The local environment as a supportive operator in innovation diffusion
Abstract
Spatial patterns of technology diffusion determine the capacity of regions and cities to compete in a global market. It is therefore, important to know in what way the local environment can contribute to the attraction of new technology to business locations and what factors differentiate in the support of the local environment in this process. The article will take its starting point in the knowledge capacity of cities. It will then discuss conventional theory on spatial diffusion and review some merits of this theory. Furthermore. a focused approach will be introduced by adopting a communication perspective on technology diffusion. This perspective allows for an exploration of potential barriers (and bridges) in the diffusion process. The article will then present new empirical results on the supportive role of the local environment in technology diffusion on the basis of a large sample of innovative companies in various European cities. Particular attention will be given to the role of the local institutes of higher' education and research. This article will conclude with some policy recommendations on an improved use of the local environment in advancing the competitiveness of innovative companies.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.