HUMAN RESOURCE MANAGEMENT IN THE ERA OF GREEN AND SUSTAINABLE BUSINESS PRACTICES: A STUDY ON EMPLOYEE ENGAGEMENT, ETHICAL LEADERSHIP, AND ENVIRONMENTAL RESPONSIBILITY
グリーンで持続可能なビジネス実践の時代における人的資源管理:従業員エンゲージメント、倫理的リーダーシップ、環境責任に関する研究 (AI 翻訳)
Manoj P. K, Sharmila Subramanian, Paras Gupta, Suby Baby, AH Alkassem, Puja Baliarsingh, Trinkul Kalita
🤖 gxceed AI 要約
日本語
本研究は、持続可能なビジネス実践への移行における人事管理(HRM)の役割を調査。倫理的リーダーシップと従業員エンゲージメントが環境責任に与える影響を、GRIやCDPなどのデータを用いて分析。結果、従業員エンゲージメントがエミッション削減などの成果に強く寄与し、倫理的リーダーシップの効果を媒介することが示された。グリーンHRM実践の重要性を提言。
English
This study examines the role of HRM in enabling sustainable business practices. Using data from GRI, CDP, and others, it finds that employee engagement strongly predicts emissions reduction and waste management, mediating the effect of ethical leadership. It recommends integrating green HRM into governance.
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
日本企業においても、人的資本経営とESG統合が進む中、本論文は従業員エンゲージメントが環境パフォーマンス向上に果たす実証的役割を示しており、SSBJ対応や統合報告書の人的資本開示に示唆を与える。
In the global GX context
Globally, this paper reinforces the linkage between HRM and ESG outcomes, supporting frameworks like ISSB and CSRD that encourage disclosure of workforce-related sustainability metrics.
👥 読者別の含意
🔬研究者:Empirical evidence on employee engagement as a mediator between ethical leadership and environmental outcomes, using SET and RBV.
🏢実務担当者:Insights on designing training and incentive systems that drive emissions reduction and waste management.
🏛政策担当者:Recommendations for linking HRM to ESG standards and SDGs, applicable to regulatory frameworks.
📄 Abstract(原文)
In the era of sustainable development, organizations are increasingly expected to embed environmental and social responsibility within their strategies. Human Resource Management (HRM) serves as a critical enabler of this transformation, with ethical leadership and employee engagement emerging as key drivers of sustainable business practices. This study adopts an exploratory mixed-methods design grounded in secondary data analysis. Data were drawn from internationally recognized repositories such as the Global Reporting Initiative (GRI), Carbon Disclosure Project (CDP), World Bank Enterprise Surveys, and International Labour Organization (ILO) reports. Quantitative indicators—including emissions reduction, energy efficiency, training adoption, and ESG-linked governance—were analyzed using descriptive and comparative statistics, while qualitative content analysis was applied to sustainability disclosures to assess leadership and engagement mechanisms. The findings show that ethical leadership is increasingly acknowledged in corporate governance but remains unevenly institutionalized, with limited board oversight and weak linkage of executive pay to ESG outcomes. Employee engagement practices, particularly formal training and incentive systems, were found to strongly predict substantive sustainability outcomes such as emissions reduction and waste management. Mediation patterns confirmed that engagement acts as the primary mechanism through which ethical leadership translates into environmental responsibility. These results align with Social Exchange Theory (SET) and the Resource-Based View (RBV), positioning employee engagement as a critical intangible resource for advancing sustainability. This study advances HRM and sustainability literature by integrating ethical leadership, employee engagement, and environmental responsibility into a unified framework. It emphasizes that organizations can achieve stronger sustainability outcomes by embedding leadership integrity, structured engagement mechanisms, and green HRM practices into their governance systems. Policy- level interventions linking HRM to ESG standards and the Sustainable Development Goals (SDGs) are recommended to reinforce accountability and drive systemic change.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.5281/zenodo.19636707first seen 2026-06-11 05:19:45 · last seen 2026-06-16 04:37:47
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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。