Reconfiguring clusters: From Contradictions to Pathways

Daniël Speldekamp*

*Corresponding author for this work

Research output: ThesisThesis fully external

Abstract

Despite advances in technology enabling faster, cheaper, more effective communication and transportation over long distances, economic activity continues to be spatially concentrated (Florida, 2005). There is no better example of this than Silicon Valley in California, which headquarters many of the world’s most renown and profitable companies, from Apple, and Facebook, to Google (Alphabet), and has an economic output that outweighs that of Finland (The Guardian, 2019). Interest in such economic hotspots has markedly increased in the thirty years since Porter’s (1990a, 1990b) seminal study The Competitive Advantage of Nations in which he conceptualizes them as clusters. An extensive scholarship has developed as a result of his work (Lazzeretti et al., 2014), establishing these “geographic concentrations of interconnected companies and institutions in a particular field” as an important determinant of organizational, regional, and national competitiveness (Porter, 1998a, p. 78). Moreover, the eye-catching success of not just Silicon Valley, but also clusters such as Silicon Roundabout, and Aerospace Valley has led to many policy makers and businesses trying to emulate it. There are now over a thousand organizations dedicated to cluster-building in the European Union alone, spanning industries from agriculture to aerospace (European Cluster Collaboration Platform, 2019; Ooms et al., 2015). These initiatives attract billions of euros in government subsidies (Brakman and van Marrewijk, 2013). However, in many cases they do not live up to expectations, with many so-called ‘Silicon Somewheres’ failing (Giest, 2017; Hospers et al., 2009). It has repeatedly been suggested that the reason for these mixed results is the pursuit of cluster initiatives and policies outpacing the understanding of what makes clusters and their members successful (Martin and Sunley, 2003; Wolman and Hincapie, 2014). Worryingly, the cluster discourse is characterized by seemingly contradictory theories and empirical findings (Frenken et al., 2015).
This dissertation aims to provide a first step towards a new approach to clusters that promises to solve many of the long-standing disagreements. By doing so it also contributes to improved cluster management and policy, moving them away from ‘copy-pasting’ ideal models characterizing clusters like Silicon Valley and towards a more differentiated, context-specific approach (Duranton, 2011; Ooms et al., 2015; Tödtling and Trippl, 2005). At the heart of the dissertation is a revaluation of the cluster concept that re-emphasizes the three dimensions at the core of Porter’s (1998a) definition: geography, networks, and institutions. It is suggested that clusters can more fully be understood through a configurational lens that emphasizes the complementarity of these dimensions, and allows for multiple configurations of cluster conditions to lead to an outcome (Ragin, 1987, 2000, 2008). Moreover, between these configurations, conditions could have different effects and degrees of importance (Schneider and Wagemann, 2012). Before exploring this application of configurational theory, I briefly present the history of the cluster concept along the lines of its three dimensions. This section culminates in a synopsis of the issues that a configurational approach can help solve, the dissertation’s broader intended contributions, and an outline of the dissertation.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Radboud University Nijmegen
Supervisors/Advisors
  • Knobben, J., Supervisor, External person
  • Saka - Helmhout, A.U., Supervisor
Award date7-Jan-2021
Place of PublicationNijmegen
Publisher
Print ISBNs978-94-6421-132-0
Electronic ISBNs978-94-6421-132-0
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • clusters
  • qualitative comparative analysis
  • geography
  • networks
  • institutions
  • complex causality

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