Uncovering the landscape of sustainable innovation funding through structural topic modeling

Sustainable innovation acts as a catalyst to address social and environmental challenges while generating economic benefits for the firms. However, the firms aiming to instigate this transformation face challenges in acquiring funds. As the realm of sustainable innovation continues to expand, a robust understanding of its funding mechanisms is necessary. The study uses an unsupervised machine-learning approach to build a precise and comprehensive knowledge of the art. The research paper employs the structural topic modeling framework, a quantitative technique that utilizes advanced statistical methods to derive semantic knowledge from extensive textual data. The study delineates prominent patterns in the domain, indicating an integrated framework that links key components of sustainable innovation and finance while emphasizing the role of green credit policy interventions. The findings from structural topic modeling identify ten distinct topics and propose multiple research prospects for forthcoming investigations on sustainable innovation funding mechanisms. The research acts as a vital tool for investors, policymakers, and entrepreneurs in optimizing resource allocation, designing targeted policies, and aligning business strategies to attract sustainable funding. From a methodological standpoint, this research leverages structural topic modeling as an innovative approach to literature review, thereby enabling an in-depth analysis of a broader range of research outputs and generating more valuable insights than conventional methodologies.