Technology Incubators and Knowledge Networks: A Rough Set Approach in Comparative Project Analysis

Abstract

Technology incubators have emerged in many places as a tool in facilitating the establishment and survival of high-technology firms. Some incubators develop quickly and produce a fast-increasing number of new ventures, while others remain stable in size. Despite a growing public investment in technology incubators, systematic studies of the factors determining their growth are scarce, meaning that policy decisions are taken without sufficient practical insights into critical conditions for growth. In response to that situation, we explore several factors in determining differences in growth patterns. We use a quantitative approach derived from the field of artificial intelligence that matches with meta-analysis and qualitative (and sometimes fuzzy) data—that is, rough set analysis. Benefits and challenges of rough set analysis are discussed, including experience with a stepwise procedure with various accuracy checks. The findings suggest that a strong performance of incubators mainly rests on diversity in stakeholder involvement and a location in nonmetropolitan areas. Rough set analysis turns out to be a helpful tool in comparative project analysis, but there is still a need for standardization of measures used in the interpretation of the results.

Fundamental Factors for the Performance of Technology Based Incubators

“…Neste trabalho, por este motivo e outros explicitados mais adiante, apenas consideramos incubadoras de base tecnológica. O tipo de suporte provisto pela incubadora pode não se restringir aos benefícios tradicionais, nomeadamente sob a forma de disponibilização de ativos tangíveis, mas podendo estender-se ao provimento de serviços de valor agregado, qual seja, a ligação a redes relacionais mais amplas e de orientação aos empreendedores (Soetanto & Van Geenhuizen, 2007).…”

Place-bound versus footloose firms: wiring metropolitan areas in a policy context

“…It is important to note that the interpretation of the rough set analysis results is valid to the extent in which the selected case studies provide a fair representation of important segments of young, innovative, companies. In addition, various testing experiments using similar rough set procedures in other studies indicate that the prediction accuracy of the rules is around 73% (Soetanto and van Geenhuizen 2007).…”

Social Networks and Competitive Growth of University Spin‐off Firms: A Tale of Two Contrasting Cities

“…The nature of support however varies considerably, ranging from merely conventional support, such as office, administrative services and financial support, to mixes of conventional and value added support. Value-added support aims to improve the capabilities of entrepreneurs (Hannon & Chaplin 2003), such as enhancing business skills, business monitoring and skills in negotiation (see also, Soetanto & Van Geenhuizen 2007).…”