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Decoding sustainable entrepreneurship current research and future direction through application of machine learning-based structured topic modeling on intellectual corpus

  • Muhammad Hassan Abbas
  • , Mehmet Bullut
  • , Hassnian Ali*
  • *Corresponding author for this work
  • Istanbul Sabahattin Zaim University

Research output: Contribution to journal › Article › peer-review

Abstract

This study leverages structured topic modeling (STM) to decode the expansive intellectual corpus on sustainable entrepreneurship, utilizing a dataset of 363 peer-reviewed articles from Scopus over a decade. Focused on "sustainable entrepreneurship" and related terms, the STM method integrated document-specific metadata to enhance the analysis of thematic developments. The findings revealed ten distinct topics, such as innovation in firm performance, sustainability in business models, and the role of education in sustainable intentions, highlighting the interplay between these themes and their evolution. This research identifies key thematic areas and examines the influence of source titles and publication years on topic prevalence, indicating shifts in academic focus and identifying emerging trends. The study's implications suggest integrating sustainability into core business and educational strategies, enhancing the understanding of sustainable entrepreneurship's dynamic nature, and providing a foundation for future scholarly and practical efforts.Cette & eacute;tude s'appuie sur la mod & eacute;lisation structur & eacute;e des sujets (STM) pour d & eacute;coder le vaste corpus intellectuel sur l'entrepreneuriat durable, en utilisant un ensemble de donn & eacute;es de 363 articles & eacute;valu & eacute;s par des pairs provenant de Scopus sur une d & eacute;cennie. Ax & eacute;e sur l' entrepreneuriat durable >> et les termes connexes, la m & eacute;thode STM a int & eacute;gr & eacute; des m & eacute;tadonn & eacute;es sp & eacute;cifiques aux documents afin d'am & eacute;liorer l'analyse des d & eacute;veloppements th & eacute;matiques. Les r & eacute;sultats ont r & eacute;v & eacute;l & eacute; dix sujets distincts, tels que l'innovation dans les performances des entreprises, la durabilit & eacute; dans les mod & egrave;les d'entreprise et le r & ocirc;le de l'& eacute;ducation dans les intentions durables, soulignant l'interaction entre ces th & egrave;mes et leur & eacute;volution. Cette recherche identifie des domaines th & eacute;matiques cl & eacute;s et examine l'influence des titres des sources et des ann & eacute;es de publication sur la pr & eacute;valence des sujets, indiquant des changements dans l'orientation acad & eacute;mique et identifiant les tendances & eacute;mergentes. Les implications de l'& eacute;tude sugg & egrave;rent d'int & eacute;grer la durabilit & eacute; dans les strat & eacute;gies commerciales et & eacute;ducatives de base, d'am & eacute;liorer la compr & eacute;hension de la nature dynamique de l'entrepreneuriat durable et de fournir une base pour les futurs efforts scientifiques et pratiques.
Original languageEnglish
Pages (from-to)466-502
Number of pages37
JournalJournal of International Entrepreneurship
Volume24
Issue number2
Early online dateFeb 2025
DOIs
Publication statusPublished - Jun 2026

Keywords

  • Apprentissage automatique
  • Avenir
  • C63
  • Entrepreneuriat durable
  • Future
  • L26
  • L31
  • Machine learning
  • Mod & eacute;lisation structurelle et th & eacute;matique
  • Q56
  • Structure topic modeling
  • Sustainable entrepreneurship

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