In Search of Creative Champions in High-Tech Spaces - Karima Kourtit; Peter Nijkamp (Tinbergen Institute, 2013)
The business performance of firms in the creative high-tech sector shows much variation. This paper examines whether the geographical location of such business firms influences the performance of these firms. The overarching analysis framework of this paper emerges from the recently developed Strategic Performance Management (SPM) concept for individual firms, which in the present study is extended with spatial meso-attributes related to the location of these firms. SPM aims to improve the firms competitive performance through the application of strict internal management principles. Our study thus adopts a micro-business perspective on the organizational determinants of a firms economic performance and its links with distinct spatial entrepreneurship conditions and general economic moderator variables. The present study focuses on both large and small and medium-sized (SME) firms, mainly operating in the creative high-tech sector in the Netherlands. The research methodology uses stepwise the following analytical tools: multivariate analysis of an extensive micro- and meso-data set on the internal performance of firms and regional covariates; Data Envelopment Analysis (DEA) and its recent extension to super-efficient DEA for mapping out in a comparative way the achievements of both regions and firms; a GIS-oriented statistical analysis to identify geographically-discriminating factors in the firms performance; and the design and estimation of a Structural Equations Model (SEM) for assessing the performance of the firms concerned (using what is called the flying disc model). Our results show significant differences in the performance of large vis-à-vis SME firms that have adopted SPM, while their geographical position in the country, in general, also plays a significant role.
Keywords: M19; M21; Q5; Q56; R10; R11; R12; R15; 330; creative industries; high-tech sector; Data Envelopment Analysis (DEA); super-efficient DEA; principal component analysis; Structural Equations Model; flying disc model