Managerial myopia and its barrier to green innovation in high-pollution enterprises: A machine learning approach

Abstract

Green technology innovation has become a vital remedy in response to the world's growing ecological problems and the urgent need for sustainable development. However, businesses are sometimes discouraged from undertaking such efforts due to the significant investments needed and the long, unpredictable innovation cycles. This study examines how managerial shortsightedness affects green innovation in highly polluting companies listed between 2007 and 2020 in China's Shanghai and Shenzhen A-share markets. The study develops measures of management shortsightedness using machine learning and text analysis tools. Then, it uses econometric techniques, such as an OLS model and a Heckman two-stage model, to assess its effect on green innovation. Essential conclusions include: Managers demonstrate greater short-term inclinations when terms representing a "short-term vision" are frequently mentioned in management discussion and analysis (MD&A) reports. In highly polluting businesses, managerial shortsightedness severely impedes green innovation initiatives. Businesses with less robust internal control systems experience this inhibiting effect more intensely. These findings provide helpful information for Policymakers, Directors, Government, and Financial advisors to assist green technology projects through tailored legislation and incentives while enhancing our understanding of the relationship between managerial myopia and green innovation. This study intends to investigate the complex connections between text analysis, machine learning, managerial myopia, green innovation, and internal control levels. The project aims to advance knowledge of how businesses can overcome obstacles to sustainable innovation and use their internal resources to achieve long-term environmental advantages by combining these factors.