Spatio-Temporal Dynamics of European Innovation—An Exploratory Approach via Multivariate Functional Data Cluster Analysis

Imke Rhoden,Daniel Weller,Ann-Katrin Voit

doi:10.3390/joitmc8010006

Abstract

We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data across 225 regions, 13 years, and eight patent classes. The approach allows us to form several regional clusters according to their specific innovation types and captures spatio-temporal dynamics too subtle for most other clustering methods. Consistent with the literature on innovation systems, our analysis supports the value of regionalized clusters that can benefit from flexible policy support to strengthen regions as well as innovation in a systematic context, adding technology specificity as a new criterion to consider. The regional innovation cluster solutions for IPC classes for ‘fixed constructions’ and ‘mechanical engineering’ are highly comparable but relatively less comparable for ‘chemistry and metallurgy’. The clusters for innovations in ‘physics’ and ‘chemistry and metallurgy’ are similar; innovations in ‘electricity’ and ‘physics’ show similar temporal dynamics. For all other innovation types, the regional clustering is different. By taking regional profiles, strengths, and developments into account, options for improved efficiency of location-based regional innovation policy to promote tailored and efficient innovation-promoting programs can be derived.

Highlights

Publisher’s Note: MDPI stays neutralInnovation is a key driver of Europe’s sustainable economic success
The analysis is based on the functional data analysis paradigm (FDA), which allows us to analyze latent functional forms, inherent dynamics, and other features in time series of multiple innovation indicators too subtle to be captured by classical time series or clustering approaches
Knowledge of regional innovation dynamics, leading to different Innovation Ginis that result in clustering regions differently across all of Europe depending on the type of innovation activity, is crucial when designing policies for supporting innovation in Europe

Summary

Introduction

Publisher’s Note: MDPI stays neutralInnovation is a key driver of Europe’s sustainable economic success. If innovation is considered in the context of geography and economic growth, there is no single theoretical framework, as there are too many interlinkages between these topics to find a universal approach [1]. We focus on approaches related to investigating innovation clustering such as that of Fornahl and Brenner [3], who find that heterogeneous types of innovation cluster differently, which points to the relevance of considering innovation as a differentiated subject. Knowledge spillovers are another link between innovation and geography to consider, as knowledge (tacit or understanding) is often only transferred locally or regionally

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Conclusion