Introduction

Silicon Valley has received a great deal of attention from scholars and decision-makers, due to its unparalleled success. It serves as a prominent exemplar for the success of high-technology clusters and is often a role model for ‘Silicon Somewheres’ around the world. Silicon Valley refers to an area of the Santa Clara Valley that begins at San Carlos, about 20 miles south of San Francisco, and extends along the San Andreas Fault south to San Jose. During the 1980s and 1990s, policy makers worldwide tried to imitate the Californian success story and something similar is happening in other high-technology fields today, where Silicon Valley remains the blueprint for much of the initiatives that are being undertaken (Cooke, 2004; Jaruzelski, 2014).

There are a variety of theories surrounding the evolution of this famous cluster and the mechanisms that kept it successful. Some emphasise the idiosyncrasies of its establishment, while others construct a historical path-dependent argument stressing ‘inevitability’ where current success arises from forces that trace back as far as the Gold Rush (Moore and Davis, 2004). Analyses find various factors including its entrepreneurs, the technologies that they commercialised and the firms they created as crucial elements for the Silicon Valley success (Kenney and Patton, 2006).And, while much attention has been paid to the success factors of Silicon Valley, there is scarce information on whether transferring this model has worked and what current transfer cases learn from earlier adopters (Hassink and Lagendijk, 2001; Karch et al, 2016).

Addressing this gap, the chapter raises the question of how the transfer of the Silicon Valley Model (SVM) plays out in various settings. The goal is to explore successful and failed Silicon Valley imitators in order to identify learning and adaptation mechanisms of the Valley-approach to different settings over time. This is relevant to the growing policy transfer literature, as transfer has witnessed an upsurge in the last couple of years; however, there is limited research on how interventions change over time (Flanagan and Uyarra, 2016).