Entrepreneurship and Machine Learning: A Review

Abstract

Machine learning (ML) is reshaping the entrepreneurial landscape, empowering startups to gain a competitive edge, develop innovative products and services, and accelerate business growth. This study conducts a review of the emerging field of ML entrepreneurship, leveraging through the pre-recording systematic reviews and meta-analysis (PRISMA) methodology to analyse publications from Web of Science (WoS) and SCOPUS. The author keywords demonstrate that the term ‘machine learning’ is the most frequently used keyword, followed by ‘entrepreneurship’, ‘performance’, ‘innovation’, ‘artificial intelligence’, ‘management’, ‘impact’, ‘technology’, ‘deep learning’, and ‘big data’. Three key themes emerged, namely start-ups, technology, and education. The prominence of the technology theme in scholarly discourse highlights the pivotal role of ML in entrepreneurial innovation. ML is enabling startups to capitalize on emerging market opportunities, automate tasks, make informed decisions based on real-time data insights, and ease and enable other tasks more efficiently and effectively. By providing a comprehensive overview of the ML entrepreneurship landscape, this study offers valuable insights for all readers. Researchers and scholars can leverage these findings to identify emerging research areas, contribute to the advancement of knowledge in this field, develop new theories and models of ML entrepreneurship.