Predicting Farmers Adoption Intention of E-Commerce for Organic Produce using Machine Learning Approaches
Reiven M. Bernardino
a
,
Gian Carlo M. Cabuguas
a
,
John Ray D. Cecilio
a
,
Norence Enoch G. EstraÑero
a
,
Alexander A. Hernandez
a
,
Julius P. Claour
a
a College of Computer Studies and Multimedia Arts, FEU Institute of Technology, Manila, Philippines
2025 23rd International Conference on ICT and Knowledge Engineering (ICT&KE), (2025), pp. 1-6
Abstract: Despite the potential for e-commerce to boost productivity and market access for farmers, adoption remains low, particularly in rural areas of developing countries. This study addresses the research gap by predicting farmers' adoption of digital platforms in the National Capital Region, Philippines, using the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Based on a survey of 615 farmers and analysis with various machine learning models, with XGBoost as the top performer, the study found that perceived usefulness, trust, and price value are the most significant factors influencing adoption. Social influence and ease of use also play important roles. The findings provide guidance for policymakers and platform developers, highlighting the need to improve digital literacy, build trust, and ensure affordability to accelerate the digital transformation of the agricultural sector.