The Impact of Institutional Pressures on Firms' Low‐Carbon Behaviors: A Configuration Approach
制度的圧力が企業の低炭素行動に与える影響：コンフィギュレーション・アプローチ (AI 翻訳)
Jiamin Zhang, Yang Qian, Christina W.Y. Wong, Jinjie Xue
🤖 gxceed AI 要約
日本語
本研究は、制度的圧力の構成が企業の低炭素行動（ハード・ソフト）に与える影響と、AI能力の調整効果を検証。中国A株上場企業のパネルデータ（2007-2022）を用い、クラスター分析、ANOVA、OLS回帰を実施。3つの企業クラスターを特定し、圧力が強いほどハードな低炭素行動が促進される一方、ソフトな行動は抑制されることを発見。AI能力がこの関係を調整することを示した。
English
This study examines how configurations of institutional pressures affect firms' low-carbon behaviors (hard and soft) and the moderating role of AI capability. Using panel data from Chinese A-share listed firms (2007-2022) with cluster analysis, ANOVA, and OLS regressions, it identifies three firm clusters. Stronger pressures promote hard low-carbon behaviors but reduce soft ones; AI capability enhances the positive effect on hard behaviors and mitigates the negative effect on soft behaviors.
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SSBJ開示義務化が進む中、制度圧力と企業行動の関係は重要。AI能力が低炭素行動を促進する可能性は、日本企業のデジタル活用戦略に示唆を与える。ただし、中国特有の制度環境を考慮する必要がある。
In the global GX context
This study contributes to global understanding of how institutional pressures (e.g., TCFD, ISSB) shape corporate low-carbon behaviors, highlighting the role of AI capability as a moderator. It offers insights for policymakers and firms in aligning AI strategies with sustainability goals, relevant to global disclosure frameworks.
👥 読者別の含意
🔬研究者:Provides empirical evidence on institutional pressure configurations and AI's moderating role, extending institutional theory in environmental behavior.
🏢実務担当者:Suggests that investing in AI capabilities can help firms respond to institutional pressures and enhance low-carbon actions.
🏛政策担当者:Highlights that policy design should consider firm heterogeneity and the potential of AI to support low-carbon transitions.
📄 Abstract（原文）
ABSTRACT Although the institutional environment is recognized as crucial for firms' low‐carbon development, how configurations of institutional pressures collectively shape firms' low‐carbon behaviors remains underexplored. Drawing on institutional theory, configuration theory, and resource‐based view, this study investigated the impact of institutional pressure configurations on firms' low‐carbon behaviors and the moderating role of artificial intelligence (AI) capability. The study used cluster analysis, analysis of variance (ANOVA), and OLS regressions with panel data from Chinese A‐share listed firms (2007–2022) to verify our research propositions. The results reveal three distinct firm clusters based on institutional pressure profiles. These configurations exert varying effects on both hard and soft low‐carbon behaviors. Generally, more intense pressures promote greater hard low‐carbon behaviors; conversely, high‐pressure firms are associated with significantly fewer soft low‐carbon behaviors compared to low‐pressure firms. Furthermore, a firm's AI capability enhances the positive influence of more intense institutional pressure on the adoption of hard low‐carbon behaviors and mitigates the negative influence of high institutional pressure on the adoption of soft low‐carbon behaviors. This study extends institutional perspectives on firms' environmental behaviors and provides actionable guidance for low‐carbon management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/bse.71377first seen 2026-08-13 05:04:50
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