Adoption of Agri-Tech and Precision Farming Among Entrepreneurs in Dakshina Kannada: An Extended TAM Approach
DOI:
https://doi.org/10.70112/arss-2025.14.1.4307Keywords:
Technology Acceptance Model (TAM), Agricultural Technology Adoption, Behavioral Intention (BI), Perceived Usefulness (PU), Technological AwarenessAbstract
Improving productivity and sustainability in agriculture depends on understanding the factors influencing technology adoption. The Technology Acceptance Model (TAM), particularly its core constructs-Attitude Toward Use (ATU), Perceived Ease of Use (PEOU), and Perceived Usefulness (PU)-has been widely used to explain user adoption behavior. However, few studies, particularly in rural and agri-tech contexts, have integrated contextual factors such as farm size, technological awareness, and gender into this framework. This study aims to examine how ATU, PEOU, and PU influence Behavioral Intention (BI) to adopt agricultural technology. It also seeks to determine whether BI is affected by contextual and demographic factors, including farm size, technological knowledge, and gender. A structured survey was conducted among 120 agricultural respondents. Structural equation modeling (SEM) was used to test the interrelationships among TAM constructs. Independent samples t-tests and one-way ANOVA were employed to examine group-based differences in BI with respect to gender, farm size, and technological awareness. PU emerged as the strongest predictor of BI, while ATU was also a significant positive influence. PEOU contributed indirectly by enhancing both ATU and PU. ATU partially mediated the relationship between PU and BI. Although gender differences were not statistically significant, BI varied significantly across groups based on farm size and technological knowledge. The findings highlight the importance of incorporating contextual variables and reaffirm the robustness of TAM in explaining agri-tech adoption. Tailored strategies that enhance technological awareness and demonstrate clear benefits to diverse user groups can improve adoption rates. These insights are valuable for policymakers, technology developers, and educators seeking to bridge gaps in the diffusion of agricultural technologies.
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