Analyzing the Ecosystem Contexts in the AI Literature Using Latent Dirichlet Allocation and Exploratory Factor Analysis

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Abstract: This study aims to explore the major topics in the recent artificial intelligence (AI) ecosystem literature and identify and categorize those topics into categories of AI ecosystems. The study analyzed 149 publications from Google Scholar using two text mining techniques: latent Dirichlet allocation (LDA) and exploratory factor analysis (EFA). The LDA identified 12 major topics, while the EFA grouped them into six common factors: (a) human resources-driven, (b) technology and algorithm-based, (c) business and entrepreneurial-driven, (d) legal, ethical, privacy, and regulatory framework, (e) innovation-based, and (f) government-supported. The goal is to suggest various AI ecosystems and their best fit for a country or region based on its characteristics and resources. Understanding these types of AI ecosystems can provide valuable insights for government agencies, policymakers, businesses, educational institutions, and other stakeholders to align strategies with resources for developing successful AI-driven ecosystems.

WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 22, 2025, Art. #29

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Kreshnik Vukatana, Elira Hoxha, Arben Asllani, "Analyzing the Ecosystem Contexts in the AI Literature Using Latent Dirichlet Allocation and Exploratory Factor Analysis," WSEAS Transactions on Information Science and Applications, vol. 22, pp. 344-357, 2025, DOI:10.37394/23209.2025.22.29
Kreshnik Vukatana, Elira Hoxha, Arben Asllani. Analyzing the Ecosystem Contexts in the AI Literature Using Latent Dirichlet Allocation and Exploratory Factor Analysis. WSEAS Transactions on Information Science and Applications. 2025;22:344-357. 10.37394/23209.2025.22.29
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