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.
Start-up companies are very important for economic dynamism and growth but often have difficulties to stay in the market (Davidsson, Lindmark, & Olofsson, 2006). In order to understand this high failure rate among start-ups, researchers within the entrepreneurship research domain have focused on identifying the determinants of new venture performance in the last couple of decades. Thus far, initial resources have been designated as important determinants of new venture performance (see e.g. the work of Bamford et al., 2000 and Cooper et al., 1994).
Resources, defined as “all tangible and intangible assets that are tied to the firm in a relatively permanent fashion” (Wernerfelt, 1984), are not only indispensable for the basic functioning of a firm, they can also serve as sources of competitive advantage. This is the key idea of the resource-based view (RBV) in which it is posited that resources can only be sources of (sustained) competitive advantage if they are valuable, rare, costly to imitate and properly exploited by the organization (Barney, 1991). Moreover, initial resources, i.e., the firm’s resources present at the point of inception, imprint the firm at start-up and thus affect its future competitive position (Bamford et al., 2004 and Boeker, 1989).
Previous research has mainly focused on the identification of resources leading to superior performance. The majority of these studies concentrated on testing universalistic (independent of other resources and context) or contingency (independent of other resources, dependent on the organizational or the environmental context) models. Despite providing useful insights, both perspectives neglect two important issues. Firstly, resources interact with each other. This means that the strategic value of a resource is dependent on other resources. Therefore, one should assess the strategic value of resources at the resource bundle level (Black & Boal, 1994). Existing research, however, has been restricted to the analysis of additive effects of resources on the overall performance. Secondly, organizations face multiple contingencies at the same time. The value of a firm’s resources needs to be evaluated within the context of the firm’s strategy, as well as the specific market environment.
Existing studies neglect this multivariate dependency of a firm’s performance on resources, organizational and environmental factors. In this paper, we address both of these concerns by taking an inductive (data mining) approach, based on survey and financial data, to identify configurations of resource bundles, strategy and environment that yield good start-up performance. The remainder of the paper is structured as follows. The next section further describes the problem setting: identifying resource combinations that impact new venture performance. In Section 3, the data gathering process, experimental setup and data mining techniques are elaborated on. Section 4 provides the results of our analysis, while Section 5 concludes the paper.
This empirical work is based on an extensive survey and data gathering process among Flemish organizations. The rule set extracted by the ALBA rule extraction technique, and corresponding decision table, provide an important insight into successful configurations of resources, combined with strategy and environment, for start-up companies. The results confirm the existence of multivariate configurations and equifinality.
We foresee further research to collaborate the findings set forth by the rule-based model in a wider setting by extending the data survey to other regions and time periods, and by qualitatively validating the different configurations observed in the model. Finally, we envision a practical use of this work by setting up entrepreneurial guidelines for setting up start-up companies.
