AI-Driven Big Data Analytics Enhances Entrepreneurial Decision-Making in Digital Economy

Strategic Insights -

Researchers from Jilin University of Finance and Economics conducted a study to investigate how AI-driven big data analytics enhances entrepreneurial decision-making in the digital economy. The study evaluated four machine learning models to predict AI service focus and found 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. The findings have implications for market entry and investment decisions, and highlight the strategic value of brand and geography.

Key Takeaways:

  • The study investigated the effectiveness of four machine learning models (Decision Trees, Random Forest, Gradient Boosting, and Histogram-Based Gradient Boosting) in predicting AI service focus.
  • Gradient Boosting outperformed the other models with a testing R² of 0.9914, identifying company reputation and location as the most influential predictors of AI adoption.
  • The results challenge assumptions about organizational size's role in digitalization and emphasize the strategic value of brand and geography.
  • The study highlights the limitations of using static datasets, which constrain real-time adaptability, and suggests the need for future research to incorporate real-time data streams and hybrid AI-human frameworks.
  • The findings have implications for market entry and investment decisions, and demonstrate AI's potential to reduce uncertainty in entrepreneurial strategy.

Statistics:

  • The testing R² of Gradient Boosting was 0.9914.
  • Company reputation was identified as one of the most influential predictors of AI adoption.
  • Location was identified as one of the most influential predictors of AI adoption.
  • The study evaluated four machine learning models: Decision Trees, Random Forest, Gradient Boosting, and Histogram-Based Gradient Boosting.
  • The study used a testing dataset to evaluate the performance of the machine learning models.

Sources:

  • Using AI and big data analytics to support entrepreneurial decisions in the digital economy.
  • Scientific Reports, 2025;15(1):36933.
  • Nature Portfolio, Heidelberger Platz 3, Berlin, 14197, Germany. (Nature Publishing Group - www.nature.com/; Scientific Reports - www.nature.com/srep/)