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<p>This research examines the factors that affect the acceptance of smart agricultural technology in Thailand. It does this by examining organizational and technological flexibility. The study uses a combined model that includes UTAUT, the IS Success Model, and TPB. A semi-structured online questionnaire gathered data from 500 Thai farmers and agriculture stakeholders. Researchers analyzed the data using PLS-SEM to test nine hypotheses about the relationships between acceptance factors and user satisfaction. Six of the nine hypotheses received statistical backing (<i>p</i> &lt; 0.001). Information quality (<i>β</i> = 0.416) and system quality (<i>β </i>= 0.396) were the strongest predictors of user satisfaction. They were followed by attitude toward behavior (<i>β</i> = 0.299), performance expectancy (<i>β</i> = 0.200), and effort expectancy (β = 0.166). User satisfaction had a significant effect on technology acceptance (β = 0.394). Perceived behavioral control, social influence, and facilitating conditions did not show any significant impact. Technology developers should focus on improving system and information quality while including flexibility features to boost acceptance. Policymakers should work on promoting positive attitudes and performance expectations instead of social factors. This study adds to the literature on flexible systems management by showing that the flexibility aspects within quality measures are key factors in adoption across diverse agricultural settings.</p>

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Integrating Theories into a Model for the Acceptance of Internet of Things Technology for Developing Smart Farm Platforms: A Study of Readiness in the Agricultural Industry

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Siwakorn Banluesapy,
  • Mahasak Ketcham,
  • Montean Rattanasiriwongwut

摘要

This research examines the factors that affect the acceptance of smart agricultural technology in Thailand. It does this by examining organizational and technological flexibility. The study uses a combined model that includes UTAUT, the IS Success Model, and TPB. A semi-structured online questionnaire gathered data from 500 Thai farmers and agriculture stakeholders. Researchers analyzed the data using PLS-SEM to test nine hypotheses about the relationships between acceptance factors and user satisfaction. Six of the nine hypotheses received statistical backing (p < 0.001). Information quality (β = 0.416) and system quality (β = 0.396) were the strongest predictors of user satisfaction. They were followed by attitude toward behavior (β = 0.299), performance expectancy (β = 0.200), and effort expectancy (β = 0.166). User satisfaction had a significant effect on technology acceptance (β = 0.394). Perceived behavioral control, social influence, and facilitating conditions did not show any significant impact. Technology developers should focus on improving system and information quality while including flexibility features to boost acceptance. Policymakers should work on promoting positive attitudes and performance expectations instead of social factors. This study adds to the literature on flexible systems management by showing that the flexibility aspects within quality measures are key factors in adoption across diverse agricultural settings.