TY - JOUR

T1 - Technology incubators and knowledge networks

T2 - a rough set approach in comparative project analysis

AU - Soetanto, D. P.

AU - van Geenhuizen, Marina

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

M3 - Journal article

VL - 34

SP - 1011

EP - 1029

JO - Environment and Planning B: Planning and Design

JF - Environment and Planning B: Planning and Design

SN - 0265-8135

ER -