Mapping Innovation Domains Using Topic Modeling

Abstract

Innovation plays a crucial role in driving economic growth and development, improving competitiveness, and creating new opportunities for individuals and businesses. It also  can lead to higher living standards and a better quality of life for citizens by addressing some of the world's most pressing challenges, such as climate change, health crises, and poverty.  Recently, artificial intelligence (AI) appeared as one of the most influential factors in everyday life. It also is revolutionizing innovation by acting as a driving force in R&D, automating tasks and by analyzing data many times faster than humans. Use of AI technologies is accelerating the creation of new products and business models and radically transforming decision-making processes. AI could compel companies to restructure the whole innovation process in response to rapid technological advancement and the rearrangement of human resources. Society at large sees AI as a representation of unlimited possibilities. 

AI is increasingly adopted by organizations to innovate, and this is even more reflected in scholarly work. To illustrate, assess and map research at the intersection of AI and innovation, we performed research of published work indexed in the Elsevier Scopus databases.

This study examines thousands of publications, indexed in the SCOPUS database, related to innovation processes (analyzing titles, abstracts, and keywords), employing BERTopic modeling and bipartite graph analysis to identify emerging patterns. The research uncovers eleven distinct innovation technologies and systematically maps their relationships with various AI methods. The results highlight that innovations in relation to AI methods are not isolated but broadly integrated across domains, suggesting the importance of cross-departmental collaboration to maximize system-wide benefits.