IoT in Agrotourism: A SEM-Neural Analysis of Smart Farming Adoption and Impacts

As agrotourism continues to thrive as a sustainable and immersive experience, the adoption of smart farming technologies, encompassing the Internet of Things (IoT), artificial intelligence, robotics, and data analytics, emerges as a pivotal driver in elevating agricultural practices and enhancing visitor experiences. This study examines the fundamental factors influencing farmers’ intentions to embrace these cutting-edge technologies, shedding light on their potential implications for agrotourism development. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research rigorously investigates the impact of performance expectancy, effort expectancy, subjective norms, and personal innovativeness on the adoption process. Employing a robust multi-methods approach, we carefully analyze data gathered from 197 planters, employing both Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis techniques. The compelling results underscore the profound significance of these predictors in shaping planters’ intentions to adopt smart farming technologies. Notably, performance expectancy, effort expectancy, personal innovativeness, and subjective norms emerge as compelling determinants of adoption intention. Remarkably, in both SEM-Neural analyses, subjective norms emerge as the most influential predictor. The findings emphasize seamless integration for sustainable agrotourism and offer actionable insights to practitioners and policymakers. This transformative research fosters a harmonious relationship between agriculture, tourism, and technology, elevating the agrotourism experience while promoting environmental sustainability.