Machine Learning Trends in Improving Startup Business Efficiency: A Bibliometric Review
DOI:
https://doi.org/10.58812/wsis.v4i06.2936Keywords:
Machine Learning, Startup Efficiency, Bibliometric Analysis, Entrepreneurship, VOSviewerAbstract
The paper performs bibliometric analysis of studies on the trends in ML in increasing business efficiency of startups. Based on the data obtained from major scientific databases and analyzed by VOSviewer, the study examines the intellectual structure, thematic development, and global collaboration patterns in the field under investigation. In particular, the results show that the concept of machine learning is the main intellectual node that is related to the concepts like startups, investments, entrepreneurship, decision-making, forecasting, and data innovations. As for temporal dynamics, there is an evolution of basic statistical and predictive modeling methods to more sophisticated tools, which involve artificial intelligence-based decision support systems, innovation ecosystems, and scalability of startups. Finally, citation analysis detects key papers devoted to implementation of AI, success predictions of startups, and intelligent systems for validation of business models. Global collaboration patterns demonstrate a semi-centralized structure, with the US and India being the main nodes in the network.
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