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グリーンな選択がサービス障害に直面するとき：航空旅客の低炭素参加への障壁とサービスリカバリー

When Green Choices Encounter Service Failures: Barriers to Air Passengers’ Low-Carbon Participation and Service Recovery (原題)

Dingyao Yu, Huali Cai, 李洪涛, Chunlei Shi, Chunjia Han, Zhen Tu, Chenxi Zheng, Yingcheng Xu, Cui Tian

Systems📚 査読済 / ジャーナル2026-10-26#AI×ESGOrigin: CN経営インパクト: 調達リスク対象セクター: transport
DOI: 10.3390/systems14111357
原典: https://doi.org/10.3390/systems14111357

🤖 gxceed AI 要約

日本語

グリーンフライトをサービスシステムとして捉え、中国民航局の苦情1161件をWord2VecとSTMで分析した研究。参加障壁として選択制限、情報開示不足、規則と旅客権益の不均衡、報酬未履行などを特定し、硬直した規則が最大の課題と示す。低炭素サービスの定着は環境意欲だけでなく、参加コストと不確実性の低減にかかっていると論じる。

English

Using 1,161 passenger complaints from China's civil aviation regulator, this study applies Word2Vec and Structural Topic Modeling to identify barriers to low-carbon green-flight participation. Key failures include restricted service choice, poor information disclosure, rigid rules, and unfulfilled rewards, with inflexible modification rules the most prominent. It argues sustainable service adoption depends on reducing participation costs and uncertainty, not just environmental willingness.

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

日本では航空・運輸セクターの脱炭素と顧客接点の質が問われる局面であり、旅客参加型の低炭素インセンティブ設計や情報開示の実務に示唆を与える。SSBJ開示とは直接連動しないが、顧客向けGX施策の設計・苦情対応ガバナンスの参考になる。

In the global GX context

Adds a consumer-facing, service-system lens to the decarbonization literature, showing that green incentive schemes fail without robust disclosure and recovery mechanisms. Relevant to global debates on the credibility and usability of voluntary low-carbon programs and customer-facing transition claims.

👥 読者別の含意

🔬研究者:Provides a service-system and NLP-based framework linking service failure, passenger demand, and recovery for participatory sustainable services.

🏢実務担当者:Airlines and travel firms can use the failure taxonomy to design clearer green-option rules, disclosure, and reward fulfillment.

🏛政策担当者:Regulators can note that rigid rules and weak disclosure undermine voluntary low-carbon participation and warrant consumer-protection guidance.

📄 Abstract（原文）

Green flight service uses incentive mechanisms to encourage passengers to participate in low-carbon value co-creation. However, willingness to make green choices does not necessarily translate into smooth participation, as service failures during implementation may create barriers to passenger participation. Drawing on Service System Theory, this study conceptualizes green flight as a service system involving passengers, airlines, service rules, and incentive mechanisms. Using 1161 passenger complaints collected from the Service Quality Supervision Platform of the Civil Aviation Administration of China between January 2021 and June 2025, Word2Vec-based semantic expansion and the Structural Topic Model (STM) were applied to identify service failures and corresponding recovery demands. The results show that major participation barriers include restricted service choice, inadequate information disclosure, imbalances between service rules and passenger rights and interests, service execution flaws, and failures in reward fulfillment and service response. Rigid rules restricting passengers’ ability to modify green flight options under irregular flight conditions represent the most prominent issue. These problems also show dynamic changes over time. Correspondingly, passengers’ recovery demands range from immediate remedies to systematic optimization, including responsibility-taking and rule improvement, compensation, explanations and notification, timely processing, reward fulfillment, service attitude improvement, and stronger internal governance. The findings indicate that sustainable service adoption depends not only on environmental willingness but also on whether the service system reduces participation costs and uncertainty. This study develops a “service failure–passenger demand–service recovery” framework for passenger-participatory sustainable services.

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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。