Indicator metadata: Taxonomy of technological transformations

This indicator classifies regions according to their prevalent technological transformation, as follows:
• Servitisation
• Industry 4.0
• digitalisation of traditional service
• robotisation of traditional manufacturing
• niches of robotisation

Theme(s): Science and Technology - Science, Technology and Innovation

Introduction

Author
ESPON project
Contact(s)
  • Roberta Capello (Project leader)
  • Lenzi Camilla (Politecnico di Milano) (Responsible party)
Territorial information
Spatial Extent Nomenclature
name version level
EU28+4 NUTS 2013 2
Years
2009-2016

Methodology

In order to identify the prevailing technological transformation occurring in each region of the ESPON space, a k-means cluster analysis has been performed on six regional sectoral specialisation variables (i.e. specialisation in ‘technology’ manufacturing sectors, specialisation in ‘carrier’ manufacturing sectors, specialisation in ‘induced’ manufacturing sectors, specialisation in ‘technology’ services, specialisation in ‘carrier’ services, specialisation in ‘induced’ services). We considered various statistical criteria with which to identify the appropriate number of clusters to be retained, such as the relationship between within-cluster and between-cluster variance, but also the number of regions per se. The balance between the information advantages provided by expanding the number of clusters and the interpretability of the results in terms of types of technological transformations supported the extraction of five clusters; each cluster included a reasonable portion of observations, so that they could be plausibly interpreted as typologies of technological transformation. They statistically and significantly differed in themain variables used for the clustering exercise, as the results of the ANOVA tests presented below show. Indeed, the magnitude of the F values performed on each dimension is an indication of how well the respective dimension discriminated between clusters. These five clusters were overall highly stable. Repeating the extraction with different similarity measures and specifying different k random initial group centers yielded highly consistent results. Only a minor portion of regions, in fact, were assigned to a different group.

Genealogy

Graph

Parents

Child

Other attributes

Id
1983
Status
Key indicator
Name
Taxonomy of technological transformations
Code
T4_15
Is standard?
True
Is base indicator?
False
Type
Single
Data type
Enumerated
Unit of measure - Numerator / Denominator Name
None
Unit of measure - Numerator / Denominator Scale
None
Is a ranking?
False
Main Theme
Science and Technology - Science, Technology and Innovation
Nature type
Typology
Labels
Name Description
3 Digitalisation of traditional services
5 Niches of robotisation
1 Servitisation
2 Industry 4.0
4 Robotisation of traditional manufacturing