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.

中文翻译：

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从失败中学习：用于检测创新创业公司失败模式的大数据分析

本文旨在确定用于分析大型数据集的合适模型，以实现双重目标：首先，更好地了解影响创新型初创公司绩效及其管理实践的动态，其次，检测其失败模式。因此，我们调查了 2012 年至 2015 年创建的 4185 家意大利创新创业公司的经济-财务、背景和治理维度的相互作用。一旦创业公司被分组，我们只关注那些不成功的公司。然后，发现失败模式，整合因素和聚类分析的使用，其中每个公司的因素分数用于基于聚类方法识别一组同质组。这些大维数据技术的综合使用允许以严格的方式对项目进行分类并展开数据的结构，这些结构在开始时并不明显。分析表明，每种失败模式都是一个多维结构，因此会产生不同的管理影响。因此，有效处理失败需要管理层针对不同公司时代特定失败模式所面临的挑战采取适当的干预措施。