Identifying financially successful start-up profiles with data mining

Martens, David, Vanhoutte, Christine, De Winne, Sophie, Baesens, Bart, Sels, Luc and Mues, Christophe (2011) Identifying financially successful start-up profiles with data mining Expert Systems with Applications, 38, (5), pp. 5794-5800. (doi:10.1016/j.eswa.2010.10.052).


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Start-ups are crucial in the modern economy as they provide dynamism and growth. Research on the performance of new ventures increasingly investigates initial resources as determinants of success. Initial resources are said to be important because they imprint the firm at start-up, limit its strategic choices, and continue to impact its performance in the long run. The purpose of this paper is to identify configurations of initial resource bundles, strategy and environment that lead to superior performance in start-ups. To date, interdependencies between resources on the one hand and between resources, strategy and environment on the other hand have been neglected in empirical research. We rely on data mining for the analysis because it accounts for premises of configurational theory, including reversed causality, intradimensional interactions, multidimensional dependencies, and equifinality.

We apply advanced data mining techniques, in the form of rule extraction from non-linear support vector machines, to induce accurate and comprehensible configurations of resource bundles, strategy and environment. We base our analysis on an extensive survey among 218 Flemish start-ups. Our experiments indicate the good performance of rule extraction technique ALBA. Finally, for comprehensibility, intuitiveness and implementation reasons, the tree is transformed into a decision table.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.eswa.2010.10.052
ISSNs: 0957-4174 (print)
Keywords: data mining, active learning, start-up companies, ideal configurations

ePrint ID: 168997
Date :
Date Event
Date Deposited: 08 Dec 2010 13:55
Last Modified: 18 Apr 2017 03:32
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