This study investigates the key factors influencing Agripreneurs’ adoption of Climate-Smart Agriculture (CSA) technologies in Karnataka, India, by integrating Diffusion of Innovation (DOI) theory and the Technology Acceptance Model (TAM). A quantitative cross-sectional survey was conducted with 306 agripreneurs across three agro-climatic regions: Bayaluseeme, Malenadu, and Karavali. Structural equation modelling using SmartPLS 4.0 was applied to assess the relationships between constructs, such as Relative Advantage (RA), Compatibility (COMP), Observability (OBS), Trialability (TR), Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Intention to adopt Climate-Smart Agriculture (CSA) technologies (IAT). PU emerged as the strongest predictor of adoption intention, followed by COMP and OBS. While PEOU significantly influenced PU, it showed a direct negative relationship with intention. Although TR was hypothesised to have a positive influence on adoption intention, the results showed no statistically significant effect. Predictive assessment using PLS-Predict confirmed the strong out-of-sample predictive performance of the model. The findings suggest that CSA technology adoption strategies should focus on showcasing visible success stories, ensuring the local COMP of technologies, and highlighting tangible benefits. Training and extension programs should prioritise usefulness over ease, ensuring region-specific and gender-inclusive delivery. This study contributes to the growing literature on sustainable agriculture by applying an integrated TAM–DOI framework in the context of Indian agripreneurs.
Suvarn et al. (Wed,) studied this question.