The purpose of the paper is to individuate appropriate models for analyzing large dataset in order to detect the patterns of failure in the case of innovative startups and understand the interaction of their economic, context and governance variables and their influence over the different patterns. The study is based on financial, governance and context data of all 180 Italian innovative startups failed from 2012 to 2015. The considered sample collects data on the entire population of Italian unsuccessful startups, so it is representative of this population as a whole. Failure patterns have been uncovered integrating the use of factor and cluster analyses, where the factor scores for each firm are used to identify a set of homogenous groups based on cluster analysis. The integrated use of those large dimensional data techniques permits to classify the data 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 intervention targeted at the challenges faced at that particular pattern of failure in different firm’s age.
|Data di pubblicazione:||2019|
|Titolo:||Learning from failure. Big Data analysis for detecting the patterns of failure in innovative startups|
|Autore/i:||Kocollari, Ulpiana; Cavicchioli, Maddalena|
|Nome del convegno:||2019 International Conference on Big Data in Business|
|Luogo del convegno:||London, United Kingdom|
|Data del convegno:||29 - 31 October 2019|
|Citazione:||Learning from failure. Big Data analysis for detecting the patterns of failure in innovative startups / Kocollari, Ulpiana; Cavicchioli, Maddalena. - (2019). ((Intervento presentato al convegno 2019 International Conference on Big Data in Business tenutosi a London, United Kingdom nel 29 - 31 October 2019.|
|Tipologia||Relazione in Atti di Convegno|
File in questo prodotto:
I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris