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Major Developments in a Decade of Entrepreneurship Research: A Machine Learning Based Review of the Literature

  • We have analyzed a pool of 37,839 articles published in 4,404 business-related journals in the entrepreneurship research field using a novel literature review approach that is based on machine learning and text data mining. Most papers have been published in the journals ‘Small Business Economics’, ‘International Journal of Entrepreneurship and Small Business’, and ‘Sustainability’ (Switzerland), while the sum of citations is highest in the ‘Journal of Business Venturing’, ‘Entrepreneurship Theory and Practice’, and ‘Small Business Economics’. We derived 29 overarching themes based on 52 identified clusters. The social entrepreneurship, development, innovation, capital, and economy clusters represent the largest ones among those with high thematic clarity. The most discussed clusters measured by the average number of citations per assigned paper are research, orientation, capital, gender, and growth. Clusters with the highest average growth in publications per year are social entrepreneurship, innovation, development, entrepreneurship education, and (business-) models. Measured by the average yearly citation rate per paper, the thematic cluster ‘research’, mostly containing literature studies, received most attention. The MLR allows for an inclusion of a significantly higher number of publications compared to traditional reviews thus providing a comprehensive, descriptive overview of the whole research field.

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Author:Rebecca Off, Kevin Reuther, Christina UngererORCiD, Guido H. BaltesORCiDGND
Parent Title (English):G-Forum Conference 2020; 24. Interdisziplinäre Jahreskonferenz zu Entrepreneurship, Innovation und Mittelstand (28. September bis 02. Oktober 2020), virtual, Förderkreis Gründungs-Forschung e.V. (FGF), Krefeld
Document Type:Conference Proceeding
Year of Publication:2020
Release Date:2021/01/19
Tag:Entrepreneurship; Literature Review; Machine Learning; Text Mining
Institutes:Institut für Strategische Innovation und Technologiemanagement - IST
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoKeine CC-Lizenz - Es gilt der Veröffentlichungsvertrag für Publikationen