The impact of shipping practitioners’ low-carbon transition cognition on corporate green practices: a moderated mediation model
海運業従事者の低炭素移行認知が企業のグリーンプラクティスに与える影響：調整媒介モデル (AI 翻訳)
Tianjiao Li, Qiong Chen, Yui-yip Lau
🤖 gxceed AI 要約
日本語
本研究は、海運業従事者の低炭素移行認知（LCTC）が企業のグリーンプラクティス（CGP）に及ぼす影響を検討した。416名の海運業専門家への質問紙調査に基づき、LCTCは直接およびグリーン行動意図とグリーン組織市民行動の連続媒介を通じてCGPに正の影響を与えることが示された。また、職業アイデンティティや政策認知、組織支援の認識が調整効果を持つことが明らかになった。この結果は、強制的な排出削減枠組みの下での心理的・行動的メカニズムを明らかにし、政策立案者や企業管理者に「人的要素」を活用したグリーンプラクティスの推進への示唆を提供する。
English
This study examines how shipping practitioners' low-carbon transition cognition (LCTC) relates to perceived corporate green practices (CGP). Based on a survey of 416 shipping professionals, results show that LCTC positively influences CGP both directly and through the serial mediation of green behavioral intention and green organizational citizenship behavior. Professional identity, policy cognition, and perceived organizational support moderate the relationships. The findings reveal micro-level psychological and behavioral mechanisms that can help policymakers and managers leverage human factors to advance green practices under mandatory emission reduction targets.
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
本研究は国際的な海運業界の低炭素移行に焦点を当てており、日本の海運企業や政策立案者にとって、従業員の認知や行動を促す施策の参考となる。日本のGHG削減目標達成に向けて、人的要因の重要性を示すエビデンスを提供する。
In the global GX context
This study provides micro-level evidence on how employee cognition drives green practices in the shipping industry, a sector facing mandatory decarbonization targets. For global GX context, it highlights the importance of behavioral interventions and organizational support in achieving IMO's 2050 net-zero goals, complementing technological and policy measures.
👥 読者別の含意
🔬研究者:Researchers can use this model to further explore the role of cognition and behavior in low-carbon transitions across industries.
🏢実務担当者:Corporate sustainability managers can leverage findings to design training and incentive programs that enhance green behavioral intention and organizational citizenship.
🏛政策担当者:Policymakers can consider incorporating human factor strategies into shipping decarbonization frameworks to complement regulatory measures.
📄 Abstract（原文）
With the incorporation of the shipping industry’s 2050 net-zero emissions target into the global agenda in the International Maritime Organization (IMO) 2023 Strategy, the industry’s low-carbon transition has moved from policy advocacy to a phase of mandatory implementation. Against this backdrop, the achievement of macro-level emission reduction goals urgently requires support from micro-level individual cognition and behavior. From a micro-level perspective, this study explores the mechanism through which shipping practitioners’ low-carbon transition cognition (LCTC) relates to perceived corporate green practices. Grounded in Social Cognitive Theory (SCT) and Social Information Processing Theory (SIPT), the study constructed and validated a moderated serial mediation model. Based on a questionnaire survey of 416 shipping industry professionals, the results indicate that low-carbon transition cognition is positively associated with corporate green practices (CGP) both directly and indirectly through the serial mediation of green behavioral intention (GBI) and green organizational citizenship behavior (GOCB). Furthermore, professional identity and policy cognition positively moderate the relationship between low-carbon transition cognition and green behavioral intention, while perceived organizational support strengthens the positive effect of green organizational citizenship behavior on corporate green practices. This study presents microlevel psychological and behavioral mechanisms associated with the green transformation within industry-mandated emission reduction frameworks and provides evidence for policy makers and corporate managers to activate the “human factor” and advance green practices integrating compliance with innovation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3389/fmars.2026.1819880first seen 2026-05-15 17:43:21
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