Entrepreneurial Success Factors and Prediction Models

Entrepreneurial success rests on a complex interplay of personal, organisational and environmental factors. Core drivers include leadership quality, team cohesion, innovative capability and the coherence of the chosen business model. External determinants such as market opportunity, regulatory context and access to finance further shape outcomes. Recent theoretical advances distinguish between a resource-based view, emphasising the strategic deployment of tangible and intangible assets, and a dynamic-capabilities perspective, which highlights a venture’s capacity to adapt swiftly to changing conditions. Complementing these conceptual frameworks, prediction models have matured through the integration of big data sources and advanced analytics. Machine-learning algorithms, from logistic regression and support-vector machines to ensemble methods like gradient boosting, now underpin decision-support systems used by investors and entrepreneurs. Survey frameworks and systematic reviews have synthesised these developments, yielding structured feature-category taxonomies that span investment metrics, operational indicators and market signals. Together, these strands contribute to a more rigorous, data-driven understanding of why some ventures thrive while others falter, with practical implications for policy-makers, incubators and venture capital firms worldwide.

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Entrepreneurial Success Factors and Prediction Models publication trend

The graph below shows the total number of articles in entrepreneurial success factors and prediction models across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic capabilities: The firm’s ability to integrate, build and reconfigure internal and external competences to address rapidly changing environments.

Resource-based view: A theoretical framework positing that a venture’s unique resources and capabilities form the basis of sustained competitive advantage.

Predictive model: A statistical or computational tool that uses historical data and algorithms to forecast future outcomes or classify new instances.

Machine learning: A branch of artificial intelligence in which systems automatically learn patterns and make decisions based on data without explicit programming.

References

1. Modeling and prediction of business success: a survey. Artificial Intelligence Review (2024).
2. What could we learn from startup failures?. Journal of Innovation and Entrepreneurship (2025).
3. A machine learning, bias-free approach for predicting business success using Crunchbase data. Information Processing & Management (2021).
4. Success Factors of Startups in Research Literature within the Entrepreneurial Ecosystem. Administrative Sciences (2022).