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The Perception of Climate Change Threats on Intention to Use AI for Sustainable Agriculture Among Thai Farmers

タイの農家における気候変動脅威の認識と持続可能な農業のためのAI利用意図 (AI 翻訳)

Surangkana Wayuparb, Supaporn Kiattisin

Sustainability📚 査読済 / ジャーナル2026-06-05#その他対象セクター: agriculture
DOI: 10.3390/su18115779
原典: https://doi.org/10.3390/su18115779
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🤖 gxceed AI 要約

日本語

本研究は、タイの農家471名を対象に、気候変動脅威の認識が持続可能な農業のためのAI利用意図に与える影響を調査しました。PMT、TPB、TAMを統合したモデルを用いた分析の結果、AI自己効力感、知覚された使いやすさ、有用性、態度、主観的規範が意図に有意な影響を与える一方、脅威の深刻さや脆弱性の認識は影響しないことが明らかになりました。この結果は、農業におけるAI導入戦略やデジタルデバイド対策に示唆を与えます。

English

This study surveys 471 Thai farmers to examine how perceived climate change threats influence intention to use AI for sustainable agriculture. Integrating PMT, TPB, and TAM, PLS-SEM analysis shows that AI self-efficacy, perceived ease of use, usefulness, attitude, and subjective norm significantly affect intention, while perceived severity, vulnerability, and response efficacy do not. Findings inform strategies for AI adoption in agriculture and addressing the AI divide.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

本論文はタイの事例ですが、日本でもスマート農業や気候変動適応技術の導入促進に示唆を与えます。特に、農家の気候変動リスク認識が技術受容に直接結びつかない点は、政策立案や普及戦略において考慮すべき知見です。

In the global GX context

This paper adds to the global literature on climate adaptation technology adoption, particularly in agriculture. While focused on Thailand, the integrated theoretical model and findings on non-significance of threat perception can inform similar studies in other developing regions and support the design of effective extension services.

👥 読者別の含意

🔬研究者:Provides a comprehensive integration of PMT, TPB, and TAM in the context of climate-adaptive AI adoption, useful for future technology acceptance research.

🏢実務担当者:Highlights that ease of use and self-efficacy are key drivers for farmer adoption; training and user-friendly design are critical for deployment.

🏛政策担当者:Suggests that merely communicating climate threats may not suffice; policies should also focus on building digital literacy and demonstrating practical benefits.

📄 Abstract（原文）

Climate change is significantly impacting sustainable agriculture and poses a threat that is likely to motivate farmers to adapt by applying AI technology to reduce risks, costs, expenses, and the impact on greenhouse gas emissions. In other contexts related to climate change, it is important to assess whether perceived climate threats and perceived vulnerability to climate change influence farmers’ intention to use artificial intelligence and whether farmers believe AI is an effective method for addressing climate change, as well as their confidence in its effectiveness. This research examines whether the ability to learn about AI independently affects the intention to use AI, aligning with Protection Motivation Theory. It further evaluates whether perceived ease of use of AI influences perceived usefulness, considering the core factors of perceived ease of use and perceived usefulness based on the Technology Acceptance Model as influencing the intention to use AI. Furthermore, it investigates whether PEOU (Perceived ease of use) and PU (Perceived usefulness) affect attitude (a key factor in the Theory of Planned Behavior) and subjective norm (another core factor in TPB (Theory of Planned Behavior)) influencing farmers’ behavioral adaptation to AI use. Therefore, exploring farmers’ behavioral intention to use AI integrates three theories: PMT (Protection Mo-tivation Theory), TPB, and TAM (Technology Acceptance Model), presenting them as a conceptual model to examine the motivating factors influencing behavioral change. This research surveyed 471 farmers in Thailand using data analyzed from PLS-SEM (Partial Least Squares Structural Equation Mod-eling). The findings revealed that only eight hypotheses (AI self-efficacy, PEOU, PU, ATT (Attitude), and SN (Social Norm)) significantly influenced the intention to use AI, while three hypotheses (PS (Perceived severity), PV (Perceived vulnerability), and RE (Response efficacy)) did not. This will be useful for planning or strategizing AI adoption among farmers, focusing on reducing problems and obstacles from insignificant factors to achieve sustainable agriculture and minimize the impact that may lead to inequality from AI use, or the AI divide, in the future.

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