Yeoh Jo AnnZURIDA ISHAKAhmad Subhi Bin ZolkaflyFarina Saffa Binti Mohamad SamsamnunGoh Chuan MengNurul Syafidah Binti Jamil2026-10-142026-10-142025-09-02979833158603410.1109/AiDAS67696.2025.11213858https://dspace-cris.utar.edu.my/handle/123456789/11725This study explores the readiness of organizations within Malaysia's palm oil industry to adopt blockchain technology (BCT) for circular economy (CE) practices. By integrating the Technology-Organisation-Environment (TOE) and Human-Organisation-Technology (HOT) fit frameworks, we developed a comprehensive theoretical model supported by quantitative methods. A dataset of 186 survey responses was analyzed using linear regression, decision tree, and random forest models to identify the relative influence of technological, organizational, environmental, and human factors. The findings show that constructs from the Technology Acceptance Model, especially perceived ease of use, perceived usefulness, and perceived behavioral control, play significant roles in determining adoption readiness. An interactive tool developed using React further enables stakeholders to simulate and visualize adoption scenarios. The study provides a substantial predictive readiness framework by combining machine learning analysis and TOE and HOT frameworks. A decision-support tool was also created to guide the stakeholders through a visualization of adoption situations. © 2025 IEEE.en-USblockchain technologycircular economymachine learningTOEHOT fit frameworksBehavioral researchBlockchainCircular economyDecision support systemsDecision treesIndustrial economicsInformation managementLearning systemsMachine learningRandom forestsBlock-chainBlockchain technologyMachine-learningMalaysiaMalaysiansOil industriesReadiness modelsTechnology organization environmentsTOEHOT fit frameworkPalm oiltext::conference output::conference proceedings::conference paper