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Research on the Diffusion Mechanism of Green Technology Innovation Based on Enterprise Perception.

Authors :
Mi, Jie
Yao, Chuanpeng
Zhao, Xiaoyang
Li, Fei
Source :
Computational Economics; May2024, Vol. 63 Issue 5, p1981-2010, 30p
Publication Year :
2024

Abstract

To address the increasingly serious global environmental issues and achieve sustainable socioeconomic development, the rapid and widespread diffusion and adoption of green technology innovation throughout interfirm networks is necessary. Previous studies have shown that diffusion strategies targeting key players can increase the adoption and diffusion of innovations. However, these studies have not taken into account the impact of firms' perceptions of the internal drive and external pressure of diffusion strategies targeting key players. Therefore, in this paper we develop a multiagent computing experimental model that, by adjusting a variety of parameters in the model, analyses the impact of firms' differing perceptions of internal drive and external pressure on diffusion strategies targeting key players in various scenarios. The results show that a firm's perception of internal drive and external pressure affect the performance of the diffusion strategies targeting key players in specific networks. In cases featuring high barriers to adoption, targeting adopter neighbours is the better communication strategy, regardless of whether the firm's perception is "low internal drive, high external pressure" or "high internal drive, low external pressure". In cases featuring low barriers to adoption, targeting firms with high horizontal influence spreads the adoption of green technology innovations more rapidly when the firm's perception is "high internal drive, low external pressure". This provides a theoretical basis for some policy makers and companies committed to green development to facilitate the accelerated adoption and diffusion of green technology innovations to generate positive social value. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09277099
Volume :
63
Issue :
5
Database :
Complementary Index
Journal :
Computational Economics
Publication Type :
Academic Journal
Accession number :
177714292
Full Text :
https://doi.org/10.1007/s10614-023-10391-6