内生性
公司治理
Nexus(标准)
独创性
企业社会责任
会计
业务
经验证据
管理控制系统
经济
营销
产业组织
控制(管理)
计量经济学
创造力
公共关系
管理
财务
哲学
认识论
嵌入式系统
计算机科学
法学
政治学
作者
Asis Kumar Sahu,Byomakesh Debata,Garima Khanna
标识
DOI:10.1108/srj-02-2024-0132
摘要
Purpose This paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship. Design/methodology/approach To verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components. Findings Empirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases. Practical implications The results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers. Originality/value To the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.
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