Using AI and big data analytics to support entrepreneurial decisions in the digital economy.
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| Titel: | Using AI and big data analytics to support entrepreneurial decisions in the digital economy. |
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| Autoren: | Cao Y; School of Business Management, Jilin University of Finance and Economics, Changchun, 130117, China. 114066@jlufe.edu.cn. |
| Quelle: | Scientific reports [Sci Rep] 2025 Oct 22; Vol. 15 (1), pp. 36933. Date of Electronic Publication: 2025 Oct 22. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH-Schlagworte: | Entrepreneurship* , Big Data* , Artificial Intelligence* , Machine Learning* , Decision Making* , Data Science*, Humans ; Data Analytics |
| Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Despite extensive research on AI's theoretical benefits in entrepreneurship, few studies compare machine learning models' effectiveness using real-world data or address challenges like model interpretability and overfitting. This study investigates how AI-driven big data analytics enhances entrepreneurial decision-making in the digital economy by evaluating four machine learning models-Decision Trees, Random Forest, Gradient Boosting, and Histogram-Based Gradient Boosting-to predict AI service focus. The results reveal that Gradient Boosting outperformed others with a testing R² of 0.9914, identifying company reputation and location as the most influential predictors of AI adoption. These findings challenge assumptions about organizational size's role in digitalization, emphasizing the strategic value of brand and geography. Key limitations include overfitting in Decision Trees and Random Forest, and reliance on static datasets that constrain real-time adaptability. The results demonstrate AI's potential to reduce uncertainty in entrepreneurial strategy, offering actionable insights for market entry and investment decisions. Future research should incorporate real-time data streams and hybrid AI-human frameworks to improve generalizability. (© 2025. The Author(s).) |
| References: | PeerJ Comput Sci. 2021 Apr 14;7:e488. (PMID: 33954253) Adv Mater. 2025 Apr;37(17):e2412006. (PMID: 40091421) |
| Contributed Indexing: | Keywords: AI service focus; AI-driven analytics; Big data analytics; Digital economy; Entrepreneurial decision-making; Machine learning |
| Entry Date(s): | Date Created: 20251022 Date Completed: 20251022 Latest Revision: 20251025 |
| Update Code: | 20251025 |
| PubMed Central ID: | PMC12546928 |
| DOI: | 10.1038/s41598-025-20871-4 |
| PMID: | 41125685 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests.<br />Despite extensive research on AI's theoretical benefits in entrepreneurship, few studies compare machine learning models' effectiveness using real-world data or address challenges like model interpretability and overfitting. This study investigates how AI-driven big data analytics enhances entrepreneurial decision-making in the digital economy by evaluating four machine learning models-Decision Trees, Random Forest, Gradient Boosting, and Histogram-Based Gradient Boosting-to predict AI service focus. The results reveal that Gradient Boosting outperformed others with a testing R² of 0.9914, identifying company reputation and location as the most influential predictors of AI adoption. These findings challenge assumptions about organizational size's role in digitalization, emphasizing the strategic value of brand and geography. Key limitations include overfitting in Decision Trees and Random Forest, and reliance on static datasets that constrain real-time adaptability. The results demonstrate AI's potential to reduce uncertainty in entrepreneurial strategy, offering actionable insights for market entry and investment decisions. Future research should incorporate real-time data streams and hybrid AI-human frameworks to improve generalizability.<br /> (© 2025. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-20871-4 |