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Published Online: 16 April 2021

Learning from Failure: Big Data Analysis for Detecting the Patterns of Failure in Innovative Startups

Publication: Big Data
Volume 9, Issue Number 2

Abstract

This article aims at identifying appropriate models for analyzing large datasets to serve a twofold goal: first, to better understand the dynamics impacting innovative startups' performance and their managerial practice and, second, to detect their patterns of failure. Therefore, we investigate the interaction of economic–financial, context, and governance dimensions of 4185 Italian innovative startups created from 2012 to 2015. Once startups have been grouped, we focus only on those that are unsuccessful. Then, failure patterns have been uncovered, integrating the use of factor and cluster analysis, where factor scores for each firm are used to identify a set of homogeneous groups based on clustering methods. The integrated use of those large-dimensional data techniques permits to classify items in rigorous ways and to unfold structures of the data, which are not apparent in the beginning. The analysis suggests that each pattern of failure is a multidimensional construct and, as a consequence can generate different managerial implications. Therefore, an effective handling of failure requires management to use appropriate interventions targeted at the challenges faced by that particular pattern of failure in the age of different firms.

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Cite this article as: Cavicchioli M, Kocollari U (2021) Learning from failure: Big data analysis for detecting the patterns of failure in innovative startups. Big Data 9:2, 79–88, DOI: 10.1089/big.2020.0047.

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Published In

cover image Big Data
Big Data
Volume 9Issue Number 2April 2021
Pages: 79 - 88
PubMed: 33259727

History

Published online: 16 April 2021
Published in print: April 2021
Published ahead of print: 1 December 2020

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Maddalena Cavicchioli* [email protected]
Department of Economics “Marco Biagi,” University of Modena and Reggio Emilia & ReCent, Modena, Italy.
Ulpiana Kocollari
Department of Economics “Marco Biagi,” University of Modena and Reggio Emilia & Softech-ICT, Modena, Italy.

Notes

*
Address correspondence to: Maddalena Cavicchioli, Department of Economics “Marco Biagi,” University of Modena and Reggio Emilia & ReCent, Viale Berengario 51, Modena 41121, Italy, [email protected]

Author Disclosure Statement

No competing financial interests exist.

Funding Information

Maddalena Cavicchioli acknowledges financial support by “Fondo di Ateneo per la Ricerca” FAR (2019) research grant of the University of Modena and Reggio Emilia, Italy.

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