Decoding drone adoption in agriculture:a comparative analysis of behavioral modelsOA

The adoption of drone technology in agriculture holds transformative potential,offering solutions to improve efficiency,productivity,and sustainability.Understanding the factors that drive or hinder this adoption is critical for leveraging these benefits.This study evaluates the adoption of drone technology among farmers by comparing three predictive models:the Technology Acceptance Model(TAM),the Theory of Planned Behavior(TPB),the Communicative Learning Model(CLM),and a Unified Adoption Behavior Model(UABM)combining the three.A survey was administered to 203 farmers of Fars province,Iran,chosen through stratified random sampling.Data were gathered using a structured questionnaire,and analyzed with SmartPLS3 and SPSS26.The convergent validity,discriminant validity,and reliability of the variables were assessed and confirmed using SmartPLS3.T-test and discriminant analysis was employed to assess the models’predictive power and their accuracy in classifying drone adopters and non-adopters.The results revealed significant differences between the two groups in variables such as behavioral intention,perceived ease of use,and access to communication channels,with adopters consistently scoring higher than non-adopters.Based on discriminant analysis,the UABM demonstrated superior predictive power,with a classification accuracy of 91.2%,surpassing TAM,TPB and CLM.Behavioral intention and perceived behavioral control emerged as the most influential factors driving adoption.The findings highlight the importance of addressing resource and confidence barriers among non-adopters and leveraging peer influence and educational programs to foster adoption.The study contributes to a deeper understanding of technology adoption behaviors,particularly in the context of agricultural innovation.It provides practical insights to enhance the adoption and effective utilization of drones in agricultural practices,addressing both theoretical and practical dimensions of this emerging technology.