From Visual Knowledge to Discovery: Leveraging Bibliometric Analysis with Visual Prompt Engineering to Explore AI’s Value in Business Ecosystems

Abstract

Digital transformation is reshaping business ecosystems through advances in artificial intelligence (AI), process automation, enhanced analytics, improved information visualization, and increased innovation. This study examines the impact of AI on ecosystems using traditional bibliometric analysis and a unique approach to processing large volumes of textual data. First, 232 documents published between 2014 and 2024 from the Scopus database were analyzed using Bibliometrix and Biblioshiny to identify influential authors, thematic clusters, and emerging research areas. In the second phase, a text network software called Infranodus was used to scan and analyze the 54 most relevant abstracts from 2023 to 2024, after which the extracted insights were refined using generative AI (genAI). Subsequently, the extracted information was further developed via prompt engineering from visual graphs and ChatGPT, revealing interesting results that demonstrated the potential of genAI in repeatedly conducting research and managing business ecosystems. Ultimately, this study shows a novel way of combining bibliometric data and visual prompt engineering to harness dynamic relations iteratively.