines the key drivers influencing the adoption of AI-based technologies and investigates how user segmentation, based on Technology Readiness Segmentation (TRS), moderates the adoption process. Considering that AI technologies behave differently from traditional and non-intelligent technologies, the study employs a descriptive research design and uses multilevel Structural Equation Modelling (SEM) to establish and validate the proposed framework. Data were gathered through structured surveys from 321 respondents from diverse backgrounds. The results indicate that social influence, intrinsic motivation, and effort expectancy drive behavioural intention to adopt AI technologies. However, the nuanced moderating role of TRS segments – Explorers, Pioneers, and Skeptics- provides additional insight into the users' adoption behaviour. Establishing and validating the user segments as crucial moderators in AI technology adoption will have significant implications for AI developers, marketers, researchers and policymakers. The study’s results will help them develop sustainable and inclusive AI adoption strategies customised and tailored to user profiles and readiness.