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Learning from Failure: Big Data Analysis for Detecting the Patterns of Failure in Innovative Startups.
Maddalena Cavicchioli1, Ulpiana Kocollari2
1Department of Economics "Marco Biagi," University of Modena and Reggio Emilia & ReCent, Modena, Italy.
Big Data
|December 1, 2020
Summary
This study identifies failure patterns in Italian innovative startups by analyzing economic, contextual, and governance data. Understanding these multidimensional failure patterns is key for targeted managerial interventions.
Area of Science:
- Business and Economics
- Data Science
- Innovation Management
Background:
- Innovative startups are crucial for economic growth but face high failure rates.
- Existing research often overlooks the complex interplay of factors contributing to startup failure.
- Understanding startup dynamics and failure patterns is essential for effective management and policy.
Purpose of the Study:
- To identify appropriate models for analyzing large datasets of innovative startups.
- To understand the dynamics impacting innovative startups' performance and managerial practices.
- To detect and analyze patterns of failure among Italian innovative startups.
Main Methods:
- Analysis of a large dataset (4185 Italian innovative startups, 2012-2015).
- Integration of economic-financial, context, and governance dimensions.
- Application of factor and cluster analysis on large-dimensional data.
Main Results:
- Failure patterns are multidimensional constructs, not isolated events.
- Specific homogeneous groups of startup failures were identified.
- The analysis revealed data structures not initially apparent.
Conclusions:
- Each failure pattern necessitates distinct managerial interventions.
- Effective handling of startup failure requires targeted strategies based on identified patterns.
- Management must adapt interventions to the specific challenges of each failure pattern.
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