A Contextual Framework for Agricultural IoT Adoption: Integrating TAM, Innovation–Decision Process, and Governmental Support in Smart Farming Contexts

Abstract

Agricultural Internet of Things (IoT) adoption in developing contexts remains constrained by a fragmented understanding of cognitive, behavioral, and contextual factors. A contextual framework is developed by integrating the Technology Acceptance Model (TAM), selected components of the Innovation–Decision Process (IDP), and key enabling conditions to explain continued use intention in smart farming environments. Data from 250 Thai agricultural stakeholders with prior exposure to or implementation experience with IoT-based agricultural applications were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that perceived usefulness is the strongest predictor of attitude toward use, which in turn drives continued use intention. Knowledge plays a critical role as a precursor, shaping perceived ease of use, persuasion, and perceived usefulness, underscoring the importance of cognitive readiness even among experienced users. Facilitating conditions significantly support continued use intention, whereas governmental support shows no direct effect. Perceived ease of use does not significantly influence attitude, suggesting that functional value may outweigh usability considerations in post-adoption or experience-based contexts. The framework offers a context-sensitive explanation of agricultural IoT adoption by linking cognitive processes with operational support mechanisms, while the model-fit results indicate that conclusions should be interpreted with appropriate caution.