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Mechanism of Enterprise Green Innovation Behavior Considering Coevolution Theory.

Xingwei LiJiachi DaiJinrong HeJingru LiYicheng HuangXiang LiuQiong Shen
Published in: International journal of environmental research and public health (2022)
Enterprise green innovation behavior is necessary for the transformation of enterprises and the enhancement of green development. However, the inconsistency of existing studies on the behavioral mechanism has not been effectively addressed. The purpose of this paper is to reveal a mechanism for enterprise green innovation behavior, taking the coevolutionary theory. Based on the coevolution theory model, this study screened 16 high-quality studies covering 11 countries and regions with 5471 independent samples from six major databases (Web of Science Core Collection (SCIE & SSCI), Science Direct, Springer Link, Wiley, Taylor & Francis, and Sage journals). The included literature was coded and tested. Meta-analysis was used to clarify the direction and intensity of the behavioral antecedent and outcome variables to explore the mechanism of enterprise green innovation behavior. Furthermore, this study also explores the moderating effect of regional heterogeneity on behavior. The results are as follows: (1) The economic, political, social, and technological environments significantly and positively influence enterprise green innovation behavior. (2) Enterprises' green innovation behavior significantly and positively influences environmental performance. (3) Regional heterogeneity can moderate the effects of enterprise green innovation behavior and antecedent and consequence variables. Then, this study proposes countermeasures based on government and enterprise perspectives. This study provides both theoretical and empirical referents for enterprises to better adopt green innovation behaviors and enhance their green development.
Keyphrases
  • systematic review
  • public health
  • healthcare
  • randomized controlled trial
  • single cell
  • machine learning
  • gene expression
  • dna methylation
  • high intensity
  • big data
  • social support
  • deep learning
  • human health