GenAI-Based Carbon Footprint Feedback as a Decision Context: A Two-Layer Extended TPB Model for Renewable Energy Product Adoption
生成AIによる炭素フットプリント・フィードバックを意思決定文脈として：再生可能エネルギー製品採用のための二層拡張TPBモデル (AI 翻訳)
Tuğba Yeğin
🤖 gxceed AI 要約
日本語
本研究は、生成AIによる炭素フットプリント・フィードバック（AI-CFB）が消費者の再生可能エネルギー製品（REPP）購入意図に与える影響を、トルコの841人を対象にPLS-SEMで分析。AI-CFBがTPB次元の先行要因となり、環境関心と技術的自己効力感が動機付け要因として機能。消費者信頼がAI-CFBと購入意図の関係を強化することを示した。
English
This study examines how GenAI-based carbon footprint feedback (AI-CFB) influences consumer purchase intentions for renewable energy-powered products (REPPs). Analyzing 841 Turkish participants with PLS-SEM, it finds AI-CFB as an antecedent to TPB dimensions, with environmental concern and technological self-efficacy as motivators, and consumer trust strengthening the AI-CFB-purchase intention link.
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
日本では、カーボンフットプリント表示の義務化やSSBJ開示が進む中、消費者向けの分かりやすい情報提供が課題。本研究のAI-CFBは、日本の小売・ECプラットフォームが低炭素消費を促す際の示唆を与える。
In the global GX context
Globally, this study contributes to the intersection of AI and sustainable consumption, offering a validated model for using GenAI to make carbon footprint information actionable in e-commerce, relevant for policymakers and platforms aiming to reduce CO2 emissions.
👥 読者別の含意
🔬研究者:Provides a validated AI-TPB model linking GenAI-based carbon feedback to purchase intention, extending sustainable consumption literature.
🏢実務担当者:E-commerce platforms can use AI-CFB to enhance product labeling and influence consumer choices toward renewable products.
🏛政策担当者:Offers evidence for policies promoting low-carbon consumption through AI-assisted carbon labeling.
📄 Abstract（原文）
Carbon footprint information can be a powerful environmental label for encouraging sustainable consumption. However, static environmental labels can be difficult for consumers to interpret during online purchasing decisions. This study examines whether generative artificial intelligence (GenAI)-based carbon footprint feedback (AI-CFB), which transforms static carbon footprint information into decision-relevant feedback, can support consumers’ evaluations and purchase intentions regarding renewable energy-powered products (REPPs). In this context, data from 841 participants in Türkiye were analyzed using PLS-SEM within a two-layer extended TPB model. Results from the first layer confirm AI-CFB as an antecedent of TPB dimensions, which, in turn, are associated with purchase intention toward renewable energy-powered products, with environmental concern and technological self-efficacy serving as motivating factors. The second layer reveals that AI-CFB functions as a decision-support mechanism, while consumer trust strengthens the relationship between AI-CFB and REPP purchase intention. This study contributes a validated AI-TPB model that explains how GenAI-based carbon footprint information is associated with consumer evaluations and moderates the relationships with purchase intention, extending the sustainable consumption literature by integrating GenAI-based systems in e-commerce. The findings offer practical recommendations for policymakers, e-commerce platforms, and carbon footprint experts to encourage low-carbon consumption and reduce CO2 emissions in Türkiye, while providing a foundation for future research at the intersection of sustainable consumption and AI-assisted decision-making.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18168183first seen 2026-08-12 04:56:04
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