Introduction to EcoPersona AI: Generative Consumer Personas Based on Carbon-Footprint Sensitivity
EcoPersona AI入門：カーボンフットプリント感度に基づく生成型消費者ペルソナ (AI 翻訳)
Riddhi Mazumdar, Debashish Sakunia, Prof. Biswajita Parida
🤖 gxceed AI 要約
日本語
本稿は、消費者のカーボンフットプリント感度（CFS）に基づくセグメンテーション手法「EcoPersona AI」を提案する。ISO 14067準拠の製品CFPと行動シグナルを組み合わせ、適応型ペルソナを生成し、マーケティング戦略を最適化する。行動科学理論（VBN、TPB、CLT）を統合し、持続可能性データの理解と信頼を高める実践的フレームワークを提供する。
English
This paper introduces EcoPersona AI, a segmentation method classifying consumers by Carbon-Footprint Sensitivity (CFS) using ISO 14067-based product carbon footprints and behavioral signals. It generates adaptive personas grounded in VBN theory, TPB, and CLT to optimize marketing communication and increase comprehension and trust in sustainability data.
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制度化が進んでおり（経済産業省・環境省）、本モデルは企業が消費者行動を理解し、SSBJや有報での非財務情報開示をマーケティングに活用する際の実用的ツールとなる。
In the global GX context
Globally, as carbon labeling proliferates (e.g., EU PEF, UK), this model offers a behavioral science-based approach to segment consumers and tailor communication, bridging sustainability data and marketing effectiveness—relevant for TCFD/ISSB reporting and green claims regulation.
👥 読者別の含意
🔬研究者:Provides a testable framework combining AI with behavioral theories for sustainability communication; useful for experimental studies on eco-labels.
🏢実務担当者:Actionable method to design targeted messaging based on carbon sensitivity, enhancing green product adoption and brand credibility.
🏛政策担当者:Highlights how consumer segmentation can improve effectiveness of carbon labeling policies and drive demand-side decarbonization.
📄 Abstract（原文）
Consumers today encounter a growing volume of sustainability-related marketing information, including carbon footprint labels, eco-scores, and product transparency dashboards. Yet, despite the global emphasis on sustainability, the behavioral impact of such information remains inconsistent. Many consumers acknowledge environmental issues but vary widely in how deeply they process, trust, or act on sustainability claims. This inconsistency highlights the need for a segmentation approach that goes beyond traditional demographic or psychographic boundaries. This paper introduces EcoPersona AI - designed to classify consumers by their Carbon-Footprint Sensitivity (CFS)—a measure of how individuals perceive and respond to product-level sustainability data. The model uses verified environmental metrics such as ISO 14067-based product carbon footprints, coupled with behavioral interaction signals, to develop adaptive personas that guide message framing, creative design, and communication strategy. Grounded in the Value-Belief-Norm (VBN) theory, the Theory of Planned Behavior (TPB), and Construal Level Theory (CLT), EcoPersona AI seeks to humanize the use of artificial intelligence in marketing by connecting behavioral science with measurable sustainability outcomes. The model aims to help mid-sized and large product-based businesses translate verified impact data into effective and credible communication strategies that increase comprehension, trust, and green product adoption.
🔗 Provenance — このレコードを発見したソース
- openalex https://doaj.org/article/5d6c5c13cdc444a391b2e76fa3e0ce6dfirst seen 2026-06-30 05:15:25
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。