Identifying financially successful start-up profiles with data mining
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. (C) 2010 Elsevier Ltd. All rights reserved.
data mining active learning start-up companies ideal configurations